Anatomical Plausibility in Deformable Image Registration Using Bayesian Optimization for Brain MRI Analysis

Deformable image registration plays a crucial role in medical imaging by aligning anatomical structures across multiple datasets, which is essential for accurate diagnosis and treatment planning. However, existing deep learning-based deformable registration models often face challenges in ensuring a...

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Bibliographic Details
Published in:Applied sciences 2024-12, Vol.14 (23), p.10890
Main Authors: Castaño-Aguirre, Mauricio, García, Hernán Felipe, Cárdenas-Peña, David, Porras-Hurtado, Gloria Liliana, Orozco-Gutiérrez, Álvaro Ángel
Format: Article
Language:English
Subjects:
anatomical plausibility
Asphyxia neonatorum
Bayesian optimization
Brain
Care and treatment
Datasets
Deep learning
deformable registration
Development and progression
Energy
Gaussian processes
Health aspects
Health care reform
Magnetic resonance imaging
Medical imaging
Medical imaging equipment
Optimization techniques
Registration
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Summary:Deformable image registration plays a crucial role in medical imaging by aligning anatomical structures across multiple datasets, which is essential for accurate diagnosis and treatment planning. However, existing deep learning-based deformable registration models often face challenges in ensuring anatomical plausibility, leading to unnatural deformations in critical brain structures. This paper proposes a novel framework that uses Bayesian optimization to address these challenges, focusing on registering 3D point clouds that represent brain structures. Our method uses probabilistic modeling to optimize non-rigid transformations, providing smooth and interpretable deformations that align with anatomical constraints. The proposed framework is validated using MRI data from patients diagnosed with hypoxic-ischemic encephalopathy (HIE) due to perinatal asphyxia. These datasets include brain scans taken at multiple time points, enabling the modeling of structural changes over time. By incorporating Bayesian optimization, we enhance the accuracy of the registration process while maintaining anatomical fidelity. Our results demonstrate that the approach provides interpretable, anatomically plausible deformations, outperforming conventional methods in terms of accuracy and reliability. This work offers an improved tool for brain MRI analysis, aiding healthcare professionals in better understanding disease progression and guiding therapeutic interventions.
ISSN:2076-3417
2076-3417
DOI:10.3390/app142310890