# Learning Spatially-Continuous Fiber Orientation Functions

arXiv: https://arxiv.org/abs/2312.05721 · HTML: https://mlsanity.com/paper/2312.05721/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 10 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Learning Continuous Image Representation with Local Implicit Image Function](https://mlsanity.com/paper/2012.09161/) (2020)
- [Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network](https://mlsanity.com/paper/1803.08664/) (2018)
- [Super-Resolution using Convolutional Neural Networks without Any Checkerboard Artifacts](https://mlsanity.com/paper/1806.02658/) (2018)
- [Neural Orientation Distribution Fields for Estimation and Uncertainty Quantification in Diffusion MRI](https://mlsanity.com/paper/2307.08138/) (2023)

## Built on by (most cited first, 5 of 5)

- [Diffusion MRI with Machine Learning](https://mlsanity.com/paper/2402.00019/) (2024)
- [Estimating Neural Orientation Distribution Fields on High Resolution Diffusion MRI Scans](https://mlsanity.com/paper/2409.09387/) (2024)
- [Implicit neural representations for accurate estimation of the standard model of white matter](https://mlsanity.com/paper/2506.15762/) (2025)
- [Prior-Guided Implicit Neural Representations for Single-Subject Diffusion MRI Super-Resolution](https://mlsanity.com/paper/2609.00981/) (2026)
- [Ground-truth effects in learning-based fiber orientation distribution estimation in neonatal brains](https://mlsanity.com/paper/2409.01195/) (2024)

## Abstract (arXiv)

Our understanding of the human connectome is fundamentally limited by the resolution of diffusion MR images. Reconstructing a connectome's constituent neural pathways with tractography requires following a continuous field of fiber directions. Typically, this field is found with simple trilinear interpolation in low-resolution, noisy diffusion MRIs. However, trilinear interpolation struggles following fine-scale changes in low-quality data. Recent deep learning methods in super-resolving diffusion MRIs have focused on upsampling to a fixed spatial grid, but this does not satisfy tractography's need for a continuous field. In this work, we propose FENRI, a novel method that learns spatially-continuous fiber orientation density functions from low-resolution diffusion-weighted images. To quantify FENRI's capabilities in tractography, we also introduce an expanded simulated dataset built for evaluating deep-learning tractography models. We demonstrate that FENRI accurately predicts high-resolution fiber orientations from realistic low-quality data, and that FENRI-based tractography offers improved streamline reconstruction over the current use of trilinear interpolation.