Probabilistic machine learning models for resolution enhancement of diffusion magnetic resonance imaging.

扩散磁共振成像（dMRI）测量水分子在生物组织（例如脑组织）中扩散的方向和速率。来自dMRI的标量度量用于评估由于不同疾病引起的脑组织变化。此外，由于扩散优先沿着大脑中白色纤维束的方向发生，dMRI可用于映射结构连接性，提供具有许多潜在应用的独特信息，包括预测脑肿瘤预后和神经外科规划。由于临床环境中通常可用的扫描时间有限，这是时间/成本和患者舒适度考虑的结果，临床dMRI协议很少允许具有足够空间分辨率的dMRI采集，以提供脑组织的复杂显微解剖的详细描述。获得这样的细节水平需要昂贵的扫描仪，具有增强的梯度硬件和长的采集时间，这两个都超出了常规临床实践中通常可用的资源。在这个项目中，我们的目标是开发新的机器学习模型，以提高扩散图像的空间分辨率。我们特别感兴趣的是开发新的方法来建立非欧流形上的深度概率模型，这些方法可以应用于提高这类图像的分辨率。成功的结果将实现双重目的，即推动最先进的分辨率，或者减少给定分辨率的扫描时间。

Diffusion magnetic resonance imaging (dMRI) measures the direction and rate at which water molecules diffuse in biological tissues, e.g. brain tissue. Scalar metrics derived from dMRI are used to assess changes in the brain tissue due to different diseases. Also, as diffusion occurs preferentially along the direction of white matter fiber bundles in the brain, dMRI can be used to map structural connectivity, providing unique information with many potential applications, including predicting brain tumour prognosis and for neurosurgical planning.Due to the limited scan time typically available in the clinical setting, a result of both time/cost and patient comfort considerations, clinical dMRI protocols rarely allow for dMRI acquisitions with sufficient spatial resolution to provide a detailed description of the complex microanatomy of the brain tissue.Obtaining such a level of detail requires expensive scanners, with enhanced gradient hardware and long acquisition times, both of which are beyond the resources typically available in routine clinical practice.In this project, our objective is to develop novel machine learning models for increasing the spatial resolution of diffusion images. We are particularly interested on developing new approaches for deep probabilistic models on non-Euclidean manifolds that can be applied to improve resolution on these type of images.A successful outcome will achieve the dual purpose of pushing the state-of-the-art in resolution, or reducing scan time for a given resolution.