Abstract #3172

Using a deep residual learning framework to enhance APT-weighted (APTw) MR images of brain tumors acquired by SENSE with compressed sensing

Jingpu Wu^1,2, Yiqing Shen^1,3, Pengfei Guo^1,3, Qianqi Huang³, Babak Moghadas¹, Hye-Young Heo¹, Jinyuan Zhou¹, and Shanshan Jiang¹

¹Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, United States, ²Department of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States, ³Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States

Synopsis

Keywords: CEST & MT, CEST & MTSensitivity encoding (SENSE) is often adopted to accelerate image acquisition for various MRI sequences, including APTw. This is further accelerated with SENSE with compressed sensing (called CS-SENSE), but the image quality degrades to some extent. We collected both SENSE- and CS-SENSE-APTw images and trained a generative model with residual learning to generate SENSE images from CS-SENSE images. The generated results were proved to be highly similar to SENSE-APTw images and less noisy than both SENSE- and CS-SENSE-APTw images. With a larger dataset, we can train more robust models and eventually replace SENSE- with CS-SENSE for a speedup of ~50%.

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