Spatiotemporal Power of Positron Emission Tomography: Pushing the Limits of Poisson Statistics in High-Resolution Human Neurotransmission Studies

Brain disorders involving dysfunctions in neurotransmissionconstitute one of the most prevalent health problems. Subtledisruptions in human neurotransmission can result in significantdysfunction of cognition, locomotion, or practically any facet ofhuman behaviour. In turn, homeostasis of a specific neurotransmitter system can often be retrieved through pharmacologicalor lifestyle interventions. At present, human neurotransmissioncan be best assayed using positron emission tomography (PET). To date, neurotransmitter-PET (nt-PET) has been employedto investigate neuroreceptor level phenomenon in human behavior/cognition as well as in treatment development. In thecurrent work the goal was to explore and enhance the temporalcapabilities of nt-PET, to allow better characterization of thetemporal facets of neurotransmission.Main obstacles limiting temporal characterization stem fromthe poor signal-to-noise-ratio of the PET measurement. Inparticular, the limitations related to image reconstruction algorithms and in turn the benefits obtained through regionalanalysis were in the focus of the investigations in this work. Themain finding was that the best temporal resolution achieved using a commonly recommended iterative reconstruction methodwas insufficient for temporal characterization, while a newlydeveloped algorithm allowing analytical reconstruction showedbetter temporal resolution without decreasing signal-to-noiseratio. Furthermore, a novel atlas-based regional analysis methodwas found superior to the currently employed manual region-ofinterest definition.The findings made through this work will directly assist theplanning of future neurotransmission studies, and it is wishedthat the observations in this work would spark new, more widespread interest on the application of nt-PET in e.g. cognitivestimulation studies

Motion Correction and Pharmacokinetic Analysis in Dynamic Positron Emission Tomography

This thesis will focus on two important aspects of dynamic Positron Emission Tomography (PET): (i) Motion-compensation , and (ii) Pharmacokinetic analysis (also called parametric imaging) of dynamic PET images. Both are required to enable fully quantitative PET imaging which is increasingly finding applications in the clinic. Motion-compensation in Dynamic Brain PET Imaging: Dynamic PET images are degraded by inter-frame and intra-frame motion artifacts that can a ffect the quantitative and qualitative analysis of acquired PET data. We propose a Generalized Inter-frame and Intra-frame Motion Correction (GIIMC) algorithm that uni fies in one framework the inter-frame motion correction capability of Multiple Acquisition Frames and the intra-frame motion correction feature of (MLEM)-type deconvolution methods. GIIMC employs a fairly simple but new approach of using time-weighted average of attenuation sinograms to reconstruct dynamic frames. Extensive validation studies show that GIIMC algorithm outperforms conventional techniques producing images with superior quality and quantitative accuracy. Parametric Myocardial Perfusion PET Imaging: We propose a novel framework of robust kinetic parameter estimation applied to absolute flow quantification in dynamic PET imaging. Kinetic parameter estimation is formulated as nonlinear least squares with spatial constraints problem where the spatial constraints are computed from a physiologically driven clustering of dynamic images, and used to reduce noise contamination. The proposed framework is shown to improve the quantitative accuracy of Myocardial Perfusion (MP) PET imaging, and in turn, has the long-term potential to enhance capabilities of MP PET in the detection, staging and management of coronary artery disease

Regularized estimation and model selection in compartment models

Dynamic imaging series acquired in medical and biological research are often analyzed with the help of compartment models. Compartment models provide a parametric, nonlinear function of interpretable, kinetic parameters describing how some concentration of interest evolves over time. Aiming to estimate the kinetic parameters, this leads to a nonlinear regression problem. In many applications, the number of compartments needed in the model is not known from biological considerations but should be inferred from the data along with the kinetic parameters. As data from medical and biological experiments are often available in the form of images, the spatial data structure of the images has to be taken into account. This thesis addresses the problem of parameter estimation and model selection in compartment models. Besides a penalized maximum likelihood based approach, several Bayesian approaches-including a hierarchical model with Gaussian Markov random field priors and a model state approach with flexible model dimension-are proposed and evaluated to accomplish this task. Existing methods are extended for parameter estimation and model selection in more complex compartment models. However, in nonlinear regression and, in particular, for more complex compartment models, redundancy issues may arise. This thesis analyzes difficulties arising due to redundancy issues and proposes several approaches to alleviate those redundancy issues by regularizing the parameter space. The potential of the proposed estimation and model selection approaches is evaluated in simulation studies as well as for two in vivo imaging applications: a dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) study on breast cancer and a study on the binding behavior of molecules in living cell nuclei observed in a fluorescence recovery after photobleaching (FRAP) experiment

Improving the Accuracy of CT-derived Attenuation Correction in Respiratory-Gated PET/CT Imaging

The effect of respiratory motion on attenuation correction in Fludeoxyglucose (18F) positron emission tomography (FDG-PET) was investigated. Improvements to the accuracy of computed tomography (CT) derived attenuation correction were obtained through the alignment of the attenuation map to each emission image in a respiratory gated PET scan. Attenuation misalignment leads to artefacts in the reconstructed PET image and several methods were devised for evaluating the attenuation inaccuracies caused by this. These methods of evaluation were extended to finding the frame in the respiratory gated PET which best matched the CT. This frame was then used as a reference frame in mono-modality compensation for misalignment. Attenuation correction was found to affect the quantification of tumour volumes; thus a regional analysis was used to evaluate the impact of mismatch and the benefits of compensating for misalignment. Deformable image registration was used to compensate for misalignment, however, there were inaccuracies caused by the poor signal-to-noise ratio (SNR) in PET images. Two models were developed that were robust to a poor SNR allowing for the estimation of deformation from very noisy images. Firstly, a cross population model was developed by statistically analysing the respiratory motion in 10 4DCT scans. Secondly, a 1D model of respiration was developed based on the physiological function of respiration. The 1D approach correctly modelled the expansion and contraction of the lungs and the differences in the compressibility of lungs and surrounding tissues. Several additional models were considered but were ruled out based on their poor goodness of fit to 4DCT scans. Approaches to evaluating the developed models were also used to assist with optimising for the most accurate attenuation correction. It was found that the multimodality registration of the CT image to the PET image was the most accurate approach to compensating for attenuation correction mismatch. Mono-modality image registration was found to be the least accurate approach, however, incorporating a motion model improved the accuracy of image registration. The significance of these findings is twofold. Firstly, it was found that motion models are required to improve the accuracy in compensating for attenuation correction mismatch and secondly, a validation method was found for comparing approaches to compensating for attenuation mismatch

Improving the Accuracy of CT-derived Attenuation Correction in Respiratory-Gated PET/CT Imaging

The effect of respiratory motion on attenuation correction in Fludeoxyglucose (18F) positron emission tomography (FDG-PET) was investigated. Improvements to the accuracy of computed tomography (CT) derived attenuation correction were obtained through the alignment of the attenuation map to each emission image in a respiratory gated PET scan. Attenuation misalignment leads to artefacts in the reconstructed PET image and several methods were devised for evaluating the attenuation inaccuracies caused by this. These methods of evaluation were extended to finding the frame in the respiratory gated PET which best matched the CT. This frame was then used as a reference frame in mono-modality compensation for misalignment. Attenuation correction was found to affect the quantification of tumour volumes; thus a regional analysis was used to evaluate the impact of mismatch and the benefits of compensating for misalignment. Deformable image registration was used to compensate for misalignment, however, there were inaccuracies caused by the poor signal-to-noise ratio (SNR) in PET images. Two models were developed that were robust to a poor SNR allowing for the estimation of deformation from very noisy images. Firstly, a cross population model was developed by statistically analysing the respiratory motion in 10 4DCT scans. Secondly, a 1D model of respiration was developed based on the physiological function of respiration. The 1D approach correctly modelled the expansion and contraction of the lungs and the differences in the compressibility of lungs and surrounding tissues. Several additional models were considered but were ruled out based on their poor goodness of fit to 4DCT scans. Approaches to evaluating the developed models were also used to assist with optimising for the most accurate attenuation correction. It was found that the multimodality registration of the CT image to the PET image was the most accurate approach to compensating for attenuation correction mismatch. Mono-modality image registration was found to be the least accurate approach, however, incorporating a motion model improved the accuracy of image registration. The significance of these findings is twofold. Firstly, it was found that motion models are required to improve the accuracy in compensating for attenuation correction mismatch and secondly, a validation method was found for comparing approaches to compensating for attenuation mismatch

Development and Evaluation of Quantitative Methods of Analysing Single Photon Emission Computed Tomography Blood Flow Images of the Brain

Development and evaluation of quantitative methods of analysing single photon emission computed tomography blood flow images of the brain. This thesis presents the investigations carried out on a particular method of functional human brain mapping (FHBM) analysis (SPM)1 as to its applicability to a routine nuclear medicine neuroimaging department. Principally designed for the investigation into positron emission tomography (PET) radiolabelled water studies of normal brain function during neuroactivation experiments the technique is still relatively novel for the purposes of interpreting single photon emission computed tomography (SPECT) images of brain function. This thesis investigates whether the functional brain mapping technique (SPM) can be extended to embrace the widely available imaging technique of SPECT and to determine whether this combination can contribute to routine diagnosis of abnormalities in brain function and to research investgiations involving functional neuroactivation. Validation of the image standardisation facility of SPM96 applied to oblique or incomplete image data sets. The image standardisation component of SPM96 was validated by subjecting it to a series of challenge conditions created from simulated data. The challenge conditions were chosen to reflect those that occur in clinical scans, for example, extreme misalignments to a standard reference orientation resulting in axial truncation of the image volume. The results of the software performance under these challenges showed that the image standardisation component of this software had particular problems correcting for large (1

Neuroimaging of fetal cell therapy in Parkinson’s disease

Parkinson’s disease is the second most common neurodegenerative disease characterised by the elevated formation of α-synuclein-immunopositive intraneuronal proteinaceous inclusions (Lewy pathology) and the progressive loss of neuromelanin-laden dopaminergic cells of the substantia nigra pars compacta, resulting in the loss of striatal dopaminergic terminals and emergence of cardinal motor features including bradykinesia, rigidity, tremor and postural instability. Dopaminomimetic agents provide effective symptomatic relief in the early stages of illness, yet due to the inherently progressive nature of the disease and the induction of debilitating side effects their efficacy is eventually lost. Cellular restorative strategies involving intrastriatal transplantation of human fetal ventral mesencephalic (hfVM) tissue gained traction from the early 1990’s, when several multi-disciplinary teams reported drastic motoric improvements concomitant with graft-derived dopaminergic re-innervation. However, outcomes of double-blind randomised controlled trials and the presentation of novel dyskinetic movements persisting in the “off-state” called for substantial revision of cell delivery strategies. The current thesis utilises positron emission tomography to examine the effects of hfVM implantation under the Transeuro protocol on dopaminergic ([18F]FDOPA, [11C]PE2I) and serotonergic ([11C]DASB) systems in patients with Parkinson’s disease and elucidate the neural underpinnings of its clinical impact. The main findings are; 1) implanted hfVM tissue led to increases in putamenal dopamine synthesis and storage capacity, dopamine and serotonin transporter density as compared to non-transplanted patients; 2) modification to surgical procedures provided inhomogenous and inconsistent re-innervation; 3) hfVM transplantation was associated with clinical improvements in measures of bradykinesia, rigidity and tremor; 4) graft-related changes in posterior putamenal dopamine and serotonin transporter density predicted symptomatic relief of bradykinesia and tremor; 5) heterogeneity of posterior putamenal re-innervation may impact upon potential clinical benefit; 6) graft-induced dyskinesia was associated with greater post-operative increases in dopamine transporter expression in the anterior putamen; 7) there was no evidence that graft-induced dyskinesia was related to serotonergic hyperinnervation. The novel findings presented in this thesis have major implications for cell-based restorative strategies beyond the hfVM era and will likely foster informed [re]consideration of many aspects of therapeutic delivery and trial design. For its ability to provide mechanistic insight in vivo, neuroimaging may continue to play a central role in the optimisation of future interventions.Open Acces