# Spatio-Angular Convolutions for Super-resolution in Diffusion MRI

arXiv: https://arxiv.org/abs/2306.00854 · HTML: https://mlsanity.com/paper/2306.00854/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 48 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 17 of 17)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Spherical CNNs](https://mlsanity.com/paper/1801.10130/) (2018)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [Point-Voxel CNN for Efficient 3D Deep Learning](https://mlsanity.com/paper/1907.03739/) (2019)
- [cuDNN: Efficient Primitives for Deep Learning](https://mlsanity.com/paper/1410.0759/) (2014)
- [Deep Parametric Continuous Convolutional Neural Networks](https://mlsanity.com/paper/2101.06742/) (2021)
- [Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning](https://mlsanity.com/paper/2011.01355/) (2020)
- [Rotation-Equivariant Deep Learning for Diffusion MRI](https://mlsanity.com/paper/2102.06942/) (2021)
- [Q-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Images from Multi-modal Structural MRI](https://mlsanity.com/paper/2106.13188/) (2021)
- [Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional Autoencoder](https://mlsanity.com/paper/2203.15598/) (2022)
- [Fast and accurate reconstruction of HARDI using a 1D encoder-decoder convolutional network](https://mlsanity.com/paper/1903.09272/) (2019)
- [ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning](https://mlsanity.com/paper/2102.12898/) (2021)

## Built on by (most cited first, 11 of 11)

- [Diffusion MRI with Machine Learning](https://mlsanity.com/paper/2402.00019/) (2024)
- [MRI Super-Resolution with Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2511.16854/) (2025)
- [Multi-Modality Conditioned Variational U-Net for Field-of-View Extension in Brain Diffusion MRI](https://mlsanity.com/paper/2409.13846/) (2024)
- [Equivariant spatio-hemispherical networks for diffusion MRI deconvolution](https://mlsanity.com/paper/2411.11819/) (2024)
- [Physics-Guided Diffusion Transformer with Spherical Harmonic Posterior Sampling for High-Fidelity Angular Super-Resolution in Diffusion MRI](https://mlsanity.com/paper/2509.07020/) (2025)
- [Prior-Guided Implicit Neural Representations for Single-Subject Diffusion MRI Super-Resolution](https://mlsanity.com/paper/2609.00981/) (2026)
- [From Promise to Practical Reality: Transforming Diffusion MRI Analysis with Fast Deep Learning Enhancement](https://mlsanity.com/paper/2508.10950/) (2025)
- [Diffusion MRI Transformer with a Diffusion Space Rotary Positional Embedding (D-RoPE)](https://mlsanity.com/paper/2603.25977/) (2026)
- [PhyMRI-SR: Toward Physics-Aware MRI Image Super-Resolution](https://mlsanity.com/paper/2607.06238/) (2026)
- [Diffusion-Encoding Gaussian Field for Joint k-q dMRI Reconstruction](https://mlsanity.com/paper/2609.02288/) (2026)
- [dMRI-F: A Foundation Model for Generalizable Diffusion MRI Analysis from Sparse Acquisitions](https://mlsanity.com/paper/2610.17968/) (2026)

## Abstract (arXiv)

Diffusion MRI (dMRI) is a widely used imaging modality, but requires long scanning times to acquire high resolution datasets. By leveraging the unique geometry present within this domain, we present a novel approach to dMRI angular super-resolution that extends upon the parametric continuous convolution (PCConv) framework. We introduce several additions to the operation including a Fourier feature mapping, global coordinates, and domain specific context. Using this framework, we build a fully parametric continuous convolution network (PCCNN) and compare against existing models. We demonstrate the PCCNN performs competitively while using significantly less parameters. Moreover, we show that this formulation generalises well to clinically relevant downstream analyses such as fixel-based analysis, and neurite orientation dispersion and density imaging.