Anatomical structure-guided joint spatiotemporal graph embedding framework for magnetic resonance fingerprint reconstruction

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Peng Li , Jianxing Liu , Yue Hu
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引用次数: 0

Abstract

Highly undersampled acquisition schemes in magnetic resonance fingerprinting (MRF) typically introduce aliasing artifacts, degrading the accuracy of quantitative imaging. While state-of-the-art graph-based reconstruction methods have shown promise in addressing this challenge by leveraging non-local and non-linear correlations in MRF data, they often face two critical limitations: high computational costs associated with large-scale graph structure estimation and limited capacity to capture complex spatiotemporal dynamics. To overcome these challenges, this study proposes an anatomical structure-guided joint spatiotemporal graph embedding framework for MRF reconstruction. By integrating anatomical segmentation and homogeneity clustering, our framework partitions MRF data into spatially contiguous regions and groups them into clusters based on tissue homogeneity. Subgraphs are then constructed for each cluster, capturing non-local spatial correlations while preserving fine-grained temporal signal dynamics. The hierarchical graph embedding architecture enables efficient focusing on critical correlations, significantly improving reconstruction performance and reducing computational complexity. Numerical experiments on both simulated and in vivo MRF datasets demonstrate that our method outperforms state-of-the-art methods, achieving a 2 dB higher signal-to-noise ratio (SNR) in reconstructed data and a 70% reduction in reconstruction time. The source code is publicly available at https://github.com/bigponglee/SP_GE_MRF.
基于解剖结构的关节时空图嵌入框架磁共振指纹重建
在磁共振指纹识别(MRF)中,高度欠采样采集方案通常会引入混叠伪影,降低定量成像的准确性。虽然最先进的基于图的重建方法已经通过利用MRF数据中的非局部和非线性相关性来解决这一挑战,但它们通常面临两个关键限制:与大规模图结构估计相关的高计算成本和捕获复杂时空动态的有限能力。为了克服这些挑战,本研究提出了一种解剖结构引导的关节时空图嵌入框架,用于磁共振成像重建。通过结合解剖分割和均匀性聚类,我们的框架将MRF数据划分为空间上连续的区域,并根据组织均匀性将其分组。然后为每个集群构建子图,在保留细粒度时间信号动态的同时捕获非局部空间相关性。分层图嵌入架构能够有效地关注关键相关性,显著提高重建性能并降低计算复杂度。在模拟和体内MRF数据集上进行的数值实验表明,我们的方法优于最先进的方法,在重建数据中实现了约2 dB的高信噪比(SNR),并将重建时间减少了约70%。源代码可在https://github.com/bigponglee/SP_GE_MRF上公开获得。
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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