Mamba-Based VoxelMorph Framework for Cardiovascular Disease Imaging and Risk Assessment
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 car...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10979302/ |
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| author | Muhammad Kashif Jabbar Huang Jianjun Ayesha Jabbar Zaka Ur Rehman |
| author_facet | Muhammad Kashif Jabbar Huang Jianjun Ayesha Jabbar Zaka Ur Rehman |
| author_sort | Muhammad Kashif Jabbar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3f42e1ea224e48fdb21ec075fad35b59 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3f42e1ea224e48fdb21ec075fad35b592025-08-20T03:52:52ZengIEEEIEEE Access2169-35362025-01-0113781207813710.1109/ACCESS.2025.356496210979302Mamba-Based VoxelMorph Framework for Cardiovascular Disease Imaging and Risk AssessmentMuhammad Kashif Jabbar0https://orcid.org/0000-0001-8963-1201Huang Jianjun1https://orcid.org/0000-0001-7040-3591Ayesha Jabbar2https://orcid.org/0009-0003-9431-4894Zaka Ur Rehman3https://orcid.org/0000-0003-1341-6366College of Electronics and Information Engineering, Shenzhen University, Shenzhen, ChinaCollege of Electronics and Information Engineering, Shenzhen University, Shenzhen, ChinaCollege of Electronics and Information Engineering, Shenzhen University, Shenzhen, ChinaFaculty of Engineering, Multimedia University, Cyberjaya, MalaysiaCardiovascular 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.https://ieeexplore.ieee.org/document/10979302/Deformable image registrationmamba optimizationcardiovascular disease (CVD) imaging component analysisVoxelMorph frameworkreal-time cardiac diagnostics |
| spellingShingle | Muhammad Kashif Jabbar Huang Jianjun Ayesha Jabbar Zaka Ur Rehman Mamba-Based VoxelMorph Framework for Cardiovascular Disease Imaging and Risk Assessment IEEE Access Deformable image registration mamba optimization cardiovascular disease (CVD) imaging component analysis VoxelMorph framework real-time cardiac diagnostics |
| title | Mamba-Based VoxelMorph Framework for Cardiovascular Disease Imaging and Risk Assessment |
| title_full | Mamba-Based VoxelMorph Framework for Cardiovascular Disease Imaging and Risk Assessment |
| title_fullStr | Mamba-Based VoxelMorph Framework for Cardiovascular Disease Imaging and Risk Assessment |
| title_full_unstemmed | Mamba-Based VoxelMorph Framework for Cardiovascular Disease Imaging and Risk Assessment |
| title_short | Mamba-Based VoxelMorph Framework for Cardiovascular Disease Imaging and Risk Assessment |
| title_sort | mamba based voxelmorph framework for cardiovascular disease imaging and risk assessment |
| topic | Deformable image registration mamba optimization cardiovascular disease (CVD) imaging component analysis VoxelMorph framework real-time cardiac diagnostics |
| url | https://ieeexplore.ieee.org/document/10979302/ |
| work_keys_str_mv | AT muhammadkashifjabbar mambabasedvoxelmorphframeworkforcardiovasculardiseaseimagingandriskassessment AT huangjianjun mambabasedvoxelmorphframeworkforcardiovasculardiseaseimagingandriskassessment AT ayeshajabbar mambabasedvoxelmorphframeworkforcardiovasculardiseaseimagingandriskassessment AT zakaurrehman mambabasedvoxelmorphframeworkforcardiovasculardiseaseimagingandriskassessment |