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Deep Learning Assisted Classification of T1ρ-MR Based Intervertebral Disc Degeneration Phases

According to the T1ρ value of nucleus pulposus, our previous study has found that intervertebral disc degeneration (IDD) can be divided into three phases based on T1ρ-MR, which is helpful for the selection of biomaterial treatment timing. However, the routine MR sequences for patients with IDD are T...

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Bibliographic Details
Published in:Journal of magnetic resonance imaging 2024-07
Main Authors: Li, Yanrun, Hu, Meiyu, Chen, Junhong, Ling, Zemin, Zou, Xuenong, Cao, Wuteng, Wei, Fuxin
Format: Article
Language:English
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Summary:According to the T1ρ value of nucleus pulposus, our previous study has found that intervertebral disc degeneration (IDD) can be divided into three phases based on T1ρ-MR, which is helpful for the selection of biomaterial treatment timing. However, the routine MR sequences for patients with IDD are T1- and T2-MR, T1ρ-MR is not commonly used due to long scanning time and extra expenses, which limits the application of T1ρ-MR based IDD phases. To build a deep learning model to achieve the classification of T1ρ-MR based IDD phases from routine T1-MR images. Retrospective. Sixty (M/F: 35/25) patients with low back pain or lower limb radiculopathy are randomly divided into training (N = 50) and test (N = 10) sets. 1.5 T MR scanner; T1-, T2-, and T1ρ-MR sequence (spin echo). The T1ρ values of the nucleus pulposus in intervertebral discs (IVDs) were measured. IVDs were divided into three phases based on the mean T1ρ value: pre-degeneration phase (mean T1ρ value >110 msec), rapid degeneration phase (mean T1ρ value: 80-110 msec), and late degeneration phase (mean T1ρ value
ISSN:1053-1807
1522-2586
1522-2586
DOI:10.1002/jmri.29499