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25 March 2016 Initial experience in primal-dual optimization reconstruction from sparse-PET patient data
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There exists interest in designing a PET system with reduced detectors due to cost concerns, while not significantly compromising the PET utility. Recently developed optimization-based algorithms, which have demonstrated the potential clinical utility in image reconstruction from sparse CT data, may be used for enabling such design of innovative PET systems. In this work, we investigate a PET configuration with reduced number of detectors, and carry out preliminary studies from patient data collected by use of such sparse-PET configuration. We consider an optimization problem combining Kullback-Leibler (KL) data fidelity with an image TV constraint, and solve it by using a primal-dual optimization algorithm developed by Chambolle and Pock. Results show that advanced algorithms may enable the design of innovative PET configurations with reduced number of detectors, while yielding potential practical PET utilities.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zheng Zhang, Jinghan Ye, Buxin Chen, Amy E. Perkins, Sean Rose, Emil Y. Sidky, Chien-Min Kao, Dan Xia, Chi-Hua Tung, and Xiaochuan Pan "Initial experience in primal-dual optimization reconstruction from sparse-PET patient data", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97831K (25 March 2016);

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