A framework for rapid, reproducible, and high-fidelity whole-brain multi-pool CEST imaging at 3T

Yupeng Wu; Siyuan Fang; Siyuan Wang; Caixia Fu; Jianqi Li

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arXiv · 2502.16986

A framework for rapid, reproducible, and high-fidelity whole-brain multi-pool CEST imaging at 3T

Yupeng Wu·Siyuan Fang·Siyuan Wang·Caixia Fu·Jianqi Li

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Abstract

Purpose: To develop and validate a framework for rapid, accurate, and reproducible whole-brain, multi-pool chemical exchange saturation transfer (CEST) imaging at 3T, addressing challenges of long acquisition times and confounding factors. Methods: A single-shot 3D true fast imaging with steady-state precession (True FISP) sequence was optimized for whole-brain multi-pool CEST. Rapid B0, B1, and T1 mapping was performed using a dual-echo modified four-angle method. A feed-forward neural network was developed for rapid B1 correction, trained against the conventional multi-power method. The apparent exchange-dependent relaxation (AREX) metric was used to correct for T1 and magnetization transfer (MT) effects. The framework was validated in phantoms and healthy human subjects (N=8), including a test-retest reproducibility assessment. Results: The True FISP sequence yielded high-quality, whole-brain images with minimal artifacts and distortion in a clinically feasible scan time (~9 minutes). Phantom studies confirmed the effectiveness of B1 correction (coefficient of variation [CV] for MT_MTRLD decreased from 22.49% to 4.61%) and AREX-based confounder correction (CV for APT_AREX reduced from 33.6% to 6.9%). The neural network B1 correction showed excellent agreement with the conventional multi-power method in vivo (ICC > 0.97). High test-retest reproducibility was demonstrated across 96 brain regions, with the average CV for APT_AREX under 10% for over 95% of regions. Conclusion: A rapid and robust framework for whole-brain quantitative multi-pool CEST imaging was successfully developed and validated. By integrating an efficient acquisition sequence with a streamlined correction pipeline, this approach overcomes key barriers to clinical translation, enabling reliable metabolic imaging for widespread brain pathologies.

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Yupeng Wu, Siyuan Fang, Siyuan Wang, Caixia Fu, Jianqi Li. 2025-09-13. A framework for rapid, reproducible, and high-fidelity whole-brain multi-pool CEST imaging at 3T. https://arxiv.org/abs/2502.16986

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