Abstract #3834

FuseMorph: accurate and time-efficient MRI 3D T1 Image deformable registration with iterative search and deep learning

Pei-Mao Sun¹, Teng-Yi Huang¹, Tzu-Chao Chuang², Yi-Ru Lin³, and Hsiao-Wen Chung⁴

¹Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ²Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan, ³Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ⁴Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan

Synopsis

Keywords: Analysis/Processing, AI/ML Software

Motivation: Deformable MRI brain registration is critical in various research areas. However, it is generally time-consuming. Advanced deep learning methods can enhance both efficiency and accuracy.

Goal(s): This study aims to develop an optimized, iterative approach for deformable MRI image registration, targeting both improved alignment accuracy and reduced computation time.

Approach: We introduce FuseMorph, which integrates a VoxelMorph-based model with iterative optimization and grid search. We benchmark its performance against ANTs' SyNCC.

Results: FuseMorph improves MRI registration accuracy compared to SyNCC and significantly reduces overall processing time.

Impact: This method improves MRI alignment accuracy and accelerates processing, offering a reliable tool for both research and clinical applications. It also enhances downstream tasks, such as VBM analysis, allowing them to be performed with greater speed and precision.

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