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Improving Image Quality in Cardiac Computed Tomography using Deep Learning
Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för kardiovaskulär medicin.
2019 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgaveAlternativ tittel
Att förbättra bildkvalitet från datortomografier av hjärtat med djupinlärning (svensk)
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.

sted, utgiver, år, opplag, sider
2019. , s. 53
Emneord [en]
deep learning, neural network, GAN, cycleGAN, CT, heart, imaging, medical imaging
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-154506ISRN: LiU-IMH-EX-19/01-SEOAI: oai:DiVA.org:liu-154506DiVA, id: diva2:1289740
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Medical Technology
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Examiner
Tilgjengelig fra: 2019-02-19 Laget: 2019-02-18 Sist oppdatert: 2019-02-19bibliografisk kontrollert

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