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Riemannian DTI Filters: Developing ITK Modules for Tensor Field Image Processing

This project focuses on creating algorithms and ITK modules for fundamental image processing tasks on tensor fields using Riemannian approaches. We aim to develop filters for interpolation, resampling, and smoothing of DTI images. The team, composed of experts from various institutions, will tackle programming challenges, ensuring compatibility with existing ITK frameworks. Clinical validation of the outcomes is scheduled by the end of the programming week, alongside efforts to resolve tensor/DWI IO issues and advance both Euclidean and symmetric space processing methods.

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Riemannian DTI Filters: Developing ITK Modules for Tensor Field Image Processing

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  1. Riemannian DTI Filters:Develop algorithms and ITK modules for basic image processing on tensor fields using Riemannian approaches. Plan/Expected Challenges/Publication Team Algorithms: development of Riemannian tensor processing ideas. Develop filters for interpolation, resampling, smoothing, etc. Saurav Basu, Utah (algorithms) (contact) Casey Goodlett, UNC (algorithms) Tom Fletcher, Utah (algorithms) Karthik Krishnan, Kitware Xiadong Tao, GE Software: Develop ITK filters that fit well with established frameworks for resampling, interpolation,et c. Clinical: Validation of results. Accomplished by end of Programming Week • Resolve Tensor/DWI IO Issues • ITK Registration Framework working on Vector/Tensor Images • Filters for Euclidean space processing of DTI • Progress on Symmetric space processing of DTI (Design of classes/filters established)

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