Accelerated spine MRI with deep learning based image reconstruction: a prospective comparison with standard MRI

Jie Li; Yi Xia; Taohu Zhou; Qian Dong; Xiaoqing Lin; Lingling Gu; Song Jiang; Meiling Xu; Xinyi Wan; Guangwen Duan; Dongqing Zhu; Rutan Chen; Zhihao Zhang; Lei Xiang; Li Fan; Shiyuan Liu

doi:10.1016/j.acra.2024.11.004

Accelerated spine MRI with deep learning based image reconstruction: a prospective comparison with standard MRI

Acad Radiol. 2024 Nov 22:S1076-6332(24)00850-X. doi: 10.1016/j.acra.2024.11.004. Online ahead of print.

Authors

Jie Li¹, Yi Xia², Taohu Zhou³, Qian Dong⁴, Xiaoqing Lin⁵, Lingling Gu⁶, Song Jiang⁷, Meiling Xu⁸, Xinyi Wan⁹, Guangwen Duan¹⁰, Dongqing Zhu¹¹, Rutan Chen¹², Zhihao Zhang¹³, Lei Xiang¹⁴, Li Fan¹⁵, Shiyuan Liu¹⁶

Affiliations

¹ Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, PR China (J.L., Y.X., T.Z., X.L., L.G., S.J., M.X., X.W., G.D., D.Z., R.C., L.F., S.L.); College of Health Sciences and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Shanghai 200093, PR China (J.L., X.L.). Electronic address: [email protected].
² Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, PR China (J.L., Y.X., T.Z., X.L., L.G., S.J., M.X., X.W., G.D., D.Z., R.C., L.F., S.L.). Electronic address: [email protected].
³ Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, PR China (J.L., Y.X., T.Z., X.L., L.G., S.J., M.X., X.W., G.D., D.Z., R.C., L.F., S.L.). Electronic address: [email protected].
⁴ Department of Radiology, University of Michigan Taubman Center, Room 2904, 1500 E., Medical Center Dr., SPC 5326, Ann Arbor, MI 48109 (Q.D.). Electronic address: [email protected].
⁵ Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, PR China (J.L., Y.X., T.Z., X.L., L.G., S.J., M.X., X.W., G.D., D.Z., R.C., L.F., S.L.); College of Health Sciences and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Shanghai 200093, PR China (J.L., X.L.). Electronic address: [email protected].
⁶ Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, PR China (J.L., Y.X., T.Z., X.L., L.G., S.J., M.X., X.W., G.D., D.Z., R.C., L.F., S.L.). Electronic address: [email protected].
⁷ Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, PR China (J.L., Y.X., T.Z., X.L., L.G., S.J., M.X., X.W., G.D., D.Z., R.C., L.F., S.L.). Electronic address: [email protected].
⁸ Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, PR China (J.L., Y.X., T.Z., X.L., L.G., S.J., M.X., X.W., G.D., D.Z., R.C., L.F., S.L.). Electronic address: [email protected].
⁹ Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, PR China (J.L., Y.X., T.Z., X.L., L.G., S.J., M.X., X.W., G.D., D.Z., R.C., L.F., S.L.). Electronic address: [email protected].
¹⁰ Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, PR China (J.L., Y.X., T.Z., X.L., L.G., S.J., M.X., X.W., G.D., D.Z., R.C., L.F., S.L.). Electronic address: [email protected].
¹¹ Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, PR China (J.L., Y.X., T.Z., X.L., L.G., S.J., M.X., X.W., G.D., D.Z., R.C., L.F., S.L.). Electronic address: [email protected].
¹² Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, PR China (J.L., Y.X., T.Z., X.L., L.G., S.J., M.X., X.W., G.D., D.Z., R.C., L.F., S.L.). Electronic address: [email protected].
¹³ Shentou Medical Inc, Shentou Medical Room 1105, No. 938 Jinshajiang Road, Shanghai 200062, PR China (Z.Z., L.X.). Electronic address: [email protected].
¹⁴ Shentou Medical Inc, Shentou Medical Room 1105, No. 938 Jinshajiang Road, Shanghai 200062, PR China (Z.Z., L.X.). Electronic address: [email protected].
¹⁵ Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, PR China (J.L., Y.X., T.Z., X.L., L.G., S.J., M.X., X.W., G.D., D.Z., R.C., L.F., S.L.). Electronic address: [email protected].
¹⁶ Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, PR China (J.L., Y.X., T.Z., X.L., L.G., S.J., M.X., X.W., G.D., D.Z., R.C., L.F., S.L.). Electronic address: [email protected].

PMID: 39580249
DOI: 10.1016/j.acra.2024.11.004

Abstract

Rationale and objectives: To evaluate the performance of deep learning (DL) reconstructed MRI in terms of image acquisition time, overall image quality and diagnostic interchangeability compared to standard-of-care (SOC) MRI.

Materials and methods: This prospective study recruited participants between July 2023 and August 2023 who had spinal discomfort. All participants underwent two separate MRI examinations (Standard and accelerated scanning). Signal-to-noise ratios (SNR), contrast-to-noise ratios (CNR) and similarity metrics were calculated for quantitative evaluation. Four radiologists performed subjective quality and lesion characteristic assessment. Wilcoxon test was used to assess the differences of SNR, CNR and subjective image quality between DL and SOC. Various lesions of spine were also tested for interchangeability using individual equivalence index. Interreader and intrareader agreement and concordance (κ and Kendall τ and W statistics) were computed and McNemar tests were performed for comprehensive evaluation.

Results: 200 participants (107 male patients, mean age 46.56 ± 17.07 years) were included. Compared with SOC, DL enabled scan time reduced by approximately 40%. The SNR and CNR of DL were significantly higher than those of SOC (P < 0.001). DL showed varying degrees of improvement (0-0.35) in each of similarity metrics. All absolute individual equivalence indexes were less than 4%, indicating interchangeability between SOC and DL. Kappa and Kendall showed a good to near-perfect agreement in range of 0.72-0.98. There is no difference between SOC and DL regarding subjective scoring and frequency of lesion detection.

Conclusion: Compared to SOC, DL provided high-quality image for diagnosis and reduced examination time for patients. DL was found to be interchangeable with SOC in detecting various spinal abnormalities.

Keywords: Deep learning reconstruction; Interchangeability; Magnetic resonance imaging; Spine.