Deep learning image reconstruction for pancreatic low-dose computed tomography: comparison with hybrid iterative reconstruction

Evaluation of SR-DLR in low-dose abdominal CT: superior image quality and noise reduction

OBJECTIVES

To evaluate the effectiveness of super-resolution deep learning reconstruction (SR-DLR) in low-dose abdominal computed tomography (CT) imaging compared with hybrid iterative reconstruction (HIR) and conventional deep learning reconstruction (cDLR) algorithms.

METHODS

We retrospectively analyzed abdominal CT scans performed using a low-dose protocol. Three different image reconstruction algorithms-HIR, cDLR, and SR-DLR-were applied to the same raw image data. Objective evaluations included noise magnitude and contrast-to-noise ratio (CNR), as well as noise power spectrum (NPS) and edge rise slope (ERS). Subjective evaluations were performed by radiologists, who assessed image quality in terms of noise, artifacts, sharpness, and overall diagnostic utility.

RESULTS

Raw CT image data were obtained from 35 patients (mean CTDIvol 11.0 mGy; mean DLP 344.8 mGy/cm). cDLR yielded the lowest noise levels and highest CNR (p < 0.001). However, SR-DLR outperformed cDLR in terms of noise texture and resolution, achieving the lowest NPS peak and highest ERS (p < 0.001 and p = 0.005, respectively). Subjectively, SR-DLR was rated highest across all categories, including noise, artifacts, sharpness, and overall image quality, with statistically significant differences compared to cDLR and HIR (p < 0.001).

CONCLUSION

SR-DLR was the most effective reconstruction algorithm for low-dose abdominal CT imaging, offering superior image quality and noise reduction compared to cDLR and HIR. This suggests that SR-DLR can enhance the reliability and diagnostic accuracy of abdominal imaging, particularly in low-dose settings, making it a valuable tool in clinical practice.

Abdominal image quality and dose reduction with energy-integrating or photon-counting detectors dual-source CT: A phantom study

PURPOSE

The purpose of this study was to assess image-quality and dose reduction potential using a photon-counting computed tomography (PCCT) system by comparison with two different dual-source CT (DSCT) systems using two phantoms.

MATERIALS AND METHODS

Acquisitions on phantoms were performed using two DSCT systems (DSCT1 [Somatom Force] and DSCT2 [Somatom Pro.Pulse]) and one PCCT system (Naeotom Alpha) at four dose levels (13/6/3.4/1.8 mGy). Noise power spectrum (NPS) and task-based transfer function (TTF) were computed to assess noise magnitude and noise texture and spatial resolution (f50), respectively. Detectability indexes (d') were computed to model the detection of abdominal lesions: one unenhanced high-contrast task, one contrast-enhanced high-contrast task and one unenhanced low-contrast task. Image quality was subjectively assessed on an anthropomorphic phantom by two radiologists.

RESULTS

For all dose levels, noise magnitude values were lower with PCCT than with DSCTs. For all CT systems, similar noise texture values were found at 13 and 6 mGy, but the greatest noise texture values were found for DSCT2 and the lowest for PCCT at 3.4 and 1.8 mGy. For high-contrast inserts, similar or lower f50 values were found with PCCT than with DSCT1 and the opposite pattern was found for the low-contrast insert. For the three simulated lesions, d' values were greater with PCCT than with DSCTs. Abdominal images were rated satisfactory for clinical use by the radiologists for all dose levels with PCCT and for 13 and 6 mGy with DSCTs.

CONCLUSION

By comparison with DSCTs, PCCT reduces image-noise and improves detectability of simulated abdominal lesions without altering the spatial resolution and image texture. Image-quality obtained with PCCT seem to indicate greater potential for dose optimization than those obtained with DSCTs.

Improving diagnostic confidence in low-dose dual-energy CTE with low energy level and deep learning reconstruction

OBJECTIVE

To demonstrate the value of using 50 keV virtual monochromatic images with deep learning image reconstruction (DLIR) in low-dose dual-energy CT enterography (CTE).

METHODS

In this prospective study, 114 participants (62 % M; 41.9 ± 16 years) underwent dual-energy CTE. The early-enteric phase was performed using standard-dose (noise index (NI): 8) and images were reconstructed at 70 keV and 50 keV with 40 % strength ASIR-V (ASIR-V40%). The late-enteric phase used low-dose (NI: 12) and images were reconstructed at 50 keV with ASIR-V40%, and DLIR at medium (DLIR-M) and high strength (DLIR-H). Image standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge-rise-slope (ERS) were computed. The quantitative comb sign score was calculated for the 27 patients with Crohn's disease. The subjective noise, image contrast, display of rectus artery were scored using a 5-point scale by two radiologists blindly.

RESULTS

Effective dose was reduced by 50 % (P < 0.001) in the late-enteric phase to 3.26 mSv. The lower-dose 50 keV-DLIR-H images (SD:17.7 ± 0.5HU) had similar image noise (P = 0.97) as the standard-dose 70 keV-ASIR-V40% images (SD:17.7 ± 0.73HU), but with higher (P < 0.001) SNR, CNR, ERS and quantitative comb sign score (5.7 ± 0.17, 1.8 ± 0.12, 156.04 ± 5.21 and 5.05 ± 0.73, respectively). Furthermore, the lower-dose 50 keV-DLIR-H images obtained the highest score in the rectus artery visibility (4.27 ± 0.6).

CONCLUSIONS

The 50 keV images in dual-energy CTE with DLIR provides high-quality images, with a 50 % reduction in radiation dose. Images with high contrast and density resolutions significantly enhance the diagnostic confidence of Crohn's disease and are essential for the clinical development of individualized treatment plans.

Improved overall image quality in low-dose dual-energy computed tomography enterography using deep-learning image reconstruction

OBJECTIVE

To demonstrate the clinical advantages of a deep-learning image reconstruction (DLIR) in low-dose dual-energy computed tomography enterography (DECTE) by comparing images with standard-dose adaptive iterative reconstruction-Veo (ASIR-V) images.

METHODS

In this Institutional review board approved prospective study, 86 participants who underwent DECTE were enrolled. The early-enteric phase scan was performed using standard-dose (noise index: 8) and images were reconstructed at 5 mm and 1.25 mm slice thickness with ASIR-V at a level of 40% (ASIR-V40%). The late-enteric phase scan used low-dose (noise index: 12) and images were reconstructed at 1.25 mm slice thickness with ASIR-V40%, and DLIR at medium (DLIR-M) and high (DLIR-H). The 70 keV monochromatic images were used for image comparison and analysis. For objective assessment, image noise, artifact index, SNR and CNR were measured. For subjective assessment, subjective noise, image contrast, bowel wall sharpness, mesenteric vessel clarity, and small structure visibility were scored by two radiologists blindly. Radiation dose was compared between the early- and late-enteric phases.

RESULTS

Radiation dose was reduced by 50% in the late-enteric phase [(6.31 ± 1.67) mSv] compared with the early-enteric phase [(3.01 ± 1.09) mSv]. For the 1.25 mm images, DLIR-M and DLIR-H significantly improved both objective and subjective image quality compared to those with ASIR-V40%. The low-dose 1.25 mm DLIR-H images had similar image noise, SNR, CNR values as the standard-dose 5 mm ASIR-V40% images, but significantly higher scores in image contrast [5(5-5), P < 0.05], bowel wall sharpness [5(5-5), P < 0.05], mesenteric vessel clarity [5(5-5), P < 0.05] and small structure visibility [5(5-5), P < 0.05].

CONCLUSIONS

DLIR significantly reduces image noise at the same slice thickness, but significantly improves spatial resolution and lesion conspicuity with thinner slice thickness in DECTE, compared to conventional ASIR-V40% 5 mm images, all while providing 50% radiation dose reduction.

Artificial Intelligence in Pancreatic Image Analysis: A Review

Pancreatic cancer is a highly lethal disease with a poor prognosis. Its early diagnosis and accurate treatment mainly rely on medical imaging, so accurate medical image analysis is especially vital for pancreatic cancer patients. However, medical image analysis of pancreatic cancer is facing challenges due to ambiguous symptoms, high misdiagnosis rates, and significant financial costs. Artificial intelligence (AI) offers a promising solution by relieving medical personnel's workload, improving clinical decision-making, and reducing patient costs. This study focuses on AI applications such as segmentation, classification, object detection, and prognosis prediction across five types of medical imaging: CT, MRI, EUS, PET, and pathological images, as well as integrating these imaging modalities to boost diagnostic accuracy and treatment efficiency. In addition, this study discusses current hot topics and future directions aimed at overcoming the challenges in AI-enabled automated pancreatic cancer diagnosis algorithms.

Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans

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.

Pancreatic cancer detection with dual-energy CT: diagnostic performance of 40 keV and 70 keV virtual monoenergetic images

PURPOSE

To compare the diagnostic performance of 40 keV and 70 keV virtual monoenergetic images (VMIs) generated from dual-energy CT in the detection of pancreatic cancer.

METHODS

This retrospective study included patients who underwent pancreatic protocol dual-energy CT from January 2019 to August 2022. Four radiologists (1-11 years of experience), who were blinded to the final diagnosis, independently and randomly interpreted 40 keV and 70 keV VMIs and graded the presence or absence of pancreatic cancer. For each image set (40 keV and 70 keV VMIs), the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated. The diagnostic performance of each image set was compared using generalized estimating equations.

RESULTS

Overall, 137 patients (median age, 71 years; interquartile range, 63-78 years; 77 men) were included. Among them, 62 patients (45%) had pathologically proven pancreatic cancer. The 40 keV VMIs had higher specificity (75% vs. 67%; P < .001), PPV (76% vs. 71%; P < .001), and accuracy (85% vs. 81%; P = .001) than the 70 keV VMIs. On the contrary, 40 keV VMIs had lower sensitivity (96% vs. 98%; P = .02) and NPV (96% vs. 98%; P = .004) than 70 keV VMIs. However, the diagnostic confidence in patients with (P < .001) and without (P = .001) pancreatic cancer was improved in 40 keV VMIs than in 70 keV VMIs.

CONCLUSIONS

The 40 keV VMIs showed better diagnostic performance in diagnosing pancreatic cancer than the 70 keV VMIs, along with higher reader confidence.

Contribution of CT scan to patient's radiation exposure in parathyroid SPECT/CT scintigraphy

INTRODUCTION

Dual phase technetium-99mTc-methoxy isobutyl isonitrile (MIBI) single-photon emission computed tomography with computed tomography (SPECT/CT) may be the most accurate conventional imaging approach for localization of enlarged parathyroid gland (EPG). The imaging is based on the radiopharmaceutical (RP) retention in EPG compared to washout from normal thyroid and normal parathyroid glands. This study aimed to estimate and optimize the contribution of computed tomography (CT) scan and scan range to effective dose (ED) in dual-phase MIBI SPECT/CT parathyroid scintigraphy.

METHODS

The study included seventy-four patients; thirty-seven with reduced and thirty-seven with extended CT scan range. The ED caused by the CT scan was calculated using Dose Length Product (DLP) data and estimated using the Imaging Performance Assessment of CT scanners (ImPACT) calculator.

RESULTS

For all patients, the contribution of CT to the ED in a combined SPECT/CT examination was 2.62 ± 0.29 mSv (48%). The contribution of CT to the total ED was 1.8 ± 0.18 mSv (33%) when using reduced and 3.44 ± 0.23 mSv (64%) when using extended scan range. The DLP and ED were statistically significantly different between the reduced and extended CT scan range (p < 0.001) in the first and second phases. The individual organ dose was reduced from 8% to 94%.

CONCLUSION

The hybrid SPECT/CT improves the interpretation of nuclear medicine images and also increases the radiation dose to the patient. An adequately defined CT scan range on SPECT/CT imaging, can significantly reduce a patient's ED.

IMPLICATIONS FOR PRACTICE

The research findings showed that knowledge of anatomy, pathology and technology can provide optimising diagnostic procedures and reduce patient ED after SPECT/CT scans.

Low-dose liver CT: image quality and diagnostic accuracy of deep learning image reconstruction algorithm

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.

Deep learning reconstruction vs standard reconstruction for abdominal CT: the influence of BMI

OBJECTIVE

This study aimed to evaluate the image quality and lesion conspicuity of the deep learning image reconstruction (DLIR) algorithm compared with standard image reconstruction algorithms on abdominal enhanced computed tomography (CT) scanning with a wide range of body mass indexes (BMIs).

METHODS

A total of 112 participants who underwent contrast-enhanced abdominal CT scans were divided into three groups according to BMIs: the 80-kVp group (BMI ≤ 23.9 kg/m2), 100-kVp group (BMI 24-28.9 kg/m2), and 120-kVp group (BMI ≥ 29 kg/m2). All images were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction-V of 50% level (IR), and DLIR at low, medium, and high levels (DL, DM, and DH, respectively). Subjective noise, artifact, overall image quality, and low- and high-contrast hepatic lesion conspicuity were all graded on a 5-point scale. The CT attenuation value (in HU), image noise, and contrast-to-noise ratio (CNR) were quantified and compared.

RESULTS

DM and DH improved the qualitative and quantitative parameters compared with FBP and IR for all three BMI groups. DH had the lowest image noise and highest CNR value, while DM had the highest subjective overall image quality and low- and high-contrast lesion conspicuity scores for the three BMI groups. Based on the FBP, the improvement in image quality and lesion conspicuity of DM and DH images was greater in the 80-kVp group than in the 100-kVp and 120-kVp groups.

CONCLUSION

For all BMIs, DLIR improves both image quality and hepatic lesion conspicuity, of which DM would be the best choice to balance both.

CLINICAL RELEVANCE STATEMENT

The study suggests that utilizing DLIR, particularly at the medium level, can significantly enhance image quality and lesion visibility on abdominal CT scans across a wide range of BMIs.

KEY POINTS

• DLIR improved the image quality and lesion conspicuity across a wide range of BMIs. • DLIR at medium level had the highest subjective parameters and lesion conspicuity scores among all reconstruction levels. • On the basis of the FBP, the 80-kVp group had improved image quality and lesion conspicuity more than the 100-kVp and 120-kVp groups.

A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction

Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.

Deep learning imaging reconstruction of reduced-dose 40 keV virtual monoenergetic imaging for early detection of colorectal cancer liver metastases

OBJECTIVE

To explore whether reduced-dose (RD) gemstone spectral imaging (GSI) and deep learning image reconstruction (DLIR) of 40 keV virtual monoenergetic image (VMI) enhanced the early detection and diagnosis of colorectal cancer liver metastases (CRLM).

METHODS

Thirty-five participants with pathologically confirmed colorectal cancer were prospectively enrolled from March to August 2022 after routine care abdominal computed tomography (CT). GSI mode was used for contrast-enhanced CT, and two portal venous phase CT images were obtained [standard-dose (SD) CT dose index (CTDIvol) = 15.51 mGy, RD CTDIvol = 7.95 mGy]. The 40 keV-VMI were reconstructed via filtered back projection (FBP) and iterative reconstruction (ASIR-V 60 %, AV60) of both SD and RD images. RD medium-strength deep learning image reconstruction (DLIR-M) and RD high-strength deep learning image reconstruction (DLIR-H) were used to reconstruct the 40 keV-VMI. The contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of the liver and the lesions were objectively evaluated. The overall image quality, lesion conspicuity, and diagnostic confidence were subjectively evaluated, to compare the differences in evaluation results among the different images.

RESULTS

All 35 participants (mean age: 59.51 ± 11.01 years; 14 females) underwent SD and RD GSI portal venous-phase CT scans. The dose-length product of the RD GSI scan was reduced by 49-53 % lower than that of the SD GSI scan (420.22 ± 31.95) vs (817.58 ± 60.56). A total of 219 lesions were identified, including 55 benign lesions and 164 metastases, with an average size of 7.37 ± 4.14 mm. SD-FBP detected 207 lesions, SD-AV60 detected 201 lesions, and DLIR-M and DLIR-H detected 199 and 190 lesions, respectively. For lesions ≤ 5 mm, there was no statistical difference between SD-FBP vs DLIR-M (χ2McNemar = 1.00, P = 0.32) and SD-AV60 vs DLIR-M (χ2McNemar = 0.33, P = 0.56) in the detection rate. The CNR, SNR, and noise of DLIR-M and DLIR-H 40 keV-VMI images were better than those of SD-FBP images (P < 0.01) but did not differ significantly from those of SD-AV60 images (P > 0.05). When the lesions ≤ 5 mm, there were statistical differences in the overall diagnostic sensitivity of lesions compared with SD-FBP, SD-AV60, DLIR-M and DLIR-H (P＜0.01). There were no statistical differences in the sensitivity of lesions diagnosis between SD-FBP, SD-AV60 and DLIR-M (both P＞0.05). However, the DLIR-M subjective image quality and lesion diagnostic confidence were higher for SD-FBP (both P < 0.01).

CONCLUSION

Reduced dose DLIR-M of 40 keV-VMI can be used for routine follow-up care of colorectal cancer patients, to optimize evaluations and ensure CT image quality. Meanwhile, the detection rate and diagnostic sensitivity and specificity of small lesions, early liver metastases is not obviously reduced.

Implementation of AI image reconstruction in CT-how is it validated and what dose reductions can be achieved

CT reconstruction has undergone a substantial change over the last decade with the introduction of iterative reconstruction (IR) and now with deep learning reconstruction (DLR). In this review, DLR will be compared to IR and filtered back-projection (FBP) reconstructions. Comparisons will be made using image quality metrics such as noise power spectrum, contrast-dependent task-based transfer function, and non-prewhitening filter detectability index (dNPW'). Discussion on how DLR has impacted CT image quality, low-contrast detectability, and diagnostic confidence will be provided. DLR has shown the ability to improve in areas that IR is lacking, namely: noise magnitude reduction does not alter noise texture to the degree that IR did, and the noise texture found in DLR is more aligned with noise texture of an FBP reconstruction. Additionally, the dose reduction potential for DLR is shown to be greater than IR. For IR, the consensus was dose reduction should be limited to no more than 15-30% to preserve low-contrast detectability. For DLR, initial phantom and patient observer studies have shown acceptable dose reduction between 44 and 83% for both low- and high-contrast object detectability tasks. Ultimately, DLR is able to be used for CT reconstruction in place of IR, making it an easy "turnkey" upgrade for CT reconstruction. DLR for CT is actively being improved as more vendor options are being developed and current DLR options are being enhanced with second generation algorithms being released. DLR is still in its developmental early stages, but is shown to be a promising future for CT reconstruction.

Image Quality and Lesion Detectability of Pancreatic Phase Thin-Slice Computed Tomography Images With a Deep Learning-Based Reconstruction Algorithm

OBJECTIVE

To evaluate the image quality and lesion detectability of pancreatic phase thin-slice computed tomography (CT) images reconstructed with a deep learning-based reconstruction (DLR) algorithm compared with filtered-back projection (FBP) and hybrid iterative reconstruction (IR) algorithms.

METHODS

Fifty-three patients who underwent dynamic contrast-enhanced CT including pancreatic phase were enrolled in this retrospective study. Pancreatic phase thin-slice (0.625 mm) images were reconstructed with each FBP, hybrid IR, and DLR. Objective image quality and signal-to-noise ratio of the pancreatic parenchyma, and contrast-to-noise ratio of pancreatic lesions were compared between the 3 reconstruction algorithms. Two radiologists independently assessed the image quality of all images. The diagnostic performance for the detection of pancreatic lesions was compared among the reconstruction algorithms using jackknife alternative free-response receiver operating characteristic analysis.

RESULTS

Deep learning-based reconstruction resulted in significantly lower image noise and higher signal-to-noise ratio and contrast-to-noise ratio than hybrid IR and FBP ( P < 0.001). Deep learning-based reconstruction also yielded significantly higher visual scores than hybrid IR and FBP ( P < 0.01). The diagnostic performance of DLR for detecting pancreatic lesions was highest for both readers, although a significant difference was found only between DLR and FBP in one reader ( P = 0.02).

CONCLUSIONS

Deep learning-based reconstruction showed improved objective and subjective image quality of pancreatic phase thin-slice CT relative to other reconstruction algorithms and has potential for improving lesion detectability.

Impact of a reduced iodine load with deep learning reconstruction on abdominal MDCT

To evaluate the impact of a reduced iodine load using deep learning reconstruction (DLR) on the hepatic parenchyma compared to conventional iterative reconstruction (hybrid IR) and its consequence on the radiation dose and image quality. This retrospective monocentric intraindividual comparison study included 66 patients explored at the portal phase using different multidetector computed tomography parameters: Group A, hybrid IR algorithm (hybrid IR) and a nonionic low-osmolality contrast agent (350 mgI/mL); Group B, DLR algorithm (DLR) and a nonionic iso-osmolality contrast agent (270 mgI/mL). We recorded the attenuation of the liver parenchyma, image quality, and radiation dose parameters. The mean hounsfield units (HU) value of the liver parenchyma was significantly lower in group B, at 105.9 ± 10.9 HU versus 118.5 ± 14.6 HU in group A. However, the 90%IC of mean liver attenuation in the group B (DLR) was between 100.8 HU and 109.3 HU. The signal-to-noise ratio of the liver parenchyma was significantly higher on DLR images, increasing by 56%. However, for both the contrast-to-noise ratio (CNR) and CNR liver/PV no statistical difference was found, even if the CNR liver/PV ratio was slightly higher for group A. The mean dose-length product and computed tomography dose index volume values were significantly lower with DLR, corresponding to a radiation dose reduction of 36% for the DLR. Using a DLR algorithm for abdominal multidetector computed tomography with a low iodine load can provide sufficient enhancement of the liver parenchyma up to 100 HU in addition to the advantages of a higher image quality, a better signal-to-noise ratio and a lower radiation dose.

The efficacy of low-dose CT with deep learning image reconstruction in the surveillance of incidentally detected pancreatic cystic lesions

PURPOSE

To evaluate the efficacy of low-dose CT (LDCT) with deep learning image reconstruction (DLIR) for the surveillance of pancreatic cystic lesions (PCLs) compared with standard-dose CT (SDCT) with adaptive statistical iterative reconstruction (ASIR-V).

METHODS

The study enrolled 103 patients who underwent pancreatic CT for follow-up of incidentally detected PCLs. The CT protocol included LDCT in the pancreatic phase with 40% ASIR-V, DLIR at medium (DLIR-M) and high levels (DLIR-H), and SDCT in the portal-venous phase with 40% ASIR-V. The overall image quality and conspicuity of PCLs were qualitatively assessed using five-point scales by two radiologists. The size of PCLs, presence of thickened/enhancing walls, enhancing mural nodules, and main pancreatic duct dilatation were reviewed. CT noise and cyst-to-pancreas contrast-to-noise ratio (CNR) were measured. Qualitative and quantitative parameters were analyzed using the chi-squared test, one-way ANOVA, and t-test. Additionally, interobserver agreement was analyzed using the kappa and weighted-kappa statistics.

RESULTS

The volume CT dose-indexes in LDCT and SDCT were 3.0 ± 0.6 mGy and 8.4 ± 2.9 mGy, respectively. LDCT with DLIR-H showed the highest overall image quality, the lowest noise, and the highest CNR. The PCL conspicuity in LDCT with either DLIR-M or DLIR-H was not significantly different from that in SDCT with ASIR-V. Other findings depicting PCLs also revealed no significant differences between LDCT with DLIR and SDCT with ASIR-V. Moreover, the results revealed good or excellent interobserver agreement.

CONCLUSION

LDCT with DLIR has a comparable performance with SDCT for the follow-up of incidentally detected PCLs.

Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis

OBJECTIVE

To perform a systematic literature review and meta-analysis of the two most common commercially available deep-learning algorithms for CT.

METHODS

We used PubMed, Scopus, Embase, and Web of Science to conduct systematic searches for studies assessing the most common commercially available deep-learning CT reconstruction algorithms: True Fidelity (TF) and Advanced intelligent Clear-IQ Engine (AiCE) in the abdomen of human participants since only these two algorithms currently have adequate published data for robust systematic analysis.

RESULTS

Forty-four articles fulfilled inclusion criteria. 32 studies evaluated TF and 12 studies assessed AiCE. DLR algorithms produced images with significantly less noise (22-57.3% less than IR) but preserved a desirable noise texture with increased contrast-to-noise ratios and improved lesion detectability on conventional CT. These improvements with DLR were similarly noted in dual-energy CT which was only assessed for a single vendor. Reported radiation reduction potential was 35.1-78.5%. Nine studies assessed observer performance with the two dedicated liver lesion studies being performed on the same vendor reconstruction (TF). These two studies indicate preserved low contrast liver lesion detection (> 5 mm) at CTDIvol 6.8 mGy (BMI 23.5 kg/m2) to 12.2 mGy (BMI 29 kg/m2). If smaller lesion detection and improved lesion characterization is needed, a CTDIvol of 13.6-34.9 mGy is needed in a normal weight to obese population. Mild signal loss and blurring have been reported at high DLR reconstruction strengths.

CONCLUSION

Deep learning reconstructions significantly improve image quality in CT of the abdomen. Assessment of other dose levels and clinical indications is needed. Careful choice of radiation dose levels is necessary, particularly for small liver lesion assessment.

Application of Deep Learning-Based Denoising Technique for Radiation Dose Reduction in Dynamic Abdominal CT: Comparison with Standard-Dose CT Using Hybrid Iterative Reconstruction Method

The purpose is to evaluate whether deep learning-based denoising (DLD) algorithm provides sufficient image quality for abdominal computed tomography (CT) with a 30% reduction in radiation dose, compared to standard-dose CT reconstructed with conventional hybrid iterative reconstruction (IR). The subjects consisted of 50 patients who underwent abdominal CT with standard dose and reconstructed with hybrid IR (ASiR-V50%) and another 50 patients who underwent abdominal CT with approximately 30% less dose and reconstructed with ASiR-V50% and DLD at low-, medium- and high-strength (DLD-L, DLD-M and DLD-H, respectively). The standard deviation of attenuation in liver parenchyma was measured as image noise. Contrast-to-noise ratio (CNR) for portal vein on portal venous phase was calculated. Lesion conspicuity in 23 abdominal solid mass on the reduced-dose CT was rated on a 5-point scale: 0 (best) to -4 (markedly inferior). Compared with hybrid IR of standard-dose CT, DLD-H of reduced-dose CT provided significantly lower image noise (portal phase: 9.0 (interquartile range, 8.7-9.4) HU vs 12.0 (11.4-12.7) HU, P < 0.0001) and significantly higher CNR (median, 5.8 (4.4-7.4) vs 4.3 (3.3-5.3), P = 0.0019). As for DLD-M of reduced-dose CT, no significant difference was found in image noise and CNR compared to hybrid IR of standard-dose CT (P > 0.99). Lesion conspicuity scores for DLD-H and DLD-M were significantly better than hybrid IR (P < 0.05). Dynamic contrast-enhanced abdominal CT acquired with approximately 30% lower radiation dose and generated with the DLD algorithm exhibit lower image noise and higher CNR compared to standard-dose CT with hybrid IR.

First Results of a New Deep Learning Reconstruction Algorithm on Image Quality and Liver Metastasis Conspicuity for Abdominal Low-Dose CT

The study's aim was to assess the impact of a deep learning image reconstruction algorithm (Precise Image; DLR) on image quality and liver metastasis conspicuity compared with an iterative reconstruction algorithm (IR). This retrospective study included all consecutive patients with at least one liver metastasis having been diagnosed between December 2021 and February 2022. Images were reconstructed using level 4 of the IR algorithm (i4) and the Standard/Smooth/Smoother levels of the DLR algorithm. Mean attenuation and standard deviation were measured by placing the ROIs in the fat, muscle, healthy liver, and liver tumor. Two radiologists assessed the image noise and image smoothing, overall image quality, and lesion conspicuity using Likert scales. The study included 30 patients (mean age 70.4 ± 9.8 years, 17 men). The mean CTDI_vol was 6.3 ± 2.1 mGy, and the mean dose-length product 314.7 ± 105.7 mGy.cm. Compared with i4, the HU values were similar in the DLR algorithm at all levels for all tissues studied. For each tissue, the image noise significantly decreased with DLR compared with i4 (p < 0.01) and significantly decreased from Standard to Smooth (-26 ± 10%; p < 0.01) and from Smooth to Smoother (-37 ± 8%; p < 0.01). The subjective image assessment confirmed that the image noise significantly decreased between i4 and DLR (p < 0.01) and from the Standard to Smoother levels (p < 0.01), but the opposite occurred for the image smoothing. The highest scores for overall image quality and conspicuity were found for the Smooth and Smoother levels.

Improving spatial resolution and diagnostic confidence with thinner slice and deep learning image reconstruction in contrast-enhanced abdominal CT

OBJECTIVE

To evaluate image quality and diagnostic confidence improvement using a thin slice and a deep learning image reconstruction (DLIR) in contrast-enhanced abdominal CT.

METHODS

Forty patients with hepatic lesions in enhanced abdominal CT were retrospectively analyzed. Images in the portal phase were reconstructed at 5 mm and 1.25 mm slice thickness using the 50% adaptive statistical iterative reconstruction (ASIR-V) (ASIR-V50%) and at 1.25 mm using DLIR at medium (DLIR-M) and high (DLIR-H) settings. CT number and standard deviation of the hepatic parenchyma, spleen, portal vein, and subcutaneous fat were measured, and contrast-to-noise ratio (CNR) was calculated. Edge-rise-slope (ERS) was measured on the portal vein to reflect spatial resolution and the CT number skewness on liver parenchyma was calculated to reflect image texture. Two radiologists blindly assessed the overall image quality including subjective noise, image contrast, visibility of small structures using a 5-point scale, and object sharpness and lesion contour using a 4-point scale.

RESULTS

For the 1.25-mm images, DLIR significantly reduced image noise, improved CNR and overall subjective image quality compared to ASIR-V50%. Compared to the 5-mm ASIR-V50% images, DLIR images had significantly higher scores in the visibility and contour for small structures and lesions; as well as significantly higher ERS and lower CT number skewness. At a quarter of the signal strength, the 1.25-mm DLIR-H images had a similar subjective noise score as the 5-mm ASIR-V50% images.

CONCLUSION

DLIR significantly reduces image noise and maintains a more natural image texture; image spatial resolution and diagnostic confidence can be improved using thin slice images and DLIR in abdominal CT.

KEY POINTS

• DLIR further reduces image noise compared with ASIR-V while maintaining favorable image texture. • In abdominal CT, thinner slice images improve image spatial resolution and small object visualization but suffer from higher image noise. • Thinner slice images combined with DLIR in abdominal CT significantly suppress image noise for detecting low-density lesions while significantly improving image spatial resolution and overall image quality.

Ultra-low-dose CT lung screening with artificial intelligence iterative reconstruction: evaluation via automatic nodule-detection software

AIM

To test the feasibility of ultra-low-dose (ULD) computed tomography (CT) combined with an artificial intelligence iterative reconstruction (AIIR) algorithm for screening pulmonary nodules using computer-assisted diagnosis (CAD).

MATERIALS AND METHODS

A chest phantom with artificial pulmonary nodules was first scanned using the routine protocol and the ULD protocol (3.28 versus 0.18 mSv) to compare the image quality and to test the acceptability of the ULD CT protocol. Next, 147 lung-screening patients were enrolled prospectively, undergoing an additional ULD CT immediately after their routine CT examination for clinical validation. Images were reconstructed with filtered back-projection (FBP), hybrid iterative reconstruction (HIR), the AIIR, and were imported to the CAD software for preliminary nodule detection. Subjective image quality on the phantom was scored using a five-point scale and compared using the Mann-Whitney U-test. Nodule detection using CAD was evaluated for ULD HIR and AIIR images using the routine dose image as reference.

RESULTS

Higher image quality was scored for AIIR than for FBP and HIR at ULD (p<0.001). As reported by CAD, 107 patients were presented with more than five nodules on routine dose images and were chosen to represent the challenging cases at an early stage of pulmonary disease. Among such, the performance of nodule detection by CAD on ULD HIR and AIIR images was 75.2% and 92.2% of the routine dose image, respectively.

CONCLUSION

Combined with AIIR, it was feasible to use an ULD CT protocol with 95% dose reduction for CAD-based screening of pulmonary nodules.

A Review of Deep Learning CT Reconstruction: Concepts, Limitations, and Promise in Clinical Practice

Purpose of Review

Deep Learning reconstruction (DLR) is the current state-of-the-art method for CT image formation. Comparisons to existing filter back-projection, iterative, and model-based reconstructions are now available in the literature. This review summarizes the prior reconstruction methods, introduces DLR, and then reviews recent findings from DLR from a physics and clinical perspective.

Recent Findings

DLR has been shown to allow for noise magnitude reductions relative to filtered back-projection without suffering from “plastic” or “blotchy” noise texture that was found objectionable with most iterative and model-based solutions. Clinically, early reader studies have reported increases in subjective quality scores and studies have successfully implemented DLR-enabled dose reductions.

Summary

The future of CT image reconstruction is bright; deep learning methods have only started to tackle problems in this space via addressing noise reduction. Artifact mitigation and spectral applications likely be future candidates for DLR applications.

Radiation and iodine dose reduced thoraco-abdomino-pelvic dual-energy CT at 40 keV reconstructed with deep learning image reconstruction

OBJECTIVE

To evaluate the feasibility of a simultaneous reduction of radiation and iodine doses in dual-energy thoraco-abdomino-pelvic CT reconstructed with deep learning image reconstruction (DLIR).

METHODS

Thoraco-abdomino-pelvic CT was prospectively performed in 111 participants; 52 participants underwent a standard-dose single-energy CT with a standard iodine dose (600 mgI/kg; SD group), while 59 underwent a low-dose dual-energy CT with a reduced iodine dose [300 mgI/kg; double low-dose (DLD) group]. CT data were reconstructed with a hybrid iterative reconstruction in the SD group and a high-strength level of DLIR at 40 keV in the DLD group. Two radiologists measured the CT numbers of the descending and abdominal aorta, portal vein, hepatic vein, inferior vena cava, liver, pancreas, spleen, and kidney, and background noise. Two other radiologists assessed diagnostic acceptability using a 5-point scale. The CT dose-index volume (CTDIvol), iodine weight, CT numbers of anatomical structures, background noise, and diagnostic acceptability were compared between the two groups using Mann-Whitney U test.

RESULTS

The median CTDIvol [10 mGy; interquartile range (IQR), 9-13 mGy vs 4 mGy; IQR, 4-5 mGy] and median iodine weight (35 g; IQR, 31-38 g vs 16 g; IQR, 14-18 g) were lower in the DLD group than in the SD group (p < 0.001 for each). The CT numbers of all anatomical structures and background noise were higher in the DLD group than in the SD group (p < 0.001 for all). The diagnostic image quality was obtained in 100% (52/52) of participants in the SD group and 95% (56/59) of participants in the DLD group.

CONCLUSION

Virtual monochromatic images at 40 keV reconstructed with DLIR could achieve half doses of radiation and iodine while maintaining diagnostic image quality.

ADVANCES IN KNOWLEDGE

Virtual monochromatic images at 40 keV reconstructed with DLIR algorithm allowed to reduce the doses of radiation and iodine while maintaining diagnostic image quality.

Deep learning reconstruction allows low-dose imaging while maintaining image quality: comparison of deep learning reconstruction and hybrid iterative reconstruction in contrast-enhanced abdominal CT

We aimed to compare the radiation dose and image quality of a low-dose abdominal computed tomography (CT) protocol reconstructed with deep learning reconstruction (DLR) with those of a routine-dose protocol reconstructed with hybrid-iterative reconstruction. This retrospective study enrolled 71 patients [61 men; average age, 71.9 years; mean body mass index (BMI), 24.3 kg/m2] who underwent both low-dose abdominal CT with DLR [advanced intelligent clear-IQ engine (AiCE)] and routine-dose abdominal CT with hybrid-iterative reconstruction [adaptive iterative dose reduction 3D (AIDR 3D)]. Radiation dose parameters included volume CT dose index (CTDIvol), effective dose (ED), and size-specific dose estimate (SSDE). Mean image noise and contrast-to-noise ratio (CNR) were calculated. Image noise was measured in the hepatic parenchyma and bilateral erector spinae muscles. Moreover, subjective assessment of perceived image quality and diagnostic acceptability was performed. The low-dose protocol helped reduce the CTDIvol by 44.3%, ED by 43.7%, and SSDE by 44.9%. Moreover, the noise was significantly lower and CNR significantly higher with the low-dose protocol than with the normal-dose protocol (P<0.001). In the subjective assessment of image quality, there was no significant difference between the protocols with regard to image noise. Overall, AiCE was superior to AIDR 3D in terms of diagnostic acceptability (P=0.001). The use of AiCE can reduce overall radiation dose by more than 40% without loss of image quality compared to routine-dose abdominal CT with AIDR 3D.

Unenhanced abdominal low-dose CT reconstructed with deep learning-based image reconstruction: image quality and anatomical structure depiction

Purpose

To evaluate the utility of deep learning-based image reconstruction (DLIR) algorithm in unenhanced abdominal low-dose CT (LDCT).

Materials and methods

Two patient groups were included in this prospective study: 58 consecutive patients who underwent unenhanced abdominal standard-dose CT reconstructed with hybrid iterative reconstruction (SDCT group) and 48 consecutive patients who underwent unenhanced abdominal LDCT reconstructed with high strength level of DLIR (LDCT group). The background noise and signal-to-noise ratio (SNR) of the liver, pancreas, spleen, kidney, abdominal aorta, inferior vena cava, and portal vein were calculated. Two radiologists qualitatively assessed the overall image noise, overall image quality, and abdominal anatomical structures depiction. Quantitative and qualitative parameters and size-specific dose estimates (SSDE) were compared between SDCT and LDCT groups.

Results

The background noise was lower in LDCT group than in SDCT group (P = 0.02). SNRs were higher in LDCT group than in SDCT group (P < 0.001–0.004) except for the liver. Overall image noise was superior in LDCT group than in SDCT group (P < 0.001). Overall image quality was not different between SDCT and LDCT groups (P = 0.25–0.26). Depiction of almost all abdominal anatomical structures was equal to or better in LDCT group than in SDCT group (P < 0.001–0.88). The SSDE was lower in LDCT group (4.0 mGy) than in SDCT group (20.6 mGy) (P < 0.001).

Conclusions

DLIR facilitates substantial radiation dose reduction of > 75% and significantly reduces background noise. DLIR can maintain image quality and anatomical structure depiction in unenhanced abdominal LDCT.

The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis

OBJECTIVE

To determine the difference in CT values and image quality of abdominal CT images reconstructed by filtered back-projection (FBP), hybrid iterative reconstruction (IR), and deep learning reconstruction (DLR).

METHODS

PubMed and Embase were systematically searched for articles regarding CT densitometry in the abdomen and the image reconstruction techniques FBP, hybrid IR, and DLR. Mean differences in CT values between reconstruction techniques were analyzed. A comparison between signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of FBP, hybrid IR, and DLR was made. A comparison of diagnostic confidence between hybrid IR and DLR was made.

RESULTS

Sixteen articles were included, six being suitable for meta-analysis. In the liver, the mean difference between hybrid IR and DLR was - 0.633 HU (p = 0.483, SD ± 0.902 HU). In the spleen, the mean difference between hybrid IR and DLR was - 0.099 HU (p = 0.925, SD ± 1.061 HU). In the pancreas, the mean difference between hybrid IR and DLR was - 1.372 HU (p = 0.353, SD ± 1.476 HU). In 14 articles, CNR was described. In all cases, DLR showed a significantly higher CNR. In 9 articles, SNR was described. In all cases but one, DLR showed a significantly higher SNR. In all cases, DLR showed a significantly higher diagnostic confidence.

CONCLUSIONS

There were no significant differences in CT values reconstructed by FBP, hybrid IR, and DLR in abdominal organs. This shows that these reconstruction techniques are consistent in reconstructing CT values. DLR images showed a significantly higher SNR and CNR, compared to FBP and hybrid IR.

KEY POINTS

CT values of abdominal CT images are similar between deep learning reconstruction (DLR), filtered back-projection (FBP), and hybrid iterative reconstruction (IR). DLR results in improved image quality in terms of SNR and CNR compared to FBP and hybrid IR images. DLR can thus be safely implemented in the clinical setting resulting in improved image quality without affecting CT values.

Improved precision of noise estimation in CT with a volume-based approach

Assessment of image noise is a relevant issue in computed tomography (CT). Noise is routinely measured by the standard deviation of density values (Hounsfield units, HU) within a circular region of interest (ROI). We explored the effect of a spherical volume of interest (VOI) on noise measurements. Forty-nine chronic obstructive pulmonary disease patients underwent CT with clinical protocol (regular dose [RD], volumetric CT dose index [CTDIvol] 3.04 mGy, 64-slice unit), and ultra-low dose (ULD) protocol (median CTDIvol 0.38 mGy, dual-source unit). Noise was measured in 27 1-cm² ROIs and 27 0.75-cm³ VOIs inside the trachea. Median true noise was 21 HU (range 17-29) for RD-CT and 33 HU (26-39) for ULD-CT. The VOI approach resulted in a lower mean distance between limits of agreement compared to ROI: 5.9 versus 10.0 HU for RD-CT (-40%); 4.7 versus 9.9 HU for ULD-CT (-53%). Mean systematic bias barely changed: -1.6 versus -0.9HU for RD-CT; 0.0 to 0.4HU for ULD-CT. The average measurement time was 6.8 s (ROI) versus 9.7 (VOI), independent of dose level. For chest CT, measuring noise with a VOI-based instead of a ROI-based approach reduces variability by 40-53%, without a relevant effect on systematic bias and measurement time.