Biomedical Imaging Lab

Magnetic Resonance Imaging


CUDA-accelerated SPM image registration

Image registration has been an important topic in the MRI applications, such as longitudinal follow-up studies, brain-normalization for group statistics and motion correction for fMRI studies. However, the automatic registration requires a lot of iteration loops and a huge amount computation for linear transformations and thus it is generally very time-consuming task. In our study, we proposed to use the parallel computing on recently advanced general-purpose computation on graphic processing units (GPGPU) to accelerate the registration calculations, especially for the popular SPM system.

(1) System requirement: Windows 32-bit (XP), Matlab, SPM, Nvidia CUDA driver

(2) remove or rename the original spm_hist2.dll

(3)download the program spm_hist2.dll  to SPM directory.

(4) This program can speed up SPM processes related to spm_hist2.dll, for exmaple, spm_coreg.

(optional) For other OS system, please download the source code to recompile

Ref. : Huang TY*, Tang YW, Ju SY (2011) “Accelerating image registration of MRI by GPU-based parallel computation.” Magnetic Resonance Imaging 2011 June; 29 (5):712-716


Automatic image registration for MRI applications generally requires many iteration loops and is, therefore, a time-consuming task. This drawback prolongs data analysis and delays the workflow of clinical routines. Recent advances in the massively parallel computation of graphic processing units (GPUs) may be a solution to this problem. This study proposes a method to accelerate registration calculations, especially for the popular statistical parametric mapping (SPM) system. This study reimplemented the image registration of SPM system to achieve an approximately 14-fold increase in speed in registering single-modality intrasubject data sets. The proposed program is fully compatible with SPM, allowing the user to simply replace the original image registration library of SPM to gain the benefit of the computation power provided by commodity graphic processors. In conclusion, the GPU computation method is a practical way to accelerate automatic image registration. This technology promises a broader scope of application in the field of image registration.

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