Transformer-Based Parameter Fitting of Models Derived from Bloch-McConnell Equations for CEST MRI Analysis

2026-02-06

MLMI@MICCAI

AI Summary

This work proposes a novel paradigm for quantitative CEST MRI by introducing, for the first time, a self-supervised Transformer architecture to directly estimate physical parameters—such as metabolite concentration, proton exchange rate, and relaxation rates—from in vitro CEST spectra. These parameters are grounded in the Bloch-McConnell equations, which describe the complex coupling of multiple physiological factors that traditionally hinder accurate CEST signal quantification. Conventional gradient-based optimization methods suffer from low efficiency and susceptibility to local minima, whereas the proposed end-to-end neural network overcomes these limitations. The method demonstrates substantial improvements over classical solvers in both accuracy and computational efficiency, establishing a new framework for robust and rapid CEST parameter mapping.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Matrix & Tensor Methods
• Computer Vision: Learning & Optimization for CV

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Chemical exchange saturation transfer (CEST) MRI is a non-invasive imaging modality for detecting metabolites. It offers higher resolution and sensitivity compared to conventional magnetic resonance spectroscopy (MRS). However, quantification of CEST data is challenging because the measured signal results from a complex interplay of many physiological variables. Here, we introduce a transformer-based neural network to fit parameters such as metabolite concentrations, exchange and relaxation rates of a physical model derived from Bloch-McConnell equations to in-vitro CEST spectra. We show that our self-supervised trained neural network clearly outperforms the solution of classical gradient-based solver.

Problem

• CEST MRI
• parameter fitting
• Bloch-McConnell equations
• quantification
• metabolite concentration

Innovation

• Transformer
• CEST MRI
• Bloch-McConnell equations
• parameter fitting
• self-supervised learning