Abstract #1304

Attention-based convolutional network quantifying the importance of quantitative MR metrics in the multiple sclerosis lesion classification

Po-Jui Lu^1,2,3, Reza Rahmanzadeh^1,2, Riccardo Galbusera^1,2, Matthias Weigel^1,2,4, Youngjin Yoo³, Pascal Ceccaldi³, Yi Wang⁵, Jens Kuhle², Ludwig Kappos^1,2, Philippe Cattin⁶, Benjamin Odry⁷, Eli Gibson³, and Cristina Granziera^1,2

¹Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, ²Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, ³Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, United States, ⁴Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland, ⁵Department of Radiology, Weill Cornell Medical College, New York, NY, United States, ⁶Center for medical Image Analysis & Navigation, Department of Biomedical Engineering, University of Basel, Basel, Switzerland, ⁷Covera Health, New York, NY, United States

White matter lesions in multiple sclerosis patients exhibit distinct characteristics depending on their locations in the brain. Multiple quantitative MR sequences sensitive to white matter micro-environment are necessary for the assessment of those lesions; but how to judge which sequences contain the most relevant information remains a challenge. In this abstract, we are proposing a convolutional neural network with a gated attention mechanism to quantify the importance of MR metrics in classifying juxtacortical and periventricular lesions. The results show the statistically significant order of quantitative importance of metrics, one step closer to combining more relevant metrics for better interpretation.

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