论文标题

MRXCAT-CDI:数值心脏扩散张量成像幻影

MRXCAT-CDTI: A Numerical Cardiac Diffusion Tensor Imaging Phantom

论文作者

van Gorkum, Robbert J. H., Weine, Jonathan L., Segars, William P., Stoeck, Christian T., Kozerke, Sebastian

论文摘要

磁共振心脏扩散量张量成像(CDTI)和心静脉内外运动成像使体内肌纤维结构和心肌灌注替代物探测。为了研究实验参数(例如分辨率,离子和心率变化)等实验参数的影响,我们提出了一个称为MRXCAT-CDI的数值开源框架。它允许模拟自旋回波(SE)和刺激回声采集模式(Steam)CDTI序列的扩散和灌注对比度。傅立叶编码器支持平面内和/或通过斜线离子效应,以及在单发图像编码期间的T2*效果。包括可选病变以模仿缺血和梗塞的心肌区域。 MRXCAT-CDI允许评估对数据获取的现实影响,以及这些影响如何影响数据编码过程以及随后的数据处理。例如,心率的变化导致磁化的部分饱和度和磁化松弛的差异,如果不考虑CDTI角度指标的误差为9%至30%。对于SE Echo-Planar CDTI,与通过SLICE的离子相比,平面内外谐波效应对CDTI指标的影响更大。通过这项工作,我们提出了一个开源的MRXCAT-CDTI数值模拟框架,该框架提供了在心脏扩散和灌注数据中发现的逼真的图像编码效果,以系统地研究数据编码,重建和后处理的影响,以促进可重复的研究。

Magnetic Resonance cardiac diffusion tensor imaging (cDTI) and cardiac intravoxel incoherent motion imaging enables probing of in vivo myofiber architecture and myocardial perfusion surrogates. To study the impact of experimental parameters such as resolution, off-resonances and heart-rate variations, we propose a numerical open-source framework called MRXCAT-CDTI. It allows simulating diffusion and perfusion contrast for spin-echo (SE) and stimulated echo acquisition mode (STEAM) cDTI sequences. The Fourier encoder supports in-plane and/or through-slice off-resonance effects, as well as T2* effects during single-shot image encoding. Optional lesions are included to mimic ischemic and infarcted myocardial regions. MRXCAT-CDTI allows assessing realistic influences on data acquisition, and how these affect the data encoding process and subsequent data processing. As an example, heart-rate variations lead to differences in partial saturation and relaxation of magnetization that end up in errors of 9 to 30% for cDTI angle metrics if not accounted for. For SE echo-planar cDTI, in-plane off-resonance effects more adversely affect cDTI metrics compared to through-slice off-resonances. With this work we propose an open-source MRXCAT-CDTI numerical simulation framework that offers realistic image encoding effects found in cardiac diffusion and perfusion data to systematically study influences of data encoding, reconstruction, and post-processing to promote reproducible research.

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