Purpose: To develop an automated pipeline based on convolutional neural networks to segment lumbar intervertebral discs and characterize their biochemical composition using voxel-based relaxometry, and establish local associations with clinical measures of disability, muscle changes, and other symptoms of lower back pain.
Methods: This work proposes a new methodology using MRI (n = 31, across the spectrum of disc degeneration) that combines deep learning-based segmentation, atlas-based registration, and statistical parametric mapping for voxel-based analysis of T1ρ and T2 relaxation time maps to characterize disc degeneration and its associated disability.
Results: Across degenerative grades, the segmentation algorithm produced accurate, high-confidence segmentations of the lumbar discs in two independent data sets. Manually and automatically extracted mean disc T1ρ and T2 relaxation times were in high agreement for all discs with minimal bias. On a voxel-by-voxel basis, imaging-based degenerative grades were strongly negatively correlated with T1ρ and T2 , particularly in the nucleus. Stratifying patients by disability grades revealed significant differences in the relaxation maps between minimal/moderate versus severe disability: The average T1ρ relaxation maps from the minimal/moderate disability group showed clear annulus nucleus distinction with a visible midline, whereas the severe disability group had lower average T1ρ values with a homogeneous distribution.
Conclusion: This work presented a scalable pipeline for fast, automated assessment of disc relaxation times, and voxel-based relaxometry that overcomes limitations of current region of interest-based analysis methods and may enable greater insights and associations between disc degeneration, disability, and lower back pain.
Keywords: deep learning; registration; relaxometry; segmentation; spine.
© 2020 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.