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Title: Exploiting MRI information for improved kinetic modelling of dynamic PET data
Author: Sari, H.
ISNI:       0000 0004 8498 3381
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2016
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Kinetic analysis of dynamic PET data requires an accurate estimation of the concen- tration of the available tracer in blood plasma, also known as the arterial input function (AIF). The gold standard method to determine the AIF involves serial blood sampling and is avoided in practice due to its invasiveness. An image derived input function (IDIF) can be a blood-free alternative but its accuracy is limited due to partial volume (PV) effects caused by the restricted spatial resolution of PET scanners. Furthermore, IDIFs are not accurate when metabolite products are present in the blood. Magnetic resonance imaging (MRI) can provide complementary information to PET with high spatial resolution and excellent soft tissue contrast. Furthermore, dynamic MRI techniques can be reliably used to measure the AIF, the concentration of contrast agent in plasma, due to their high temporal resolution. The underlying aim of this research is to improve IDIF estimation in PET, utilising spatial and temporal information from MRI. An IDIF measurement method was developed which involves segmentation of carotid arteries from MR angiography images and uses a practical PVC method to correct for PV effects. It was demonstrated that the IDIFs can be used to compute the cerebral metabolic rate of glucose in the brain with no significant difference compared to arterial sampling. The simultaneous estimation method (SIME) is an alternative technique used to estimate the AIF by fitting time activity curves derived from multiple regions. Due to its computational complexity, SIME is usually complemented with blood samples. In this work, we observed that the early part of an image derived blood curve or an MRI derived AIF could provide prior knowledge regarding the AIF. This was incorporated into SIME to make more accurate kinetic parameter estimations and to perform blood-free analysis of tracers with metabolites.
Supervisor: Hutton, B. ; Erlandsson, K. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available