# Spherical Harmonic Residual Network for Diffusion Signal Harmonization

arXiv: https://arxiv.org/abs/1808.01595 · HTML: https://mlsanity.com/paper/1808.01595/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2018.

## Built on by (most cited first, 9 of 9)

- [Domain Generalization for Medical Image Analysis: A Review](https://mlsanity.com/paper/2310.08598/) (2023)
- [Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions](https://mlsanity.com/paper/2201.06505/) (2022)
- [A microstructure estimation Transformer inspired by sparse representation for diffusion MRI](https://mlsanity.com/paper/2205.06450/) (2022)
- [Diffusion MRI with Machine Learning](https://mlsanity.com/paper/2402.00019/) (2024)
- [RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling under Continuous Representation](https://mlsanity.com/paper/2408.01944/) (2024)
- [SamRobNODDI: Q-Space Sampling-Augmented Continuous Representation Learning for Robust and Generalized NODDI](https://mlsanity.com/paper/2411.06444/) (2024)
- [Polyhedra Encoding Transformers: Enhancing Diffusion MRI Analysis Beyond Voxel and Volumetric Embedding](https://mlsanity.com/paper/2501.13352/) (2025)
- [Parsing altered brain connectivity in neurodevelopmental disorders by integrating graph-based normative modeling and deep generative networks](https://mlsanity.com/paper/2410.11064/) (2024)
- [Generalizable field of view extension for diffusion MRI via conditional flow matching](https://mlsanity.com/paper/2610.17401/) (2026)

## Abstract (arXiv)

Diffusion imaging is an important method in the field of neuroscience, as it is sensitive to changes within the tissue microstructure of the human brain. However, a major challenge when using MRI to derive quantitative measures is that the use of different scanners, as used in multi-site group studies, introduces measurement variability. This can lead to an increased variance in quantitative metrics, even if the same brain is scanned. Contrary to the assumption that these characteristics are comparable and similar, small changes in these values are observed in many clinical studies, hence harmonization of the signals is essential. In this paper, we present a method that does not require additional preprocessing, such as segmentation or registration, and harmonizes the signal based on a deep learning residual network. For this purpose, a training database is required, which consist of the same subjects, scanned on different scanners. The results show that harmonized signals are significantly more similar to the ground truth signal compared to no harmonization, but also improve in comparison to another deep learning method. The same effect is also demonstrated in commonly used metrics derived from the diffusion MRI signal.