In-vivo high-resolution χ-separation at 7T

作者：Jiye Kim, Minjun Kim, Sooyeon Ji, Kyeongseon Min, Hwihun Jeong, Hyeong‐Geol Shin, Chungseok Oh, Robert J. Fox, Ken Sakaie, Mark J. Lowe, Se‐Hong Oh, Sina Straub, Seong‐Gi Kim, Jongho Lee · 发表于：NeuroImage · 年份：2025 · DOI：10.1016/j.neuroimage.2025.121060 · 被引用次数：1 · 研究领域：Advanced MRI Techniques and Applications、Medical Imaging Techniques and Applications、Advanced NMR Techniques and Applications

• An in-vivo high-resolution χ-separation method at 7T using a deep neural network is proposed. • The method is validated against 3T χ-separation maps and outperforms alternative pipelines. • The method effectively delineates detailed brain structures related to iron and myelin distribution. A recently introduced quantitative susceptibility mapping (QSM) technique, χ -separation, offers the capability to separate paramagnetic ( χ para ) and diamagnetic ( χ dia ) susceptibility distribution within the brain. In-vivo high-resolution mapping of iron and myelin distribution, estimated by χ -separation, could provide a deeper understanding of brain substructures, assisting the investigation of their functions and alterations. This can be achieved using 7T MRI, which benefits from a high signal-to-noise ratio and susceptibility effects. However, applying χ -separation at 7T presents difficulties due to the requirement of an R 2 map, coupled with issues such as high specific absorption rate (SAR), large B 1 transmit field inhomogeneities, and prolonged scan time. To address these challenges, we developed a novel deep neural network, R2PRIMEnet 7T , designed to convert a 7T R 2 * map into a 3T R 2 ′ map. Building on this development, we present a new pipeline for χ -separation at 7T, enabling us to generate high-resolution χ -separation maps from multi-echo gradient-echo data. The proposed method is compared with alternative pipelines, such as an end-to-end network and linearly-scale...