Diffusion MRI is a non-invasive, in-vivo biomedical imaging method for mapping tissue microstructure. Applications include structural connectivity imaging of the human brain and detecting microstructural neural changes. However, acquiring high signal-to-noise ratio dMRI datasets with high angular and spatial resolution requires prohibitively long scan times, limiting usage in many important clinical settings, especially for children, the elderly, and in acute neurological disorders that may require conscious sedation or general anesthesia. We employ a Swin UNEt Transformers model, trained on augmented Human Connectome Project data and conditioned on registered T1 scans, to perform generalized denoising of dMRI. We also qualitatively demonstrate super-resolution with artificially downsampled HCP data in normal adult volunteers. Remarkably, Swin UNETR can be fine-tuned for an out-of-domain dataset with a single example scan, as we demonstrate on dMRI of children with neurodevelopmental disorders and of adults with acute evolving traumatic brain injury, each cohort scanned on different models of scanners with different imaging protocols at different sites. We exceed current state-of-the-art denoising methods in accuracy and test-retest reliability of rapid diffusion tensor imaging requiring only 90 seconds of scan time. Applied to tissue microstructural modeling of dMRI, Swin UNETR denoising achieves dramatic improvements over the state-of-the-art for test-retest reliability of intracellular volume fraction and free water fraction measurements and can remove heavy-tail noise, improving biophysical modeling fidelity. Swin UNeTR enables rapid diffusion MRI with unprecedented accuracy and reliability, especially for probing biological tissues for scientific and clinical applications. The code and model are publicly available at https://github.com/ucsfncl/dmri-swin.

翻译：扩散磁共振成像（dMRI）是一种无创、在体的生物医学成像方法，用于绘制组织微观结构。其应用包括人脑结构连接成像及检测微结构神经变化。然而，获取具有高角度和高空间分辨率的高信噪比dMRI数据集需要过长的扫描时间，这限制了其在许多重要临床场景中的使用，尤其是在儿童、老年人以及可能需要清醒镇静或全身麻醉的急性神经系统疾病患者中。我们采用基于Swin UNEt Transformers（Swin UNETR）模型，并利用增强的人脑连接组计划数据（HCP）进行训练，同时以配准后的T1扫描为条件，实现dMRI的广义去噪。我们还通过人工降采样的HCP数据，在正常成年志愿者中定性展示了超分辨率效果。值得注意的是，Swin UNETR可通过单次示例扫描针对域外数据集进行微调，正如我们在神经发育障碍儿童dMRI和急性进展性创伤性脑损伤成人dMRI中所展示的——两个队列均在不同现场、使用不同成像协议的不同型号扫描仪完成采集。在仅需90秒扫描时间的快速扩散张量成像中，我们的方法在精度和重测信度上均超越了当前最先进的去噪方法。应用于dMRI组织微观结构建模时，Swin UNETR去噪在细胞内体积分数和自由水分数测量值的重测信度方面实现了对现有技术的显著提升，并能去除重尾噪声，改善生物物理建模的保真度。Swin UNETR使得快速扩散磁共振成像具有前所未有的精度和可靠性，尤其适用于科学及临床应用中生物组织的探测。代码和模型已在https://github.com/ucsfncl/dmri-swin公开。