# Learning Multi-Site Harmonization of Magnetic Resonance Images Without Traveling Human Phantoms

arXiv: https://arxiv.org/abs/2110.00041 · HTML: https://mlsanity.com/paper/2110.00041/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 30 Sep 2021.

## Builds on (most cited first, 15 of 15)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Toward Multimodal Image-to-Image Translation](https://mlsanity.com/paper/1711.11586/) (2017)
- [Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1808.00948/) (2018)
- [DRIT++: Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1905.01270/) (2019)
- [Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis](https://mlsanity.com/paper/1701.02096/) (2017)
- [ComboGAN: Unrestrained Scalability for Image Domain Translation](https://mlsanity.com/paper/1712.06909/) (2017)
- [Test-time Unsupervised Domain Adaptation](https://mlsanity.com/paper/2010.01926/) (2020)
- [Information-based Disentangled Representation Learning for Unsupervised MR Harmonization](https://mlsanity.com/paper/2103.13283/) (2021)

## Built on by (most cited first, 12 of 12)

- [From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare](https://mlsanity.com/paper/2409.09727/) (2024)
- [IGUANe: a 3D generalizable CycleGAN for multicenter harmonization of brain MR images](https://mlsanity.com/paper/2402.03227/) (2024)
- [IHF-Harmony: Multi-Modality Magnetic Resonance Images Harmonization using Invertible Hierarchy Flow Model](https://mlsanity.com/paper/2602.21536/) (2026)
- [Harmonization Across Imaging Locations(HAIL): One-Shot Learning for Brain MRI](https://mlsanity.com/paper/2308.11047/) (2023)
- [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)
- [Self-Supervised Multi-Stage Domain Unlearning for White-Matter Lesion Segmentation](https://mlsanity.com/paper/2603.13328/) (2026)
- [CoMNet: A MedNeXt-CorrDiff Framework for Multi-Site Brain Tumor Segmentation](https://mlsanity.com/paper/2606.15305/) (2026)
- [Feynman Kac Reweighted Schr"odinger Bridge Matching for Surface-Based Tau PET Harmonization](https://mlsanity.com/paper/2606.17420/) (2026)
- [Native space based pipelines outperform template space based pipeline in subcortical segmentation](https://mlsanity.com/paper/2606.21463/) (2026)
- [Task-Adaptive 3D Cross-Field MRI Translation via Field-Conditioned Content-Style Pretraining](https://mlsanity.com/paper/2608.09264/) (2026)
- [Weakly-supervised Kidney Tumor Classification from CT Scans with Multi-Instance Learning and Anatomical Filtering](https://mlsanity.com/paper/2609.07178/) (2026)

## Abstract (arXiv)

Harmonization improves data consistency and is central to effective integration of diverse imaging data acquired across multiple sites. Recent deep learning techniques for harmonization are predominantly supervised in nature and hence require imaging data of the same human subjects to be acquired at multiple sites. Data collection as such requires the human subjects to travel across sites and is hence challenging, costly, and impractical, more so when sufficient sample size is needed for reliable network training. Here we show how harmonization can be achieved with a deep neural network that does not rely on traveling human phantom data. Our method disentangles site-specific appearance information and site-invariant anatomical information from images acquired at multiple sites and then employs the disentangled information to generate the image of each subject for any target site. We demonstrate with more than 6,000 multi-site T1- and T2-weighted images that our method is remarkably effective in generating images with realistic site-specific appearances without altering anatomical details. Our method allows retrospective harmonization of data in a wide range of existing modern large-scale imaging studies, conducted via different scanners and protocols, without additional data collection.