SuperMAP: Deep ultrafast MR relaxometry with joint spatiotemporal undersampling.

ABSTRACT:

Purpose

To develop an ultrafast and robust MR parameter mapping network using deep learning.

Theory and methods

We design a deep learning framework called SuperMAP that directly converts a series of undersampled (both in k-space and parameter-space) parameter-weighted images into several quantitative maps, bypassing the conventional exponential fitting procedure. We also present a novel technique to simultaneously reconstruct T1rho and T2 relaxation maps within a single scan. Full data were acquired and retrospectively undersampled for training and testing using traditional and state-of-the-art techniques for comparison. Prospective data were also collected to evaluate the trained network. The performance of all methods is evaluated using the parameter qualification errors a

SUBMITTER: Li H 

PROVIDER: S-EPMC9617769 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature