Deep learning with diffusion basis spectrum imaging for classification of multiple sclerosis lesions. Issue 5 (18th April 2020)

• Record Type: Journal Article
• Authors: Ye, Zezhong; George, Ajit; Wu, Anthony T.; Niu, Xuan; Lin, Joshua; Adusumilli, Gautam; Naismith, Robert T.; Cross, Anne H.; Sun, Peng; Song, Sheng‐Kwei
• Is Part Of: Annals of clinical and translational neurology. Volume 7:Issue 5(2020)
• Page Start: 695
• Page End: 706
• Publication Date: 2020-04-18
• Subjects: Nervous system -- Diseases -- Periodicals; Neurology -- Periodicals; 616.8005
• DOI: 10.1002/acn3.51037
• Languages: English
• ISSNs: 2328-9503

Abstract

Abstract: Objective: Multiple sclerosis (MS) lesions are heterogeneous with regard to inflammation, demyelination, axonal injury, and neuronal loss. We previously developed a diffusion basis spectrum imaging (DBSI) technique to better address MS lesion heterogeneity. We hypothesized that the profiles of multiple DBSI metrics can identify lesion‐defining patterns. Here we test this hypothesis by combining a deep learning algorithm using deep neural network (DNN) with DBSI and other imaging methods. Methods: Thirty‐eight MS patients were scanned with diffusion‐weighted imaging, magnetization transfer imaging, and standard conventional MRI sequences (cMRI). A total of 499 regions of interest were identified on standard MRI and labeled as persistent black holes (PBH), persistent gray holes (PGH), acute black holes (ABH), acute gray holes (AGH), nonblack or gray holes (NBH), and normal appearing white matter (NAWM). DBSI, diffusion tensor imaging (DTI), and magnetization transfer ratio (MTR) were applied to the 43, 261 imaging voxels extracted from these ROIs. The optimized DNN with 10 fully connected hidden layers was trained using the imaging metrics of the lesion subtypes and NAWM. Results: Concordance, sensitivity, specificity, and accuracy were determined for the different imaging methods. DBSI‐DNN derived lesion classification achieved 93.4% overall concordance with predetermined lesion types, compared with 80.2% for DTI‐DNN model, 78.3% for MTR‐DNN model, and 74.2% for cMRI‐DNN model. DBSI‐DNN also produced the highest specificity, sensitivity, and accuracy. Conclusions: DBSI‐DNN improves the classification of different MS lesion subtypes, which could aid clinical decision making. The efficacy and efficiency of DBSI‐DNN shows great promise for clinical applications in automatic MS lesion detection and classification.