Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) is the only in vivo method to non-invasively examine the microstructure of the human heart. Current research in DT-CMR aims to improve the understanding of how the cardiac microstructure relates to the macroscopic function of the healthy heart as well as how microstructural dysfunction contributes to disease. To get the final DT-CMR metrics, we need to acquire diffusion weighted images of at least 6 directions. However, due to DWI's low signal-to-noise ratio, the standard voxel size is quite big on the scale for microstructures. In this study, we explored the potential of deep-learning-based methods in improving the image quality volumetrically (x4 in all dimensions). This study proposed a novel framework to enable volumetric super-resolution, with an additional model input of high-resolution b0 DWI. We demonstrated that the additional input could offer higher super-resolved image quality. Going beyond, the model is also able to super-resolve DWIs of unseen b-values, proving the model framework's generalizability for cardiac DWI superresolution. In conclusion, we would then recommend giving the model a high-resolution reference image as an additional input to the low-resolution image for training and inference to guide all super-resolution frameworks for parametric imaging where a reference image is available.

翻译：弥散张量心脏磁共振（DT-CMR）是唯一能无创检测人体心脏微结构的在体方法。当前DT-CMR研究旨在深入理解心脏微结构与健康心脏宏观功能之间的关联，以及微结构功能障碍如何导致疾病。为获取最终DT-CMR指标，需采集至少6个方向的弥散加权图像（DWI）。然而，由于DWI信噪比较低，其标准体素尺寸在微结构尺度上通常较大。本研究探索了基于深度学习方法在三维空间（所有维度各放大4倍）提升图像质量的潜力。我们提出了一种新型框架实现容积超分辨率，该框架额外输入高分辨率b0 DWI作为模型输入。实验证明，额外的输入能提供更优的超分辨率图像质量。进一步地，该模型还能对未见过的b值DWI进行超分辨率处理，证实了模型框架在心脏DWI超分辨率中的泛化能力。结论建议：在训练和推理过程中，将高分辨率参考图像作为低分辨率图像的附加输入，为所有可获取参考图像的参数成像超分辨率框架提供指导。