Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction: a comparison study
Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction: a comparison study
复制标题
DOI：
10.1038/s41598-024-55880-2
复制
发表时间：
2024-03-07
影响因子：
4.6
通讯作者：
Yang，Guang
中科院分区：
文献类型：
--
作者：
Huang，Jiahao;Ferreira，Pedro F.;Yang，Guang
体内心脏弥散张量成像（cDTI）是一种很有前途的磁共振成像（MRI）技术，用于评估活体心脏心肌组织的微观结构，提供心脏功能的见解，并使创新治疗策略的发展成为可能。然而，由于采集过程中涉及的技术障碍，如低信噪比和长时间的扫描时间，将cDTI整合到常规临床实践中面临挑战。在这项研究中，我们研究并实现了三种不同类型的基于深度学习的MRI重建模型，用于cDTI重建。我们从重建质量评估、扩散张量参数评估和计算成本评估三个方面对这些模型的性能进行了评价。我们的研究结果表明，本研究中讨论的模型可以以10倍的加速因子（AF）应用于临床，其中D5C5模型在重建方面表现出更好的保真度，而SwinMR模型提供了更高的感知评分。对于AF下的大多数DT参数，所有扩散张量参数与参考值没有统计学差异，并且大多数扩散张量参数映射的质量在视觉上是可以接受的。SwinMR被推荐为AF和AF重建的最佳方法。然而，我们认为本研究中讨论的模型尚未准备好在更高AF下临床使用。在AF下，所有讨论的模型的性能仍然有限，只有一半的扩散张量参数恢复到与参考文献没有统计学差异的水平。一些扩散张量参数图甚至提供了错误和误导性的信息。
In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the microstructure of myocardial tissue in living hearts, providing insights into cardiac function and enabling the development of innovative therapeutic strategies. However, the integration of cDTI into routine clinical practice poses challenging due to the technical obstacles involved in the acquisition, such as low signal-to-noise ratio and prolonged scanning times. In this study, we investigated and implemented three different types of deep learning-based MRI reconstruction models for cDTI reconstruction. We evaluated the performance of these models based on the reconstruction quality assessment, the diffusion tensor parameter assessment as well as the computational cost assessment. Our results indicate that the models discussed in this study can be applied for clinical use at an acceleration factor (AF) ofand, with the D5C5 model showing superior fidelity for reconstruction and the SwinMR model providing higher perceptual scores. There is no statistical difference from the reference for all diffusion tensor parameters at AFor most DT parameters at AF, and the quality of most diffusion tensor parameter maps is visually acceptable. SwinMR is recommended as the optimal approach for reconstruction at AFand AF. However, we believe that the models discussed in this study are not yet ready for clinical use at a higher AF. At AF, the performance of all models discussed remains limited, with only half of the diffusion tensor parameters being recovered to a level with no statistical difference from the reference. Some diffusion tensor parameter maps even provide wrong and misleading information.