Optimizing MRI Registration using Software/Hardware Co-Design Model on FPGA
Yasmeen Farouk1, Sherine Rady2

1Yasmeen Farouk*, Department of Information Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.
2Sherine Rady, Department of Information Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.

Manuscript received on November 21, 2020. | Revised Manuscript received on December 01, 2020. | Manuscript published on December 10, 2021. | PP: 128-137 | Volume-10 Issue-2, December 2020 | Retrieval Number: 100.1/ijitee.B83001210220| DOI: 10.35940/ijitee.B8300.1210220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The correct localization of brain tissue deformation and determination of the tumor growth relies majorly on the accuracy of the process known by image registration. Poor registration may lead to misclassified diseases and highly affect image-guided surgery and radiation therapies. Voxel-based morphometry (VBM) is an image analytical technique encompassing accurate registration but suffers from intensive time computations, similar to most of image registration techniques. Achieving the compromise between accuracy and computations is a challenging mission. Field programmable gate arrays have fast-evolving and customizable hardware acceleration capabilities that promise to help speed up computational tasks. This paper presents a software/hardware co-design model for accelerating the implementation of the diffeomorphic image registration algorithm ‘DARTEL’ as a part of VBM that analyzes MRI images. An optimized and pipelined hardware architecture is proposed and integrated into the Statistical Parametric Mapping (SPM) software tool that runs the DARTEL. Acceleration of the DARTEL registration algorithm resulted in a speedup factor of 114x on function-level, compared to the CPU with a contribution of 8x faster for the overall performance in the registration process of the SPM. The proposed model is successfully validated for the identification of Alzheimer’s disease based on T1-weighted MRI. A proposed software/hardware co-design model for VBM achieves remarkable acceleration while maintaining classification accuracy and proving proficiency against other CPU and GPU implementations. 
Keywords: Alzheimer’s disease, Field programmable gate array, Image registration, Magnetic resonance imaging, Software Hardware co-design, Voxel-based morphometry.