Highly Accelerated CEST Imaging with Stack-of-stars Acquisition using Unsupervised Implicit Neural Representation Networks

Motivation: Reduction of scan time in CEST imaging is clinically meaningful. Goal(s): Our goal is to develop an undersampled reconstruction algorithm to help vastly reduce the acquisition time. Approach: A novel unsupervised deep-learning based algorithm is proposed to accelerate steady-state pulsed CEST imaging with golden-angle stack-of-stars trajectory using mixed-feature hash encoding implicit neural representation. Additionally, Imaging quality is further improved using the explicit prior knowledge of weighted joint sparsity in subtle structural features of CEST image domain. The low rankness and sparsity in the Z‐spectra domain are used to reduce acquisition time. Results: It is possible to achieve a 30-fold acceleration for CEST imaging. Impact: An unsupervised deep-learning algorithm is proposed to accelerate steady-state pulsed CEST imaging with golden-angle stack-of-stars trajectory using mixed-feature hash encoding implicit neural representation and weighted joint sparsity. It can vastly reduce the acquisition time and has potential for clinical applications.