SepPetReconstruction

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

Diploma thesis

[Subtitle]
Thesis by:
Advisor:
Supervision by:
Due date:

Abstract

Iterative expectation-maximization algorithms are nowadays the most widespread choice of image reconstruction for tomographic medical scanners. However, due to the exceptional requirements in terms of memory and computation, these algorithms have often been implemented using certain simplifications in the underlying system model.

Within the context of our MR/PET research project, our group is currently developing a modified version of the popular OSEM algorithm which does not rely on a simplified system model. This implementation should provide us with the required flexibility to perform an interesting set of experiments on dynamic, motion-compensated image reconstruction.

The student will be in charge of analyzing the proposed reconstruction algorithm (C++) and writing an optimized version that minimizes convergence time. In a first stage the code must be studied and modified to reduce memory usage and improve cache reutilization. In a second stage, vectorization and/or GPU programming must be used to accelerate the algorithm.

The resulting code will be benchmarked with real clinical data from PET/CT studies.

Contact:

Dr. Gaspar Delso
Nuklearmedizinische Klinik und Poliklinik der TU München
Klinikum rechts der Isar

Tel.: 089 – 4140 6459 (English)
Email:

Students.ProjectForm
Title: Optimization of a Positron Emission Tomography (PET) reconstruction algorithm
Abstract: Iterative expectation-maximization algorithms are nowadays the most widespread choice of image reconstruction for tomographic medical scanners. However, due to the exceptional requirements in terms of memory and computation, these algorithms have often been implemented using certain simplifications in the underlying system model. Within the context of our MR/PET research project, our group is currently developing a modified version of the popular OSEM algorithm which does not rely on a simplified system model. This implementation should provide us with the required flexibility to perform an interesting set of experiments on dynamic, motion-compensated image reconstruction. The student will be in charge of analyzing the proposed reconstruction algorithm (C++) and writing an optimized version that minimizes convergence time. In a first stage the code must be studied and modified to reduce memory usage and improve cache reutilization. In a second stage, vectorization and/or GPU programming must be used to accelerate the algorithm. The resulting code will be benchmarked with real clinical data from PET/CT studies.
Student:  
Director: Prof. Nassir Navab
Supervisor: Gaspar Delso
Type: SEP
Area: Medical Imaging, Molecular Imaging
Status: draft
Start:  
Finish:  
Thesis (optional):  
Picture:  


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