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 Mahajan1,4
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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