Angular upsampling in diffusion MRI using contextual HemiHex sub-sampling in q-space

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.

DLpN: Single-Shell NODDI Using Deep Learner Estimated Isotropic Volume Fraction

Neurite orientation dispersion and density imaging (NODDI) enables assessment of intracellular, extracellular and free water signals from multi-shell diffusion MRI data. It is an insightful approach to characterize the brain tissue microstructure. Single-shell reconstruction for NODDI parameters has been discouraged in previous literature based on failure when fitting especially for the neurite density index (NDI). Here, we investigated the possibility to create robust NODDI parameter maps with single-shell data, using isotropic volume fraction (f_{ISO}) as prior. We made the prior estimation independent of NODDI model constraint using a dictionary based deep learning approach. First, we proposed a stochastic sparse dictionary-based network, DictNet in predicting f_{ISO} . In single-shell cases, fractional anisotropy (FA) and T2 signal without diffusion weighting ( S_0 ) were incorporated in the dictionary for f_{ISO} estimation. Then, NODDI framework was used in a prior setting to estimate the NDI and orientation dispersion index (ODI). Using both synthetic data simulation and human data collected on a 3T scanner, we compared the performance of our dictionary based deep learning prior NODDI (DLpN) with original NODDI method for both single-shell and multi-shell data. Our results suggest that DLpN derived NDI and ODI parameters for single-shell protocols are comparable with original multi-shell NODDI, and protocol with b=2000 s/mm 2 performs the best (error ~2% in white matter and ~4% in grey matter). This may allow NODDI evaluation of retrospective studies on single-shell data by additional scanning of two subjects for DictNet f_{ISO} training.