Academic literature on the topic 'Positron emission tomography'

Acquisition and reconstruction of data in three-dimensional positron emission tomography (3D PET) was introduced in 1990 almost 20 years after the first PET scanners were developed. 3D PET offers a significant sensitivity improvement over conventional, sliceoriented 2D PET, but at the cost of a three-fold increase in acceptance of scattered events. In addition, processing time is increased and new methods for applying corrections such as for photon attenuation, calibration, and detector/geometry normalisation are required. 3D PET raised concerns that the high quantitative accuracy that was possible with 2D PET (with its moderate sensitivity) would not be matched in 3D, primarily because of the greatly increased scattered photon component in the measured data. The aim of this thesis was to develop methods that enable quantitatively accurate measurements with 3D PET. A technique to correct for scattered photons prior to reconstruction has been developed, implemented and assessed. A device for normalising the data for detector efficiency and the geometry of the cylindrical detector system has been developed, and the factors affecting reconstruction investigated. A new approach to calibration of the reconstructed data to produce images of activity concentration which is independent of scatter has been implemented. Finally, the techniques have been applied to data from brain scans of human subjects. Evaluation of images reconstructed from 3D PET demonstrates that the methodology developed in this work produces data accurate to within 10% of the true activity concentration in an object with reasonably homogeneous density. 3D PET is shown to be as accurate as 2D PET, but with a sensitivity advantage that improves signal-to-noise by approximately a factor of three in the human brain and slightly less in other regions of the body.

Image noise in Positron Emission Tomography (PET) is the result of statistical fluctuation in projection data. The variance properties of images obtained with the UBC/TRIUMF PETT VI tomograph are studied by analytical methods, computer simulations, and phantom experiments. The PETT VI image reconstruction algorithm is described and analyzed for noise propagation properties. Procedures for estimating both point-wise (pixel) and region of interest (ROI) variances are developed: these include the effects of corrections for non-uniform sampling, detector efficiency variation, object self-attenuation and random coincidences. The analytical expression for image-plane variance is used in computer simulations to isolate the effects of the various data corrections: It is shown that the image precision is degraded due to non-uniform sampling of the projections. The RMS noise is found to be increased by 9% due to the wobble motion employed in PETT VI. Analytical predictions for both pixel and ROI variances are verified with phantom experiments. The average error between measured and predicted ROI variances due to noise in emission data for a set of seven regions placed on a 20 cm cylindrical phantom is 9.5%. Images showing variance distributions due to noise in emission data and due to noise in transmission data are produced from human subject brain scan data collected by the UBC/TRIUMF PET group. The maximum ratio of image variance due to noise in transmission data to that due to noise in emission data is calculated as 2.6 for a typical FDG study, and 0.082 for a typical fluorodopa study. Total RMS noise varies between 0.4% and 11.6% for a typical set of ROI's placed on mid-brain slices reconstructed from these data sets. Procedures are suggested for improving the statistical accuracy of quantitative PET measurements.<br>Applied Science, Faculty of<br>Electrical and Computer Engineering, Department of<br>Graduate

Alzheimer's Disease (AD) is a major concern for the elderly population, currently affecting over 670,000 people in the UK. With the continual increase in the age of the population the problem is expected to rise. There is no known cure to the condition and a definite diagnosis cannot be made in life. Clinical diagnosis is considered to be approximately 80% - 90% accurate, sometimes taking up to a year to assess. Early detection could aid in the care and possible development of better treatments or even a cure. AD has been shown to alter the structure and global texture of the brain. Studies using Magnetic Resonance imaging (MRI) and Computerised Tomography (CT) have been used to detect these changes with some success by some researchers. Positron Emission Tomography (PET) imaging is a functional imaging modality and in theory before structural changes are evident functional changes should be apparent. Therefore we utilise PET images for this study. This thesis will exploit the fact that AD alters the global texture of the brain. Texture features extracted from fluoro-deoxy-glucose (FDG) PET images and sinograms of the brain will be used. Most texture feature extraction methods fail, due to poor signal to noise ratio so we will use a novel texture feature extraction method known as the Trace transform - triple features, which can extract features directly from raw data acquired by PET scanners. Classifiers will be used to aid in the separation of the two groups, namely AD patients and normal controls. The Trace transform - triple feature method has proven its potential as a good feature extraction technique. It enabled us to achieve classification accuracy of up to 93% on raw sinogram data using a combination of five features. This result is very good compared with the clinical accuracy of 80% reported by most researcher. It is comparable to results obtained by Kippenhan et al [52, 53, 51, 50], who used regional metabolic activity using PET and a neural network classifier. Monomial features extracted from images achieved accuracies as high as 87%. These features are good discriminators, however, they suffer from lack of scaling invariance. This is problematic as brain sizes do vary considerably. The use of registration and extraction of regional information failed to produce fruitful results. This is principally due to poor registration. The registration failed primarily because a very small cross section of the brain was available. Also the effect of AD alters the structure of the brain. Since the registration relies on matching structure, it becomes questionable whether one can actually register automatically a very degraded AD brain. Gender and age are crucial to the progress of Alzheimer's disease. Age and gender matching is not sufficient to get the best results. This thesis has shown that performance gains of up to 11% can be attained by simply incorporating age and/or gender into the classification model. However, the maximum classification accuracy was not improved any further.