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Deep learning with diffusion basis spectrum imaging for classification of multiple sclerosis lesions.

Zezhong Ye1, Ajit George1, Anthony T Wu2

  • 1Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, 63110.

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Diffusion basis spectrum imaging combined with deep neural networks (DBSI-DNN) accurately classifies multiple sclerosis (MS) lesion subtypes. This advanced imaging technique shows promise for improving MS diagnosis and clinical decision-making.

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Area of Science:

  • Neuroimaging
  • Medical Imaging Analysis
  • Artificial Intelligence in Medicine

Background:

  • Multiple sclerosis (MS) lesions exhibit significant heterogeneity in inflammation, demyelination, and axonal injury.
  • Existing imaging techniques struggle to fully capture the complexity of MS lesion characteristics.
  • Diffusion basis spectrum imaging (DBSI) was previously developed to better characterize MS lesion heterogeneity.

Purpose of the Study:

  • To test the hypothesis that multiple DBSI metrics can identify distinct MS lesion patterns.
  • To combine DBSI with a deep neural network (DNN) algorithm for enhanced MS lesion classification.
  • To evaluate the performance of DBSI-DNN against other imaging modalities and DNN models.

Main Methods:

  • Thirty-eight MS patients underwent diffusion-weighted imaging, magnetization transfer imaging, and conventional MRI (cMRI).
  • Regions of interest (ROIs) were identified and labeled, including various lesion subtypes and normal-appearing white matter (NAWM).
  • DBSI, diffusion tensor imaging (DTI), and magnetization transfer ratio (MTR) metrics were extracted from voxels within ROIs and used to train an optimized 10-layer DNN.

Main Results:

  • The DBSI-DNN model achieved 93.4% overall concordance in classifying MS lesion types, outperforming DTI-DNN (80.2%), MTR-DNN (78.3%), and cMRI-DNN (74.2%).
  • DBSI-DNN demonstrated superior specificity, sensitivity, and accuracy compared to the other tested models.
  • The deep learning model effectively utilized DBSI metrics to differentiate between various MS lesion subtypes.

Conclusions:

  • DBSI-DNN significantly improves the classification accuracy of different MS lesion subtypes.
  • This enhanced classification can potentially aid in clinical decision-making for MS patients.
  • The efficacy and efficiency of DBSI-DNN suggest strong potential for automatic MS lesion detection and classification in clinical settings.