Towards Virtual MR Imaging: Predicting Diffusion-Weighted Brain MR Images from T2-Weighted Images Using Convolutional Neural Networks
Oral Presentation at the European Congress of Radiology, Vienna, 2019
In a quest for standardisation and speeding-up for MR Imaging, there is a move towards having a 'universal' MRI sequence from which all MR contrasts can be obtained. We propose first-of-its-kind Virtual MR Imaging where we predict "Virtual" Diffusion-Weighted (DW) images of the brain from axial T2W images using Convolutional Neural Networks (CNN).
Methods and Materials
Binary Cross Entropy of 0.15 for normal and 0.11 for infarct cases was obtained. The model took 750 ms to produce each image. In the test cases, 6 out of 7 T2 hyperintensities in group-1 showed diffusion restriction and all 16 T2 hyperintensities in group-2 did not show restriction. The one 'missed' lesion in group-1 was 2.5
We demonstrate a novel use-case for Deep Learning in reducing the MRI exam time and potentially creating a single universal MRI sequence.