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Abstract #3915

T2 Mapping of the Prostate with a Convolutional Neural Network

Sara L Saunders1,2, Mitchell J Gross2, Gregory J Metzger1, and Patrick J Bolan1
1Center for MR Research / Radiology, University of Minnesota, MINNEAPOLIS, MN, United States, 2Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States


This work compares T2 maps calculated from multi-echo spin-echo MR images using a conventional non-linear least squares (NLLS) fitting method to those constructed with a U-Net, a type of convolutional neural network. The performance of the U-Net and NLLS methods was compared in two retrospectively simulated experiments with a) reduced echo train lengths and b) decreased SNR to emulate accelerated acquisitions. The U-Net generally gave higher accuracy than NLLS fitting, with the trade-off of a modest increase of blurring of the resultant T2 maps.

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