Abstract #4771

Virtual Imaging Using Generative Adversarial Networks for Image Translation (VIGANIT): Deep Learning based Prediction of Diffusion-Weighted Images from T2-Weighted Brain MR Images

Vidur Mahajan¹, Aravind Upadhyaya², Vasantha Kumar Venugopal¹, Abhishek S Venkataram², Mukundhan Srinivasan³, Murali Murugavel¹, and Harsh Mahajan^1,4

¹Centre for Advanced Research in Imaging, Neuroscience and Genomics, Mahajan Imaging, New Delhi, India, ²Triocula technologies, Bangalore, India, ³Nvidia, Bangalore, India, ⁴Mahajan Imaging, New Delhi, India

100 whole brain MRI scans of patients with no abnormality and 30 with acute infarcts, comprising of 25 T2-weighted and Diffusion-Weighted (b=1000) images each, were fed into a Deep Learning model with a 75-25 training-validation split. The T2W image was assigned as the input to predict DW images. Binary Cross entropy of 0.15 for normal and 0.11 for infarct cases was obtained and the predicted images were able to successfully delineate acute and chronic infarcts in all test cases.

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