BaSparseCtDictionaryLearning

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

Bachelors's thesis: Reconstruction of sparsely sampled X-ray Computed Tomography data using dictionary learning

Thesis by: David Frank
Advisor: Bjoern Menze
Supervision by: Tobias Lasser

Abstract

Reconstruction of x-ray computed tomography (CT) data enables an insight to the human body without the need of opening it. The basic concept comes down to sending X-rays through the human body and measuring the overall change the X-ray recieved on the other side of the patient. Such methods are called projective imaging methods. Tomographic reconstruction aims at reconstructing a 3D volume of the human body providing a map of the physical properties which led to the according projective measurements. In order to reduce noise and/or improve reconstruction quality (e.g. with respect to a specific task) one can incorporate prior assumptions or knowledge into this reconstruction process, i.e. regularization.

Tasks

Subject of this Bachelor's thesis is the implementation of dictionary learning methods for sparsely sampled data, as well as the quantitative evaluation of their performance for the application to tomographic reconstruction.

Requirements

Scientific programming, strong mathematic background, C++

Contact


Students.ProjectForm
Title: Reconstruction of sparsely sampled X-ray Computed Tomography data using dictionary learning
Abstract: Reconstruction of x-ray computed tomography (CT) data enables an insight to the human body without the need of opening it. The basic concept comes down to sending X-rays through the human body and measuring the overall change the X-ray recieved on the other side of the patient. Such methods are called projective imaging methods. Tomographic reconstruction aims at reconstructing a 3D volume of the human body providing a map of the physical properties which led to the according projective measurements. In order to reduce noise and/or improve reconstruction quality (e.g. with respect to a specific task) one can incorporate prior assumptions or knowledge into this reconstruction process, i.e. regularization.
Student: David Frank
Director: Bjoern Menze
Supervisor: Tobias Lasser
Type: Bachelor Thesis
Area: Medical Imaging
Status: finished
Start: 2016/05/15
Finish: 2016/09/15
Thesis (optional):  
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Edit | Attach | Refresh | Diffs | More | Revision r1.4 - 17 Oct 2016 - 05:41 - TobiasLasser