Angular upsampling in diffusion MRI using contextual HemiHex sub-sampling in q-space
This work addresses a domain-specific challenge in medical imaging by enhancing the efficiency of diffusion MRI data acquisition while retaining clinical features, though it appears incremental as it builds on existing regression techniques with geometric optimizations.
The paper tackles the problem of upsampling diffusion MRI data to achieve high angular resolution with fewer gradient directions, using a novel HemiHex subsampling method and nearest neighbor regression to infer unknown q-space points, resulting in improved performance over earlier regression-based approaches.
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.