MaCtProjector

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Master's thesis: Efficient GPU Projectors for X-ray Computed Tomography

Thesis by: Michal Szymczak
Advisor: Nassir Navab
Supervision by: Tobias Lasser, 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

The aim of this Master's thesis is the educated selection of a suitable cone-beam projection / back-projection method based on established literature, and its efficient implementation in our existing C++ framework for tomographic reconstruction, CAMPRecon, using a massively parallel implementation on GPUs with OpenCL. The results should be tested and evaluated both on simulated and real clinical data (in cooperation with our partners at the university clinic MRI).

Requirements

GPU programming with OpenCL, C++ programming skills.

Contact


ProjectForm
Title: Efficient GPU Projectors for X-ray Computed Tomography
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.
Student: Michal Szymczak
Director: Nassir Navab
Supervisor: Tobias Lasser, Matthias Wieczorek
Type: Master Thesis
Area: Medical Imaging
Status: finished
Start: 2016/04/18
Finish: 2016/12/18
Thesis (optional):  
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