OBJECTIVES: A novel approach to the PET image reconstruction is presented, based on the inclusion of image deconvolution during conventional OSEM reconstruction. Deconvolution is here used to provide a recovered PET image to be included as "a priori" information to guide OSEM toward an improved solution. METHODS: Deconvolution was implemented using the Lucy-Richardson (LR) algorithm: Two different deconvolution schemes were tested, modifying the conventional OSEM iterative formulation: 1) We built a regularizing penalty function on the recovered PET image obtained by deconvolution and included it in the OSEM iteration. 2) After each conventional global OSEM iteration, we deconvolved the resulting PET image and used this "recovered" version as the initialization image for the next OSEM iteration. Tests were performed on both simulated and acquired data. RESULTS: Compared to the conventional OSEM, both these strategies, applied to simulated and acquired data, showed an improvement in image spatial resolution with better behavior in the second case. In this way, small lesions, present on data, could be better discriminated in terms of contrast. CONCLUSIONS: Application of this approach to both simulated and acquired data suggests its efficacy in obtaining PET images of enhanced quality.
OBJECTIVES: A novel approach to the PET image reconstruction is presented, based on the inclusion of image deconvolution during conventional OSEM reconstruction. Deconvolution is here used to provide a recovered PET image to be included as "a priori" information to guide OSEM toward an improved solution. METHODS: Deconvolution was implemented using the Lucy-Richardson (LR) algorithm: Two different deconvolution schemes were tested, modifying the conventional OSEM iterative formulation: 1) We built a regularizing penalty function on the recovered PET image obtained by deconvolution and included it in the OSEM iteration. 2) After each conventional global OSEM iteration, we deconvolved the resulting PET image and used this "recovered" version as the initialization image for the next OSEM iteration. Tests were performed on both simulated and acquired data. RESULTS: Compared to the conventional OSEM, both these strategies, applied to simulated and acquired data, showed an improvement in image spatial resolution with better behavior in the second case. In this way, small lesions, present on data, could be better discriminated in terms of contrast. CONCLUSIONS: Application of this approach to both simulated and acquired data suggests its efficacy in obtaining PET images of enhanced quality.
Authors: Benjamin A Thomas; Kjell Erlandsson; Marc Modat; Lennart Thurfjell; Rik Vandenberghe; Sebastien Ourselin; Brian F Hutton Journal: Eur J Nucl Med Mol Imaging Date: 2011-02-19 Impact factor: 9.236
Authors: Jiarui Yang; Chenhui Hu; Ning Guo; Joyita Dutta; Lucia M Vaina; Keith A Johnson; Jorge Sepulcre; Georges El Fakhri; Quanzheng Li Journal: Sci Rep Date: 2017-10-12 Impact factor: 4.379