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Abstract #1878

MR image enhancement using a multi-task neural network trained using only synthetic data

Kevin Blansit1, Zhehao Hu1, Greg Zaharchuk1, Enhao Gong1, and Keshav Datta1
1Subtle Medical, Menlo Park, CA, United States


Faster MRI scans can be achieved by acquiring low resolution images or low SNR images and enhancing them to standard-of-care using deep learning techniques. However, to achieve clinical diagnostic quality images, this requires a large number of paired clinical datasets to train the model. Here we show that a multi-task deep convolutional neural network (DCNN) trained using only simulated motion artifact, low SNR, and low resolution images is capable of improving the quality of clinically acquired images from motion corrupted and accelerated sequences.

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