期刊
MEDICAL IMAGE ANALYSIS
卷 84, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.media.2022.102700
关键词
Magnetic susceptibility; Electromagnetic tissue properties Simulated training data; Simulated training data; Magnetic susceptibility Data-consistent deep learning
Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great potential, but current methods lack data consistency and often result in error propagation. We developed a new framework, NeXtQSM, that solves the QSM problem jointly and overcomes these limitations.
Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great potential in recent years, obtaining similar results to established non-learning approaches. Many current deep learning approaches are not data consistent, require in vivo training data or solve the QSM problem in consecutive steps resulting in the propagation of errors. Here we aim to overcome these limitations and developed a framework to solve the QSM processing steps jointly. We developed a new hybrid training data generation method that enables the end-to-end training for solving background field correction and dipole inversion in a data-consistent fashion using a variational network that combines the QSM model term and a learned regularizer. We demonstrate that NeXtQSM overcomes the limitations of previous deep learning methods. NeXtQSM offers a new deep learning based pipeline for computing quantitative susceptibility maps that integrates each processing step into the training and provides results that are robust and fast.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据