Contributions to Signal Processing for MRI
2015 (English)Doctoral thesis, monograph (Other academic)
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
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
Research subject Electrical Engineering with specialization in Signal Processing
IdentifiersURN: urn:nbn:se:uu:diva-246537ISBN: 978-91-554-9204-5OAI: oai:DiVA.org:uu-246537DiVA: diva2:796353
2015-05-08, ITC 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 13:15 (English)
Jakobsson, Andreas, Professor
Stoica, Peter, Professor
FunderEU, European Research Council, 247035