Omni-QALAS: Optimized Multiparametric Imaging for Simultaneous T1, T2 and Myelin Water Mapping

作者：Song Li, Unay Dorken Gallastegi, Shohei Fujita, Yuting Chen, Pengcheng Xu, Yangsean Choi, Borjan Gagoski, Ye, Huihui, Huafeng Liu, Berkin Bilgiç, Yohan Jun · 发表于：arXiv (Cornell University) · 年份：2025 · DOI：10.48550/arxiv.2510.13118 · 被引用次数：1 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Metabolomics and Mass Spectrometry Studies、Machine Learning in Healthcare

Purpose: To improve the accuracy of multiparametric estimation, including myelin water fraction (MWF) quantification, and reduce scan time in 3D-QALAS by optimizing sequence parameters, using a self-supervised multilayer perceptron network. Methods: We jointly optimize flip angles, T2 preparation durations, and sequence gaps for T1 recovery using a self-supervised MLP trained to minimize a Cramer-Rao bound-based loss function, with explicit constraints on total scan time. The optimization targets white matter, gray matter, and myelin water tissues, and its performance was validated through simulation, phantom, and in vivo experiments. Results: Building on our previously proposed MWF-QALAS method for simultaneous MWF, T1, and T2 mapping, the optimized sequence reduces the number of readouts from six to five and achieves a scan time nearly one minute shorter, while also yielding higher T1 and T2 accuracy and improved MWF maps. This sequence enables simultaneous multiparametric quantification, including MWF, at 1 mm isotropic resolution within 3 minutes and 30 seconds. Conclusion: This study demonstrated that optimizing sequence parameters using a self-supervised MLP network improved T1, T2 and MWF estimation accuracy, while reducing scan time.