Multi Modal Medical Image Registration: A New Data Driven Approach

Image registration is a challenging task in building computer-based diagnostic systems. One type of image modality will not be able to provide all information needed for better diagnostic. Hence data from multiple sources/image modalities should be combined. In this work canonical correlation analysis (CCA) based image registration approach has been proposed. CCA provides the framework to integrate information from multiple sources. In this work, the information contained in both images is used for image registration task. T1-weighted, T2- weighted and FLAIR MRI images has Multimodal registration done on it. The algorithm provided better results when compared with mutual information based image registration approach. The work has been carried out using the 3D rigid registration of CT and MRI images. The work is carried out using the public datasets, and later performance is evaluated with the work carried out by Research scholars previously. Our algorithm performs better with mutual information based image registration. Medical image registration of multimodality images like MRI, MRI-CT, and MRI-CT-PET. In this paper for MRI-CT Medical Image Registration CT image is used as a fixed image and MRI image as moving image and later compared results with some benchmark algorithm presented in literature such as correlation coefficient, correlation ratio, and mutual information and normalized mutual information methods

Quantification of 3D spatial correlations between state variables and distances to the grain boundary network in full-field crystal plasticity spectral method simulations

Deformation microstructure heterogeneities play a pivotal role during dislocation patterning and interface network restructuring. Thus, they affect indirectly how an alloy recrystallizes if at all. Given this relevance, it has become common practice to study the evolution of deformation microstructure heterogeneities with 3D experiments and full-field crystal plasticity computer simulations including tools such as the spectral method. Quantifying material point to grain or phase boundary distances, though, is a practical challenge with spectral method crystal plasticity models because these discretize the material volume rather than mesh explicitly the grain and phase boundary interface network. This limitation calls for the development of interface reconstruction algorithms which enable us to develop specific data post-processing protocols to quantify spatial correlations between state variable values at each material point and the points' corresponding distance to the closest grain or phase boundary. This work contributes to advance such post-processing routines. Specifically, two grain reconstruction and three distancing methods are developed to solve above challenge. The individual strengths and limitations of these methods surplus the efficiency of their parallel implementation is assessed with an exemplary DAMASK large scale crystal plasticity study. We apply the new tool to assess the evolution of subtle stress and disorientation gradients towards grain boundaries.Comment: Manuscript submitted to Modelling and Simulation in Materials Science and Engineerin