Accelerated 3D qCEST of the Spine in a Porcine Model Using MR Multitasking at 3T.

Journal: NMR in biomedicine
Published Date: Sep 1, 2025

Abstract

To assess lower back pain using quantitative chemical exchange saturation transfer (qCEST) imaging in a porcine model by comparing exchange rate maps obtained from multitasking qCEST with conventional qCEST. Use a permuted random forest (PRF) model trained on CEST-derived magnetization transfer ratio (MTR) and exchange rate (k) features to predict Glasgow pain scores. Six Yucatan minipigs were scanned at baseline and at four post-injury time points (weeks 4, 8, 12, and 16) following intervertebral disc injury. Conventional qCEST imaging was performed at four B1 powers using a two-dimensional reduced field of view turbo spin-echo (TSE) sequence, with a total acquisition time of 24 min per slice. Multitasking steady-state (SS) CEST imaging was performed with pulsed saturation to achieve a steady state, acquiring 32 slices at 59 offsets for 4 B1 powers in 36 min. Exchange rate maps were generated using omega plot analysis, and CEST images were analyzed using a multi-pool fitting model to produce MTR and k maps. Permuted random forest (PRF) model was trained on MTR and k values to predict pain scores. Modic changes were assessed using T2-weighted MR images. The Pearson correlation coefficient between exchange rate maps from multitasking qCEST and conventional qCEST was 0.82, demonstrating strong agreement. The 3D qCEST (SS-CEST) technique effectively differentiated between healthy and injured discs, with injured discs exhibiting significantly higher k values. Using MTR and k, the PRF model achieved 80% accuracy in predicting pain scores disc-by-disc, outperforming the correlation with Modic changes (r = 0.45, p < 0.05); with a Cohen's Kappa of 0.4. 3D steady-state qCEST with whole-spine coverage can be done at 3T within 32 min using MR Multitasking (acceleration factor of 22), and qCEST-derived biomarkers (MTR and k) can predict pain scores with an accuracy of 80%.

Authors

  • Karandeep Cheema
  • Dante Rigo De Righi
    Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Chushu Shen
    Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Hsu-Lei Lee
    Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Giselle Kaneda
    Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Jacob Wechsler
    Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Melissa Chavez
    Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Pablo Avalos
    Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Candace Floyd
    Department of Emergency Medicine, Emory University, Atlanta, Georgia, USA.
  • Wafa Tawackoli
    Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Yibin Xie
    Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Anthony G Christodoulou
    Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Dmitriy Sheyn
    Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Debiao Li

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