G5 Artikkeliväitöskirja
Algorithmic Analysis Techniques for Molecular Imaging
Tekijät: Merisaari Harri
Kustantaja: TUCS Dissertations
Kustannuspaikka: Turku
Julkaisuvuosi: 2016
ISBN: 978-952-12-3442-2
Verkko-osoite: https://www.doria.fi/bitstream/handle/10024/125696/harri merisaari.pdf
Tiivistelmä
This study addresses image processing techniques for two medical imagingmodalities: Positron Emission Tomography (PET) and Magnetic ResonanceImaging (MRI), which can be used in studies of human body functions andanatomy in a non-invasive manner.In PET, the so-called Partial Volume Effect (PVE) is caused by lowspatial resolution of the modality. The efficiency of a set of PVE-correctionmethods is evaluated in the present study. These methods use informationabout tissue borders which have been acquired with the MRI technique. Asanother technique, a novel method is proposed for MRI brain image segmen-tation.A standard way of brain MRI is to use spatial prior informationin 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-healthypremature infant brain MR images. Diffusion Weighted Imaging (DWI) isa MRI-based technique that can be used to create images for measuringphysiological properties of cells on the structural level.We optimise thescanning parameters of DWI so that the required acquisition time can bereduced while still maintaining good image quality.In the present work, PVE correction methods, and physiological DWImodels are evaluated in terms of repeatabilityof the results. This gives in-formation on the reliability of the measures given by the methods. Theevaluations are done using physical phantom objects, correlation measure-ments against expert segmentations, computer simulations with realisticnoise modelling, and with repeated measurements conducted on real pa-tients. In PET, the applicability and selection of a suitable partial volumecorrection method was found to depend on the target application. For MRI,the data-driven segmentation offers an alternative when using spatial prior isnot feasible. For DWI, the distribution of b-values turns out to be a centralfactor affecting the time-quality ratio of the DWI acquisition. An optimalb-value distribution was determined. This helps to shorten the imaging timewithout hampering the diagnostic accuracy.
This study addresses image processing techniques for two medical imagingmodalities: Positron Emission Tomography (PET) and Magnetic ResonanceImaging (MRI), which can be used in studies of human body functions andanatomy in a non-invasive manner.In PET, the so-called Partial Volume Effect (PVE) is caused by lowspatial resolution of the modality. The efficiency of a set of PVE-correctionmethods is evaluated in the present study. These methods use informationabout tissue borders which have been acquired with the MRI technique. Asanother technique, a novel method is proposed for MRI brain image segmen-tation.A standard way of brain MRI is to use spatial prior informationin 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-healthypremature infant brain MR images. Diffusion Weighted Imaging (DWI) isa MRI-based technique that can be used to create images for measuringphysiological properties of cells on the structural level.We optimise thescanning parameters of DWI so that the required acquisition time can bereduced while still maintaining good image quality.In the present work, PVE correction methods, and physiological DWImodels are evaluated in terms of repeatabilityof the results. This gives in-formation on the reliability of the measures given by the methods. Theevaluations are done using physical phantom objects, correlation measure-ments against expert segmentations, computer simulations with realisticnoise modelling, and with repeated measurements conducted on real pa-tients. In PET, the applicability and selection of a suitable partial volumecorrection method was found to depend on the target application. For MRI,the data-driven segmentation offers an alternative when using spatial prior isnot feasible. For DWI, the distribution of b-values turns out to be a centralfactor affecting the time-quality ratio of the DWI acquisition. An optimalb-value distribution was determined. This helps to shorten the imaging timewithout hampering the diagnostic accuracy.
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