Guided Research: Implementation of efficient computational modules for forward- and backward-projection in X-ray computed tomography
Project by: David Frank
Advisor:
Tobias Lasser
Supervision by:
Matthias Wieczorek
Abstract
X-Ray Computed Tomography (CT) is one of the cornerstones of medical imaging for many decades now. The tomographic reconstruction of CT is quite well understood theoretically and practically, but many open research issues remain. A central point for any reconstruction method is the projector and back-projector pair, which models the interaction process of X-rays with matter, the detection process in the detector and the acquisition geometry. Several standard methods for this are described in the literature, each with specific advantages and disadvantages. Common to all these methods are high computational requirements, necessitating the use of massively parallel computing devices, such as GPUs.
Tasks
Subject of this project is the efficient computation of accurate line integrals, which is the main computational complexity of any reconstruction algorithm in X-ray Computed Tomography.
The aim of this work is the investigation of a very recent 2D approach using summed area tables and regression models, which promises a combination of high performance and great accuracy.
This approach should be investigated and extended to 3D, with an implementation in our existing C++ reconstruction framework and quantitative evaluation of the results, comparing to other standard methods already implemented in our framework.
The implementation should be parallelized either on regular CPUs, Xeon Phi's or GPUs (OpenCL).
Requirements
C++, parallel programming, some geometry knowledge
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