TY - JOUR
T1 - Synthesized Image Reconstruction for Post-Reconstruction Resolution Recovery
AU - Vass, Laurence
AU - Reader, Andrew
N1 - Funding Information:
This work was supported in part by the Centre of Excellence in Medical Engineering Funded by the Wellcome Trust and the Engineering and Physical Sciences Research Council (EPSRC) under GrantWT 203148/Z/16/Z; in part by the National Institute for Health Research Biomedical Research Centre Based at Guy's and St. Thomas' NHS Foundation Trust and King's College London (KCL); and in part by the EPSRC Programme under Grant EP/S032789/1 "MITHRAS."
Publisher Copyright:
© 2017 IEEE.
PY - 2023/2/22
Y1 - 2023/2/22
N2 - Resolution recovery (RR) techniques in positron emission tomography (PET) imaging aim to mitigate spatial resolution losses and related inaccuracies in quantification by using a model of the system's point spread function (PSF) during reconstruction or post-processing. However, including PSF modeling in fully 3-D image reconstruction is far from trivial as access to the scanner-specific forward and back-projectors is required, along with access to the 3-D sinogram data. Hence, post-reconstruction RR methods, such as the Richardson-Lucy (RL) algorithm, can be more practical. However, the RL method leads to relatively rapid noise amplification in early image iterations, giving inferior image quality compared to iterates obtained by placing the PSF model in the reconstruction algorithm. We propose a post-reconstruction RR method by synthesizing PET data by a forward projection of an initial real data reconstruction (such reconstructions are usually available via a scanner's standard reconstruction software). The synthetic PET data are then used to reconstruct an image, but crucially now including a modeled PSF within the system model used during reconstruction. Results from simulations and real data demonstrate the proposed method improves image quality compared to the RL algorithm, whilst avoiding the need for scanner-specific projectors and raw sinogram data (as required by standard PSF modeling within reconstruction).
AB - Resolution recovery (RR) techniques in positron emission tomography (PET) imaging aim to mitigate spatial resolution losses and related inaccuracies in quantification by using a model of the system's point spread function (PSF) during reconstruction or post-processing. However, including PSF modeling in fully 3-D image reconstruction is far from trivial as access to the scanner-specific forward and back-projectors is required, along with access to the 3-D sinogram data. Hence, post-reconstruction RR methods, such as the Richardson-Lucy (RL) algorithm, can be more practical. However, the RL method leads to relatively rapid noise amplification in early image iterations, giving inferior image quality compared to iterates obtained by placing the PSF model in the reconstruction algorithm. We propose a post-reconstruction RR method by synthesizing PET data by a forward projection of an initial real data reconstruction (such reconstructions are usually available via a scanner's standard reconstruction software). The synthetic PET data are then used to reconstruct an image, but crucially now including a modeled PSF within the system model used during reconstruction. Results from simulations and real data demonstrate the proposed method improves image quality compared to the RL algorithm, whilst avoiding the need for scanner-specific projectors and raw sinogram data (as required by standard PSF modeling within reconstruction).
UR - http://www.scopus.com/inward/record.url?scp=85149398985&partnerID=8YFLogxK
U2 - 10.1109/TRPMS.2023.3247489
DO - 10.1109/TRPMS.2023.3247489
M3 - Article
SN - 2469-7303
VL - 7
SP - 473
EP - 482
JO - Transactions on Radiation and Plasma Medical Sciences
JF - Transactions on Radiation and Plasma Medical Sciences
IS - 5
ER -