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Yao H, Jia B, Pan X, Sun J. Validation and Feasibility of Ultrafast Cervical Spine MRI Using a Deep Learning-Assisted 3D Iterative Image Enhancement System. J Multidiscip Healthc 2024; 17:2499-2509. [PMID: 38799011 PMCID: PMC11128255 DOI: 10.2147/jmdh.s465002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/13/2024] [Indexed: 05/29/2024] Open
Abstract
Purpose This study aimed to evaluate the feasibility of ultrafast (2 min) cervical spine MRI protocol using a deep learning-assisted 3D iterative image enhancement (DL-3DIIE) system, compared to a conventional MRI protocol (6 min 14s). Patients and Methods Fifty-one patients were recruited and underwent cervical spine MRI using conventional and ultrafast protocols. A DL-3DIIE system was applied to the ultrafast protocol to compensate for the spatial resolution and signal-to-noise ratio (SNR) of images. Two radiologists independently assessed and graded the quality of images from the dimensions of artifacts, boundary sharpness, visibility of lesions and overall image quality. We recorded the presence or absence of different pathologies. Moreover, we examined the interchangeability of the two protocols by computing the 95% confidence interval of the individual equivalence index, and also evaluated the inter-protocol intra-observer agreement using Cohen's weighted kappa. Results Ultrafast-DL-3DIIE images were significantly better than conventional ones for artifacts and equivalent for other qualitative features. The number of cases with different kinds of pathologies was indistinguishable based on the MR images from ultrafast-DL-3DIIE and conventional protocols. With the exception of disc degeneration, the 95% confidence interval for the individual equivalence index across all variables did not surpass 5%, suggesting that the two protocols are interchangeable. The kappa values of these evaluations by the two radiologists ranged from 0.65 to 0.88, indicating good-to-excellent agreement. Conclusion The DL-3DIIE system enables 67% spine MRI scan time reduction while obtaining at least equivalent image quality and diagnostic results compared to the conventional protocol, suggesting its potential for clinical utility.
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Affiliation(s)
- Hui Yao
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, People’s Republic of China
| | - Bangsheng Jia
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, People’s Republic of China
| | - Xuelin Pan
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, People’s Republic of China
| | - Jiayu Sun
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, People’s Republic of China
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Ni M, He M, Yang Y, Wen X, Zhao Y, Gao L, Yan R, Xu J, Zhang Y, Chen W, Jiang C, Li Y, Zhao Q, Wu P, Li C, Qu J, Yuan H. Application research of AI-assisted compressed sensing technology in MRI scanning of the knee joint: 3D-MRI perspective. Eur Radiol 2024; 34:3046-3058. [PMID: 37932390 DOI: 10.1007/s00330-023-10368-x] [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: 07/13/2023] [Revised: 08/29/2023] [Accepted: 09/04/2023] [Indexed: 11/08/2023]
Abstract
OBJECTIVE To investigate the potential applicability of AI-assisted compressed sensing (ACS) in knee MRI to enhance and optimize the scanning process. METHODS Volunteers and patients with sports-related injuries underwent prospective MRI scans with a range of acceleration techniques. The volunteers were subjected to varied ACS acceleration levels to ascertain the most effective level. Patients underwent scans at the determined optimal 3D-ACS acceleration level, and 3D compressed sensing (CS) and 2D parallel acquisition technology (PAT) scans were performed. The resultant 3D-ACS images underwent 3.5 mm/2.0 mm multiplanar reconstruction (MPR). Experienced radiologists evaluated and compared the quality of images obtained by 3D-ACS-MRI and 3D-CS-MRI, 3.5 mm/2.0 mm MPR and 2D-PAT-MRI, diagnosed diseases, and compared the results with the arthroscopic findings. The diagnostic agreement was evaluated using Cohen's kappa correlation coefficient, and both absolute and relative evaluation methods were utilized for objective assessment. RESULTS The study involved 15 volunteers and 53 patients. An acceleration factor of 10.69 × was identified as optimal. The quality evaluation showed that 3D-ACS provided poorer bone structure visualization, and improved cartilage visualization and less satisfactory axial images with 3.5 mm/2.0 mm MPR than 2D-PAT. In terms of objective evaluation, the relative evaluation yielded satisfactory results across different groups, while the absolute evaluation revealed significant variances in most features. Nevertheless, high levels of diagnostic agreement (κ: 0.81-0.94) and accuracy (0.83-0.98) were observed across all diagnoses. CONCLUSION ACS technology presents significant potential as a replacement for traditional CS in 3D-MRI knee scans, allowing thinner MPRs and markedly faster scans without sacrificing diagnostic accuracy. CLINICAL RELEVANCE STATEMENT 3D-ACS-MRI of the knee can be completed in the 160 s with good diagnostic consistency and image quality. 3D-MRI-MPR can replace 2D-MRI and reconstruct images with thinner slices, which helps to optimize the current MRI examination process and shorten scanning time. KEY POINTS • AI-assisted compressed sensing technology can reduce knee MRI scan time by over 50%. • 3D AI-assisted compressed sensing MRI and related multiplanar reconstruction can replace traditional accelerated MRI and yield thinner 2D multiplanar reconstructions. • Successful application of 3D AI-assisted compressed sensing MRI can help optimize the current knee MRI process.
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Affiliation(s)
- Ming Ni
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Miao He
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, People's Republic of China
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, People's Republic of China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, People's Republic of China
| | - Yuxin Yang
- United Imaging Research Institute of Intelligent Imaging, Beijing, People's Republic of China
| | - Xiaoyi Wen
- Institute of Statistics and Big Data, Renmin University of China, Beijing, People's Republic of China
| | - Yuqing Zhao
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Lixiang Gao
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Ruixin Yan
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Jiajia Xu
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Yarui Zhang
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Wen Chen
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Chenyu Jiang
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Yali Li
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Qiang Zhao
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Peng Wu
- United Imaging Healthcare Co, Shanghai, People's Republic of China
| | - Chunlin Li
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, People's Republic of China
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, People's Republic of China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, People's Republic of China
| | - Junda Qu
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, People's Republic of China.
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, People's Republic of China.
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, People's Republic of China.
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China.
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Sui H, Gong Y, Liu L, Lv Z, Zhang Y, Dai Y, Mo Z. Comparison of Artificial Intelligence-Assisted Compressed Sensing (ACS) and Routine Two-Dimensional Sequences on Lumbar Spine Imaging. J Pain Res 2023; 16:257-267. [PMID: 36744117 PMCID: PMC9891076 DOI: 10.2147/jpr.s388219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 12/20/2022] [Indexed: 01/29/2023] Open
Abstract
Purpose To evaluate and compare the image quality and diagnostic accuracy of Artificial Intelligence-assisted Compressed Sensing (ACS) sequences for lumbar disease, as an acceleration method for MRI combining parallel imaging, half-Fourier, compressed sensing and neural network and routine 2D sequences for lumbar spine. Methods We collected data from 82 healthy subjects and 213 patients who used 2D ACS accelerated sequences to examine the lumbar spine while 95 healthy subjects and 234 patients used routine 2D sequences. Acquisitions included axial T2WI, sagittal T2WI, T1WI, and T2-fs sequences. All obtained images of these subjects were analyzed in the light of calculating image quality factors such as signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for selected regions of interest. The lumbar image quality, artifacts and visibility of lesion structure were assessed by two radiologists independently. Differences between the evaluation values above were tested for statistical significance by the Wilcoxon signed-ranks test. Inter-observer agreements of image quality between two radiologists were measured using Cohen's kappa correlation coefficient. Results The ACS accelerated sequences not only reduced the scanning time by 18.9%, but also retained basically the same image quality as the routine 2D sequences in both healthy subjects and patients. Artifacts are less produced on ACS accelerated sequences compared with routine 2D sequences (p < 0.05). Apart from this, there were no significant differences in quantitative SNR, CNR measurements and qualitative scores within reviewing radiologists for each group (p > 0.05). Moreover, inter-observer agreement between two radiologists in scoring image quality was substantial consistently for ACS accelerated sequences and routine sequences (kappa = 0.622-0.986). Conclusion Compared with routine 2D sequences, ACS accelerated sequences allow for faster lumbar spine imaging with similar imaging quality and present reliable diagnostic accuracy, which can potentially improve workflow and patient comfort in musculoskeletal examinations.
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Affiliation(s)
- He Sui
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
| | - Yu Gong
- Medical Imaging Department, Linyi People’s Hospital, Linyi, People’s Republic of China
| | - Lin Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
| | - Zhongwen Lv
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
| | - Yunfei Zhang
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, People’s Republic of China
| | - Yongming Dai
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, People’s Republic of China
| | - Zhanhao Mo
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China,Correspondence: Zhanhao Mo, Department of Radiology, China-Japan Union Hospital of Jilin University, No. 126 Xiantai St., Erdao Dist., Changchun, People’s Republic of China, Email
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Kashiwagi N, Sakai M, Tsukabe A, Yamashita Y, Fujiwara M, Yamagata K, Nakamoto A, Nakanishi K, Tomiyama N. Ultrafast cervcial spine MRI protocol using deep learning-based reconstruction: Diagnostic equivalence to a conventional protocol. Eur J Radiol 2022; 156:110531. [PMID: 36179465 DOI: 10.1016/j.ejrad.2022.110531] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/18/2022] [Accepted: 09/16/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE A major drawback of magnetic resonance imaging (MRI) is its limited imaging speed. This study proposed an ultrafast cervical spine MRI protocol (2 min 57 s) using deep learning-based reconstruction (DLR) and compared the diagnostic results to those of conventional MRI protocols (12 min 54 s). METHODS Fifty patients who underwent cervical spine MRI using both conventional and ultrafast protocols, including sagittal T1-weighted, T2-weighted, short-TI inversion recovery, and axial T2*-weighted imaging were included in this study. The ultrafast protocol shortened the acquisition time to approximately-one-fourth of that of the conventional protocol by reducing the phase matrix, oversampling rate, and number of excitations, and by applying compressed sensing. To compensate for the decreased signal-to-noise ratio caused by acceleration, noise reduction using DLR was performed. For image interpretation, three neuroradiologists graded or classified degenerative changes, including central canal stenosis, foraminal stenosis, endplate degeneration, disc degeneration, and disc hernia. The presence of other pathologies was also recorded. Given the absence of a reference standard, we tested the interchangeability of the two protocols by calculating the 95% confidence interval (CI) of the individual equivalence index. We also assessed the inter-protocol intra-reader agreement using kappa statistics. RESULTS Except for endplate degeneration, the 95 % CI of the individual equivalence index for all variables did not exceed 5 %, indicating interchangeability between the two protocols. The kappa values ranged from 0.600 to 0.977, indicating substantial to almost perfect agreement. CONCLUSIONS The proposed ultrafast MRI protocol yielded almost equivalent diagnostic results compared as the conventional protocol.
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Affiliation(s)
- Nobuo Kashiwagi
- Department of Diagnostic and Interventional Radiology, Osaka International Cancer Institute, Japan.
| | - Mio Sakai
- Department of Diagnostic and Interventional Radiology, Osaka International Cancer Institute, Japan.
| | - Akio Tsukabe
- Department of Radiology, Toyonaka Municipal Hospital, Japan
| | | | - Masahiro Fujiwara
- Department of Radiology, Osaka Medical and Pharmaceutical University Hospital, Japan.
| | - Kazuki Yamagata
- Department of Radiology, Osaka University Graduate School of Medicine, Japan.
| | - Atsushi Nakamoto
- Department of Future Diagnostic Radiology, Osaka University Graduate School of Medicine, Japan.
| | - Katsuyuki Nakanishi
- Department of Diagnostic and Interventional Radiology, Osaka International Cancer Institute, Japan.
| | - Noriyuki Tomiyama
- Department of Radiology, Osaka University Graduate School of Medicine, Japan.
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