Harmonizing Flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonization
Farzad Beizaee, Gregory A. Lodygensky, Chris L. Adamson, Deanne K., Thompso, Jeanie L.Y. Cheon, Alicia J. Spittl. Peter J. Anderso, Christian, Desrosier, Jose Dolz

TL;DR
This paper introduces an unsupervised, source-free MRI harmonization method using normalizing flows to align images across different domains, improving the robustness of downstream tasks like segmentation and age estimation.
Contribution
It presents a novel framework combining normalizing flows and a shallow harmonizer network for unsupervised MRI harmonization without requiring source data during inference.
Findings
Outperforms existing harmonization methods in MRI segmentation tasks.
Effective across adult and neonatal brain MRI datasets.
Enhances generalization in MRI-based age estimation.
Abstract
Lack of standardization and various intrinsic parameters for magnetic resonance (MR) image acquisition results in heterogeneous images across different sites and devices, which adversely affects the generalization of deep neural networks. To alleviate this issue, this work proposes a novel unsupervised harmonization framework that leverages normalizing flows to align MR images, thereby emulating the distribution of a source domain. The proposed strategy comprises three key steps. Initially, a normalizing flow network is trained to capture the distribution characteristics of the source domain. Then, we train a shallow harmonizer network to reconstruct images from the source domain via their augmented counterparts. Finally, during inference, the harmonizer network is updated to ensure that the output images conform to the learned source domain distribution, as modeled by the normalizing…
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Taxonomy
TopicsAdvanced MRI Techniques and Applications · Medical Imaging Techniques and Applications · Atomic and Subatomic Physics Research
MethodsNormalizing Flows · ALIGN
