Low-Rank Tensor Models for Improved Multidimensional MRI: Application to Dynamic Cardiac $T_1$ Mapping
Low-Rank Tensor Models for Improved Multidimensional MRI: Application to Dynamic Cardiac $T_1$ Mapping
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DOI：
10.1109/tci.2019.2940916
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发表时间：
2020
影响因子：
5.4
通讯作者：
Burhaneddin Yaman;Sebastian Weingärtner;Nikos Kargas;N. Sidiropoulos;M. Akçakaya
中科院分区：
文献类型：
--
作者：
Burhaneddin Yaman;Sebastian Weingärtner;Nikos Kargas;N. Sidiropoulos;M. Akçakaya
多维、多对比磁共振成像 (MRI) 已越来越多地用于对各种病理进行全面、高效的评估，提供大量数据并为改进图像重建提供新的机会。最近，引入了心脏相位分辨心肌 $T_1$ 绘图方法，以提供有关组织活力的动态信息。在临床可接受的扫描时间内提高时空分辨率是非常理想的，但需要高加速因子。张量非常适合描述此类多维数据集中的多维隐藏结构。在本研究中，我们试图在不使用辅助导航器数据的情况下利用和比较不同的张量分解方法。我们探索了多种处理方法，以实现高分辨率心脏相位分辨心肌 $T_1$ 绘图。通过对六名健康志愿者获得的 $T_1$ 图中的准确度和精确度进行定量分析，评估了八种不同的低秩张量近似和处理方法。所有方法都提供了可比较的 $T_1$ 值。然而，使用本地处理以及直接张量秩近似可以显着提高精度。低秩张量近似方法非常适合在高时空分辨率下实现动态 $T_1$ 映射。
Multidimensional, multicontrast magnetic resonance imaging (MRI) has become increasingly available for comprehensive and time-efficient evaluation of various pathologies, providing large amounts of data and offering new opportunities for improved image reconstructions. Recently, a cardiac phase-resolved myocardial $T_1$ mapping method has been introduced to provide dynamic information on tissue viability. Improved spatio-temporal resolution in clinically acceptable scan times is highly desirable but requires high acceleration factors. Tensors are well-suited to describe interdimensional hidden structures in such multi-dimensional datasets. In this study, we sought to utilize and compare different tensor decomposition methods, without the use of auxiliary navigator data. We explored multiple processing approaches in order to enable high-resolution cardiac phase-resolved myocardial $T_1$ mapping. Eight different low-rank tensor approximation and processing approaches were evaluated using quantitative analysis of accuracy and precision in $T_1$ maps acquired in six healthy volunteers. All methods provided comparable $T_1$ values. However, the precision was significantly improved using local processing, as well as a direct tensor rank approximation. Low-rank tensor approximation approaches are well-suited to enable dynamic $T_1$ mapping at high spatio-temporal resolutions.