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

Munendra Singh^1,2, Sultan Z Mahmud¹, Kevin Ju^1,3

¹Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.

Magnetic Resonance in Medicine

|May 11, 2026

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This summary is machine-generated.

An interpretable deep-learning framework, importance-ranking network (IRnet), accelerates quantitative chemical exchange saturation transfer (CEST) imaging. IRnet significantly reduces scan time while maintaining accuracy, enabling efficient tissue quantification.

Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Biophysics

Background:

Quantitative imaging requires optimized acquisition sequences for accuracy and speed.
Saturation-transfer MR fingerprinting (ST-MRF) offers rich tissue parameter information but can be time-consuming.
Accelerating ST-MRF is crucial for clinical translation and improved patient experience.

Purpose of the Study:

To develop an interpretable deep-learning framework, IRnet, for optimizing ST-MRF acquisition.
To identify the most informative dynamic scans for efficient tissue quantification.
To maintain quantitative accuracy with a minimal number of scans.

Main Methods:

Developed IRnet, a deep-learning framework to learn scan contributions to tissue parameter representations.

Keywords:

CEST MRI sequence optimization deep‐learning importance‐ranking tissue quantification

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Trained an encoder network on simulated ST-MRF signals and tissue parameters using Bloch-McConnell equations.

Utilized a shallow network to rank scan importance based on learned weights.

Main Results:

IRnet achieved over a two-fold reduction in acquisition time with 6.2% NRMSE compared to full ST-MRF.
Outperformed pseudo-random and LASSO methods, especially for challenging APT parameters.
Demonstrated excellent consistency between IRnet-derived and reference tissue parameters.

Conclusions:

IRnet enables efficient and accurate tissue quantification through sparse, informative scan selection.
This interpretable, data-driven approach accelerates quantitative CEST imaging.
IRnet shows potential for clinical translation in quantitative imaging protocols.