Simple image intensity compensation (SIMIC) method prior to application of distortion correction algorithms in brain diffusion Tensor Magnetic Resonance Imaging: Validation test for two cost functions of distortion correction algorithms

Abstract

The purpose of this study is to design a simple image intensity compensation SIMIC method prior to the application of a variety of cost functions for distortion correction in diffusion tensor imaging DTI. The synthetic dataset consists of each direction of diffusion weighted imaging DWI made by multiplication of nondiffusion weighted image b = 0 image and tensor matrices. We added the effects of patient motion and eddy current distortion using translation, rotation, scaling and shearing matrices. We calculated the b = 0 image of each direction from original DTI, inversely. A co-registration method was applied to the extracted b = 0 images of each direction based on the original b = 0 image and then, the transformation matrices were generated and the original DTI were transformed using this transformation matrix. For the DTI distortion correction, two kinds of cost functions, normalized mutual information NMI and normalized cross-correlation NCC, were used. Visual assessments and quantitative measurements were used to evaluate the results. When using the NMI as a cost function, the quantitative results showed no significant differences between NMI and NMI with SIMIC method. However, there are significant differences compared with using the NCC as a cost function. Our study showed cost function for image distortion correction with SIMIC method improved the results both quantitatively and in terms of qualitative accuracy. This method may be helpful for DTI analysis and helpful for increasing accuracy. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 328-33, 2015