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Contributions to Signal Processing for MRI
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. (Signal processing)
2015 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

Magnetic Resonance Imaging (MRI) is an important diagnostic tool for imaging soft tissue without the use of ionizing radiation. Moreover, through advanced signal processing, MRI can provide more than just anatomical information, such as estimates of tissue-specific physical properties.

Signal processing lies at the very core of the MRI process, which involves input design, information encoding, image reconstruction, and advanced filtering. Based on signal modeling and estimation, it is possible to further improve the images, reduce artifacts, mitigate noise, and obtain quantitative tissue information.

In quantitative MRI, different physical quantities are estimated from a set of collected images. The optimization problems solved are typically nonlinear, and require intelligent and application-specific algorithms to avoid suboptimal local minima. This thesis presents several methods for efficiently solving different parameter estimation problems in MRI, such as multi-component T2 relaxometry, temporal phase correction of complex-valued data, and minimizing banding artifacts due to field inhomogeneity. The performance of the proposed algorithms is evaluated using both simulation and in-vivo data. The results show improvements over previous approaches, while maintaining a relatively low computational complexity. Using new and improved estimation methods enables better tissue characterization and diagnosis.

Furthermore, a sequence design problem is treated, where the radio-frequency excitation is optimized to minimize image artifacts when using amplifiers of limited quality. In turn, obtaining higher fidelity images enables improved diagnosis, and can increase the estimation accuracy in quantitative MRI.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2015. , xviii+176 p.
Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1104-2516 ; 113
Keyword [en]
Parameter estimation, efficient estimation algorithms, non-convex optimization, multicomponent T2 relaxometry, artifact reduction, T2 mapping, denoising, phase estimation, RF design, MR thermometry, in-vivo brain
National Category
Signal Processing
Research subject
Electrical Engineering with specialization in Signal Processing
URN: urn:nbn:se:uu:diva-246537ISBN: 978-91-554-9204-5OAI: oai:DiVA.org:uu-246537DiVA: diva2:796353
Public defence
2015-05-08, ITC 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 13:15 (English)
EU, European Research Council, 247035
Available from: 2015-04-16 Created: 2015-03-09 Last updated: 2015-07-07Bibliographically approved

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Björk, Marcus
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