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Implementation of Image Registration Toolbox
The project aims to modernize and improve the process of medical image registration, currently performed through a method known as pTV. Offering a unique combination of numerical programming and practical software implementation, this project promises visibility and application in the ever-evolving field of medical imaging technology. Suitable as a semester-long or master's project.
Keywords: image registration, medical imaging, programming, torch, numpy, imaging, porting
Image registration plays a crucial role in the realm of medical image acquisition and analysis. It encompasses various complex processes including image alignment, optical flow estimation, tissue tracking, motion compensation, and deformation estimation. Particularly, within dynamic MRI reconstruction, the focus lies in motion tracking of the subject during acquisition to enhance the quality of reconstruction.
We have been successfully utilizing a method known as pTV registration in our workflow. This method has also been incorporated into other libraries, including Matlab, for broader usage. Further information and examples on pTV registration can be found on the following GitHub page:
https://github.com/visva89/pTVreg
(For examples: https://github.com/visva89/pTVreg/blob/master/Examples.md )
The project at hand consists of two major tasks:
* Since Matlab is gradually becoming obsolete, a significant portion of the codebase needs to be ported to Python. This will ensure the continued accessibility and usability of the pTV registration.
* Additionally, the pTV method needs to be rewritten using PyTorch to make it compatible with the context of differentiable programming.
This project is ideal for those who possess strong programming skills and a comprehensive understanding of numerical programming. Given the wide usage and citation of the code, the ported version will be made public, offering visibility to the individual who successfully completes the task.
This project can be undertaken as a semester-long assignment or as a master's project.
Reference:
Vishnevskiy, V. (2016). Effective Joint Regularization in Medical Imaging for Nonsmooth Nonlinear Priors (pp.1-36). ETH Zurich. https://www.research-collection.ethz.ch/handle/20.500.11850/155735
Image registration plays a crucial role in the realm of medical image acquisition and analysis. It encompasses various complex processes including image alignment, optical flow estimation, tissue tracking, motion compensation, and deformation estimation. Particularly, within dynamic MRI reconstruction, the focus lies in motion tracking of the subject during acquisition to enhance the quality of reconstruction.
We have been successfully utilizing a method known as pTV registration in our workflow. This method has also been incorporated into other libraries, including Matlab, for broader usage. Further information and examples on pTV registration can be found on the following GitHub page:
* Since Matlab is gradually becoming obsolete, a significant portion of the codebase needs to be ported to Python. This will ensure the continued accessibility and usability of the pTV registration.
* Additionally, the pTV method needs to be rewritten using PyTorch to make it compatible with the context of differentiable programming.
This project is ideal for those who possess strong programming skills and a comprehensive understanding of numerical programming. Given the wide usage and citation of the code, the ported version will be made public, offering visibility to the individual who successfully completes the task.
This project can be undertaken as a semester-long assignment or as a master's project.
Reference:
Vishnevskiy, V. (2016). Effective Joint Regularization in Medical Imaging for Nonsmooth Nonlinear Priors (pp.1-36). ETH Zurich. https://www.research-collection.ethz.ch/handle/20.500.11850/155735