<scp>DeepCEST 7 T</scp>: Fast and homogeneous mapping of <scp>7 T CEST MRI</scp> parameters and their uncertainty quantification

Abstract

Purpose In this work, we investigated the ability of neural networks to rapidly and robustly predict Lorentzian parameters of multi‐pool CEST MRI spectra at 7 T with corresponding uncertainty maps to make them quickly and easily available for routine clinical use. Methods We developed a deepCEST 7 T approach that generates CEST contrasts from just 1 scan with robustness against B 1 inhomogeneities. The input data for a neural feed‐forward network consisted of 7 T in vivo uncorrected Z ‐spectra of a single B 1 level, and a B 1 map. The 7 T raw data were acquired using a 3D snapshot gradient echo multiple interleaved mode saturation CEST sequence. These inputs were mapped voxel‐wise to target data consisting of Lorentzian amplitudes generated conventionally by 5‐pool Lorentzian fitting of normalized, denoised, B 0 ‐ and B 1 ‐corrected Z ‐spectra. The deepCEST network was trained with Gaussian negative log‐likelihood loss, providing an uncertainty quantification in addition to the Lorentzian amplitudes. Results The deepCEST 7 T network provides fast and accurate prediction of all Lorentzian parameters also when only a single B 1 level is used. The prediction was highly accurate with respect to the Lorentzian fit amplitudes, and both healthy tissues and hyperintensities in tumor areas are predicted with a low uncertainty. In corrupted cases, high uncertainty indicated wrong predictions reliably. Conclusion The proposed deepCEST 7 T approach reduces scan time by 50% to now 6:42 min, but still delivers both B 0 ‐ and B 1 ‐corrected homogeneous CEST contrasts along with an uncertainty map, which can increase diagnostic confidence. Multiple accurate 7 T CEST contrasts are delivered within seconds.

Topics & Concepts