χ-sepnet: Deep neural network for magnetic susceptibility source separation
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arXiv
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| Natura: | Preprint |
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2024
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| _version_ | 1866909356199510016 |
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| author | Kim, Minjun Ji, Sooyeon Kim, Jiye Min, Kyeongseon Jeong, Hwihun Youn, Jonghyo Kim, Taechang Jang, Jinhee Bilgic, Berkin Shin, Hyeong-Geol Lee, Jongho |
| author_facet | Kim, Minjun Ji, Sooyeon Kim, Jiye Min, Kyeongseon Jeong, Hwihun Youn, Jonghyo Kim, Taechang Jang, Jinhee Bilgic, Berkin Shin, Hyeong-Geol Lee, Jongho |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14030 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | χ-sepnet: Deep neural network for magnetic susceptibility source separation Kim, Minjun Ji, Sooyeon Kim, Jiye Min, Kyeongseon Jeong, Hwihun Youn, Jonghyo Kim, Taechang Jang, Jinhee Bilgic, Berkin Shin, Hyeong-Geol Lee, Jongho Image and Video Processing 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. |
| title | χ-sepnet: Deep neural network for magnetic susceptibility source separation |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2409.14030 |