An Interpretable Deep-Learning Approach for Efficient CEST Parameter Quantification: Importance-Ranked Saturation...

An optimal design of saturation-transfer MR fingerprinting (ST-MRF) sequences is essential to accelerate imaging and improve tissue quantification accuracy. This study aims to develop an interpretable deep-learning framework, importance-ranking network (IRnet), which can rank and identify the most informative dynamic scans, enabling optimized acquisition with a minimal number of scans while maintaining quantitative accuracy.

IRnet was developed to learn the scan-specific contributions to the latent representation of tissue parameters derived from ST-MRF. It consists of an encoder network trained on ST-MRF signals and corresponding ground-truth tissue parameters simulated using three-pool Bloch-McConnell equations. A shallow network was then used to predict the latent tissue representations, enabling scan importance to be ranked based on the magnitude of the learned weights.

IRnet achieved more than a two-fold reduction in acquisition time while maintaining good reconstruction accuracy, with a normalized root-mean-square error of 6.2% when compared to ST-MRF with a full range of dynamic scans as a reference. The method consistently outperformed the pseudo-random selection and the least absolute shrinkage and selection operator-based approach, particularly for challenging amide proton transfer (APT) parameters, proton exchange rates and pool size ratios, to which ST-MRF is less sensitive than to magnetization transfer contrast (MTC) and water parameters. The tissue parameters obtained from IRnet and reference sequences demonstrated excellent consistency.

IRnet enabled efficient and accurate tissue quantification by selecting a sparse, informative subset of acquisition parameters. This interpretable data-driven approach achieved accelerated quantitative CEST imaging and holds potential for translation into clinical protocols.