CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping.

IF 1.5 4区 数学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xiaojian Xu, Weijie Gan, Satya V V N Kothapalli, Dmitriy A Yablonskiy, Ulugbek S Kamilov
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引用次数: 0

Abstract

Quantitative MRI (qMRI) refers to a class of MRI methods for quantifying the spatial distribution of biological tissue parameters. Traditional qMRI methods usually deal separately with artifacts arising from accelerated data acquisition, involuntary physical motion, and magnetic field inhomogeneities, leading to sub-optimal end-to-end performance. This paper presents CoRRECT, a unified deep unfolding (DU) framework for qMRI consisting of a model-based end-to-end neural network, a method for motion artifact reduction, and a self-supervised learning scheme. The network is trained to produce R2* maps whose k-space data matches the real data by also accounting for motion and field inhomogeneities. When deployed, CoRRECT only uses the k-space data without any pre-computed parameters for motion or inhomogeneity correction. Our results on experimentally collected multi-gradient recalled echo (mGRE) MRI data show that CoRRECT recovers motion and inhomogeneity artifact-free R2* maps in highly accelerated acquisition settings. This work opens the door to DU methods that can integrate physical measurement models, biophysical signal models, and learned prior models for high-quality qMRI.

正确:一个深度展开框架的运动校正定量R2*映射。
定量磁共振成像(Quantitative MRI, qMRI)是一类量化生物组织参数空间分布的MRI方法。传统的qMRI方法通常单独处理由加速数据采集、非自愿物理运动和磁场不均匀性引起的伪影,导致次优的端到端性能。本文提出了一个统一的qMRI深度展开(DU)框架CoRRECT,该框架由基于模型的端到端神经网络、运动伪影减少方法和自监督学习方案组成。通过考虑运动和场的不均匀性,该网络被训练生成R2*映射,其k空间数据与真实数据相匹配。部署时,CoRRECT只使用k空间数据,而不使用任何预先计算的参数进行运动或非均匀性校正。我们对实验收集的多梯度回忆回波(mGRE) MRI数据的研究结果表明,在高度加速的采集设置下,CoRRECT可以恢复运动和不均匀的无伪影R2*图。这项工作为DU方法打开了大门,该方法可以集成物理测量模型、生物物理信号模型和高质量qMRI的学习先验模型。
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来源期刊
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision 工程技术-计算机:人工智能
CiteScore
4.30
自引率
5.00%
发文量
70
审稿时长
3.3 months
期刊介绍: The Journal of Mathematical Imaging and Vision is a technical journal publishing important new developments in mathematical imaging. The journal publishes research articles, invited papers, and expository articles. Current developments in new image processing hardware, the advent of multisensor data fusion, and rapid advances in vision research have led to an explosive growth in the interdisciplinary field of imaging science. This growth has resulted in the development of highly sophisticated mathematical models and theories. The journal emphasizes the role of mathematics as a rigorous basis for imaging science. This provides a sound alternative to present journals in this area. Contributions are judged on the basis of mathematical content. Articles may be physically speculative but need to be mathematically sound. Emphasis is placed on innovative or established mathematical techniques applied to vision and imaging problems in a novel way, as well as new developments and problems in mathematics arising from these applications. The scope of the journal includes: computational models of vision; imaging algebra and mathematical morphology mathematical methods in reconstruction, compactification, and coding filter theory probabilistic, statistical, geometric, topological, and fractal techniques and models in imaging science inverse optics wave theory. Specific application areas of interest include, but are not limited to: all aspects of image formation and representation medical, biological, industrial, geophysical, astronomical and military imaging image analysis and image understanding parallel and distributed computing computer vision architecture design.
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