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.