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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

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Unsupervised Multi-parametric MRI Registration Using Neural Optimal Transport.

Boah Kim¹, Tejas Sudharshan Mathai¹, Ronald M Summers¹

¹Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, United States.

Proceedings of Spie--The International Society for Optical Engineering

|October 7, 2024

Summary

This summary is machine-generated.

We developed OTMorph, an unsupervised method using neural optimal transport for multi-parametric MRI registration. It accurately aligns medical images with different data distributions, improving disease diagnosis.

Keywords:

Image Registration MRI Multi-parametric Optimal Transport Unsupervised Learning

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Area of Science:

Medical Imaging
Machine Learning
Radiology

Background:

Accurate deformable image registration of multi-parametric MRI is crucial for diagnosing diseases like prostate cancer and lymphoma.
Unsupervised learning methods face challenges with diverse data distributions in volumetric medical image registration.

Purpose of the Study:

To propose OTMorph, an unsupervised domain-transported registration method for multi-parametric MRI sequences.
To address the challenge of registering volumetric medical images with varying data distributions.

Main Methods:

Developed a novel framework with a transport module and a registration module.
Employed neural optimal transport to learn an optimal transport plan for mapping different data distributions.
Utilized end-to-end learning for effective deformable registration.

Main Results:

OTMorph demonstrated superior performance, achieving 67-85% improvement in deforming MRI volumes compared to existing learning-based methods.
Experimental results on abdominal multi-parametric MRI data validated the method's effectiveness.
The method successfully learned deformable registration for volumes with different distributions.

Conclusions:

OTMorph offers an effective solution for unsupervised deformable registration of multi-parametric MRI.
The method's generic nature allows for inter-/intra-modality image registration by mapping diverse data distributions.
This approach enhances the ability to identify abnormalities and diagnose diseases through improved image alignment.