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Improving Image Quality in Cardiac Computed Tomography using Deep Learning
Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Att förbättra bildkvalitet från datortomografier av hjärtat med djupinlärning (Swedish)
Abstract [en]

Cardiovascular diseases are the largest mortality factor globally, and early diagnosis is essential for a proper medical response. Cardiac computed tomography can be used to acquire images for their diagnosis, but without radiation dose reduction the radiation emitted to the patient becomes a significant risk factor. By reducing the dose, the image quality is often compromised, and determining a diagnosis becomes difficult. This project proposes image quality enhancement with deep learning. A cycle-consistent generative adversarial neural network was fed low- and high-quality images with the purpose to learn to translate between them. By using a cycle-consistency cost it was possible to train the network without paired data. With this method, a low-quality image acquired from a computed tomography scan with dose reduction could be enhanced in post processing.

The results were mixed but showed an increase of ventricular contrast and artifact mitigation. The technique comes with several problems that are yet to be solved, such as structure alterations, but it shows promise for continued development.

Place, publisher, year, edition, pages
2019. , p. 53
Keywords [en]
deep learning, neural network, GAN, cycleGAN, CT, heart, imaging, medical imaging
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-154506ISRN: LiU-IMH-EX-19/01-SEOAI: oai:DiVA.org:liu-154506DiVA, id: diva2:1289740
Subject / course
Medical Technology
Supervisors
Examiners
Available from: 2019-02-19 Created: 2019-02-18 Last updated: 2019-02-19Bibliographically approved

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Thesis(5063 kB)95 downloads
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File name FULLTEXT01.pdfFile size 5063 kBChecksum SHA-512
8b7704ebb97aa602c60966dab78906cd2142aa442acae78103b2cf23804d5dd8f16b2f9f94380278c343e54ae97c911abe8b6df66cc26ac7b31fd23eaa110806
Type fulltextMimetype application/pdf

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf