# Scanner Invariant Representations for Diffusion MRI Harmonization

arXiv: https://arxiv.org/abs/1904.05375 · HTML: https://mlsanity.com/paper/1904.05375/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2019.

## Built on by (most cited first, 27 of 27)

- [Domain Generalization for Medical Image Analysis: A Review](https://mlsanity.com/paper/2310.08598/) (2023)
- [Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptation](https://mlsanity.com/paper/2103.12857/) (2021)
- [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)
- [Challenges for machine learning in clinical translation of big data imaging studies](https://mlsanity.com/paper/2107.05630/) (2021)
- [Harmonization with Flow-based Causal Inference](https://mlsanity.com/paper/2106.06845/) (2021)
- [Improved Brain Age Estimation with Slice-based Set Networks](https://mlsanity.com/paper/2102.04438/) (2021)
- [Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience](https://mlsanity.com/paper/2206.07081/) (2022)
- [Diffusion MRI with Machine Learning](https://mlsanity.com/paper/2402.00019/) (2024)
- [FedHarmony: Unlearning Scanner Bias with Distributed Data](https://mlsanity.com/paper/2205.15970/) (2022)
- [Learning Invariant Representations using Inverse Contrastive Loss](https://mlsanity.com/paper/2102.08343/) (2021)
- [Are demographically invariant models and representations in medical imaging fair?](https://mlsanity.com/paper/2305.01397/) (2023)
- [Resolving Domain Shift For Representations Of Speech In Non-Invasive Brain Recordings](https://mlsanity.com/paper/2410.19986/) (2024)
- [Pooling Image Datasets With Multiple Covariate Shift and Imbalance](https://mlsanity.com/paper/2403.02598/) (2024)
- [Harmonization and the Worst Scanner Syndrome](https://mlsanity.com/paper/2101.06255/) (2021)
- [Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI](https://mlsanity.com/paper/2108.06518/) (2021)
- [Adapting Machine Learning Diagnostic Models to New Populations Using a Small Amount of Data: Results from Clinical Neuroscience](https://mlsanity.com/paper/2308.03175/) (2023)
- [Robust Fiber Orientation Distribution Function Estimation Using Deep Constrained Spherical Deconvolution for Diffusion MRI](https://mlsanity.com/paper/2306.02900/) (2023)
- [Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Data-scarce Medical image segmentation?](https://mlsanity.com/paper/2507.19575/) (2025)
- [Clinical-ComBAT: a diffusion-weighted MRI harmonization method for clinical applications](https://mlsanity.com/paper/2511.04871/) (2025)
- [Site Is Decodable Before Pretraining: Negative Controls for Probing Frozen Brain-MRI Foundation Models](https://mlsanity.com/paper/2608.10295/) (2026)
- [SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysis](https://mlsanity.com/paper/2303.15965/) (2023)
- [ComBAT Harmonization for diffusion MRI: Challenges and Best Practices](https://mlsanity.com/paper/2505.14722/) (2025)
- [Scanner-Agnostic MRI Harmonization via SSIM-Guided Disentanglement](https://mlsanity.com/paper/2510.22073/) (2025)
- [HARP: HARmonizing in-vivo diffusion MRI using Phantom-only training](https://mlsanity.com/paper/2603.06696/) (2026)
- [FedDOSE: Federated Learning Framework Decomposing Site Effects for Modeling Brain Dynamic Functional Connectivity](https://mlsanity.com/paper/2608.07393/) (2026)
- [Decoupling Disease, Covariates, and Individual Variability: A Unified Disentanglement Framework for Medical Image Classification](https://mlsanity.com/paper/2609.25650/) (2026)
- [Is invariance all you need for algorithmic fairness? Removing demographic information can create new bias](https://mlsanity.com/paper/2609.32004/) (2026)

## Abstract (arXiv)

Purpose: In the present work we describe the correction of diffusion-weighted MRI for site and scanner biases using a novel method based on invariant representation. Theory and Methods: Pooled imaging data from multiple sources are subject to variation between the sources. Correcting for these biases has become very important as imaging studies increase in size and multi-site cases become more common. We propose learning an intermediate representation invariant to site/protocol variables, a technique adapted from information theory-based algorithmic fairness; by leveraging the data processing inequality, such a representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to underlying structures. To implement this, we use a deep learning method based on variational auto-encoders (VAE) to construct scanner invariant encodings of the imaging data. Results: To evaluate our method, we use training data from the 2018 MICCAI Computational Diffusion MRI (CDMRI) Challenge Harmonization dataset. Our proposed method shows improvements on independent test data relative to a recently published baseline method on each subtask, mapping data from three different scanning contexts to and from one separate target scanning context. Conclusion: As imaging studies continue to grow, the use of pooled multi-site imaging will similarly increase. Invariant representation presents a strong candidate for the harmonization of these data.