Key result
Mamba-Optimized VoxelMorph boosts registration accuracy and cuts computational time ~40% versus standard methods.
Why the study?
Existing deformable image registration methods, including VoxelMorph, face limitations in computational efficiency and memory usage that restrict real-time applicability for high-resolution cardiac imaging.
Does the Mamba-Optimized VoxelMorph framework improve registration accuracy and computational efficiency in cardiovascular imaging compared to existing methods?
Does the Mamba-Optimized VoxelMorph framework improve registration accuracy and computational efficiency in cardiovascular imaging compared to existing methods?
The Mamba-Optimized VoxelMorph framework improves the accuracy and computational efficiency of deformable image registration for cardiovascular imaging.
May support efficient cardiovascular image registration; leaves open prospective clinical validation before adoption.
Cardiovascular diseases (CVDs), particularly coronary artery disease (CAD), remain the leading cause of global mortality, necessitating advanced diagnostic solutions. Accurate deformable image registration plays a crucial role in enhancing segmentation precision and classification performance in cardiovascular imaging. However, existing registration methods, including VoxelMorph, face limitations in computational efficiency and memory usage, restricting their real-time applicability for high-resolution cardiac imaging. This study proposes the Mamba-Optimized VoxelMorph framework, which leverages GPU-based parallelization and memory optimization to address these challenges. The framework achieves superior registration accuracy, yielding a Dice Similarity Coefficient (DSC) of 0.95 and Normalized Cross-Correlation (NCC) of 0.90, while reducing computational time by 40% and memory usage to 800 MB. These advancements ensure efficient alignment of complex cardiac structures, thereby improving segmentation accuracy and classification reliability. By addressing these critical limitations, the Mamba-Optimized VoxelMorph framework significantly enhances cardiovascular imaging, enabling precise, scalable, and real-time deformable image registration for improved CAD diagnosis and treatment planning.
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Jabbar et al. (2025) studied Cardiovascular diseases (CVDs), particularly coronary artery disease (CAD). Mamba-Optimized VoxelMorph framework vs. existing registration methods, including VoxelMorph was evaluated on Registration accuracy (Dice Similarity Coefficient and Normalized Cross-Correlation), computational time, and memory usage. The Mamba-Optimized VoxelMorph framework achieved superior registration accuracy with a Dice Similarity Coefficient of 0.95 and Normalized Cross-Correlation of 0.90, while reducing computational time by 40%.
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