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Yangsean Choi

Publications and source records attributed to Yangsean Choi.

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Longitudinal QSM: Enhancing consistency of multiple time point susceptibility maps via simultaneous reconstruction

Quantitative susceptibility mapping (QSM) has been increasingly applied in longitudinal studies of neurodegenerative diseases and aging to assess temporal alterations in brain iron and myelin. The accuracy of such investigations depends on the repeatability and sensitivity of measurements. However, the ill-posed nature of the QSM processing steps makes the reconstruction vulnerable to background field changes, head orientation changes, noise, and imperfect registration, which compromise repeatability and sensitivity and hinder reliable detection of true changes. To address these limitations, we propose Longitudinal QSM, a simultaneous reconstruction framework that jointly estimates susceptibility maps across time points while enforcing spatial sparsity of temporal changes. The method was evaluated through simulations and in-vivo experiments and compared with conventional reconstruction methods. Longitudinal QSM consistently reduced inter-scan variability and accurately recovered simulated lesion changes. Application to stroke patient and multiple sclerosis patient data further demonstrated that the framework stabilizes non-lesion variability while preserving lesion-related temporal changes. This approach offers a promising tool for monitoring subtle temporal changes in brain iron and myelin in various neurodegenerative diseases as well as throughout aging and development.

q-bio.QM

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

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.

q-bio.QM