1
|
Xiao H, Wang X, Yang P, Wang L, Xi J, Xu J. Impact of CT reconstruction algorithms on pericoronary and epicardial adipose tissue attenuation. Eur J Radiol 2025; 188:112132. [PMID: 40344712 DOI: 10.1016/j.ejrad.2025.112132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2024] [Revised: 02/24/2025] [Accepted: 04/22/2025] [Indexed: 05/11/2025]
Abstract
OBJECTIVE This study aims to investigate the impact of adaptive statistical iterative reconstruction-Veo (ASIR-V) and deep learning image reconstruction (DLIR) algorithms on the quantification of pericoronary adipose tissue (PCAT) and epicardial adipose tissue (EAT). Furthermore, we propose to explore the feasibility of correcting the effects through fat threshold adjustment. METHODS A retrospective analysis was conducted on the imaging data of 134 patients who underwent coronary CT angiography (CCTA) between December 2023 and January 2024. These data were reconstructed into seven datasets using filtered back projection (FBP), ASIR-V at three different intensities (ASIR-V 30%, ASIR-V 50%, ASIR-V 70%), and DLIR at three different intensities (DLIR-L, DLIR-M, DLIR-H). Repeated-measures ANOVA was used to compare differences in fat, PCAT and EAT attenuation values among the reconstruction algorithms, and Bland-Altman plots were used to analyze the agreement between ASIR-V or DLIR and FBP algorithms in PCAT attenuation values. RESULTS Compared to FBP, ASIR-V 30 %, ASIR-V 50 %, ASIR-V 70 %, DLIR-L, DLIR-M, and DLIR-H significantly increased fat attenuation values (-103.91 ± 12.99 HU, -102.53 ± 12.68 HU, -101.14 ± 12.78 HU, -101.81 ± 12.41 HU, -100.87 ± 12.25 HU, -99.08 ± 12.00 HU vs. -105.95 ± 13.01 HU, all p < 0.001). When the fat threshold was set at -190 to -30 HU, ASIR-V and DLIR algorithms significantly increased PCAT and EAT attenuation values compared to FBP algorithm (all p < 0.05), with these values increasing as the reconstruction intensity level increased. After correction with a fat threshold of -200 to -35 HU for ASIR-V 30 %, -200 to -40 HU for ASIR-V 50 % and DLIR-L, and -200 to -45 HU for ASIR-V 70 %, DLIR-M, and DLIR-H, the mean differences in PCAT attenuation values between ASIR-V or DLIR and FBP algorithms decreased (-0.03 to 1.68 HU vs. 2.35 to 8.69 HU), and no significant difference was found in PCAT attenuation values between FBP and ASIR-V 30 %, ASIR-V 50 %, ASIR-V 70 %, DLIR-L, and DLIR-M (all p > 0.05). CONCLUSION Compared to the FBP algorithm, ASIR-V and DLIR algorithms increase PCAT and EAT attenuation values. Adjusting the fat threshold can mitigate the impact of ASIR-V and DLIR algorithms on PCAT attenuation values.
Collapse
Affiliation(s)
- Huawei Xiao
- Heart Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, 310014 Hangzhou, China
| | - Xiangquan Wang
- Heart Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, 310014 Hangzhou, China; Zhejiang Chinese Medical University, China
| | - Panfeng Yang
- Heart Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, 310014 Hangzhou, China
| | - Ling Wang
- Heart Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, 310014 Hangzhou, China
| | - Jiada Xi
- Heart Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, 310014 Hangzhou, China
| | - Jian Xu
- Heart Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, 310014 Hangzhou, China.
| |
Collapse
|
2
|
Hao H, Tong J, Xu S, Wang J, Ding N, Liu Z, Zhao W, Huang X, Li Y, Jin C, Yang J. Does the deep learning-based iterative reconstruction affect the measuring accuracy of bone mineral density in low-dose chest CT? Br J Radiol 2025; 98:974-980. [PMID: 40127198 DOI: 10.1093/bjr/tqaf059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 11/07/2024] [Accepted: 03/07/2025] [Indexed: 03/26/2025] Open
Abstract
OBJECTIVES To investigate the impacts of a deep learning-based iterative reconstruction algorithm on image quality and measuring accuracy of bone mineral density (BMD) in low-dose chest CT. METHODS Phantom and patient studies were separately conducted in this study. The same low-dose protocol was used for phantoms and patients. All images were reconstructed with filtered back projection, hybrid iterative reconstruction (HIR) (KARL®, level of 3,5,7), and deep learning-based iterative reconstruction (artificial intelligence iterative reconstruction [AIIR], low, medium, and high strength). The noise power spectrum (NPS) and the task-based transfer function (TTF) were evaluated using phantom. The accuracy and the relative error (RE) of BMD were evaluated using a European spine phantom. The subjective evaluation was performed by 2 experienced radiologists. BMD was measured using quantitative CT (QCT). Image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), BMD values, and subjective scores were compared with Wilcoxon signed-rank test. The Cohen's kappa test was used to evaluate the inter-reader and inter-group agreement. RESULTS AIIR reduced noise and improved resolution on phantom images significantly. There were no significant differences among BMD values in all groups of images (all P > 0.05). RE of BMD measured using AIIR images was smaller. In objective evaluation, all strengths of AIIR achieved less image noise and higher SNR and CNR (all P < 0.05). AIIR-H showed the lowest noise and highest SNR and CNR (P < 0.05). The increase in AIIR algorithm strengths did not affect BMD values significantly (all P > 0.05). CONCLUSION The deep learning-based iterative reconstruction did not affect the accuracy of BMD measurement in low-dose chest CT while reducing image noise and improving spatial resolution. ADVANCES IN KNOWLEDGE The BMD values could be measured accurately in low-dose chest CT with deep learning-based iterative reconstruction while reducing image noise and improving spatial resolution.
Collapse
Affiliation(s)
- Hui Hao
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, P.R. China
- Shaanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi'an 710061, P.R. China
- Xi'an Key Laboratory of Medical Computational Imaging, Xi'an, 710061, P.R. China
| | - Jiayin Tong
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, P.R. China
- Shaanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi'an 710061, P.R. China
- Xi'an Key Laboratory of Medical Computational Imaging, Xi'an, 710061, P.R. China
| | - Shijie Xu
- Collaborative Innovation Department, United Imaging Healthcare, Shanghai 201800, P.R. China
| | - Jingyi Wang
- Collaborative Innovation Department, United Imaging Healthcare, Shanghai 201800, P.R. China
| | - Ningning Ding
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, P.R. China
- Shaanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi'an 710061, P.R. China
- Xi'an Key Laboratory of Medical Computational Imaging, Xi'an, 710061, P.R. China
| | - Zhe Liu
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, P.R. China
- Shaanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi'an 710061, P.R. China
- Xi'an Key Laboratory of Medical Computational Imaging, Xi'an, 710061, P.R. China
| | - Wenzhe Zhao
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, P.R. China
- Shaanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi'an 710061, P.R. China
- Xi'an Key Laboratory of Medical Computational Imaging, Xi'an, 710061, P.R. China
| | - Xin Huang
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, P.R. China
- Shaanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi'an 710061, P.R. China
- Xi'an Key Laboratory of Medical Computational Imaging, Xi'an, 710061, P.R. China
| | - Yanshou Li
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, P.R. China
- Shaanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi'an 710061, P.R. China
- Xi'an Key Laboratory of Medical Computational Imaging, Xi'an, 710061, P.R. China
| | - Chao Jin
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, P.R. China
- Shaanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi'an 710061, P.R. China
- Xi'an Key Laboratory of Medical Computational Imaging, Xi'an, 710061, P.R. China
| | - Jian Yang
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, P.R. China
- Shaanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi'an 710061, P.R. China
- Xi'an Key Laboratory of Medical Computational Imaging, Xi'an, 710061, P.R. China
| |
Collapse
|
3
|
Mei J, Chen C, Liu R, Ma H. Effect of Deep Learning Image Reconstruction on Image Quality and Pericoronary Fat Attenuation Index. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2025; 38:1881-1890. [PMID: 39299956 DOI: 10.1007/s10278-024-01234-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 08/04/2024] [Accepted: 08/12/2024] [Indexed: 09/22/2024]
Abstract
To compare the image quality and fat attenuation index (FAI) of coronary artery CT angiography (CCTA) under different tube voltages between deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction V (ASIR-V). Three hundred one patients who underwent CCTA with automatic tube current modulation were prospectively enrolled and divided into two groups: 120 kV group and low tube voltage group. Images were reconstructed using ASIR-V level 50% (ASIR-V50%) and high-strength DLIR (DLIR-H). In the low tube voltage group, the voltage was selected according to Chinese BMI classification: 70 kV (BMI < 24 kg/m2), 80 kV (24 kg/m2 ≤ BMI < 28 kg/m2), 100 kV (BMI ≥ 28 kg/m2). At the same tube voltage, the subjective and objective image quality, edge rise distance (ERD), and FAI between different algorithms were compared. Under different tube voltages, we used DLIR-H to compare the differences between subjective, objective image quality, and ERD. Compared with the 120 kV group, the DLIR-H image noise of 70 kV, 80 kV, and 100 kV groups increased by 36%, 25%, and 12%, respectively (all P < 0.001); contrast-to-noise ratio (CNR), subjective score, and ERD were similar (all P > 0.05). In the 70 kV, 80 kV, 100 kV, and 120 kV groups, compared with ASIR-V50%, DLIR-H image noise decreased by 50%, 53%, 47%, and 38-50%, respectively; CNR, subjective score, and FAI value increased significantly (all P < 0.001), ERD decreased. Compared with 120 kV tube voltage, the combination of DLIR-H and low tube voltage maintains image quality. At the same tube voltage, compared with ASIR-V, DLIR-H improves image quality and FAI value.
Collapse
Affiliation(s)
- Junqing Mei
- Department of Radiology, BenQ Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Chang Chen
- Department of Radiology, BenQ Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Ruoting Liu
- Department of Radiology, BenQ Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Hongbing Ma
- Department of Radiology, BenQ Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.
| |
Collapse
|
4
|
Li H, Zhang Y, Hua S, Sun R, Zhang Y, Yang Z, Peng Y, Sun J. Deep Learning Image Reconstruction (DLIR) Algorithm to Maintain High Image Quality and Diagnostic Accuracy in Quadruple-low CT Angiography of Children with Pulmonary Sequestration: A Case Control Study. Acad Radiol 2025:S1076-6332(25)00433-7. [PMID: 40410108 DOI: 10.1016/j.acra.2025.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2025] [Revised: 05/02/2025] [Accepted: 05/04/2025] [Indexed: 05/25/2025]
Abstract
RATIONALE AND OBJECTIVES CT angiography (CTA) is a commonly used clinical examination to detect abnormal arteries and diagnose pulmonary sequestration (PS). Reducing the radiation dose, contrast medium dosage, and injection pressure in CTA, especially in children, has always been an important research topic, but few research is proven by pathology. The current study aimed to evaluate the diagnostic accuracy for children with PS in a quadruple-low CTA (4L-CTA: low tube voltage, radiation, contrast medium, and injection flow rate) using deep learning image reconstruction (DLIR) in comparison with routine protocol CTA with adaptive statistical iterative reconstruction-V (ASIR-V) MATERIALS AND METHODS: 53 patients (1.50±1.36years) suspected with PS were enrolled to undergo chest 4L-CTA using 70kVp tube voltage with radiation dose or 0.90 mGy in volumetric CT dose index (CTDIvol) and contrast medium dose of 0.8 ml/kg injected in 16 s. Images were reconstructed using DLIR. Another 53 patients (1.25±1.02years) with a routine dose protocol was used for comparison, and images were reconstructed with ASIR-V. The contrast-to-noise ratio (CNR) and edge-rise distance (ERD) of the aorta were calculated. The subjective overall image quality and artery visualization were evaluated using a 5-point scale (5, excellent; 3, acceptable). All patients underwent surgery after CT, the sensitivity and specificity for diagnosing PS were calculated. RESULTS 4L-CTA reduced radiation dose by 51%, contrast dose by 47%, injection flow rate by 44% and injection pressure by 44% compared to the routine CTA (all p<0.05). Both groups had satisfactory subjective image quality and achieved 100% in both sensitivity and specificity for diagnosing PS. 4L-CTA had a reduced CNR (by 27%, p<0.05) but similar ERD, which reflects the image spatial resolution (p>0.05) compared to the routine CTA. 4L-CTA revealed small arteries with a diameter of 0.8 mm. CONCLUSION DLIR ensures the realization of 4L-CTA in children with PS for significant radiation and contrast dose reduction, while maintaining image quality, visualization of small arteries, and high diagnostic accuracy.
Collapse
Affiliation(s)
- Haoyan Li
- Department of radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No.56, Nanlishi Road, Xicheng District, Beijing 100045, China (H.L., Y.Z., R.S., Y.P., J.S.).
| | - Yuchen Zhang
- Department of radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No.56, Nanlishi Road, Xicheng District, Beijing 100045, China (H.L., Y.Z., R.S., Y.P., J.S.); School of Biomedical Engineering, Capital Medical University, No.10 Xitoutiao Road, Fengtai District, Beijing 100069, China (Y.Z., Z.Y.).
| | - Shan Hua
- Medical Imaging Department, Children's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hospital of Beijing Children's Hospital, No.393 Altay Road, Saibak District, Urumqi 830000, China (S.H., J.S.).
| | - Ruifang Sun
- Department of radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No.56, Nanlishi Road, Xicheng District, Beijing 100045, China (H.L., Y.Z., R.S., Y.P., J.S.).
| | - Yunxian Zhang
- Medical Science Center, Yangtze University, No .1 Xueyuan Road, Jingzhou 434023, Hubei, China (Y.Z.).
| | - Zhi Yang
- School of Biomedical Engineering, Capital Medical University, No.10 Xitoutiao Road, Fengtai District, Beijing 100069, China (Y.Z., Z.Y.).
| | - Yun Peng
- Department of radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No.56, Nanlishi Road, Xicheng District, Beijing 100045, China (H.L., Y.Z., R.S., Y.P., J.S.).
| | - Jihang Sun
- Department of radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No.56, Nanlishi Road, Xicheng District, Beijing 100045, China (H.L., Y.Z., R.S., Y.P., J.S.); Medical Imaging Department, Children's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hospital of Beijing Children's Hospital, No.393 Altay Road, Saibak District, Urumqi 830000, China (S.H., J.S.).
| |
Collapse
|
5
|
Luo Y, Tang Y, Chen S, Tang H, Cheng Y, Zhang F, Shen Y, Wang Q. Improving image quality and diagnostic performance using deep learning image reconstruction in 100-kVp CT enterography for patients with wide-range body mass index. Eur J Radiol 2025; 189:112167. [PMID: 40398003 DOI: 10.1016/j.ejrad.2025.112167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2024] [Revised: 03/10/2025] [Accepted: 05/13/2025] [Indexed: 05/23/2025]
Abstract
OBJECTIVE To assess the clinical value of the deep learning image reconstruction (DLIR) algorithm compared with conventional adaptive statistical iterative reconstruction-Veo (ASiR-V) in image quality, diagnostic confidence, and intestinal lesion detection in 100-kVp CT enterography (CTE) for patients with wide-range body mass index (BMI). METHODS A total of 84 patients underwent 100-kVp dual-phase CTE were included. Images were reconstructed using filtered back projection (FBP), ASiR-V 30 %, ASiR-V 60 %, and DLIR with low, medium, and high levels (DLIR-L, DLIR-M, and DLIR-H). The CT value, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of small and large intestines were compared using repeated measures analysis of variance with the Bonferroni correction or Friedman test. The correlation between relative CNR increment and BMI was analyzed using Pearson's correlation coefficient. The overall image quality and diagnostic confidence scores were evaluated. Additionally, lesion detection of intestinal disease was conducted by three readers with different experience and compared between DLIR-M and ASiR-V 60 % images using McNemar's test. RESULTS SD decreased sequentially from FBP, ASiR-V 30 %, DLIR-L, ASiR-V 60 %, DLIR-M, to DLIR-H, which corresponded with improvements in CNR and SNR (all p < 0.001). The relative CNR increment of DLIR exhibited a significantly positive linear correlation with BMI (r:0.307-0.506, all p ≤ 0.005). For overall image quality scores, the ranking was: FBP < ASiR-V 30 % < ASiR-V 60 % ≈DLIR-L < DLIR-M ≈ DLIR-H. DLIR-M outperformed ASiR-V 60 % in diagnostic confidence (p ≤ 0.018 for all three readers). In lesion detection, for the two junior readers, DLIR-M exhibited higher sensitivity for inflammatory lesions compared to ASiR-V 60 % (0.700 (95 % confidence interval [95 % CI]: 0.354-0.919) vs. 0.300 (95 % CI: 0.081-0.646) for reader 1 and 0.700 (95 %CI: 0.354-0.919) vs. 0.500 (95 % CI: 0.201-0.799) for reader 2), though no statistical significance was reached. CONCLUSION DLIR effectively reduces noise and improves image quality in 100-kVp dual-phase CTE for wide-range BMIs. DLIR-M exhibits superior performance in image quality and diagnostic confidence, also provide potential value in improving intestinal inflammatory lesion detection in junior readers and sheds lights on benefiting clinical decision making, which needs further investigation.
Collapse
Affiliation(s)
- Yan Luo
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Youfa Tang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Suping Chen
- CT Research Center, GE HealthCare, Changsha, China
| | - Hao Tang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yiqi Cheng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fan Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yaqi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Qiuxia Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| |
Collapse
|
6
|
Okumura T, Koganezawa AS, Nakashima T, Ochi Y, Tsubouchi K, Murakami Y. Assessment of CT-to-physical density table for multiple image reconstruction functions with a large-bore scanner for radiotherapy treatment planning. Phys Med 2025; 133:104970. [PMID: 40187130 DOI: 10.1016/j.ejmp.2025.104970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 12/04/2024] [Accepted: 03/26/2025] [Indexed: 04/07/2025] Open
Abstract
PURPOSE To evaluate the performance of the Aquilion Exceed LB computed tomography (CT) scanner for radiotherapy treatment planning, this study examined the effect of different combinations of the image reconstruction function (IRF) (AiCE and AIDR) and scan parameters on the CT-to-physical density (CT-PD) table and radiation dose in the phantom, and the effect of different object positions on CT values. METHODS To investigate IRF's influence on each material, we calculated CT values by varying tube current, pitch, field of view (FOV), and phantom position for each IRF, comparing them with reference values using filtered back projection (FBP). Furthermore, we evaluated changes in depth dose values due to IRF differences using a solid phantom. RESULTS In the combinations of changes in IRF and scan parameters the change in CT value (ΔHU) of each material was within ±10 HU, except for most conditions. The change in physical density (ΔPD) was within ±0.02 g/cm3 for all combinations. For changes in phantom position, ΔHU was within ±25 HU for changes within the scan FOV, except for Bone 200 mg/cc and 1250 mg/cc. In areas outside the scan FOV with an expanded FOV, ΔHU was significantly larger than within the scan FOV. Variations in depth dose for different IRFs using solid phantoms were within ±0.5 %, except at material boundaries. CONCLUSION Our evaluations of the CT values and dose calculations suggested no need to change the CT-PD table, even with multiple IRFs.
Collapse
Affiliation(s)
- Takuro Okumura
- Department of Clinical Practice and Support, Hiroshima University Hospital, Hiroshima 734-8551, Japan
| | - Akito S Koganezawa
- Department of Information and Electronic Engineering, Faculty of Science and Engineering, Teikyo University, Tochigi 320-8551, Japan.
| | - Takeo Nakashima
- Department of Clinical Practice and Support, Hiroshima University Hospital, Hiroshima 734-8551, Japan
| | - Yusuke Ochi
- Department of Clinical Practice and Support, Hiroshima University Hospital, Hiroshima 734-8551, Japan
| | - Kento Tsubouchi
- Department of Clinical Practice and Support, Hiroshima University Hospital, Hiroshima 734-8551, Japan
| | - Yuji Murakami
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima 734-8551, Japan
| |
Collapse
|
7
|
Bocquet W, Bouzerar R, François G, Leleu A, Renard C. Detection of Pulmonary Nodules on Ultra-low Dose Chest Computed Tomography With Deep-learning Image Reconstruction Algorithm. J Thorac Imaging 2025; 40:e0806. [PMID: 39267547 DOI: 10.1097/rti.0000000000000806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/17/2024]
Abstract
PURPOSE To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR). MATERIAL AND METHODS This prospective cross-sectional study included 60 patients referred to our institution for assessment or follow-up of solid pulmonary nodules. All patients underwent low-dose (LD) and ULD chest CT within the same examination session. LD CT data were reconstructed using Adaptive Statistical Iterative Reconstruction-V (ASIR-V), whereas ULD CT data were reconstructed using DLIR and ASIR-V. ULD CT images were reviewed by 2 readers and LD CT images were reviewed by an experienced thoracic radiologist as the reference standard. Quantitative image quality analysis was performed, and the detectability of pulmonary nodules was assessed according to their size and location. RESULTS The effective radiation dose for ULD CT and LD CT were 0.13±0.01 and 1.16±0.6 mSv, respectively. Over the whole population, LD CT revealed 733 nodules. At ULD, DLIR images significantly exhibited better image quality than ASIR-V images. The overall sensitivity of DLIR reconstruction for the detection of solid pulmonary nodules from the ULD CT series was 93% and 82% for the 2 readers, with a good to excellent agreement with LD CT (ICC=0.82 and 0.66, respectively). The best sensitivities were observed in the middle lobe (97% and 85%, respectively). CONCLUSIONS At ULD, DLIR reconstructions, with minimal radiation exposure that could facilitate large-scale screening, allow the detection of pulmonary nodules with high sensitivity in an unrestricted BMI population.
Collapse
Affiliation(s)
| | | | - Géraldine François
- Department of Pneumology and Transplantation, Amiens University Hospital, Amiens, France
| | | | | |
Collapse
|
8
|
Li H, Chen Z, Gao S, Hu J, Yang Z, Peng Y, Sun J. Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom Study. Tomography 2025; 11:51. [PMID: 40423253 DOI: 10.3390/tomography11050051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2025] [Revised: 04/18/2025] [Accepted: 04/24/2025] [Indexed: 05/28/2025] Open
Abstract
Objectives: To evaluate the medical image segmentation performance of monochromatic images in various energy levels. Methods: The low-density module (25 mm in diameter, 6 Hounsfield Unit (HU) in density difference from background) from the ACR464 phantom was scanned at both 10 mGy and 5 mGy dose levels. Virtual monoenergetic images (VMIs) at different energy levels of 40, 50, 60, 68, 74, and 100 keV were generated. The images at 10 mGy reconstructed with 50% adaptive statistical iterative reconstruction veo (ASIR-V50%) were used to train an image segmentation model based on U-Net. The evaluation set used 5 mGy VMIs reconstructed with various reconstruction algorithms: FBP, ASIR-V50%, ASIR-V100%, deep learning image reconstruction (DLIR) with low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength levels. U-Net was employed as a tool to compare algorithm performance. Image noise and segmentation metrics, such as the DICE coefficient, intersection over union (IOU), sensitivity, and Hausdorff distance, were calculated to assess both image quality and segmentation performance. Results: DLIR-M and DLIR-H consistently achieved lower image noise and better segmentation performance, with the highest results observed at 60 keV, and DLIR-H had the lowest image noise across all energy levels. The performance metrics, including IOU, DICE, and sensitivity, were ranked in descending order with energy levels of 60 keV, 68 keV, 50 keV, 74 keV, 40 keV, and 100 keV. Specifically, at 60 keV, the average IOU values for each reconstruction method were 0.60 for FBP, 0.67 for ASIR-V50%, 0.68 for ASIR-V100%, 0.72 for DLIR-L, 0.75 for DLIR-M, and 0.75 for DLIR-H. The average DICE values were 0.75, 0.80, 0.82, 0.83, 0.85, and 0.86. The sensitivity values were 0.93, 0.91, 0.96, 0.95, 0.98, and 0.98. Conclusions: For low-density, non-enhancing objects under a low dose, the 60 keV VMIs performed better in automatic segmentation. DLIR-M and DLIR-H algorithms delivered the best results, whereas DLIR-H provided the lowest image noise and highest sensitivity.
Collapse
Affiliation(s)
- Haoyan Li
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, China
| | - Zhenpeng Chen
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
| | - Shuaiyi Gao
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, China
| | - Jiaqi Hu
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, China
| | - Zhihao Yang
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, China
| | - Yun Peng
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, China
| | - Jihang Sun
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, China
- Children's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hospital of Beijing Children' s Hospital, Urumqi 830054, China
| |
Collapse
|
9
|
Njølstad TH, Jensen K, Andersen HK, Berstad AE, Hagen G, Johansen CK, Øye K, Glittum J, Dybwad A, Thingstad E, Guren MG, Dormagen JB, Schulz A. Deep learning reconstruction for detection of liver lesions at standard-dose and reduced-dose abdominal CT. Eur Radiol 2025:10.1007/s00330-025-11596-z. [PMID: 40251443 DOI: 10.1007/s00330-025-11596-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2024] [Revised: 02/19/2025] [Accepted: 03/20/2025] [Indexed: 04/20/2025]
Abstract
OBJECTIVES Deep learning reconstruction (DLR) has shown promising image denoising ability, but its radiation dose reduction potential remains unknown. The objective of this study was to investigate the diagnostic performance of DLR compared to iterative reconstruction (IR) in the detection of liver lesions at standard-dose and reduced-dose CT. MATERIALS AND METHODS Participants with known liver metastases from gastrointestinal and pancreatic adenocarcinoma were prospectively included from routine follow-up (October 2020 to March 2022). Participants received standard-dose CT and two additional reduced-dose scans during the same contrast administration, each reconstructed with IR and high-strength DLR. Two radiologists evaluated images for the presence of liver lesions, and a third established a reference standard. Diagnostic performance was compared using McNemar's test and mixed effects logistic regression. RESULTS Forty-four participants (mean age 66 years ± 11 [standard deviation], 28 men) were evaluated with 348 included liver lesions ≤ 20 mm (297 metastases, 51 benign; mean size 9.1 ± 4.3 mm). Mean volume CT dose index was 14.2, 7.8 mGy, and 5.1 mGy. Between algorithms, no significant difference in lesion detection was observed within dose levels. Detection of 233 lesions ≤ 10 mm was deteriorated with lower dose levels despite DLR denoising, with 185 detected at standard-dose IR (79.4%; 95% CI: 73.5-84.3) vs 128 at medium-dose DLR (54.9%; 95% CI: 48.3-61.4; p < 0.001) and 105 at low-dose DLR (45.1%; 95% CI: 38.6-51.7; p < 0.001). CONCLUSION Diagnostic performance for liver lesion detection was comparable between algorithms. When the detection of smaller lesions is important, DLR did not facilitate substantial dose reduction. KEY POINTS Question Methods to reduce CT radiation dose are desirable in clinical practice, and DLR has shown promising image denoising capabilities. Findings Liver lesion detection was comparable for DLR and IR across dose levels, but detection of smaller lesions deteriorated with lower dose levels. Clinical relevance Although potent in image noise reduction, the diagnostic performance of DLR is comparable to IR at standard-dose and reduced-dose CT. Care must be taken in pursuit of dose reduction when the detection and characterization of smaller liver lesions are of clinical importance.
Collapse
Affiliation(s)
| | - Kristin Jensen
- Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway
| | - Hilde K Andersen
- Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway
| | - Audun E Berstad
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Gaute Hagen
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Cathrine K Johansen
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Kjetil Øye
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Jan Glittum
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Anniken Dybwad
- Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway
| | - Emma Thingstad
- Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway
| | - Marianne G Guren
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Oncology, Oslo University Hospital, Oslo, Norway
| | | | - Anselm Schulz
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| |
Collapse
|
10
|
Zellner M, Sartoretti T, Flohr T, Frauenfelder T, Alkadhi H, Kellenberger CJ, Mergen V. Paediatric high-pitch lung imaging with photon-counting detector computed tomography: a dose reduction phantom study. Pediatr Radiol 2025:10.1007/s00247-025-06235-0. [PMID: 40229451 DOI: 10.1007/s00247-025-06235-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2025] [Revised: 03/26/2025] [Accepted: 03/29/2025] [Indexed: 04/16/2025]
Abstract
BACKGROUND Photon-counting detector computed tomography (PCD-CT) can reduce radiation dose in paediatric lung imaging. OBJECTIVE The aim of this study was to determine the lowest radiation dose maintaining adequate image quality for high-pitch lung imaging using a PCD-CT in a chest phantom replicating the characteristics of a 5-year-old child. MATERIALS AND METHODS The phantom was imaged on a dual-source PCD-CT with five different volume CT dose indices (CTDIvol): 0.45 mGy, 0.30 mGy, 0.15 mGy, 0.07 mGy, and 0.01 mGy. Scans were acquired with Sn100 kV in standard and ultra-high resolution modes. Polychromatic images were reconstructed with a 1-mm slice thickness, lung kernel Bl60, without quantum iterative reconstruction and with quantum iterative reconstruction at strengths 2 and 4. Two paediatric radiologists rated reconstructions subjectively, defining adequate image quality as the visibility of small peripheral structures. Objective evaluation included global noise index and global signal-to-noise ratio index. RESULTS Exposure times were 0.42 s and 0.84 s for standard and ultra-high resolution modes, respectively. Subjective assessments showed no significant differences across scan modes or quantum iterative reconstruction strengths for both readers at all doses (all, P > 0.05). Scans at 0.07 mGy with quantum iterative reconstruction 4 were deemed to maintain adequate image quality at the lowest dose. Global noise index was always lower and global signal-to-noise ratio index always higher in ultra-high resolution compared with standard mode, underscoring noise reduction achieved via ultra-high resolution mode's small pixel effect. CONCLUSIONS PCD-CT enables high-pitch lung imaging while maintaining adequate image quality at a radiation dose as low as 0.07 mGy, with quantum iterative reconstruction 4, in a paediatric phantom representing a 5-year-old child.
Collapse
Affiliation(s)
- Michael Zellner
- Department of Diagnostic Imaging, University Children's Hospital Zurich, Lenggstrasse 30, Zurich, 8008, Switzerland.
| | - Thomas Sartoretti
- Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland
| | - Thomas Flohr
- Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland
| | - Thomas Frauenfelder
- Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland
| | - Hatem Alkadhi
- Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland
| | - Christian J Kellenberger
- Department of Diagnostic Imaging, University Children's Hospital Zurich, Lenggstrasse 30, Zurich, 8008, Switzerland
| | - Victor Mergen
- Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland
| |
Collapse
|
11
|
Pimenta EB, Costa PR. Model observers and detectability index in x-ray imaging: historical review, applications and future trends. Phys Med Biol 2025; 70:07TR02. [PMID: 40081014 DOI: 10.1088/1361-6560/adc070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 03/13/2025] [Indexed: 03/15/2025]
Abstract
The detectability index, originally developed in psychophysics, has been applied in medical imaging to integrate objective metrics with subjective assessments. This index accounts for both image processing properties and the limitations of the human visual system, thus enhancing the clinical efficacy of imaging technologies. By providing a single metric that captures multiple aspects of image quality, the detectability index offers a comprehensive evaluation of clinical images. Numerous applications of this index across various areas of medical imaging are documented in the literature, along with recommendations for its use in periodic performance evaluations of imaging devices. However, since different modalities of images may require different detectability indices, it is crucial to assess the adequacy of the properties of the image being analyzed and those from the adopted index. A thorough understanding of this metric, including its statistical nature and complex relationship with model observers, is essential to ensure its proper application and interpretation, and to prevent misuse. Medical physicists face the challenge of a lack of organized guidance on the detectability index, necessitating a comprehensive review of its merits and drawbacks. This paper aims to trace the origins, concepts, and clinical applications of the detectability index, offering insight into its strengths, limitations, and future potential. To achieve this, an extensive literature review was conducted, covering the evolution of the index from its early use in radar interpretation to its current applications in modern imaging techniques and future trends. The paper includes supplementary materials such as a compendium of fundamental concepts, ancillary information, and mathematical deductions to help readers less experienced in the subject.
Collapse
Affiliation(s)
- Elsa B Pimenta
- University of São Paulo, Institute of physics, São Paulo, SP, Brazil
| | - Paulo R Costa
- University of São Paulo, Institute of physics, São Paulo, SP, Brazil
| |
Collapse
|
12
|
Caruso D, De Santis D, Tremamunno G, Santangeli C, Polidori T, Bona GG, Zerunian M, Del Gaudio A, Pugliese L, Laghi A. Deep learning reconstruction algorithm and high-concentration contrast medium: feasibility of a double-low protocol in coronary computed tomography angiography. Eur Radiol 2025; 35:2213-2221. [PMID: 39299952 PMCID: PMC11913928 DOI: 10.1007/s00330-024-11059-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 06/28/2024] [Accepted: 08/16/2024] [Indexed: 09/22/2024]
Abstract
OBJECTIVE To evaluate radiation dose and image quality of a double-low CCTA protocol reconstructed utilizing high-strength deep learning image reconstructions (DLIR-H) compared to standard adaptive statistical iterative reconstruction (ASiR-V) protocol in non-obese patients. MATERIALS AND METHODS From June to October 2022, consecutive patients, undergoing clinically indicated CCTA, with BMI < 30 kg/m2 were prospectively included and randomly assigned into three groups: group A (100 kVp, ASiR-V 50%, iodine delivery rate [IDR] = 1.8 g/s), group B (80 kVp, DLIR-H, IDR = 1.4 g/s), and group C (80 kVp, DLIR-H, IDR = 1.2 g/s). High-concentration contrast medium was administered. Image quality analysis was evaluated by two radiologists. Radiation and contrast dose, and objective and subjective image quality were compared across the three groups. RESULTS The final population consisted of 255 patients (64 ± 10 years, 161 men), 85 per group. Group B yielded 42% radiation dose reduction (2.36 ± 0.9 mSv) compared to group A (4.07 ± 1.2 mSv; p < 0.001) and achieved a higher signal-to-noise ratio (30.5 ± 11.5), contrast-to-noise-ratio (27.8 ± 11), and subjective image quality (Likert scale score: 4, interquartile range: 3-4) compared to group A and group C (all p ≤ 0.001). Contrast medium dose in group C (44.8 ± 4.4 mL) was lower than group A (57.7 ± 6.2 mL) and B (50.4 ± 4.3 mL), all the comparisons were statistically different (all p < 0.001). CONCLUSION DLIR-H combined with 80-kVp CCTA with an IDR 1.4 significantly reduces radiation and contrast medium exposure while improving image quality compared to conventional 100-kVp with 1.8 IDR protocol in non-obese patients. CLINICAL RELEVANCE STATEMENT Low radiation and low contrast medium dose coronary CT angiography protocol is feasible with high-strength deep learning reconstruction and high-concentration contrast medium without compromising image quality. KEY POINTS Minimizing the radiation and contrast medium dose while maintaining CT image quality is highly desirable. High-strength deep learning iterative reconstruction protocol yielded 42% radiation dose reduction compared to conventional protocol. "Double-low" coronary CTA is feasible with high-strength deep learning reconstruction without compromising image quality in non-obese patients.
Collapse
Affiliation(s)
- Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Domenico De Santis
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Tremamunno
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Curzio Santangeli
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Tiziano Polidori
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Giovanna G Bona
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Marta Zerunian
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Antonella Del Gaudio
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Luca Pugliese
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Andrea Laghi
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.
| |
Collapse
|
13
|
Park HJ, Choi H, Ryu RR. Double low protocol in pediatric abdominal CT for evaluating right lower quadrant pain. Jpn J Radiol 2025:10.1007/s11604-025-01766-w. [PMID: 40156737 DOI: 10.1007/s11604-025-01766-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2024] [Accepted: 02/27/2025] [Indexed: 04/01/2025]
Abstract
PURPOSE In pediatric patients, minimizing radiation and contrast media exposure without compromising diagnostic accuracy is paramount. Double low protocol, which utilizes a low dose contrast concentration and low tube voltage, could be a safer alternative. We compare diagnostic efficacy of double low protocol (Group A, 240 mgI/ml + 80 kVp) with conventional protocol (Group B, 350 mgI/ml + 120 kVp) in pediatric patients (< 10 years) presenting with abdominal pain and suspected acute appendicitis. MATERIALS AND METHODS This retrospective study included 121 pediatric patients who underwent enhanced abdominal CT between January 2019 and February 2023: 62 with Group A and 59 with Group B. We compared radiation dose, iodine load, and quantitative image quality parameters. Two radiologists independently assessed diagnostic image quality on a 5-point scale, visualization of the appendix, and diagnostic performance for acute appendicitis and its complications. RESULTS There were no significant differences in mean age (7.6 ± 2.0 vs. 7.6 ± 2.1, p = 0.956), body weight (31.4 ± 11.2 kg vs. 31.7 ± 11.4 kg, p = 0.972), and contrast media volume used (59.3 ± 21.0 ml vs. 65.0 ± 20.0 ml, p = 135) between the two groups. However, effective dose and iodine load used were significantly lower in Group A compared to Group B (2.7 ± 1.1 mSv vs. 4.3 ± 1.5 mSv and vs. 12.7 ± 4.6gI vs.18.6 ± 6.7gI, all p < 0.001). Although diagnostic image quality, noise and signal-to-noise ratio were significantly lower in Group A, visualization of the appendix (p = 0.853) and diagnostic accuracy for appendicitis were comparable between the two groups (98.4% vs. 94.9%, p = 0.284). DISCUSSION The double low protocol offers an effective alternative for evaluating pediatric patients requiring enhanced abdomen CT, achieving comparable diagnostic performance while significantly reducing radiation dose. We believe that our findings support safer CT acquisition practices for pediatric patients requiring enhanced CT imaging.
Collapse
Affiliation(s)
- Hyun Jeong Park
- Department of Radiology, Chung-Ang University Hospitall, Chung-Ang University College of Medicine, Seoul, Republic of Korea.
| | - Hyewon Choi
- Department of Radiology, Chung-Ang University Hospitall, Chung-Ang University College of Medicine, Seoul, Republic of Korea
| | - Rae Rim Ryu
- Department of Radiology, Chung-Ang University Hospitall, Chung-Ang University College of Medicine, Seoul, Republic of Korea
| |
Collapse
|
14
|
Clement David-Olawade A, Olawade DB, Vanderbloemen L, Rotifa OB, Fidelis SC, Egbon E, Akpan AO, Adeleke S, Ghose A, Boussios S. AI-Driven Advances in Low-Dose Imaging and Enhancement-A Review. Diagnostics (Basel) 2025; 15:689. [PMID: 40150031 PMCID: PMC11941271 DOI: 10.3390/diagnostics15060689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2025] [Revised: 02/24/2025] [Accepted: 03/05/2025] [Indexed: 03/29/2025] Open
Abstract
The widespread use of medical imaging techniques such as X-rays and computed tomography (CT) has raised significant concerns regarding ionizing radiation exposure, particularly among vulnerable populations requiring frequent imaging. Achieving a balance between high-quality diagnostic imaging and minimizing radiation exposure remains a fundamental challenge in radiology. Artificial intelligence (AI) has emerged as a transformative solution, enabling low-dose imaging protocols that enhance image quality while significantly reducing radiation doses. This review explores the role of AI-assisted low-dose imaging, particularly in CT, X-ray, and magnetic resonance imaging (MRI), highlighting advancements in deep learning models, convolutional neural networks (CNNs), and other AI-based approaches. These technologies have demonstrated substantial improvements in noise reduction, artifact removal, and real-time optimization of imaging parameters, thereby enhancing diagnostic accuracy while mitigating radiation risks. Additionally, AI has contributed to improved radiology workflow efficiency and cost reduction by minimizing the need for repeat scans. The review also discusses emerging directions in AI-driven medical imaging, including hybrid AI systems that integrate post-processing with real-time data acquisition, personalized imaging protocols tailored to patient characteristics, and the expansion of AI applications to fluoroscopy and positron emission tomography (PET). However, challenges such as model generalizability, regulatory constraints, ethical considerations, and computational requirements must be addressed to facilitate broader clinical adoption. AI-driven low-dose imaging has the potential to revolutionize radiology by enhancing patient safety, optimizing imaging quality, and improving healthcare efficiency, paving the way for a more advanced and sustainable future in medical imaging.
Collapse
Affiliation(s)
| | - David B. Olawade
- Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London E16 2RD, UK
- Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK;
- Department of Public Health, York St. John University, London E14 2BA, UK
| | - Laura Vanderbloemen
- Department of Primary Care and Public Health, Imperial College London, London SW7 2AZ, UK;
- School of Health, Sport and Bioscience, University of East London, London E16 2RD, UK
| | - Oluwayomi B. Rotifa
- Department of Radiology, Afe Babalola University MultiSystem Hospital, Ado-Ekiti 360102, Ekiti State, Nigeria;
| | - Sandra Chinaza Fidelis
- School of Nursing and Midwifery, University of Central Lancashire, Preston Campus, Preston PR1 2HE, UK;
| | - Eghosasere Egbon
- Department of Tissue Engineering and Regenerative Medicine, Faculty of Life Science Engineering, FH Technikum, 1200 Vienna, Austria;
| | | | - Sola Adeleke
- Guy’s Cancer Centre, Guy’s and St. Thomas’ NHS Foundation Trust, London SE1 9RT, UK;
- School of Cancer & Pharmaceutical Sciences, King’s College London, Strand, London WC2R 2LS, UK
| | - Aruni Ghose
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK;
- United Kingdom and Ireland Global Cancer Network, Manchester M20 4BX, UK
| | - Stergios Boussios
- Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK;
- School of Cancer & Pharmaceutical Sciences, King’s College London, Strand, London WC2R 2LS, UK
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK;
- Faculty of Medicine, Health, and Social Care, Canterbury Christ Church University, Canterbury CT1 1QU, UK
- Kent Medway Medical School, University of Kent, Canterbury CT2 7NZ, UK
- AELIA Organization, 57001 Thessaloniki, Greece
| |
Collapse
|
15
|
Qi K, Xu C, Yuan D, Zhang Y, Zhang M, Zhang W, Zhang J, You B, Gao J, Liu J. Feasibility of Ultra-low Radiation and Contrast Medium Dosage in Aortic CTA Using Deep Learning Reconstruction at 60 kVp: An Image Quality Assessment. Acad Radiol 2025; 32:1506-1516. [PMID: 39542806 DOI: 10.1016/j.acra.2024.10.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Revised: 10/19/2024] [Accepted: 10/23/2024] [Indexed: 11/17/2024]
Abstract
OBJECTIVE To assess the viability of using ultra-low radiation and contrast medium (CM) dosage in aortic computed tomography angiography (CTA) through the application of low tube voltage (60kVp) and a novel deep learning image reconstruction algorithm (ClearInfinity, DLIR-CI). METHODS Iodine attenuation curves obtained from a phantom study informed the administration of CM protocols. Non-obese participants undergoing aortic CTA were prospectively allocated into two groups and then obtained three reconstruction groups. The conventional group (100kVp-CV group) underwent imaging at 100kVp and received 210 mg iodine/kg in combination with a hybrid iterative reconstruction algorithm (ClearView, HIR-CV). The experimental group was imaged at 60kVp with 105 mg iodine/kg, while images were reconstructed with HIR-CV (60kVp-CV group) and with DLIR-CI (60kVp-CI group). Student's t-test was used to compare differences in CM protocol and radiation dose. One-way ANOVA compared CT attenuation, image noise, SNR, and CNR among the three reconstruction groups, while the Kruskal-Wallis H test assessed subjective image quality scores. Post hoc analysis was performed with Bonferroni correction for multiple comparisons, and consistency analysis conducted in subjective image quality assessment was measured using Cohen's kappa. RESULTS The radiation dose (1.12 ± 0.23mSv vs. 2.03 ± 0.82mSv) and CM dosage (19.04 ± 3.03mL vs. 38.11 ± 6.47mL) provided the reduction of 45% and 50% in the experimental group compared to the conventional group. The CT attenuation, SNR, and CNR of 60kVp-CI were superior to or equal to those of 100kVp-CV. Compared to the 60kVp-CV group, images in 60kVp-CI showed higher SNR and CNR (all P < 0.001). There was no difference between the 60kVp-CI and 100kVp-CV group in terms of the subjective image quality of the aorta in various locations (all P > 0.05), with 60kVp-CI images were deemed diagnostically sufficient across all vascular segments. CONCLUSION For non-obese patients, the combined use of 60kVp and DLIR-CI algorithm can be preserving image quality while enabling radiation dose and contrast medium savings for aortic CTA compared to 100kVp using HIR-CV.
Collapse
Affiliation(s)
- Ke Qi
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou 450052, Henan Province, China (K.Q., D.Y., Y.Z., M.Z., W.Z., J.Z., B.Y., J.G., J.L.)
| | - Chensi Xu
- CT Business Unit, Neusoft Medical Systems Co., Ltd, No.177-1, Innovation Road, Hunnan District, Shenyang, Liaoning Province, China (C.X.)
| | - Dian Yuan
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou 450052, Henan Province, China (K.Q., D.Y., Y.Z., M.Z., W.Z., J.Z., B.Y., J.G., J.L.)
| | - Yicun Zhang
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou 450052, Henan Province, China (K.Q., D.Y., Y.Z., M.Z., W.Z., J.Z., B.Y., J.G., J.L.)
| | - Mengyuan Zhang
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou 450052, Henan Province, China (K.Q., D.Y., Y.Z., M.Z., W.Z., J.Z., B.Y., J.G., J.L.)
| | - Weiting Zhang
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou 450052, Henan Province, China (K.Q., D.Y., Y.Z., M.Z., W.Z., J.Z., B.Y., J.G., J.L.)
| | - Jiong Zhang
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou 450052, Henan Province, China (K.Q., D.Y., Y.Z., M.Z., W.Z., J.Z., B.Y., J.G., J.L.)
| | - Bojun You
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou 450052, Henan Province, China (K.Q., D.Y., Y.Z., M.Z., W.Z., J.Z., B.Y., J.G., J.L.)
| | - Jianbo Gao
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou 450052, Henan Province, China (K.Q., D.Y., Y.Z., M.Z., W.Z., J.Z., B.Y., J.G., J.L.)
| | - Jie Liu
- The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou 450052, Henan Province, China (K.Q., D.Y., Y.Z., M.Z., W.Z., J.Z., B.Y., J.G., J.L.).
| |
Collapse
|
16
|
Zhang S, Zhu Z, Yu Z, Sun H, Sun Y, Huang H, Xu L, Wan J. Effectiveness of AI for Enhancing Computed Tomography Image Quality and Radiation Protection in Radiology: Systematic Review and Meta-Analysis. J Med Internet Res 2025; 27:e66622. [PMID: 40053787 PMCID: PMC11907168 DOI: 10.2196/66622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2024] [Revised: 11/27/2024] [Accepted: 12/16/2024] [Indexed: 03/09/2025] Open
Abstract
BACKGROUND Artificial intelligence (AI) presents a promising approach to balancing high image quality with reduced radiation exposure in computed tomography (CT) imaging. OBJECTIVE This meta-analysis evaluates the effectiveness of AI in enhancing CT image quality and lowering radiation doses. METHODS A thorough literature search was performed across several databases, including PubMed, Embase, Web of Science, Science Direct, and Cochrane Library, with the final update in 2024. We included studies that compared AI-based interventions to conventional CT techniques. The quality of these studies was assessed using the Newcastle-Ottawa Scale. Random effect models were used to pool results, and heterogeneity was measured using the I² statistic. Primary outcomes included image quality, CT dose index, and diagnostic accuracy. RESULTS This meta-analysis incorporated 5 clinical validation studies published between 2022 and 2024, totaling 929 participants. Results indicated that AI-based interventions significantly improved image quality (mean difference 0.70, 95% CI 0.43-0.96; P<.001) and showed a positive trend in reducing the CT dose index, though not statistically significant (mean difference 0.47, 95% CI -0.21 to 1.15; P=.18). AI also enhanced image analysis efficiency (odds ratio 1.57, 95% CI 1.08-2.27; P=.02) and demonstrated high accuracy and sensitivity in detecting intracranial aneurysms, with low-dose CT using AI reconstruction showing noninferiority for liver lesion detection. CONCLUSIONS The findings suggest that AI-based interventions can significantly enhance CT imaging practices by improving image quality and potentially reducing radiation doses, which may lead to better diagnostic accuracy and patient safety. However, these results should be interpreted with caution due to the limited number of studies and the variability in AI algorithms. Further research is needed to clarify AI's impact on radiation reduction and to establish clinical standards.
Collapse
Affiliation(s)
- Subo Zhang
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, Lianyungang, China
- Department of Medical Imaging, Cancer Hospital of Lianyungang, Lianyungang, China
- Lianyungang Clinical College, Jiangsu University, Lianyungang, China
| | - Zhitao Zhu
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, Lianyungang, China
- Department of Medical Imaging, Cancer Hospital of Lianyungang, Lianyungang, China
- Lianyungang Clinical College, Jiangsu University, Lianyungang, China
| | - Zhenfei Yu
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, Lianyungang, China
- Department of Medical Imaging, Cancer Hospital of Lianyungang, Lianyungang, China
- Lianyungang Clinical College, Jiangsu University, Lianyungang, China
| | - Haifeng Sun
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, Lianyungang, China
- Department of Medical Imaging, Cancer Hospital of Lianyungang, Lianyungang, China
- Lianyungang Clinical College, Jiangsu University, Lianyungang, China
| | - Yi Sun
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, Lianyungang, China
- Department of Medical Imaging, Cancer Hospital of Lianyungang, Lianyungang, China
- Lianyungang Clinical College, Jiangsu University, Lianyungang, China
| | - Hai Huang
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, Lianyungang, China
- Department of Medical Imaging, Cancer Hospital of Lianyungang, Lianyungang, China
- Lianyungang Clinical College, Jiangsu University, Lianyungang, China
| | - Lei Xu
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, Lianyungang, China
- Department of Medical Imaging, Cancer Hospital of Lianyungang, Lianyungang, China
- Lianyungang Clinical College, Jiangsu University, Lianyungang, China
| | - Jinxin Wan
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, Lianyungang, China
- Department of Medical Imaging, Cancer Hospital of Lianyungang, Lianyungang, China
- Lianyungang Clinical College, Jiangsu University, Lianyungang, China
| |
Collapse
|
17
|
Yang G, Wang H, Liu L, Ma Q, Shi H, Yuan Y. Impact of Combined Deep Learning Image Reconstruction and Metal Artifact Reduction Algorithm on CT Image Quality in Different Scanning Conditions for Maxillofacial Region with Metal Implants: A Phantom Study. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2025:10.1007/s10278-024-01287-4. [PMID: 39953255 DOI: 10.1007/s10278-024-01287-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 08/29/2024] [Accepted: 09/26/2024] [Indexed: 02/17/2025]
Abstract
This study aims to investigate the impact of combining deep learning image reconstruction (DLIR) and metal artifacts reduction (MAR) algorithms on the quality of CT images with metal implants under different scanning conditions. Four images of the maxillofacial region in pigs were taken using different metal implants for evaluation. The scans were conducted at three different dose levels (CTDIvol: 20/10/5 mGy). The images were reconstructed using three different methods: filtered back projection (FBP), adaptive statistical iterative reconstruction with Veo at a 50% level (AV50), and DLIR at three levels (low, medium, and high). Regions of interest (ROIs) were identified in various tissues (near/far/reference fat, muscle, bone) both with and without metal implants and artifacts. Parameters such as standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and metal artifact index (MAI) were calculated. Additionally, two experienced radiologists evaluated the subjective image quality (IQ) using a 5-point Likert scale. (1) Both observers rated MAR generated significantly lower artifact scores than non-MAR in all types of tissues (P < 0.01), except for the far shadow and bloom in bone (phantoms 1, 3, 4) and the far bloom in muscle (phantom 3) without significant differences (P = 1.0). (2) Under the same scanning condition, DLIR at three levels produced a smaller SD than those of FBP and AV50 (P < 0.05). (3) Compared to FBP and AV50, DLIR denoted a better reduction of MAI and improvement of SNR and CNR (P < 0.05) for most tissues between the four phantoms. (4) Subjective overall IQ was superior with the increasement of DLIR level (P < 0.05) and both observers agreed that DLIR produced better artifact reductions compared with FBP and AV50. The combination of DLIR and MAR algorithms can enhance image quality, significantly reduce metal artifacts, and offer high clinical value.
Collapse
Affiliation(s)
- Gongxin Yang
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haowei Wang
- Department of Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ling Liu
- GE Healthcare CT Research Center, Shanghai, China
| | - Qifan Ma
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huimin Shi
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Yuan
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| |
Collapse
|
18
|
Greffier J, Viry A, Robert A, Khorsi M, Si-Mohamed S. Photon-counting CT systems: A technical review of current clinical possibilities. Diagn Interv Imaging 2025; 106:53-59. [PMID: 39304365 DOI: 10.1016/j.diii.2024.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2024] [Accepted: 09/03/2024] [Indexed: 09/22/2024]
Abstract
In recent years, computed tomography (CT) has undergone a number of developments to improve radiological care. The most recent major innovation has been the development of photon-counting detectors. By comparison with the energy-integrating detectors traditionally used in CT, these detectors offer better dose efficiency, eliminate electronic noise, improve spatial resolution and have intrinsic spectral sensitivity. These detectors also allow the energy of each photon to be counted, thus improving the sampling of the X-ray spectrum in multiple energy bins, to better distinguish between photoelectric and Compton attenuation coefficients, resulting in better spectral images and specific color K-edge images. The purpose of this article was to make the reader more familiar with the basic principles and techniques of new photon-counting CT systems equipped with photon-counting detectors and also to describe the currently available devices that could be used in clinical practice.
Collapse
Affiliation(s)
- Joël Greffier
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30900 Nîmes, France.
| | - Anaïs Viry
- Institute of Radiation Physics, Lausanne University Hospital and University of Lausanne, 1007 Lausanne, Switzerland
| | - Antoine Robert
- University of Lyon, INSA-Lyon, University Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, 69621 Villeurbanne, France
| | - Mouad Khorsi
- Institute of Radiation Physics, Lausanne University Hospital and University of Lausanne, 1007 Lausanne, Switzerland
| | - Salim Si-Mohamed
- University of Lyon, INSA-Lyon, University Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, 69621 Villeurbanne, France; Department of Radiology, Louis Pradel Hospital, Hospices Civils de Lyon, 69500 Bron, France
| |
Collapse
|
19
|
Malthiery C, Hossu G, Ayav A, Laurent V. Characterization of hepatocellular carcinoma with CT with deep learning reconstruction compared with iterative reconstruction and 3-Tesla MRI. Eur Radiol 2025:10.1007/s00330-024-11314-1. [PMID: 39775897 DOI: 10.1007/s00330-024-11314-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Revised: 11/02/2024] [Accepted: 11/19/2024] [Indexed: 01/11/2025]
Abstract
OBJECTIVES This study compared the characteristics of lesions suspicious for hepatocellular carcinoma (HCC) and their LI-RADS classifications in adaptive statistical iterative reconstruction (ASIR) and deep learning reconstruction (DLR) to those of MR images, along with radiologist confidence. MATERIALS AND METHODS This prospective single-center trial included patients who underwent four-phase liver CT and multiphasic contrast-enhanced MRI within 7 days from February to August 2023. The lesion characteristics, LI-RADS classifications and confidence scores according to two radiologists on the ASIR, DLR and MRI techniques were compared. If the patient had at least one lesion, he was included in the HCC group, otherwise in the non-HCC group. MRI being the technique with the best sensitivity, concordance of lesions characteristics and LI-RADS classifications were calculated by weighted kappa between the ASIR and MRI and between the DLR and MRI. The confidence scores are expressed as the means and standard deviations. RESULTS Eighty-nine patients were enrolled, 52 in the HCC group (67 years ± 9 [mean ± SD], 46 men) and 37 in the non-HCC group (68 years ± 9, 33 men). The concordance coefficient between the LI-RADS classification by ASIR and MRI was 0.64 [0.52; 0.76], showing good agreement, that by DLR and MRI was 0.83 [0.73; 0.92], showing excellent agreement. The diagnostic confidence in ASIR was 3.31 ± 0.95 (mean ± SD) and 3.0 ± 1.11, that in the DLR was 3.9 ± 0.88 and 4.11 ± 0.75, that in the MRI was 4.46 ± 0.80 and 4.57 ± 0.80. CONCLUSION DLR provided excellent LI-RADS classification concordance with MRI, whereas ASIR provided good concordance. The radiologists' confidence was greater in the DLR than in the ASIR but remained highest in the MR group. KEY POINTS Question Does the use of deep learning reconstructions (DLR) improve LI-RADS classification of suspicious hepatocellular carcinoma lesions compared to adaptive statistical iterative reconstructions (ASIR)? Findings DLR demonstrated superior concordance of LI-RADS classification with MRI compared to ASIR. It also provided greater diagnostic confidence than ASIR. Clinical relevance The use of DLR enhances radiologists' ability to visualize and characterize lesions suspected of being HCC, as well as their LI-RADS classification. Moreover, it also boosts their confidence in interpreting these images.
Collapse
Affiliation(s)
- Clément Malthiery
- Department of Adult Radiology, CHRU de Nancy, Vandoeuvre-lès-Nancy, France.
| | - Gabriela Hossu
- Clinical Investigation Center Technological Innovation of Nancy, Inserm, CHRU de Nancy, Vandoeuvre-lès-Nancy, France
| | - Ahmet Ayav
- Department of HPB Surgery, CHRU de Nancy, Vandoeuvre-lès-Nancy, France
| | - Valérie Laurent
- Adaptive Diagnostic and Interventional Imaging, Inserm, CHRU de Nancy, Vandoeuvre-lès-Nancy, France
| |
Collapse
|
20
|
Harashima S, Fukui R, Samejima W, Hirose Y, Kariyasu T, Nishikawa M, Yamaguchi H, Machida H. Virtual Monochromatic Imaging of Half-Iodine-Load, Contrast-Enhanced Computed Tomography with Deep Learning Image Reconstruction in Patients with Renal Insufficiency: A Clinical Pilot Study. J NIPPON MED SCH 2025; 92:69-79. [PMID: 40058838 DOI: 10.1272/jnms.jnms.2025_92-112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2025]
Abstract
BACKGROUND We retrospectively examined image quality (IQ) of thin-slice virtual monochromatic imaging (VMI) of half-iodine-load, abdominopelvic, contrast-enhanced CT (CECT) by dual-energy CT (DECT) with deep learning image reconstruction (DLIR). METHODS In 28 oncology patients with moderate-to-severe renal impairment undergoing half-iodine-load (300 mgI/kg) CECT by DECT during the nephrographic phase, we reconstructed VMI at 40-70 keV with a slice thickness of 0.625 mm using filtered back-projection (FBP), hybrid iterative reconstruction (HIR), and DLIR; measured contrast-noise ratio (CNR) of the liver, spleen, aorta, portal vein, and prostate/uterus; and determined the optimal keV to achieve the maximal CNR. At the optimal keV, two independent radiologists compared each organ's CNR and subjective IQ scores among FBP, HIR, and DLIR to subjectively grade image noise, contrast, sharpness, delineation of small structures, and overall IQ. RESULTS CNR of each organ increased continuously from 70 to 40 keV using FBP, HIR, and DLIR. At 40 keV, CNR of the prostate/uterus was significantly higher with DLIR than with FBP; however, CNR was similar between FBP and HIR and between HIR and DLIR. The CNR of all other organs increased significantly from FBP to HIR to DLIR (P < 0.05). All IQ scores significantly improved from FBP to HIR to DLIR (P < 0.05) and were acceptable in all patients with DLIR only. CONCLUSIONS The combination of 40 keV and DLIR offers the maximal CNR and a subjectively acceptable IQ for thin-slice VMI of half-iodine-load CECT.
Collapse
Affiliation(s)
- Shingo Harashima
- Department of Radiology, Tokyo Women's Medical University Adachi Medical Center
| | - Rika Fukui
- Department of Radiology, Tokyo Women's Medical University Adachi Medical Center
| | - Wakana Samejima
- Department of Radiology, Tokyo Women's Medical University Adachi Medical Center
| | - Yuta Hirose
- Department of Radiology, Tokyo Women's Medical University Adachi Medical Center
| | - Toshiya Kariyasu
- Department of Radiology, Tokyo Women's Medical University Adachi Medical Center
| | - Makiko Nishikawa
- Department of Radiology, Tokyo Women's Medical University Adachi Medical Center
| | - Hidenori Yamaguchi
- Department of Radiology, Tokyo Women's Medical University Adachi Medical Center
- Department of Radiology, Nippon Medical School Tama Nagayama Hospital
| | - Haruhiko Machida
- Department of Radiology, Tokyo Women's Medical University Adachi Medical Center
| |
Collapse
|
21
|
Bertl M, Hahne FG, Gräger S, Heinrich A. Impact of Deep Learning-Based Image Reconstruction on Tumor Visibility and Diagnostic Confidence in Computed Tomography. Bioengineering (Basel) 2024; 11:1285. [PMID: 39768103 PMCID: PMC11673264 DOI: 10.3390/bioengineering11121285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2024] [Revised: 12/11/2024] [Accepted: 12/16/2024] [Indexed: 01/11/2025] Open
Abstract
Deep learning image reconstruction (DLIR) has shown potential to enhance computed tomography (CT) image quality, but its impact on tumor visibility and adoption among radiologists with varying experience levels remains unclear. This study compared the performance of two deep learning-based image reconstruction methods, DLIR and Pixelshine, an adaptive statistical iterative reconstruction-volume (ASIR-V) method, and filtered back projection (FBP) across 33 contrast-enhanced CT staging examinations, evaluated by 20-24 radiologists. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured for tumor and surrounding organ tissues across DLIR (Low, Medium, High), Pixelshine (Soft, Ultrasoft), ASIR-V (30-100%), and FBP. In two blinded surveys, radiologists ranked eight reconstructions and assessed four using a 5-point Likert scale in arterial and portal venous phases. DLIR consistently outperformed other methods in SNR, CNR, image quality, image interpretation, structural differentiability and diagnostic certainty. Pixelshine performed comparably only to ASIR-V 50%. No significant differences were observed between junior and senior radiologists. In conclusion, DLIR-based techniques have the potential to establish a new benchmark in clinical CT imaging, offering superior image quality for tumor staging, enhanced diagnostic capabilities, and seamless integration into existing workflows without requiring an extensive learning curve.
Collapse
Affiliation(s)
| | | | | | - Andreas Heinrich
- Department of Radiology, Jena University Hospital, Friedrich Schiller University, 07747 Jena, Germany
| |
Collapse
|
22
|
Jiang H, Qin S, Jia L, Wei Z, Xiong W, Xu W, Gong W, Zhang W, Yu L. Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy. J Appl Clin Med Phys 2024; 25:e14560. [PMID: 39540681 PMCID: PMC11633815 DOI: 10.1002/acm2.14560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 08/11/2024] [Accepted: 09/12/2024] [Indexed: 11/16/2024] Open
Abstract
OBJECTIVE We investigated the feasibility of deep learning-based ultra-low dose kV-fan-beam computed tomography (kV-FBCT) image enhancement algorithm for clinical application in abdominal and pelvic tumor radiotherapy. METHODS A total of 76 patients of abdominal and pelvic tumors were prospectively selected. The Catphan504 was acquired with the same conditions as the standard phantom test set. We used a CycleGAN-based model for image enhancement. Normal dose CT (NDCT), ultra-low dose CT (LDCT) and deep learning enhanced CT (DLR) were evaluated by subjective and objective analyses in terms of imaging quality, HU accuracy, and image signal-to-noise ratio (SNR). RESULTS The image noise of DLR was significantly reduced, and the contrast-to-noise ratio (CNR) was significantly improved compared to the LDCT. The most significant improvement was the acrylic which represented soft tissue in CNR from 1.89 to 3.37, improving by 76%, nearly approaching the NDCT, and in low-density resolution from 7.64 to 12.6, improving by 64%. The spatial frequencies of MTF10 and MTF50 in DLR were 4.28 and 2.35 cycles/mm in DLR, respectively, which are higher than LDCT 3.87 and 2.12 cycles/mm, and even slightly higher than NDCT 4.15 and 2.28 cycles/mm. The accuracy and stability of HU values of DLR were similar to NDCT. The image quality evaluation of the two doctors agreed well with DLR and NDCT. A two-by-two comparison between groups showed that the differences in image scores of LDCT compared with NDCT and DLR were all statistically significant (p < 0.05), and the subjective scores of DLR were close to NDCT. CONCLUSION The image quality of DLR was close to NDCT with reduced radiation dose, which can fully meet the needs of conventional image-guided adaptive radiotherapy (ART) and achieve the quality requirements of clinical radiotherapy. The proposed method provided a technical basis for LDCT-guided ART.
Collapse
Affiliation(s)
- Hua Jiang
- Department of Radiation OncologyThe First Affiliated Hospital of Soochow UniversitySuzhouChina
| | - Songbing Qin
- Department of Radiation OncologyThe First Affiliated Hospital of Soochow UniversitySuzhouChina
| | - Lecheng Jia
- Real Time Laboratory, Shenzhen United Imaging Research Institute of Innovative Medical EquipmentShenzhenChina
- Zhejiang Engineering Research Center for Innovation and Application of Intelligent Radiotherapy TechnologyWenzhouChina
| | - Ziquan Wei
- Real Time Laboratory, Shenzhen United Imaging Research Institute of Innovative Medical EquipmentShenzhenChina
| | - Weiqi Xiong
- Radiotherapy Business UnitShanghai United Imaging Healthcare Co. Ltd.ShanghaiChina
| | - Wentao Xu
- Department of Radiation OncologyThe First Affiliated Hospital of Soochow UniversitySuzhouChina
| | - Wei Gong
- Department of Radiation OncologyThe First Affiliated Hospital of Soochow UniversitySuzhouChina
| | - Wei Zhang
- Radiotherapy Business UnitShanghai United Imaging Healthcare Co. Ltd.ShanghaiChina
| | - Liqin Yu
- Department of Radiation OncologyThe First Affiliated Hospital of Soochow UniversitySuzhouChina
| |
Collapse
|
23
|
Zellner M, Tschauner S, Weyland MS, Hotz PE, Scheidegger S, Kellenberger CJ. Low-dose lung CT: Optimizing diagnostic radiation dose - A phantom study. Eur J Radiol Open 2024; 13:100614. [PMID: 39654592 PMCID: PMC11626076 DOI: 10.1016/j.ejro.2024.100614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2024] [Revised: 11/17/2024] [Accepted: 11/18/2024] [Indexed: 12/12/2024] Open
Abstract
Background/purpose To investigate a quantitative method for assessing image quality of low dose lung computed tomography (CT) and find the lowest exposure dose providing diagnostic images. Methods Axial volumetric lung CT acquisitions (256 slice scanner) were performed on three different sized anthropomorphic phantoms at different dose levels. The maximum steepness of sigmoid curves fitted to line density profiles was measured at lung-to-pleura interfaces. For each phantom, image sharpness was calculated as the median of 468 measurements from 39 different locations. Diagnostic image quality for the adult and paediatric phantom was rated by three radiologists using 4-point Likert scales. The image sharpness cut-off for obtaining adequate image quality was determined from qualitative ratings. Results Adequate diagnostic image quality was reached at a median steepness of 713 HU/mm in the adult phantom with a corresponding CTDIvol of 0.14 mGy and an effective dose of 0.13 mSv at a dose level of 100 kVp and 10 mA. In the paediatric phantom diagnostic image quality was reached at a median steepness of 1139 HU/mm with a corresponding CTDIvol of 0.13 mGy and an effective dose of 0.08 mSv at a dose level of 100 kVp and 10 mA. Conclusions Determination of image sharpness on line density profiles can be used as quantitative measure for image quality of lung CT. Sufficient-quality lung CT can be achieved at effective radiation doses of 0.13 mSv (adult phantom) and 0.08 mSv (paediatric phantom). These findings suggest that substantial dose reduction is feasible without compromising diagnostic accuracy.
Collapse
Affiliation(s)
- Michael Zellner
- University Children’s Hospital Zürich, Department of Diagnostic Imaging, Zurich, Switzerland
- University Children’s Hospital Zürich, Children’s Research Centre, Zurich, Switzerland
| | - Sebastian Tschauner
- University Children’s Hospital Zürich, Department of Diagnostic Imaging, Zurich, Switzerland
- University Children’s Hospital Zürich, Children’s Research Centre, Zurich, Switzerland
- Medical University of Graz, Department of Radiology, Division of Paediatric Radiology, Graz, Austria
| | - Mathias S. Weyland
- Cantonal Hospital Aarau AG, Applied Complex Systems Science, Aarau, Switzerland
- University of Applied Sciences Zurich, Zurich, Switzerland
| | - Peter Eggenberger Hotz
- Cantonal Hospital Aarau AG, Applied Complex Systems Science, Aarau, Switzerland
- University of Applied Sciences Zurich, Zurich, Switzerland
| | - Stephan Scheidegger
- Cantonal Hospital Aarau AG, Applied Complex Systems Science, Aarau, Switzerland
- University of Applied Sciences Zurich, Zurich, Switzerland
| | - Christian J. Kellenberger
- University Children’s Hospital Zürich, Department of Diagnostic Imaging, Zurich, Switzerland
- University Children’s Hospital Zürich, Children’s Research Centre, Zurich, Switzerland
| |
Collapse
|
24
|
Yoshida K, Nagayama Y, Funama Y, Ishiuchi S, Motohara T, Masuda T, Nakaura T, Ishiko T, Hirai T, Beppu T. Low tube voltage and deep-learning reconstruction for reducing radiation and contrast medium doses in thin-slice abdominal CT: a prospective clinical trial. Eur Radiol 2024; 34:7386-7396. [PMID: 38753193 DOI: 10.1007/s00330-024-10793-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/23/2024] [Accepted: 04/08/2024] [Indexed: 10/29/2024]
Abstract
OBJECTIVES To investigate the feasibility of low-radiation dose and low iodinated contrast medium (ICM) dose protocol combining low-tube voltage and deep-learning reconstruction (DLR) algorithm in thin-slice abdominal CT. METHODS This prospective study included 148 patients who underwent contrast-enhanced abdominal CT with either 120-kVp (600 mgL/kg, n = 74) or 80-kVp protocol (360 mgL/kg, n = 74). The 120-kVp images were reconstructed using hybrid iterative reconstruction (HIR) (120-kVp-HIR), while 80-kVp images were reconstructed using HIR (80-kVp-HIR) and DLR (80-kVp-DLR) with 0.5 mm thickness. Size-specific dose estimate (SSDE) and iodine dose were compared between protocols. Image noise, CT attenuation, and contrast-to-noise ratio (CNR) were quantified. Noise power spectrum (NPS) and edge rise slope (ERS) were used to evaluate noise texture and edge sharpness, respectively. The subjective image quality was rated on a 4-point scale. RESULTS SSDE and iodine doses of 80-kVp were 40.4% (8.1 ± 0.9 vs. 13.6 ± 2.7 mGy) and 36.3% (21.2 ± 3.9 vs. 33.3 ± 4.3 gL) lower, respectively, than those of 120-kVp (both, p < 0.001). CT attenuation of vessels and solid organs was higher in 80-kVp than in 120-kVp images (all, p < 0.001). Image noise of 80-kVp-HIR and 80-kVp-DLR was higher and lower, respectively than that of 120-kVp-HIR (both p < 0.001). The highest CNR and subjective scores were attained in 80-kVp-DLR (all, p < 0.001). There were no significant differences in average NPS frequency and ERS between 120-kVp-HIR and 80-kVp-DLR (p ≥ 0.38). CONCLUSION Compared with the 120-kVp-HIR protocol, the combined use of 80-kVp and DLR techniques yielded superior subjective and objective image quality with reduced radiation and ICM doses at thin-section abdominal CT. CLINICAL RELEVANCE STATEMENT Scanning at low-tube voltage (80-kVp) combined with the deep-learning reconstruction algorithm may enhance diagnostic efficiency and patient safety by improving image quality and reducing radiation and contrast doses of thin-slice abdominal CT. KEY POINTS Reducing radiation and iodine doses is desirable; however, contrast and noise degradation can be detrimental. The 80-kVp scan with the deep-learning reconstruction technique provided better images with lower radiation and contrast doses. This technique may be efficient for improving diagnostic confidence and patient safety in thin-slice abdominal CT.
Collapse
Affiliation(s)
- Kenichiro Yoshida
- Department of Radiology, Yamaga Medical Center, 511 Yamaga, Kumamoto, 861-0501, Japan
- Graduate School of Health Sciences, Kumamoto University, 4-24-1 Kuhonji, Chuo-ku, Kumamoto, 862-0976, Japan
| | - Yasunori Nagayama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto, 860-8556, Japan.
| | - Yoshinori Funama
- Department of Medical Radiation Sciences, Faculty of Life Sciences, Kumamoto University, 4-24-1 Kuhonji, Chuo-ku, Kumamoto, 862-0976, Japan
| | - Soichiro Ishiuchi
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto, 860-8556, Japan
| | - Toshihiko Motohara
- Department of Gastroenterology, Yamaga Medical Center, 511 Yamaga, Kumamoto, 861-0501, Japan
| | - Toshiro Masuda
- Department of Surgery, Yamaga Medical Center, 511 Yamaga, Kumamoto, 861-0501, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto, 860-8556, Japan
| | - Takatoshi Ishiko
- Department of Surgery, Yamaga Medical Center, 511 Yamaga, Kumamoto, 861-0501, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto, 860-8556, Japan
| | - Toru Beppu
- Department of Surgery, Yamaga Medical Center, 511 Yamaga, Kumamoto, 861-0501, Japan
| |
Collapse
|
25
|
Zheng Z, Liang Y, Wu Z, Han Q, Ai Z, Ma K, Xiang Z. Effect of Deep Learning Image Reconstruction Algorithms on Radiomic Features of Pulmonary Nodules in Ultra-Low-Dose CT. J Comput Assist Tomogr 2024; 48:943-950. [PMID: 39095065 DOI: 10.1097/rct.0000000000001634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
OBJECTIVE The purpose of this study is to explore the impact of deep learning image reconstruction (DLIR) algorithm on the quantification of radiomic features in ultra-low-dose computed tomography (ULD-CT) compared with adaptive statistical iterative reconstruction-Veo (ASIR-V). METHODS One hundred eighty-three patients with pulmonary nodules underwent standard-dose computed tomography (SDCT) (4.30 ± 0.36 mSv) and ULD-CT (UL-A, 0.57 ± 0.09 mSv or UL-B, 0.33 ± 0.04 mSv). SDCT was the reference standard using (ASIR-V) at 50% strength (50%ASIR-V). ULD-CT was reconstructed with 50%ASIR-V, DLIR at medium and high strength (DLIR-M, DLIR-H). Radiomics analysis extracted 102 features, and the intraclass correlation coefficient (ICC) quantified reproducibility between ULD-CT and SDCT reconstructed by 50%ASIR-V, DLIR-M, and DLIR-H for each feature. RESULTS Among 102 radiomic features, the percentages of reproducibility of 50%ASIR-V, DLIR-M, and DLIR-H were 48.04% (49/102), 49.02% (50/102), and 52.94% (54/102), respectively. Shape and first order features demonstrated high reproducibility across different reconstruction algorithms and radiation doses, with mean ICC values exceeding 0.75. In texture features, DLIR-M and DLIR-H showed improved mean ICC values for pure ground glass nodules (pGGNs) from 0.69 ± 0.23 to 0.75 ± 0.18 and 0.81 ± 0.12, respectively, compared with 50%ASIR-V. Similarly, the mean ICC values for solid nodules (SNs) increased from 0.60 ± 0.19 to 0.66 ± 0.14 and 0.69 ± 0.13, respectively. Additionally, the mean ICC values of texture features for pGGNs and SNs in both ULD-CT groups decreased with reduced radiation dose. CONCLUSIONS DLIR can improve the reproducibility of radiomic features at ultra-low doses compared with ASIR-V. In addition, pGGNs showed better reproducibility at ultra-low doses than SNs.
Collapse
Affiliation(s)
| | - Yuying Liang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | | | - Qijia Han
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Zhu Ai
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Kun Ma
- CT Imaging Research Center, GE HealthCare China, Guangzhou, China
| | - Zhiming Xiang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| |
Collapse
|
26
|
Kazimierczak W, Wajer R, Komisarek O, Dyszkiewicz-Konwińska M, Wajer A, Kazimierczak N, Janiszewska-Olszowska J, Serafin Z. Evaluation of a Vendor-Agnostic Deep Learning Model for Noise Reduction and Image Quality Improvement in Dental CBCT. Diagnostics (Basel) 2024; 14:2410. [PMID: 39518377 PMCID: PMC11545169 DOI: 10.3390/diagnostics14212410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2024] [Revised: 10/21/2024] [Accepted: 10/22/2024] [Indexed: 11/16/2024] Open
Abstract
BACKGROUND/OBJECTIVES To assess the impact of a vendor-agnostic deep learning model (DLM) on image quality parameters and noise reduction in dental cone-beam computed tomography (CBCT) reconstructions. METHODS This retrospective study was conducted on CBCT scans of 93 patients (41 males and 52 females, mean age 41.2 years, SD 15.8 years) from a single center using the inclusion criteria of standard radiation dose protocol images. Objective and subjective image quality was assessed in three predefined landmarks through contrast-to-noise ratio (CNR) measurements and visual assessment using a 5-point scale by three experienced readers. The inter-reader reliability and repeatability were calculated. RESULTS Eighty patients (30 males and 50 females; mean age 41.5 years, SD 15.94 years) were included in this study. The CNR in DLM reconstructions was significantly greater than in native reconstructions, and the mean CNR in regions of interest 1-3 (ROI1-3) in DLM images was 11.12 ± 9.29, while in the case of native reconstructions, it was 7.64 ± 4.33 (p < 0.001). The noise level in native reconstructions was significantly higher than in the DLM reconstructions, and the mean noise level in ROI1-3 in native images was 45.83 ± 25.89, while in the case of DLM reconstructions, it was 35.61 ± 24.28 (p < 0.05). Subjective image quality assessment revealed no statistically significant differences between native and DLM reconstructions. CONCLUSIONS The use of deep learning-based image reconstruction algorithms for CBCT imaging of the oral cavity can improve image quality by enhancing the CNR and lowering the noise.
Collapse
Affiliation(s)
- Wojciech Kazimierczak
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, University Hospital No. 1 in Bydgoszcz, Marii Skłodowskiej—Curie 9, 85-094 Bydgoszcz, Poland
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Róża Wajer
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, University Hospital No. 1 in Bydgoszcz, Marii Skłodowskiej—Curie 9, 85-094 Bydgoszcz, Poland
| | - Oskar Komisarek
- Department of Otolaryngology, Audiology and Phoniatrics, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | | | - Adrian Wajer
- Dental Primus, Poznańska 18, 88-100 Inowrocław, Poland
| | - Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Joanna Janiszewska-Olszowska
- Department of Interdisciplinary Dentistry, Pomeranian Medical University in Szczecin, Al. Powstańców Wlkp. 72, 70-111 Szczecin, Poland
| | - Zbigniew Serafin
- Faculty of Medicine, Bydgoszcz University of Science and Technology, Kaliskiego 7, 85-796 Bydgoszcz, Poland
| |
Collapse
|
27
|
She Y, Liu X, Liu H, Yang H, Zhang W, Han Y, Zhou J. Combination of clinical and spectral-CT iodine concentration for predicting liver metastasis in gastric cancer: a preliminary study. Abdom Radiol (NY) 2024; 49:3438-3449. [PMID: 38744700 DOI: 10.1007/s00261-024-04346-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 04/13/2024] [Accepted: 04/16/2024] [Indexed: 05/16/2024]
Abstract
PURPOSE This study aimed to determine the diagnostic efficacy of various indicators and models for the prediction of gastric cancer with liver metastasis. METHODS Clinical and spectral computed tomography (CT) data from 80 patients with gastric adenocarcinoma who underwent surgical resection were retrospectively analyzed. Patients were divided into metastatic and non-metastatic groups based on whether or not to occur liver metastasis, and the region of interest (ROI) was measured manually on each phase iodine map at the largest level of the tumor. Iodine concentration (IC), normalized iodine concentration (nIC), and clinical data of the primary gastric lesions were analyzed. Logistic regression analysis was used to construct the clinical indicator (CI) and clinical indicator-spectral CT iodine concentration (CI-Spectral CT-IC) Models, which contained all of the parameters with statistically significant differences between the groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the accuracy of the models. RESULTS The metastatic group showed significantly higher levels of Cancer antigen125 (CA125), carcinoembryonic antigen (CEA), IC, and nIC in the arterial phase, venous phase, and delayed phase than the non-metastatic group (all p < 0.05). Normalized iodine concentration Venous Phase (nICVP) exhibited a favorable performance among all IC and nIC parameters for forecasting gastric cancer with liver metastasis (area under the curve (AUC), 0.846). The combination model of clinical data with significant differences and nICVP showed the best diagnostic accuracy for predicting liver metastasis from gastric cancer, with an AUC of 0.897. CONCLUSION nICVP showed the best diagnostic efficacy for predicting gastric cancer with liver metastasis. Clinical Indicators-normalized ICVP model can improve the prediction accuracy for this condition.
Collapse
Affiliation(s)
- Yingxia She
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Xianwang Liu
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Hong Liu
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Haiting Yang
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Wenjuan Zhang
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Yinping Han
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Junlin Zhou
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, People's Republic of China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China.
| |
Collapse
|
28
|
Cao J, Mroueh N, Mercaldo N, Lennartz S, Kongboonvijit S, Srinivas Rao S, Pisuchpen N, Baliyan V, Pierce TT, Anderson MA, Sertic M, Shenoy-Bhangle AS, Kambadakone AR, Atzen S. Detectability of Hypoattenuating Liver Lesions with Deep Learning CT Reconstruction: A Phantom and Patient Study. Radiology 2024; 313:e232749. [PMID: 39377679 PMCID: PMC11535864 DOI: 10.1148/radiol.232749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 06/12/2024] [Accepted: 07/29/2024] [Indexed: 10/09/2024]
Abstract
Background CT deep learning image reconstruction (DLIR) improves image quality by reducing noise compared with adaptive statistical iterative reconstruction-V (ASIR-V). However, objective assessment of low-contrast lesion detectability is lacking. Purpose To investigate low-contrast detectability of hypoattenuating liver lesions on CT scans reconstructed with DLIR compared with CT scans reconstructed with ASIR-V in a patient and a phantom study. Materials and Methods This single-center retrospective study included patients undergoing portal venous phase abdominal CT between February and May 2021 and a low-contrast-resolution phantom scanned with the same protocol. Four reconstructions (ASIR-V at 40% strength [ASIR-V 40] and DLIR at three strengths) were generated. Five radiologists qualitatively assessed the images using the five-point Likert scale for image quality, lesion diagnostic confidence, conspicuity, and small lesion (≤1 cm) visibility. Up to two key lesions per patient, confirmed at histopathologic testing or at prior or follow-up imaging studies, were included. Lesion-to-background contrast-to-noise ratio was calculated. Interreader variability was analyzed. Intergroup qualitative and quantitative metrics were compared between DLIR and ASIR-V 40 using proportional odds logistic regression models. Results Eighty-six liver lesions (mean size, 15 mm ± 9.5 [SD]) in 50 patients (median age, 62 years [IQR, 57-73 years]; 27 [54%] female patients) were included. Differences were not detected for various qualitative low-contrast detectability metrics between ASIR-V 40 and DLIR (P > .05). Quantitatively, medium-strength DLIR and high-strength DLIR yielded higher lesion-to-background contrast-to-noise ratios than ASIR-V 40 (medium-strength DLIR vs ASIR-V 40: odds ratio [OR], 1.96 [95% CI: 1.65, 2.33]; high-strength DLIR vs ASIR-V 40: OR, 5.36 [95% CI: 3.68, 7.82]; P < .001). Low-contrast lesion attenuation was reduced by 2.8-3.6 HU with DLIR. Interreader agreement was moderate to very good for the qualitative metrics. Subgroup analysis based on lesion size of larger than 1 cm and 1 cm or smaller yielded similar results (P > .05). Qualitatively, phantom study results were similar to those in patients (P > .05). Conclusion The detectability of low-contrast liver lesions was similar on CT scans reconstructed with low-, medium-, and high-strength DLIR and ASIR-V 40 in both patient and phantom studies. Lesion-to-background contrast-to-noise ratios were higher for DLIR medium- and high-strength reconstructions compared with ASIR-V 40. © RSNA, 2024 Supplemental material is available for this article.
Collapse
Affiliation(s)
| | | | - Nathaniel Mercaldo
- From the Department of Radiology, Division of Abdominal Radiology,
Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270,
Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P.,
V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and
Interventional Radiology, Faculty of Medicine, University Cologne, University
Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty
of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society,
Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Simon Lennartz
- From the Department of Radiology, Division of Abdominal Radiology,
Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270,
Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P.,
V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and
Interventional Radiology, Faculty of Medicine, University Cologne, University
Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty
of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society,
Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Sasiprang Kongboonvijit
- From the Department of Radiology, Division of Abdominal Radiology,
Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270,
Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P.,
V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and
Interventional Radiology, Faculty of Medicine, University Cologne, University
Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty
of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society,
Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Shravya Srinivas Rao
- From the Department of Radiology, Division of Abdominal Radiology,
Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270,
Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P.,
V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and
Interventional Radiology, Faculty of Medicine, University Cologne, University
Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty
of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society,
Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Nisanard Pisuchpen
- From the Department of Radiology, Division of Abdominal Radiology,
Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270,
Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P.,
V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and
Interventional Radiology, Faculty of Medicine, University Cologne, University
Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty
of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society,
Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Vinit Baliyan
- From the Department of Radiology, Division of Abdominal Radiology,
Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270,
Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P.,
V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and
Interventional Radiology, Faculty of Medicine, University Cologne, University
Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty
of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society,
Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Theodore T. Pierce
- From the Department of Radiology, Division of Abdominal Radiology,
Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270,
Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P.,
V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and
Interventional Radiology, Faculty of Medicine, University Cologne, University
Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty
of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society,
Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Mark A. Anderson
- From the Department of Radiology, Division of Abdominal Radiology,
Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270,
Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P.,
V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and
Interventional Radiology, Faculty of Medicine, University Cologne, University
Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty
of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society,
Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Madeleine Sertic
- From the Department of Radiology, Division of Abdominal Radiology,
Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270,
Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P.,
V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and
Interventional Radiology, Faculty of Medicine, University Cologne, University
Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty
of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society,
Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Anuradha S. Shenoy-Bhangle
- From the Department of Radiology, Division of Abdominal Radiology,
Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270,
Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P.,
V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and
Interventional Radiology, Faculty of Medicine, University Cologne, University
Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty
of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society,
Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Avinash R. Kambadakone
- From the Department of Radiology, Division of Abdominal Radiology,
Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270,
Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P.,
V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and
Interventional Radiology, Faculty of Medicine, University Cologne, University
Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty
of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society,
Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| | - Sarah Atzen
- From the Department of Radiology, Division of Abdominal Radiology,
Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270,
Boston, MA 02114-2696 (J.C., N. Mroueh, N. Mercaldo, S.L., S.K., S.S.R., N.P.,
V.B., T.T.P., M.A.A., M.S., A.S.S.B., A.R.K.); Institute for Diagnostic and
Interventional Radiology, Faculty of Medicine, University Cologne, University
Hospital Cologne, Cologne, Germany (S.L.); and Department of Radiology, Faculty
of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society,
Chulalongkorn University, Bangkok, Thailand (S.K., N.P.)
| |
Collapse
|
29
|
You Y, Zhong S, Zhang G, Wen Y, Guo D, Li W, Li Z. Exploring the Low-Dose Limit for Focal Hepatic Lesion Detection with a Deep Learning-Based CT Reconstruction Algorithm: A Simulation Study on Patient Images. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:2089-2098. [PMID: 38502435 PMCID: PMC11522246 DOI: 10.1007/s10278-024-01080-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/06/2024] [Accepted: 03/07/2024] [Indexed: 03/21/2024]
Abstract
This study aims to investigate the maximum achievable dose reduction for applying a new deep learning-based reconstruction algorithm, namely the artificial intelligence iterative reconstruction (AIIR), in computed tomography (CT) for hepatic lesion detection. A total of 40 patients with 98 clinically confirmed hepatic lesions were retrospectively included. The mean volume CT dose index was 13.66 ± 1.73 mGy in routine-dose portal venous CT examinations, where the images were originally obtained with hybrid iterative reconstruction (HIR). Low-dose simulations were performed in projection domain for 40%-, 20%-, and 10%-dose levels, followed by reconstruction using both HIR and AIIR. Two radiologists were asked to detect hepatic lesion on each set of low-dose image in separate sessions. Qualitative metrics including lesion conspicuity, diagnostic confidence, and overall image quality were evaluated using a 5-point scale. The contrast-to-noise ratio (CNR) for lesion was also calculated for quantitative assessment. The lesion CNR on AIIR at reduced doses were significantly higher than that on routine-dose HIR (all p < 0.05). Lower qualitative image quality was observed as the radiation dose reduced, while there were no significant differences between 40%-dose AIIR and routine-dose HIR images. The lesion detection rate was 100%, 98% (96/98), and 73.5% (72/98) on 40%-, 20%-, and 10%-dose AIIR, respectively, whereas it was 98% (96/98), 73.5% (72/98), and 40% (39/98) on the corresponding low-dose HIR, respectively. AIIR outperformed HIR in simulated low-dose CT examinations of the liver. The use of AIIR allows up to 60% dose reduction for lesion detection while maintaining comparable image quality to routine-dose HIR.
Collapse
Affiliation(s)
- Yongchun You
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | | | | | - Yuting Wen
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Dian Guo
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Wanjiang Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
| | - Zhenlin Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
| |
Collapse
|
30
|
Røhme LAG, Homme THF, Johansen ECK, Schulz A, Aaløkken TM, Johansson E, Johansen S, Mussmann B, Brunborg C, Eikvar LK, Martinsen ACT. Image quality and radiation doses in abdominal CT: A multicenter study. Eur J Radiol 2024; 178:111642. [PMID: 39079322 DOI: 10.1016/j.ejrad.2024.111642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 06/18/2024] [Accepted: 07/22/2024] [Indexed: 08/18/2024]
Abstract
PURPOSE To benchmark image quality and corresponding radiation doses for acute abdominal CT examination across different laboratories and CT manufacturers. METHOD An anthropomorphic phantom was scanned once with local abdominal CT protocols at 40 CT scanners, from four vendors, in thirty-three sites. Quantitative image quality was evaluated by CNR and SNR in the liver and kidney parenchyma. Qualitative image quality was assessed by visual grading analysis performed by three experienced radiologists using a five-point Likert scale to score thirteen image quality criteria. The CTDIvol was recorded for each scan. Pearson's correlation coefficient was calculated for the continuous variables, and the intraclass correlation coefficient was used to investigate interrater reliability between the radiologists. RESULTS CTDIvol ranged from 3.5 to 12 mGy (median 5.3 mGy, third quartile 6.7 mGy). SNR in liver parenchyma ranged from 4.4 to 14.4 (median 8.5), and CNR ranged from 2.7 to 11.2 (median 6.1). A weak correlation was found between CTDIvol and CNR (r = 0.270, p = 0.092). Variations in CNR across scanners at the same dose level CTDIvol were observed. No significant difference in CTDIvol or CNR was found based on scanner installation year. The oldest scanners had a 15 % higher median CTDIvol and a 12 % lower median CNR. The ICC showed acceptable agreement for all dose groups: low (ICC=0.889), medium (ICC=0.767), high (ICC=0.847), and in low (ICC=0.803) and medium (ICC=0.811) CNR groups. CONCLUSION There was large variation in radiation dose and image quality across the different CT scanners. Interestingly, the weak correlation between CTDIvol and CNR indicates that higher doses do not consistently improve CNR, indicating a need for systematic assessment and optimization of image quality and radiation doses for the abdominal CT examination.
Collapse
Affiliation(s)
- Linn Andrea Gjerberg Røhme
- Department of Life Sciences and Health, Faculty of Health Science, Oslo Metropolitan University Oslo, Norway.
| | - Tora Hilde Fjeld Homme
- Department of Life Sciences and Health, Faculty of Health Science, Oslo Metropolitan University Oslo, Norway.
| | | | - Anselm Schulz
- Department of Radiology and Nuclear Medicine, Oslo University Hospital, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Trond Mogens Aaløkken
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Radiology and Nuclear Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway.
| | - Ellen Johansson
- Department of Radiology, Drammen Hospital, Vestre Viken Hospital Trust, Norway.
| | - Safora Johansen
- Department of Life Sciences and Health, Faculty of Health Science, Oslo Metropolitan University Oslo, Norway; Department of Cancer Treatment, Oslo University Hospital, Oslo, Norway; Health and Social Science Cluster, Singapore Institute of Technology, Singapore.
| | - Bo Mussmann
- Department of Life Sciences and Health, Faculty of Health Science, Oslo Metropolitan University Oslo, Norway; Department of Radiology, Odense University Hospital, Odense, Denmark; Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark.
| | - Cathrine Brunborg
- Oslo Centre for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital, Oslo, Norway.
| | - Lars Kristian Eikvar
- Department of Medicine and Health Services, The South-Eastern Norway Health Authority, Hamar, Norway.
| | - Anne Catrine T Martinsen
- Department of Life Sciences and Health, Faculty of Health Science, Oslo Metropolitan University Oslo, Norway; Centre for Research and Innovation, Sunnaas Rehabilitation Hospital, Bjornemyr, Norway.
| |
Collapse
|
31
|
Hwang MH, Kang S, Lee JW, Lee G. Deep Learning-Based Reconstruction Algorithm With Lung Enhancement Filter for Chest CT: Effect on Image Quality and Ground Glass Nodule Sharpness. Korean J Radiol 2024; 25:833-842. [PMID: 39197828 PMCID: PMC11361802 DOI: 10.3348/kjr.2024.0472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2024] [Revised: 07/04/2024] [Accepted: 07/18/2024] [Indexed: 09/01/2024] Open
Abstract
OBJECTIVE To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone. MATERIALS AND METHODS Five artificial spherical GGNs with various densities (-250, -350, -450, -550, and -630 Hounsfield units) and 10 mm in diameter were placed in a thorax anthropomorphic phantom. Four scans at four different radiation dose levels were performed using a 256-slice CT (Revolution Apex CT, GE Healthcare). Each scan was reconstructed using three different reconstruction algorithms: adaptive statistical iterative reconstruction-V at a level of 50% (AR50), Truefidelity (TF), which is a DLIR method, and TF with a lung enhancement filter (TF + Lu). Thus, 12 sets of reconstructed images were obtained and analyzed. Image noise, signal-to-noise ratio, and contrast-to-noise ratio were compared among the three reconstruction algorithms. Nodule sharpness was compared among the three reconstruction algorithms using the full-width at half-maximum value. Furthermore, subjective image quality analysis was performed. RESULTS AR50 demonstrated the highest level of noise, which was decreased by using TF + Lu and TF alone (P = 0.001). TF + Lu significantly improved nodule sharpness at all radiation doses compared to TF alone (P = 0.001). The nodule sharpness of TF + Lu was similar to that of AR50. Using TF alone resulted in the lowest nodule sharpness. CONCLUSION Adding a lung enhancement filter to DLIR (TF + Lu) significantly improved the nodule sharpness compared to DLIR alone (TF). TF + Lu can be an effective reconstruction technique to enhance image quality and GGN evaluation in ultralow-dose chest CT scans.
Collapse
Affiliation(s)
- Min-Hee Hwang
- Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea
| | | | - Ji Won Lee
- Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea
| | - Geewon Lee
- Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea.
| |
Collapse
|
32
|
Wu Y, Xia S, Liang Z, Chen R, Qi S. Artificial intelligence in COPD CT images: identification, staging, and quantitation. Respir Res 2024; 25:319. [PMID: 39174978 PMCID: PMC11340084 DOI: 10.1186/s12931-024-02913-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 07/09/2024] [Indexed: 08/24/2024] Open
Abstract
Chronic obstructive pulmonary disease (COPD) stands as a significant global health challenge, with its intricate pathophysiological manifestations often demanding advanced diagnostic strategies. The recent applications of artificial intelligence (AI) within the realm of medical imaging, especially in computed tomography, present a promising avenue for transformative changes in COPD diagnosis and management. This review delves deep into the capabilities and advancements of AI, particularly focusing on machine learning and deep learning, and their applications in COPD identification, staging, and imaging phenotypes. Emphasis is laid on the AI-powered insights into emphysema, airway dynamics, and vascular structures. The challenges linked with data intricacies and the integration of AI in the clinical landscape are discussed. Lastly, the review casts a forward-looking perspective, highlighting emerging innovations in AI for COPD imaging and the potential of interdisciplinary collaborations, hinting at a future where AI doesn't just support but pioneers breakthroughs in COPD care. Through this review, we aim to provide a comprehensive understanding of the current state and future potential of AI in shaping the landscape of COPD diagnosis and management.
Collapse
Affiliation(s)
- Yanan Wu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Shuyue Xia
- Respiratory Department, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China
- Key Laboratory of Medicine and Engineering for Chronic Obstructive Pulmonary Disease in Liaoning Province, Shenyang, China
| | - Zhenyu Liang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Rongchang Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Shenzhen Institute of Respiratory Disease, Shenzhen People's Hospital, Shenzhen, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| |
Collapse
|
33
|
Zheng Z, Ai Z, Liang Y, Li Y, Wu Z, Wu M, Han Q, Ma K, Xiang Z. Clinical value of deep learning image reconstruction on the diagnosis of pulmonary nodule for ultra-low-dose chest CT imaging. Clin Radiol 2024; 79:628-636. [PMID: 38749827 DOI: 10.1016/j.crad.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 03/20/2024] [Accepted: 04/15/2024] [Indexed: 07/10/2024]
Abstract
PURPOSE To compare the image quality and pulmonary nodule detectability between deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) in ultra-low-dose CT (ULD-CT). METHODS 142 participants required lung examination who underwent simultaneously ULD-CT (UL-A, 0.57 ± 0.04 mSv or UL-B, 0.33 ± 0.03 mSv), and standard CT (SDCT, 4.32 ± 0.33 mSv) plain scans were included in this prospective study. SDCT was the reference standard using ASIR-V at 50% strength (50%ASIR-V). ULD-CT was reconstructed with 50%ASIR-V, DLIR at medium and high strength (DLIR-M, DLIR-H). The noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective scores were measured. The presence and accuracy of nodules were analyzed using a combination of a deep learning-based nodule evaluation system and a radiologist. RESULTS A total of 710 nodules were detected by SDCT, including 358 nodules in UL-A and 352 nodules in UL-B. DLIR-H exhibited superior noise, SNR, and CNR performance, and achieved comparable or even higher subjective scores compared to 50%ASIR-V in ULD-CT. Nodules sensitivity detection of 50%ASIR-V, DLIR-M, and DLIR-H in ULD-CT were identical (96.90%). In multivariate analysis, body mass index (BMI), nodule diameter, and type were independent predictors for the sensitivity of nodule detection (p<.001). DLIR-H provided a lower absolute percent error (APE) in volume (3.10% ± 95.11% vs 8.29% ± 99.14%) compared to 50%ASIR-V of ULD-CT (P<.001). CONCLUSIONS ULD-CT scanning has a high sensitivity for detecting pulmonary nodules. Compared with ASIR-V, DLIR can significantly reduce image noise, and improve image quality, and accuracy of the nodule measurement in ULD-CT.
Collapse
Affiliation(s)
- Z Zheng
- Postgraduate Cultivation Base of Guangzhou University of Chinese Medicine, Panyu Central Hospital, Guangzhou, China; Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - Z Ai
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - Y Liang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - Y Li
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - Z Wu
- Postgraduate Cultivation Base of Guangzhou University of Chinese Medicine, Panyu Central Hospital, Guangzhou, China; Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - M Wu
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - Q Han
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - K Ma
- CT Imaging Research Center, GE HealthCare China, Guangzhou, China.
| | - Z Xiang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
| |
Collapse
|
34
|
Lan T, Zeng F, Yi Z, Xu X, Zhu M. ICNoduleNet: Enhancing Pulmonary Nodule Detection Performance on Sharp Kernel CT Imaging. IEEE J Biomed Health Inform 2024; 28:4751-4760. [PMID: 38758615 DOI: 10.1109/jbhi.2024.3402186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2024]
Abstract
Thoracic computed tomography (CT) currently plays the primary role in pulmonary nodule detection, where the reconstruction kernel significantly impacts performance in computer-aided pulmonary nodule detectors. The issue of kernel selection affecting performance has been overlooked in pulmonary nodule detection. This paper first introduces a novel pulmonary nodule detection dataset named Reconstruction Kernel Imaging for Pulmonary Nodule Detection (RKPN) for quantifying algorithm differences between the two imaging types. The dataset contains pairs of images taken from the same patient on the same date, featuring both smooth (B31f) and sharp kernel (B60f) reconstructions. All other imaging parameters and pulmonary nodule labels remain entirely consistent across these pairs. Extensive quantification reveals mainstream detectors perform better on smooth kernel imaging than on sharp kernel imaging. To address suboptimal detection on the sharp kernel imaging, we further propose an image conversion-based pulmonary nodule detector called ICNoduleNet. A lightweight 3D slice-channel converter (LSCC) module is introduced to convert sharp kernel images into smooth kernel images, which can sufficiently learn inter-slice and inter-channel feature information while avoiding introducing excessive parameters. We conduct thorough experiments that validate the effectiveness of ICNoduleNet, it takes sharp kernel images as input and can achieve comparable or even superior detection performance to the baseline that uses the smooth kernel images. The evaluation shows promising results and proves the effectiveness of ICNoduleNet.
Collapse
|
35
|
Zhou Z, Gong H, Hsieh S, McCollough CH, Yu L. Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution. Med Phys 2024; 51:5399-5413. [PMID: 38555876 PMCID: PMC11321944 DOI: 10.1002/mp.17029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 02/19/2024] [Accepted: 02/26/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND Deep-learning-based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images. PURPOSE In this work, we aim to develop a patient-data-based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method. METHODS The patient-data-based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR-processed images from projection or image data; (4) creation of ensembles of DLIR-processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (-500, -100, -50, -20, -10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered-backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN-weak, DCNN-medium, and DCNN-strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion-present images from the lesion absent images. Modulation transfer functions (MTFs) both in-plane and along the z-axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t-test was used to compare the z-axis and in-plane spatial resolution of five algorithms for five different contrasts and three dose levels. RESULTS The in-plane and z-axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in-plane and z-axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (-10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in-plane and z-axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of -100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in-plane and cross-plane spatial resolution: FBP > DCNN-Weak > IR > DCNN-Medium > DCNN-Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels. CONCLUSIONS A patient-data-based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non-linear image reconstruction and post-processing techniques, the evaluated DCNN method degraded the in-plane and z-axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.
Collapse
Affiliation(s)
| | - Hao Gong
- Department of Radiology, Mayo Clinic, Rochester, MN, US
| | - Scott Hsieh
- Department of Radiology, Mayo Clinic, Rochester, MN, US
| | | | - Lifeng Yu
- Department of Radiology, Mayo Clinic, Rochester, MN, US
| |
Collapse
|
36
|
Bellmann Q, Peng Y, Genske U, Yan L, Wagner M, Jahnke P. Low-contrast lesion detection in neck CT: a multireader study comparing deep learning, iterative, and filtered back projection reconstructions using realistic phantoms. Eur Radiol Exp 2024; 8:84. [PMID: 39046565 PMCID: PMC11269546 DOI: 10.1186/s41747-024-00486-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 06/18/2024] [Indexed: 07/25/2024] Open
Abstract
BACKGROUND Computed tomography (CT) reconstruction algorithms can improve image quality, especially deep learning reconstruction (DLR). We compared DLR, iterative reconstruction (IR), and filtered back projection (FBP) for lesion detection in neck CT. METHODS Nine patient-mimicking neck phantoms were examined with a 320-slice scanner at six doses: 0.5, 1, 1.6, 2.1, 3.1, and 5.2 mGy. Each of eight phantoms contained one circular lesion (diameter 1 cm; contrast -30 HU to the background) in the parapharyngeal space; one phantom had no lesions. Reconstruction was made using FBP, IR, and DLR. Thirteen readers were tasked with identifying and localizing lesions in 32 images with a lesion and 20 without lesions for each dose and reconstruction algorithm. Receiver operating characteristic (ROC) and localization ROC (LROC) analysis were performed. RESULTS DLR improved lesion detection with ROC area under the curve (AUC) 0.724 ± 0.023 (mean ± standard error of the mean) using DLR versus 0.696 ± 0.021 using IR (p = 0.037) and 0.671 ± 0.023 using FBP (p < 0.001). Likewise, DLR improved lesion localization, with LROC AUC 0.407 ± 0.039 versus 0.338 ± 0.041 using IR (p = 0.002) and 0.313 ± 0.044 using FBP (p < 0.001). Dose reduction to 0.5 mGy compromised lesion detection in FBP-reconstructed images compared to doses ≥ 2.1 mGy (p ≤ 0.024), while no effect was observed with DLR or IR (p ≥ 0.058). CONCLUSION DLR improved the detectability of lesions in neck CT imaging. Dose reduction to 0.5 mGy maintained lesion detectability when denoising reconstruction was used. RELEVANCE STATEMENT Deep learning enhances lesion detection in neck CT imaging compared to iterative reconstruction and filtered back projection, offering improved diagnostic performance and potential for x-ray dose reduction. KEY POINTS Low-contrast lesion detectability was assessed in anatomically realistic neck CT phantoms. Deep learning reconstruction (DLR) outperformed filtered back projection and iterative reconstruction. Dose has little impact on lesion detectability against anatomical background structures.
Collapse
Affiliation(s)
- Quirin Bellmann
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Yang Peng
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, Hubei Province, China
| | - Ulrich Genske
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Li Yan
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Moritz Wagner
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Paul Jahnke
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
- Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Str. 2, 10178, Berlin, Germany.
| |
Collapse
|
37
|
Shin DJ, Choi YH, Lee SB, Cho YJ, Lee S, Cheon JE. Low-iodine-dose computed tomography coupled with an artificial intelligence-based contrast-boosting technique in children: a retrospective study on comparison with conventional-iodine-dose computed tomography. Pediatr Radiol 2024; 54:1315-1324. [PMID: 38839610 PMCID: PMC11254996 DOI: 10.1007/s00247-024-05953-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 05/12/2024] [Accepted: 05/13/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND Low-iodine-dose computed tomography (CT) protocols have emerged to mitigate the risks associated with contrast injection, often resulting in decreased image quality. OBJECTIVE To evaluate the image quality of low-iodine-dose CT combined with an artificial intelligence (AI)-based contrast-boosting technique in abdominal CT, compared to a standard-iodine-dose protocol in children. MATERIALS AND METHODS This single-center retrospective study included 35 pediatric patients (mean age 9.2 years, range 1-17 years) who underwent sequential abdominal CT scans-one with a standard-iodine-dose protocol (standard-dose group, Iobitridol 350 mgI/mL) and another with a low-iodine-dose protocol (low-dose group, Iohexol 240 mgI/mL)-within a 4-month interval from January 2022 to July 2022. The low-iodine CT protocol was reconstructed using an AI-based contrast-boosting technique (contrast-boosted group). Quantitative and qualitative parameters were measured in the three groups. For qualitative parameters, interobserver agreement was assessed using the intraclass correlation coefficient, and mean values were employed for subsequent analyses. For quantitative analysis of the three groups, repeated measures one-way analysis of variance with post hoc pairwise analysis was used. For qualitative analysis, the Friedman test followed by post hoc pairwise analysis was used. Paired t-tests were employed to compare radiation dose and iodine uptake between the standard- and low-dose groups. RESULTS The standard-dose group exhibited higher attenuation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of organs and vessels compared to the low-dose group (all P-values < 0.05 except for liver SNR, P = 0.12). However, noise levels did not differ between the standard- and low-dose groups (P = 0.86). The contrast-boosted group had increased attenuation, CNR, and SNR of organs and vessels, and reduced noise compared with the low-dose group (all P < 0.05). The contrast-boosted group showed no differences in attenuation, CNR, and SNR of organs and vessels (all P > 0.05), and lower noise (P = 0.002), than the standard-dose group. In qualitative analysis, the contrast-boosted group did not differ regarding vessel enhancement and lesion conspicuity (P > 0.05) but had lower noise (P < 0.05) and higher organ enhancement and artifacts (all P < 0.05) than the standard-dose group. While iodine uptake was significantly reduced in low-iodine-dose CT (P < 0.001), there was no difference in radiation dose between standard- and low-iodine-dose CT (all P > 0.05). CONCLUSION Low-iodine-dose abdominal CT, combined with an AI-based contrast-boosting technique exhibited comparable organ and vessel enhancement, as well as lesion conspicuity compared to standard-iodine-dose CT in children. Moreover, image noise decreased in the contrast-boosted group, albeit with an increase in artifacts.
Collapse
Affiliation(s)
- Dong-Joo Shin
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Young Hun Choi
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea.
- Department of Radiology, Seoul National University College of Medicine, Jongno-Gu, Seoul, Republic of Korea.
| | - Seul Bi Lee
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Jongno-Gu, Seoul, Republic of Korea
| | - Yeon Jin Cho
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Jongno-Gu, Seoul, Republic of Korea
| | - Seunghyun Lee
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Jongno-Gu, Seoul, Republic of Korea
| | - Jung-Eun Cheon
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Jongno-Gu, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Jongno-Gu, Seoul, Republic of Korea
| |
Collapse
|
38
|
Zhu K, Shen Z, Wang M, Jiang L, Zhang Y, Yang T, Zhang H, Zhang M. Visual Knowledge Domain of Artificial Intelligence in Computed Tomography: A Review Based on Bibliometric Analysis. J Comput Assist Tomogr 2024; 48:652-662. [PMID: 38271538 DOI: 10.1097/rct.0000000000001585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
ABSTRACT Artificial intelligence (AI)-assisted medical imaging technology is a new research area of great interest that has developed rapidly over the last decade. However, there has been no bibliometric analysis of published studies in this field. The present review focuses on AI-related studies on computed tomography imaging in the Web of Science database and uses CiteSpace and VOSviewer to generate a knowledge map and conduct the basic information analysis, co-word analysis, and co-citation analysis. A total of 7265 documents were included and the number of documents published had an overall upward trend. Scholars from the United States and China have made outstanding achievements, and there is a general lack of extensive cooperation in this field. In recent years, the research areas of great interest and difficulty have been the optimization and upgrading of algorithms, and the application of theoretical models to practical clinical applications. This review will help researchers understand the developments, research areas of great interest, and research frontiers in this field and provide reference and guidance for future studies.
Collapse
|
39
|
Quaia E, Kiyomi Lanza de Cristoforis E, Agostini E, Zanon C. Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms. Tomography 2024; 10:912-921. [PMID: 38921946 PMCID: PMC11209234 DOI: 10.3390/tomography10060069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 06/05/2024] [Accepted: 06/06/2024] [Indexed: 06/27/2024] Open
Abstract
Deep learning image reconstruction (DLIR) algorithms employ convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at a low radiation dose. The aim of this study was to assess whether the DLIR algorithm reduces the CT effective dose (ED) and improves CT image quality in comparison with filtered back projection (FBP) and iterative reconstruction (IR) algorithms in intensive care unit (ICU) patients. We identified all consecutive patients referred to the ICU of a single hospital who underwent at least two consecutive chest and/or abdominal contrast-enhanced CT scans within a time period of 30 days using DLIR and subsequently the FBP or IR algorithm (Advanced Modeled Iterative Reconstruction [ADMIRE] model-based algorithm or Adaptive Iterative Dose Reduction 3D [AIDR 3D] hybrid algorithm) for CT image reconstruction. The radiation ED, noise level, and signal-to-noise ratio (SNR) were compared between the different CT scanners. The non-parametric Wilcoxon test was used for statistical comparison. Statistical significance was set at p < 0.05. A total of 83 patients (mean age, 59 ± 15 years [standard deviation]; 56 men) were included. DLIR vs. FBP reduced the ED (18.45 ± 13.16 mSv vs. 22.06 ± 9.55 mSv, p < 0.05), while DLIR vs. FBP and vs. ADMIRE and AIDR 3D IR algorithms reduced image noise (8.45 ± 3.24 vs. 14.85 ± 2.73 vs. 14.77 ± 32.77 and 11.17 ± 32.77, p < 0.05) and increased the SNR (11.53 ± 9.28 vs. 3.99 ± 1.23 vs. 5.84 ± 2.74 and 3.58 ± 2.74, p < 0.05). CT scanners employing DLIR improved the SNR compared to CT scanners using FBP or IR algorithms in ICU patients despite maintaining a reduced ED.
Collapse
Affiliation(s)
- Emilio Quaia
- Department of Radiology, University of Padova, Via Giustiniani 2, 35128 Padova, Italy
| | | | | | | |
Collapse
|
40
|
Prod'homme S, Bouzerar R, Forzini T, Delabie A, Renard C. Detection of urinary tract stones on submillisievert abdominopelvic CT imaging with deep-learning image reconstruction algorithm (DLIR). Abdom Radiol (NY) 2024; 49:1987-1995. [PMID: 38470506 DOI: 10.1007/s00261-024-04223-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 03/14/2024]
Abstract
PURPOSE Urolithiasis is a chronic condition that leads to repeated CT scans throughout the patient's life. The goal was to assess the diagnostic performance and image quality of submillisievert abdominopelvic computed tomography (CT) using deep learning-based image reconstruction (DLIR) in urolithiasis. METHODS 57 patients with suspected urolithiasis underwent both non-contrast low-dose (LD) and ULD abdominopelvic CT. Raw image data of ULD CT were reconstructed using hybrid iterative reconstruction (ASIR-V 70%) and high-strength-level DLIR (DLIR-H). The performance of ULD CT for the detection of urinary stones was assessed by two readers and compared with LD CT with ASIR-V 70% as a reference standard. Image quality was assessed subjectively and objectively. RESULTS 266 stones were detected in 38 patients. Mean effective dose was 0.59 mSv for ULD CT and 1.96 mSv for LD CT. For diagnostic performance, sensitivity and specificity were 89% and 94%, respectively, for ULDCT with DLIR-H. There was an almost perfect intra-observer concordance on ULD CT with DLIR-H versus LDCT with ASIR-V 70% (ICC = 0.90 and 0.90 for the two readers). Image noise was significantly lower and signal-to-noise ratio significantly higher with DLIR-H compared to ASIR-V 70%. Subjective image quality was also significantly better with ULDCT with DLIR-H. CONCLUSION ULD CT with Deep Learning Image Reconstruction maintains a good diagnostic performance in urolithiasis, with better image quality than hybrid iterative reconstruction and a significant radiation dose reduction.
Collapse
Affiliation(s)
- Sarah Prod'homme
- Department of Radiology, Amiens University Hospital, 1 Rond-Point du Professeur Christian Cabrol, 80054, Amiens Cedex 01, France
| | - Roger Bouzerar
- Biophysics and Image Processing Unit, Amiens University Hospital, Amiens, France
| | - Thomas Forzini
- Department of Urology and Transplantation, Amiens University Hospital, Amiens, France
| | - Aurélien Delabie
- Department of Radiology, Amiens University Hospital, 1 Rond-Point du Professeur Christian Cabrol, 80054, Amiens Cedex 01, France
| | - Cédric Renard
- Department of Radiology, Amiens University Hospital, 1 Rond-Point du Professeur Christian Cabrol, 80054, Amiens Cedex 01, France.
| |
Collapse
|
41
|
Bos D, Demircioğlu A, Neuhoff J, Haubold J, Zensen S, Opitz MK, Drews MA, Li Y, Styczen H, Forsting M, Nassenstein K. Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans. Sci Rep 2024; 14:11810. [PMID: 38782976 PMCID: PMC11116440 DOI: 10.1038/s41598-024-62394-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 05/16/2024] [Indexed: 05/25/2024] Open
Abstract
In this retrospective study, we aimed to assess the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast head computed tomography (CT) images. In total, 152 adult head CT scans (77 female, 75 male; mean age 69.4 ± 18.3 years) obtained from three different CT scanners using different protocols between March and April 2021 were included. CT images were reconstructed using filtered-back projection (FBP), iterative reconstruction (IR), and post-processed using a deep learning-based algorithm (PS). Post-processing significantly reduced noise in FBP-reconstructed images (up to 15.4% reduction) depending on the protocol, leading to improvements in signal-to-noise ratio of up to 19.7%. However, when deep learning-based post-processing was applied to FBP images compared to IR alone, the differences were inconsistent and partly non-significant, which appeared to be protocol or site specific. Subjective assessments showed no significant overall improvement in image quality for all reconstructions and post-processing. Inter-rater reliability was low and preferences varied. Deep learning-based denoising software improved objective image quality compared to FBP in routine head CT. A significant difference compared to IR was observed for only one protocol. Subjective assessments did not indicate a significant clinical impact in terms of improved subjective image quality, likely due to the low noise levels in full-dose images.
Collapse
Affiliation(s)
- Denise Bos
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany.
| | - Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Julia Neuhoff
- Faculty of Medicine, University Duisburg-Essen, Hufelandstraße 55, 45122, Essen, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Sebastian Zensen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Marcel K Opitz
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Marcel A Drews
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Yan Li
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Hanna Styczen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Kai Nassenstein
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| |
Collapse
|
42
|
D'hondt L, Franck C, Kellens PJ, Zanca F, Buytaert D, Van Hoyweghen A, Addouli HE, Carpentier K, Niekel M, Spinhoven M, Bacher K, Snoeckx A. Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT. Cancer Imaging 2024; 24:60. [PMID: 38720391 PMCID: PMC11080267 DOI: 10.1186/s40644-024-00703-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 04/27/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduced radiation doses. This is essential in the context of low-dose CT lung cancer screening where accurate volumetry and characterization of pulmonary nodules in repeated CT scanning are indispensable. MATERIALS AND METHODS A standardized CT dataset was established using an anthropomorphic chest phantom (Lungman, Kyoto Kaguku Inc., Kyoto, Japan) containing a set of 3D-printed lung nodules including six diameters (4 to 9 mm) and three morphology classes (lobular, spiculated, smooth), with an established ground truth. Images were acquired at varying radiation doses (6.04, 3.03, 1.54, 0.77, 0.41 and 0.20 mGy) and reconstructed with combinations of reconstruction kernels (soft and hard kernel) and reconstruction algorithms (ASIR-V and DLIR at low, medium and high strength). Semi-automatic volumetry measurements and subjective image quality scores recorded by five radiologists were analyzed with multiple linear regression and mixed-effect ordinal logistic regression models. RESULTS Volumetric errors of nodules imaged with DLIR are up to 50% lower compared to ASIR-V, especially at radiation doses below 1 mGy and when reconstructed with a hard kernel. Also, across all nodule diameters and morphologies, volumetric errors are commonly lower with DLIR. Furthermore, DLIR renders higher subjective IQ, especially at the sub-mGy doses. Radiologists were up to nine times more likely to score the highest IQ-score to these images compared to those reconstructed with ASIR-V. Lung nodules with irregular margins and small diameters also had an increased likelihood (up to five times more likely) to be ascribed the best IQ scores when reconstructed with DLIR. CONCLUSION We observed that DLIR performs as good as or even outperforms conventionally used reconstruction algorithms in terms of volumetric accuracy and subjective IQ of nodules in an anthropomorphic chest phantom. As such, DLIR potentially allows to lower the radiation dose to participants of lung cancer screening without compromising accurate measurement and characterization of lung nodules.
Collapse
Affiliation(s)
- L D'hondt
- Department of Human structure and repair, Faculty of Medicine and Health Sciences, Ghent University, Proeftuinstraat 86, 9000, Ghent, Belgium.
- Faculty of Medicine, University of Antwerp, Universiteitsplein 1, Wilrijk, Belgium.
| | - C Franck
- Department of Radiology, Antwerp University Hospital, Drie Eikenstraat 655, Edegem, Belgium
| | - P-J Kellens
- Department of Human structure and repair, Faculty of Medicine and Health Sciences, Ghent University, Proeftuinstraat 86, 9000, Ghent, Belgium
| | - F Zanca
- Center of Medical Physics in Radiology, Leuven University, University Hospitals Leuven, Herestraat 49, Leuven, Belgium
| | - D Buytaert
- Cardiovascular Research Center, OLV Ziekenhuis Aalst, Moorselbaan 164, Aalst, Belgium
| | - A Van Hoyweghen
- Department of Radiology, Antwerp University Hospital, Drie Eikenstraat 655, Edegem, Belgium
| | - H El Addouli
- Department of Radiology, Antwerp University Hospital, Drie Eikenstraat 655, Edegem, Belgium
| | - K Carpentier
- Department of Radiology, Antwerp University Hospital, Drie Eikenstraat 655, Edegem, Belgium
| | - M Niekel
- Department of Radiology, Antwerp University Hospital, Drie Eikenstraat 655, Edegem, Belgium
| | - M Spinhoven
- Department of Radiology, Antwerp University Hospital, Drie Eikenstraat 655, Edegem, Belgium
| | - K Bacher
- Department of Human structure and repair, Faculty of Medicine and Health Sciences, Ghent University, Proeftuinstraat 86, 9000, Ghent, Belgium
| | - A Snoeckx
- Faculty of Medicine, University of Antwerp, Universiteitsplein 1, Wilrijk, Belgium
- Department of Radiology, Antwerp University Hospital, Drie Eikenstraat 655, Edegem, Belgium
| |
Collapse
|
43
|
Onnis C, van Assen M, Muscogiuri E, Muscogiuri G, Gershon G, Saba L, De Cecco CN. The Role of Artificial Intelligence in Cardiac Imaging. Radiol Clin North Am 2024; 62:473-488. [PMID: 38553181 DOI: 10.1016/j.rcl.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Artificial intelligence (AI) is having a significant impact in medical imaging, advancing almost every aspect of the field, from image acquisition and postprocessing to automated image analysis with outreach toward supporting decision making. Noninvasive cardiac imaging is one of the main and most exciting fields for AI development. The aim of this review is to describe the main applications of AI in cardiac imaging, including CT and MR imaging, and provide an overview of recent advancements and available clinical applications that can improve clinical workflow, disease detection, and prognostication in cardiac disease.
Collapse
Affiliation(s)
- Carlotta Onnis
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA; Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, SS 554 km 4,500 Monserrato, Cagliari 09042, Italy. https://twitter.com/CarlottaOnnis
| | - Marly van Assen
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA. https://twitter.com/marly_van_assen
| | - Emanuele Muscogiuri
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA; Division of Thoracic Imaging, Department of Radiology, University Hospitals Leuven, Herestraat 49, Leuven 3000, Belgium
| | - Giuseppe Muscogiuri
- Department of Diagnostic and Interventional Radiology, Papa Giovanni XXIII Hospital, Piazza OMS, 1, Bergamo BG 24127, Italy. https://twitter.com/GiuseppeMuscog
| | - Gabrielle Gershon
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA. https://twitter.com/gabbygershon
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari-Polo di Monserrato, SS 554 km 4,500 Monserrato, Cagliari 09042, Italy. https://twitter.com/lucasabaITA
| | - Carlo N De Cecco
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Department of Radiology and Imaging Sciences, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA; Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University, Emory University Hospital, 1365 Clifton Road Northeast, Suite AT503, Atlanta, GA 30322, USA.
| |
Collapse
|
44
|
Varghese BA, Cen SY, Jensen K, Levy J, Andersen HK, Schulz A, Lei X, Duddalwar VA, Goodenough DJ. Investigating the role of imaging factors in the variability of CT-based texture analysis metrics. J Appl Clin Med Phys 2024; 25:e14192. [PMID: 37962032 PMCID: PMC11005980 DOI: 10.1002/acm2.14192] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 08/02/2023] [Accepted: 10/12/2023] [Indexed: 11/15/2023] Open
Abstract
OBJECTIVE This study assesses the robustness of first-order radiomic texture features namely interquartile range (IQR), coefficient of variation (CV) and standard deviation (SD) derived from computed tomography (CT) images by varying dose, reconstruction algorithms and slice thickness using scans of a uniform water phantom, a commercial anthropomorphic liver phantom, and a human liver in-vivo. MATERIALS AND METHODS Scans were acquired on a 16 cm detector GE Revolution Apex Edition CT scanner with variations across three different nominal slice thicknesses: 0.625, 1.25, and 2.5 mm, three different dose levels: CTDIvol of 13.86 mGy for the standard dose, 40% reduced dose and 60% reduced dose and two different reconstruction algorithms: a deep learning image reconstruction (DLIR-high) algorithm and a hybrid iterative reconstruction (IR) algorithm ASiR-V50% (AV50) were explored, varying one at a time. To assess the effect of non-linear modifications of images by AV50 and DLIR-high, images of the water phantom were also reconstructed using filtered back projection (FBP). Quantitative measures of IQR, CV and SD were extracted from twelve pre-selected, circular (1 cm diameter) regions of interest (ROIs) capturing different texture patterns across all scans. RESULTS Across all scans, imaging, and reconstruction settings, CV, IQR and SD were observed to increase with reduction in dose and slice thickness. An exception to this observation was found when using FBP reconstruction. Lower values of CV, IQR and SD were observed in DLIR-high reconstructions compared to AV50 and FBP. The Poisson statistics were more stringently noted in FBP than DLIR-high and AV50, due to the non-linear nature of the latter two algorithms. CONCLUSION Variation in image noise due to dose reduction algorithms, tube current, and slice thickness show a consistent trend across phantom and patient scans. Prospective evaluation across multiple centers, scanners and imaging protocols is needed for establishing quality assurance standards of radiomics.
Collapse
Affiliation(s)
- Bino Abel Varghese
- Keck Medical CenterDepartment of RadiologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Steven Yong Cen
- Keck Medical CenterDepartment of RadiologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Kristin Jensen
- Department of Physics and Computational RadiologyOsloNorway
| | | | | | - Anselm Schulz
- Department of Radiology and Nuclear MedicineOslo University HospitalOsloNorway
| | - Xiaomeng Lei
- Keck Medical CenterDepartment of RadiologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Vinay Anant Duddalwar
- Keck Medical CenterDepartment of RadiologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - David John Goodenough
- Department of RadiologyGeorge Washington UniversityWashingtonDistrict of ColumbiaUSA
| |
Collapse
|
45
|
Caruso D, De Santis D, Del Gaudio A, Guido G, Zerunian M, Polici M, Valanzuolo D, Pugliese D, Persechino R, Cremona A, Barbato L, Caloisi A, Iannicelli E, Laghi A. Low-dose liver CT: image quality and diagnostic accuracy of deep learning image reconstruction algorithm. Eur Radiol 2024; 34:2384-2393. [PMID: 37688618 PMCID: PMC10957592 DOI: 10.1007/s00330-023-10171-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/11/2023] [Accepted: 07/20/2023] [Indexed: 09/11/2023]
Abstract
OBJECTIVES To perform a comprehensive within-subject image quality analysis of abdominal CT examinations reconstructed with DLIR and to evaluate diagnostic accuracy compared to the routinely applied adaptive statistical iterative reconstruction (ASiR-V) algorithm. MATERIALS AND METHODS Oncologic patients were prospectively enrolled and underwent contrast-enhanced CT. Images were reconstructed with DLIR with three intensity levels of reconstruction (high, medium, and low) and ASiR-V at strength levels from 10 to 100% with a 10% interval. Three radiologists characterized the lesions and two readers assessed diagnostic accuracy and calculated signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), figure of merit (FOM), and subjective image quality, the latter with a 5-point Likert scale. RESULTS Fifty patients (mean age: 70 ± 10 years, 23 men) were enrolled and 130 liver lesions (105 benign lesions, 25 metastases) were identified. DLIR_H achieved the highest SNR and CNR, comparable to ASiR-V 100% (p ≥ .051). DLIR_M returned the highest subjective image quality (score: 5; IQR: 4-5; p ≤ .001) and significant median increase (29%) in FOM (p < .001). Differences in detection were identified only for lesions ≤ 0.5 cm: 32/33 lesions were detected with DLIR_M and 26 lesions were detected with ASiR-V 50% (p = .031). Lesion accuracy of was 93.8% (95% CI: 88.1, 97.3; 122 of 130 lesions) for DLIR and 87.7% (95% CI: 80.8, 92.8; 114 of 130 lesions) for ASiR-V 50%. CONCLUSIONS DLIR yields superior image quality and provides higher diagnostic accuracy compared to ASiR-V in the assessment of hypovascular liver lesions, in particular for lesions ≤ 0.5 cm. CLINICAL RELEVANCE STATEMENT Deep learning image reconstruction algorithm demonstrates higher diagnostic accuracy compared to iterative reconstruction in the identification of hypovascular liver lesions, especially for lesions ≤ 0.5 cm. KEY POINTS • Iterative reconstruction algorithm impacts image texture, with negative effects on diagnostic capabilities. • Medium-strength deep learning image reconstruction algorithm outperforms iterative reconstruction in the diagnostic accuracy of ≤ 0.5 cm hypovascular liver lesions (93.9% vs 78.8%), also granting higher objective and subjective image quality. • Deep learning image reconstruction algorithm can be safely implemented in routine abdominal CT protocols in place of iterative reconstruction.
Collapse
Affiliation(s)
- Damiano Caruso
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Domenico De Santis
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Antonella Del Gaudio
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Gisella Guido
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Marta Zerunian
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Michela Polici
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Daniela Valanzuolo
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Dominga Pugliese
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Raffaello Persechino
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Antonio Cremona
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Luca Barbato
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Andrea Caloisi
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Elsa Iannicelli
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Andrea Laghi
- Department of Medical-Surgical Sciences and Translational Medicine, Radiology Unit, Sant'Andrea University Hospital, Sapienza University of Rome, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy.
| |
Collapse
|
46
|
Dillenseger JP, Gillet R, Louis M, Bach J, Sieffert C, Meylheuc L, Palpacuer C, Bierry G, Garnon J, Blum A. Quantitative and qualitative evaluation of three MSCT for high resolution bone imaging. Eur J Radiol 2024; 173:111394. [PMID: 38428256 DOI: 10.1016/j.ejrad.2024.111394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/09/2023] [Accepted: 02/23/2024] [Indexed: 03/03/2024]
Abstract
INTRODUCTION Strategies for achieving high resolution varies between manufacturers. In CT, the helical mode with narrow collimation has long been considered as the gold standard for high-resolution imaging. More recently, incremental modes with small dexels and focal spot, have been developed but have not been compared with helical acquisitions under optimal conditions. The aim of this work is to compare the high-resolution acquisition strategies currently proposed by recent MSCT. METHODS Three CT systems were compared. A phantom was used to evaluate geometric accuracy, uniformity, scan slice geometry, and spatial resolution. Human dry bones were used to test different protocols on real bone architecture. A blind visual analysis was conducted by trained CT users for classifying the different acquisitions (p-values). RESULTS All systems give satisfactory results in terms of geometric accuracy and uniformity. The in-plane MTF at 5% were respectively 13.4, 15.9 and 18.1 lp/cm. Dry-bones evaluation confirms that acquisition#3 is considered as the best. CONCLUSIONS The incremental acquisition coupled with à small focal spot, and a high-sampling detector, overpasses the reference of low-pitch helical acquisitions for high-resolution imaging. Cortical bone, bony vessels, and tumoral matrix analysis are the very next challenges that will have to be managed to improve normal and pathologic bone imaging thanks to the availability UHR-CT systems.
Collapse
Affiliation(s)
- Jean-Philippe Dillenseger
- ICube - UMR 7357, CNRS, Université de Strasbourg, Strasbourg, France; Pole d'imagerie médicale, Hôpitaux universitaire de Strasbourg, Strasbourg, France.
| | - Romain Gillet
- Service D'imagerie Guilloz, CHRU Nancy, Nancy, France; IADI - U1254, Inserm, Université de Lorraine, Nancy, France
| | | | - Justin Bach
- Pole d'imagerie médicale, Hôpitaux universitaire de Strasbourg, Strasbourg, France.
| | - Cléa Sieffert
- ICube - UMR 7357, CNRS, Université de Strasbourg, Strasbourg, France.
| | - Laurence Meylheuc
- ICube - UMR 7357, CNRS, Université de Strasbourg, Strasbourg, France.
| | - Clément Palpacuer
- Clinical research department, Groupe Hospitalier Mulhouse et Sud Alsace, Mulhouse, France.
| | - Guillaume Bierry
- ICube - UMR 7357, CNRS, Université de Strasbourg, Strasbourg, France; Pole d'imagerie médicale, Hôpitaux universitaire de Strasbourg, Strasbourg, France.
| | - Julien Garnon
- ICube - UMR 7357, CNRS, Université de Strasbourg, Strasbourg, France; Pole d'imagerie médicale, Hôpitaux universitaire de Strasbourg, Strasbourg, France.
| | - Alain Blum
- Service D'imagerie Guilloz, CHRU Nancy, Nancy, France; IADI - U1254, Inserm, Université de Lorraine, Nancy, France.
| |
Collapse
|
47
|
Tachibana Y, Takaji R, Shiroo T, Asayama Y. Deep-learning reconstruction with low-contrast media and low-kilovoltage peak for CT of the liver. Clin Radiol 2024; 79:e546-e553. [PMID: 38238148 DOI: 10.1016/j.crad.2023.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 12/13/2023] [Accepted: 12/19/2023] [Indexed: 03/09/2024]
Abstract
AIM To compare images using reduced CM, low-kVp scanning and DLR reconstruction with conventional images (no CM reduction, normal tube voltage, reconstructed with HBIR. To compare images using reduced contrast media (CM), low kilovoltage peak (kVp) scanning and deep-learning reconstruction (DLR) with conventional image quality (no CM reduction, normal tube voltage, reconstructed with hybrid-type iterative reconstruction method [HBIR protocol]). MATERIALS AND METHODS A retrospective analysis was performed on 70 patients with liver disease and three-phase dynamic imaging using computed tomography (CT) from April 2020 to March 2022 at Oita University Hospital. Of these cases, 39 were reconstructed using the DLR protocol at a tube voltage of 80 kVp and CM of 300 mg iodine/kg while 31 were imaged at a tube voltage of 120 kVp with CM of 600 mg iodine/kg and were reconstructed by the usual HBIR protocol. Images from the DLR and HBIR protocols were analysed and compared based on the contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), figure-of-merit (FOM), and visual assessment. The CT dose index (CTDI)vol and size-specific dose estimates (SSDE) were compared with respect to radiation dose. RESULTS The DLR protocol was superior, with significant differences in CNR, SNR, and FOM except hepatic parenchyma in the arterial phase. For visual assessment, the DLR protocol had better values for vascular visualisation for the portal vein, image noise, and contrast enhancement of the hepatic parenchyma. Regarding comparison of the radiation dose, the DLR protocol was superior for all values of CTDIvol and SSDE, with significant differences (p<0.01; max. 52%). CONCLUSION Protocols using DLR with reduced CM and low kVp have better image quality and lower radiation dose compared to protocols using conventional HBIR.
Collapse
Affiliation(s)
- Y Tachibana
- Graduate School of Medicine, Oita University, 1-1 Idaigaoka, Hasama-machi, Yufu, 879-5593, Japan
| | - R Takaji
- Department of Radiology, Oita University Faculty of Medicine, 1-1 Idaigaoka, Hasama-machi, Yufu, 879-5593, Japan
| | - T Shiroo
- Radiology Department, Division of Medical Technology, Oita University Hospital, 1-1 Idaigaoka, Hasama-machi, Yufu, 879-5593, Japan
| | - Y Asayama
- Department of Radiology, Oita University Faculty of Medicine, 1-1 Idaigaoka, Hasama-machi, Yufu, 879-5593, Japan.
| |
Collapse
|
48
|
Kazimierczak W, Kędziora K, Janiszewska-Olszowska J, Kazimierczak N, Serafin Z. Noise-Optimized CBCT Imaging of Temporomandibular Joints-The Impact of AI on Image Quality. J Clin Med 2024; 13:1502. [PMID: 38592413 PMCID: PMC10932444 DOI: 10.3390/jcm13051502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 02/28/2024] [Accepted: 03/04/2024] [Indexed: 04/10/2024] Open
Abstract
Background: Temporomandibular joint disorder (TMD) is a common medical condition. Cone beam computed tomography (CBCT) is effective in assessing TMD-related bone changes, but image noise may impair diagnosis. Emerging deep learning reconstruction algorithms (DLRs) could minimize noise and improve CBCT image clarity. This study compares standard and deep learning-enhanced CBCT images for image quality in detecting osteoarthritis-related degeneration in TMJs (temporomandibular joints). This study analyzed CBCT images of patients with suspected temporomandibular joint degenerative joint disease (TMJ DJD). Methods: The DLM reconstructions were performed with ClariCT.AI software. Image quality was evaluated objectively via CNR in target areas and subjectively by two experts using a five-point scale. Both readers also assessed TMJ DJD lesions. The study involved 50 patients with a mean age of 28.29 years. Results: Objective analysis revealed a significantly better image quality in DLM reconstructions (CNR levels; p < 0.001). Subjective assessment showed high inter-reader agreement (κ = 0.805) but no significant difference in image quality between the reconstruction types (p = 0.055). Lesion counts were not significantly correlated with the reconstruction type (p > 0.05). Conclusions: The analyzed DLM reconstruction notably enhanced the objective image quality in TMJ CBCT images but did not significantly alter the subjective quality or DJD lesion diagnosis. However, the readers favored DLM images, indicating the potential for better TMD diagnosis with CBCT, meriting more study.
Collapse
Affiliation(s)
- Wojciech Kazimierczak
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
- Department of Interdisciplinary Dentistry, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
| | - Kamila Kędziora
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | | | - Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| |
Collapse
|
49
|
Greffier J, Pastor M, Si-Mohamed S, Goutain-Majorel C, Peudon-Balas A, Bensalah MZ, Frandon J, Beregi JP, Dabli D. Comparison of two deep-learning image reconstruction algorithms on cardiac CT images: A phantom study. Diagn Interv Imaging 2024; 105:110-117. [PMID: 37949769 DOI: 10.1016/j.diii.2023.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 11/12/2023]
Abstract
PURPOSE The purpose of this study was to compare the performance of Precise IQ Engine (PIQE) and Advanced intelligent Clear-IQ Engine (AiCE) algorithms on image-quality according to the dose level in a cardiac computed tomography (CT) protocol. MATERIALS AND METHODS Acquisitions were performed using the CT ACR 464 phantom at three dose levels (volume CT dose indexes: 7.1/5.2/3.1 mGy) using a prospective cardiac CT protocol. Raw data were reconstructed using the three levels of AiCE and PIQE (Mild, Standard and Strong). The noise power spectrum (NPS) and task-based transfer function (TTF) for bone and acrylic inserts were computed. The detectability index (d') was computed to model the detectability of the coronary lumen (350 Hounsfield units and 4-mm diameter) and non-calcified plaque (40 Hounsfield units and 2-mm diameter). RESULTS Noise magnitude values were lower with PIQE than with AiCE (-13.4 ± 6.0 [standard deviation (SD)] % for Mild, -20.4 ± 4.0 [SD] % for Standard and -32.6 ± 2.6 [SD] % for Strong levels). The average NPS spatial frequencies shifted towards higher frequencies with PIQE than with AiCE (21.9 ± 3.5 [SD] % for Mild, 20.1 ± 3.0 [SD] % for Standard and 12.5 ± 3.5 [SD] % for Strong levels). The TTF values at fifty percent (f50) values shifted towards higher frequencies with PIQE than with AiCE for acrylic inserts but, for bone inserts, f50 values were found to be close. Whatever the dose and DLR level, d' values of both simulated cardiac lesions were higher with PIQE than with AiCE. For the simulated coronary lumen, d' values were better by 35.1 ± 9.3 (SD) % on average for all dose levels for Mild, 43.2 ± 5.0 (SD) % for Standard, and 62.6 ± 1.2 (SD) % for Strong levels. CONCLUSION Compared to AiCE, PIQE reduced noise, improved spatial resolution, noise texture and detectability of simulated cardiac lesions. PIQE seems to have a greater potential for dose reduction in cardiac CT acquisition.
Collapse
Affiliation(s)
- Joël Greffier
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30029 Nîmes, France.
| | - Maxime Pastor
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30029 Nîmes, France
| | - Salim Si-Mohamed
- University Lyon, INSA-Lyon, University Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, 69100 Villeurbanne, France; Department of Radiology, Louis Pradel Hospital, Hospices Civils de Lyon, 69500 Bron, France
| | | | - Aude Peudon-Balas
- Department of Medical Imaging, Centre Hospitalier de Perpignan, 66000 Perpignan, France
| | | | - Julien Frandon
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30029 Nîmes, France
| | - Jean-Paul Beregi
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30029 Nîmes, France
| | - Djamel Dabli
- IMAGINE UR UM 103, Montpellier University, Department of Medical Imaging, Nîmes University Hospital, 30029 Nîmes, France
| |
Collapse
|
50
|
Zhang J, Gong W, Ye L, Wang F, Shangguan Z, Cheng Y. A Review of deep learning methods for denoising of medical low-dose CT images. Comput Biol Med 2024; 171:108112. [PMID: 38387380 DOI: 10.1016/j.compbiomed.2024.108112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/18/2024] [Accepted: 02/04/2024] [Indexed: 02/24/2024]
Abstract
To prevent patients from being exposed to excess of radiation in CT imaging, the most common solution is to decrease the radiation dose by reducing the X-ray, and thus the quality of the resulting low-dose CT images (LDCT) is degraded, as evidenced by more noise and streaking artifacts. Therefore, it is important to maintain high quality CT image while effectively reducing radiation dose. In recent years, with the rapid development of deep learning technology, deep learning-based LDCT denoising methods have become quite popular because of their data-driven and high-performance features to achieve excellent denoising results. However, to our knowledge, no relevant article has so far comprehensively introduced and reviewed advanced deep learning denoising methods such as Transformer structures in LDCT denoising tasks. Therefore, based on the literatures related to LDCT image denoising published from year 2016-2023, and in particular from 2020 to 2023, this study presents a systematic survey of current situation, and challenges and future research directions in LDCT image denoising field. Four types of denoising networks are classified according to the network structure: CNN-based, Encoder-Decoder-based, GAN-based, and Transformer-based denoising networks, and each type of denoising network is described and summarized from the perspectives of structural features and denoising performances. Representative deep-learning denoising methods for LDCT are experimentally compared and analyzed. The study results show that CNN-based denoising methods capture image details efficiently through multi-level convolution operation, demonstrating superior denoising effects and adaptivity. Encoder-decoder networks with MSE loss, achieve outstanding results in objective metrics. GANs based methods, employing innovative generators and discriminators, obtain denoised images that exhibit perceptually a closeness to NDCT. Transformer-based methods have potential for improving denoising performances due to their powerful capability in capturing global information. Challenges and opportunities for deep learning based LDCT denoising are analyzed, and future directions are also presented.
Collapse
Affiliation(s)
- Ju Zhang
- College of Information Science and Technology, Hangzhou Normal University, Hangzhou, China.
| | - Weiwei Gong
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Lieli Ye
- College of Information Science and Technology, Hangzhou Normal University, Hangzhou, China.
| | - Fanghong Wang
- Zhijiang College, Zhejiang University of Technology, Shaoxing, China.
| | - Zhibo Shangguan
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Yun Cheng
- Department of Medical Imaging, Zhejiang Hospital, Hangzhou, China.
| |
Collapse
|