Vahid Abdollah1,2, Eric C Parent3, Samin Dolatabadi4, Erica Marr4, Keith Wachowicz5,6, Michele Battié7,8. 1. Department of Physical Therapy, Faculty of Rehabilitation Medicine, University of Alberta, 2-50 Corbett Hall, Edmonton, AB, T6G2G4, Canada. 2. Neuromuscular Control and Biomechanics Laboratory, Department of Mechanical Engineering, Faculty of Engineering, Nanotechnology Research Centre, University of Alberta, 5-046, Edmonton, AB, T6G 2M9, Canada. 3. Department of Physical Therapy, Faculty of Rehabilitation Medicine, University of Alberta, 2-50 Corbett Hall, Edmonton, AB, T6G2G4, Canada. eparent@ualberta.ca. 4. Faculty of Science, University of Alberta, 32 Athabasca Hall, Edmonton, AB, T6G 2E8, Canada. 5. Department of Oncology, Medical Physics Division, University of Alberta, 11560, Edmonton, AB, T6G 1Z2, Canada. 6. Department of Medical Physics, Cross Cancer Institute, 11560 University Avenue, Edmonton, AB, T6G 1Z2, Canada. 7. Western Research Chair in Musculoskeletal Exercise, Mobility and Health, School of Physical Therapy and Western's Bone & Joint Institute, Western University, London, ON, N6A 3K7, Canada. 8. Faculty of Rehabilitation Medicine, University of Alberta, 2-50 Corbett Hall, Edmonton, AB, T6G 2G4, Canada.
Abstract
BACKGROUND: Recent advances in texture analysis and machine learning offer new opportunities to improve the application of imaging to intervertebral disc biomechanics. This study employed texture analysis and machine learning on MRIs to investigate the lumbar disc's response to loading. METHODS: Thirty-five volunteers (30 (SD 11) yrs.) with and without chronic back pain spent 20 min lying in a relaxed unloaded supine position, followed by 20 min loaded in compression, and then 20 min with traction applied. T2-weighted MR images were acquired during the last 5 min of each loading condition. Custom image analysis software was used to segment discs from adjacent tissues semi-automatically and segment each disc into the nucleus, anterior and posterior annulus automatically. A grey-level, co-occurrence matrix with one to four pixels offset in four directions (0°, 45°, 90° and 135°) was then constructed (320 feature/tissue). The Random Forest Algorithm was used to select the most promising classifiers. Linear mixed-effect models and Cohen's d compared loading conditions. FINDINGS: All statistically significant differences (p < 0.001) were observed in the nucleus and posterior annulus in the 135° offset direction at the L4-5 level between lumbar compression and traction. Correlation (P2-Offset, P4-Offset) and information measure of correlation 1 (P3-Offset, P4-Offset) detected significant changes in the nucleus. Statistically significant changes were also observed for homogeneity (P2-Offset, P3-Offset), contrast (P2-Offset), and difference variance (P4-Offset) of the posterior annulus. INTERPRETATION: MRI textural features may have the potential of identifying the disc's response to loading, particularly in the nucleus and posterior annulus, which appear most sensitive to loading. LEVEL OF EVIDENCE: Diagnostic: individual cross-sectional studies with consistently applied reference standard and blinding.
BACKGROUND: Recent advances in texture analysis and machine learning offer new opportunities to improve the application of imaging to intervertebral disc biomechanics. This study employed texture analysis and machine learning on MRIs to investigate the lumbar disc's response to loading. METHODS: Thirty-five volunteers (30 (SD 11) yrs.) with and without chronic back pain spent 20 min lying in a relaxed unloaded supine position, followed by 20 min loaded in compression, and then 20 min with traction applied. T2-weighted MR images were acquired during the last 5 min of each loading condition. Custom image analysis software was used to segment discs from adjacent tissues semi-automatically and segment each disc into the nucleus, anterior and posterior annulus automatically. A grey-level, co-occurrence matrix with one to four pixels offset in four directions (0°, 45°, 90° and 135°) was then constructed (320 feature/tissue). The Random Forest Algorithm was used to select the most promising classifiers. Linear mixed-effect models and Cohen's d compared loading conditions. FINDINGS: All statistically significant differences (p < 0.001) were observed in the nucleus and posterior annulus in the 135° offset direction at the L4-5 level between lumbar compression and traction. Correlation (P2-Offset, P4-Offset) and information measure of correlation 1 (P3-Offset, P4-Offset) detected significant changes in the nucleus. Statistically significant changes were also observed for homogeneity (P2-Offset, P3-Offset), contrast (P2-Offset), and difference variance (P4-Offset) of the posterior annulus. INTERPRETATION: MRI textural features may have the potential of identifying the disc's response to loading, particularly in the nucleus and posterior annulus, which appear most sensitive to loading. LEVEL OF EVIDENCE: Diagnostic: individual cross-sectional studies with consistently applied reference standard and blinding.
Authors: Yoichiro Hirasawa; Waseem A Bashir; Francis W Smith; Marianne L Magnusson; Malcolm H Pope; Keisuke Takahashi Journal: Spine (Phila Pa 1976) Date: 2007-02-15 Impact factor: 3.468
Authors: Miranda L Davies-Tuck; Anita E Wluka; Andrew Forbes; Yuanyuan Wang; Dallas R English; Graham G Giles; Richard O'Sullivan; Flavia M Cicuttini Journal: Arthritis Res Ther Date: 2010-01-19 Impact factor: 5.156