In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the micro-structure of myocardial tissue in the living heart, providing insights into cardiac function and enabling the development of innovative therapeutic strategies. However, the integration of cDTI into routine clinical practice is challenging due to the technical obstacles involved in the acquisition, such as low signal-to-noise ratio and long scanning times. In this paper, we investigate and implement three different types of deep learning-based MRI reconstruction models for cDTI reconstruction. We evaluate the performance of these models based on reconstruction quality assessment and diffusion tensor parameter assessment. Our results indicate that the models we discussed in this study can be applied for clinical use at an acceleration factor (AF) of $\times 2$ and $\times 4$, with the D5C5 model showing superior fidelity for reconstruction and the SwinMR model providing higher perceptual scores. There is no statistical difference with the reference for all diffusion tensor parameters at AF $\times 2$ or most DT parameters at AF $\times 4$, and the quality of most diffusion tensor parameter maps are visually acceptable. SwinMR is recommended as the optimal approach for reconstruction at AF $\times 2$ and AF $\times 4$. However, we believed the models discussed in this studies are not prepared for clinical use at a higher AF. At AF $\times 8$, 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.

翻译：在体心脏扩散张量成像（cDTI）是一种很有前景的磁共振成像（MRI）技术，用于评估活体心脏心肌组织的微观结构，为心脏功能提供见解，并促进创新治疗策略的开发。然而，由于采集过程中面临的技术障碍，如低信噪比和长时间扫描，cDTI在常规临床实践中的整合仍具有挑战性。在本文中，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。我们基于重建质量评估和扩散张量参数评估对这些模型的性能进行了评价。我们的结果表明，本研究中讨论的模型在加速因子（AF）为×2和×4时可应用于临床，其中D5C5模型在重建方面表现出更高的保真度，而SwinMR模型提供了更高的感知评分。在AF ×2时，所有扩散张量参数与参考值均无统计学差异；在AF ×4时，大部分DT参数与参考值也无统计学差异，且大多数扩散张量参数图的质量在视觉上可接受。推荐SwinMR作为AF ×2和AF ×4时的最优重建方法。然而，我们认为本研究中讨论的模型尚不适用于更高AF下的临床使用。在AF ×8时，所有讨论模型的表现仍有限，仅有一半的扩散张量参数恢复至与参考值无统计学差异的水平，部分扩散张量参数图甚至提供错误和误导性信息。