χ-sepnet: Deep neural network for magnetic susceptibility source separation

Minjun Kim; Sooyeon Ji; Jiye Kim; Kyeongseon Min; Hwihun Jeong; Jonghyo Youn; Taechang Kim; Jinhee Jang; Berkin Bilgic; Hyeong-Geol Shin; Jongho Lee

arXiv · 2409.14030

χ-sepnet: Deep neural network for magnetic susceptibility source separation

Minjun Kim·Sooyeon Ji·Jiye Kim·Kyeongseon Min·Hwihun Jeong·Jonghyo Youn·Taechang Kim·Jinhee Jang·Berkin Bilgic·Hyeong-Geol Shin·Jongho Lee

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Abstract

Magnetic susceptibility source separation ($χ$-separation), an advanced quantitative susceptibility mapping (QSM) method, enables the separate estimation of para- and diamagnetic susceptibility source distributions in the brain. The method utilizes reversible transverse relaxation (R2'=R2*-R2) to complement frequency shift information for estimating susceptibility source concentrations, requiring time-consuming data acquisition for R2 in addition R2*. To address this challenge, we develop a new deep learning network, $χ$-sepnet, and propose two deep learning-based susceptibility source separation pipelines, $χ$-sepnet-R2' for inputs with multi-echo GRE and multi-echo spin-echo, and $χ$-sepnet-R2* for input with multi-echo GRE only. $χ$-sepnet is trained using multiple head orientation data that provide streaking artifact-free labels, generating high-quality $χ$-separation maps. The evaluation of the pipelines encompasses both qualitative and quantitative assessments in healthy subjects, and visual inspection of lesion characteristics in multiple sclerosis patients. The susceptibility source-separated maps of the proposed pipelines delineate detailed brain structures with substantially reduced artifacts compared to those from conventional regularization-based reconstruction methods. In quantitative analysis, $χ$-sepnet-R2' achieves the best outcomes followed by $χ$-sepnet-R2*, outperforming the conventional methods. When the lesions of multiple sclerosis patients are assessed, both pipelines report identical lesion characteristics in most lesions ($χ$para: 99.6% and $χ$dia: 98.4% out of 250 lesions). The $χ$-sepnet-R2* pipeline, which only requires multi-echo GRE data, has demonstrated its potential to offer broad clinical and scientific applications, although further evaluations for various diseases and pathological conditions are necessary.

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Minjun Kim, Sooyeon Ji, Jiye Kim, Kyeongseon Min, Hwihun Jeong, Jonghyo Youn, Taechang Kim, Jinhee Jang, Berkin Bilgic, Hyeong-Geol Shin, Jongho Lee. 2024-10-21. χ-sepnet: Deep neural network for magnetic susceptibility source separation. https://arxiv.org/abs/2409.14030

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