Algorithmic Analysis Techniques for Molecular Imaging

dc.contributorMatemaattis-luonnontieteellinen tiedekunta / Faculty of Mathematics and Natural Sciences, Turku centre for computer science-
dc.contributor.authorMerisaari, Harri
dc.contributor.departmentfi=Tulevaisuuden teknologioiden laitos|en=Department of Future Technologies|
dc.contributor.facultyfi=Matemaattis-luonnontieteellinen tiedekunta|en=Faculty of Mathematics and Natural Sciences|-
dc.date.accessioned2016-10-21T06:27:44Z
dc.date.available2016-10-21T06:27:44Z
dc.date.issued2016-11-18
dc.description.abstractThis study addresses image processing techniques for two medical imaging modalities: Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI), which can be used in studies of human body functions and anatomy in a non-invasive manner. In PET, the so-called Partial Volume Effect (PVE) is caused by low spatial resolution of the modality. The efficiency of a set of PVE-correction methods is evaluated in the present study. These methods use information about tissue borders which have been acquired with the MRI technique. As another technique, a novel method is proposed for MRI brain image segmen- tation. A standard way of brain MRI is to use spatial prior information in image segmentation. While this works for adults and healthy neonates, the large variations in premature infants preclude its direct application. The proposed technique can be applied to both healthy and non-healthy premature infant brain MR images. Diffusion Weighted Imaging (DWI) is a MRI-based technique that can be used to create images for measuring physiological properties of cells on the structural level. We optimise the scanning parameters of DWI so that the required acquisition time can be reduced while still maintaining good image quality. In the present work, PVE correction methods, and physiological DWI models are evaluated in terms of repeatabilityof the results. This gives in- formation on the reliability of the measures given by the methods. The evaluations are done using physical phantom objects, correlation measure- ments against expert segmentations, computer simulations with realistic noise modelling, and with repeated measurements conducted on real pa- tients. In PET, the applicability and selection of a suitable partial volume correction method was found to depend on the target application. For MRI, the data-driven segmentation offers an alternative when using spatial prior is not feasible. For DWI, the distribution of b-values turns out to be a central factor affecting the time-quality ratio of the DWI acquisition. An optimal b-value distribution was determined. This helps to shorten the imaging time without hampering the diagnostic accuracy.-
dc.description.accessibilityfeatureei tietoa saavutettavuudesta
dc.description.notificationSiirretty Doriasta
dc.format.contentfulltext
dc.identifier978-952-12-3442-2-
dc.identifier.olddbid141452
dc.identifier.oldhandle10024/125696
dc.identifier.urihttps://www.utupub.fi/handle/11111/28144
dc.identifier.urnURN:ISBN:978-952-12-3442-2
dc.language.isoeng-
dc.publisherTurku Centre for Computer Science
dc.relation.ispartofseriesTUCS Dissertations
dc.relation.issn1239-1883
dc.relation.numberinseries217-
dc.source.identifierhttps://www.utupub.fi/handle/10024/125696
dc.titleAlgorithmic Analysis Techniques for Molecular Imaging-
dc.type.ontasotfi=Artikkeliväitöskirja|en=Doctoral dissertation (article-based)|

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