Iterative reconstruction of simulated low count data: a comparison of post-filtering versus regularised OSEM

[摘要] Low count PET data is a challenge for medical image reconstruction. The statistics of a dataset is a key factor of the quality of the reconstructed images. Reconstruction algorithms which would be able to compensate for low count datasets could provide the means to reduce the patient injected doses and/or reduce the scan times. It has been shown that the use of priors improve the image quality in low count conditions. In this study we compared regularised versus post-filtered OSEM for their performance on challenging simulated low count datasets. Initial visual comparison demonstrated that both algorithms improve the image quality, although the use of regularization does not introduce the undesired blurring as post-filtering.

[发布机构] Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St. Thomas' Hospital, London; SE1 7EH, United Kingdom^1;Department of Biomedical Engineering, Technological Educational Institute of Athens, Ag. Spyridonos 28, Egaleo, Athens; 12210, Greece^2;Division of Biomedical Imaging, Faculty of Medicine and Health, 8.001a, Worsley Building Clarendon way, Leeds; LS2 9NL, United Kingdom^3

[效力级别] 医药卫生 [学科分类] 卫生学

[关键词] Count datum;Iterative reconstruction;Key factors;Post-filtering;Reconstructed image;Reconstruction algorithms;Scan time;Visual comparison