Increasing the speed of multiparametric prostate MRI (mpMRI) is highly desirable. However, usual tradeoffs between signal-to-noise (SNR), scan time and lesion conspicuity must be considered. One recently proposed approach consists of using a bi-parametric protocol, whereby only the T2 and diffusion-weighted images are collected, thus highlighting the particular significance of achieving a robust, high-quality T2-weighted acquisition. As such, this work focuses on evaluating a Deep Learning reconstruction technique which shows promises to cut acquisition time of prostate T2-weighted imaging in half and would therefore benefit both bi-parametric as well as mpMRI.
This abstract and the presentation materials are available to members only; a login is required.