论文标题
在扩散MRI中使用上下文中的Hemihex子采样在Q空间中的Angular UPS采样
Angular upsampling in diffusion MRI using contextual HemiHex sub-sampling in q-space
论文作者
论文摘要
人工智能（深度学习（DL）/机器学习（ML））技术被广泛用于解决和克服医学成像中各种不适的问题，或者实际上似乎是不可能的。在保留临床特征的MR中减少梯度方向，但利用MR中的高角度分辨率（HAR）扩散数据是该领域的重要且具有挑战性的问题。虽然DL/ML方法很有希望，但重要的是要合并数据的相关上下文，以确保为AI模型提供最大的先验信息以推断后部。在本文中，我们介绍了Hemihex（HH）子采样，以暗示解决Q-Space几何形状上的训练数据采样，然后在HH-Samples上进行最近的邻居回归训练，以最终对DMRI数据进行示例。较早的研究试图使用回归来进行更新DMRI数据，但由于无法提供结构化的几何措施，因此产生了性能问题。我们提出的方法是一种几何优化的回归技术，它渗透了未知的Q空间，从而解决了早期研究中的局限性。
Artificial Intelligence (Deep Learning(DL)/ Machine Learning(ML)) techniques are widely being used to address and overcome all kinds of ill-posed problems in medical imaging which was or in fact is seemingly impossible. Reducing gradient directions but harnessing high angular resolution(HAR) diffusion data in MR that retains clinical features is an important and challenging problem in the field. While the DL/ML approaches are promising, it is important to incorporate relevant context for the data to ensure that maximum prior information is provided for the AI model to infer the posterior. In this paper, we introduce HemiHex (HH) subsampling to suggestively address training data sampling on q-space geometry, followed by a nearest neighbor regression training on the HH-samples to finally upsample the dMRI data. Earlier studies has tried to use regression for up-sampling dMRI data but yields performance issues as it fails to provide structured geometrical measures for inference. Our proposed approach is a geometrically optimized regression technique which infers the unknown q-space thus addressing the limitations in the earlier studies.
