定量磁化率图
深度学习
计算机科学
人工智能
一致性(知识库)
数据一致性
忠诚
概化理论
模式识别(心理学)
磁共振成像
数据挖掘
机器学习
数学
放射科
医学
电信
统计
操作系统
作者
Naveen Paluru,Raji Susan Mathew,Phaneendra K. Yalavarthy
摘要
Abstract Quantitative Susceptibility Mapping (QSM) is an advanced magnetic resonance imaging (MRI) technique to quantify the magnetic susceptibility of the tissue under investigation. Deep learning methods have shown promising results in deconvolving the susceptibility distribution from the measured local field obtained from the MR phase. Although existing deep learning based QSM methods can produce high‐quality reconstruction, they are highly biased toward training data distribution with less scope for generalizability. This work proposes a hybrid two‐step reconstruction approach to improve deep learning based QSM reconstruction. The susceptibility map prediction obtained from the deep learning methods has been refined in the framework developed in this work to ensure consistency with the measured local field. The developed method was validated on existing deep learning and model‐based deep learning methods for susceptibility mapping of the brain. The developed method resulted in improved reconstruction for MRI volumes obtained with different acquisition settings, including deep learning models trained on constrained (limited) data settings.
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