Reconstructing simulated breast phantoms using neural networks inspired by the problem geometry

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http://urn.fi/URN:NBN:fi:hulib-202009304162
Title: Reconstructing simulated breast phantoms using neural networks inspired by the problem geometry
Author: Enwald, Joel
Contributor: University of Helsinki, Faculty of Science
Publisher: Helsingin yliopisto
Date: 2020
Language: eng
URI: http://urn.fi/URN:NBN:fi:hulib-202009304162
http://hdl.handle.net/10138/319776
Thesis level: master's thesis
Discipline: Matematiikka
Abstract: Mammography is used as an early detection system for breast cancer, which is one of the most common types of cancer, regardless of one’s sex. Mammography uses specialised X-ray machines to look into the breast tissue for possible tumours. Due to the machine’s set-up as well as to reduce the radiation patients are exposed to, the number of X-ray measurements collected is very restricted. Reconstructing the tissue from this limited information is referred to as limited angle tomography. This is a complex mathematical problem and ordinarily leads to poor reconstruction results. The aim of this work is to investigate how well a neural network whose structure utilizes pre-existing models and known geometry of the problem performs at this task. In this preliminary work, we demonstrate the results on simulated two-dimensional phantoms and discuss the extension of the results to 3-dimensional patient data.
Subject: neural networks
deep learning
breast cancer
shift-and-add
tomosynthesis
convolution


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