Cade, SC; (2015) Attenuation correction of myocardial perfusion scintigraphy images without transmission scanning. Doctoral thesis , UCL (University College London).

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Abstract

Attenuation correction is essential for reliable interpretation of emission tomography; however the use of transmission measurements to generate attenuation maps is limited by availability of equipment and potential mismatches between the transmission and emission measurements. This work investigates the possibility of estimating an attenuation map using measured scatter data without a transmission scan. A scatter model has been developed that predicts the distribution of photons which have been scattered once. The scatter model has been used as the basis of a maximum likelihood gradient ascent method (SMLGA) to estimate an attenuation map from measured scatter data. The SMLGA algorithm has been combined with an existing algorithm using photopeak data to estimate an attenuation map (MLAA) in order to obtain a more accurate attenuation map than using either algorithm alone. Iterations of the SMLGA-MLAA algorithm are alternated with iterations of the MLEM algorithm to estimate the activity distribution. Initial tests of the algorithm were performed in 2 dimensions using idealised data before extension to 3 dimensions. The basic algorithm has been tested in 3 dimensions using projection data simulated using a Monte Carlo simulator with software phantoms. All soft tissues within the body have similar attenuation characteristics and so only a small number of different values are normally present. A Level-Set technique to restrict the attenuation map to a piecewise constant function has therefore been investigated as a potential way to improve the quality of the reconstructed attenuation map. The basic SMLGA-MLAA algorithm contains a number of assumptions; the effect of these has been investigated and the model extended to include the effect of photons which are scattered more than once and scatter correction of the photopeak. The effect of different phantom shapes and activity distributions has been assessed and the final algorithm tested using data acquired using a physical phantom.

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