CECNN-B1: Confidence-Enhanced CNN in B1 inhomogeneity Correction for Quantitative CEST MRI at 5 T

Motivation: B1-correction is important in CEST MRI. Conventional correction methods require multiple B1 acquisitions, making them less practical in clinical adoptions. Goal(s): This study proposed a CNN-based model that can correct B1 inhomogeneity from a single B1 acquisition. Approach: A CECNN-B1 model incorporating B1 distribution was designed to enhance the confidence of CEST quantification, and its performance was evaluated on a creatine phantom at 5 T. Results: Substantial variation of B1 distribution was observed, resulting in inhomogeneous CEST map. After correction, the B1-induced artifact was effectively alleviated. The image quality of the corrected CEST map was superior to that calculated with conventional interpolation method. Impact: The proposed CECNN-B1 model enabled B1 inhomogeneity correction from single B1 acquisition. A creatine phantom study showed its superiority over the conventional interpolation methods requiring multiple B1 acquisitions, providing an efficient way for improved CEST MRI on high-field scanners.