Accurate 2D Reconstruction for PET Scanners based on the Analytical White Image Model

Fuente: arXiv
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Main Authors: Matulić, Tomislav, Seršić, Damir
Format: Preprint
Published: 2023
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author Matulić, Tomislav
Seršić, Damir
author_facet Matulić, Tomislav
Seršić, Damir
contents In this paper, we provide a precise mathematical model of crystal-to-crystal response which is used to generate the white image - a necessary compensation model needed to overcome the physical limitations of the PET scanner. We present a closed-form solution, as well as several accurate approximations, due to the complexity of the exact mathematical expressions. We prove, experimentally and analytically, that the difference between the best approximations and real crystal-to-crystal response is insignificant. The obtained responses are used to generate the white image compensation model. It can be written as a single closed-form expression making it easy to implement in known reconstruction methods. The maximum likelihood expectation maximization (MLEM) algorithm is modified and our white image model is integrated into it. The modified MLEM algorithm is not based on the system matrix, rather it is based on ray-driven projections and back-projections. The compensation model provides all necessary information about the system. Finally, we check our approach on synthetic and real data. For the real-world acquisition, we use the Raytest ClearPET camera for small animals and the NEMA NU 4-2008 phantom. The proposed approach overperforms competitive, non-compensated reconstruction methods.
format Preprint
id arxiv_https___arxiv_org_abs_2306_17652
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Accurate 2D Reconstruction for PET Scanners based on the Analytical White Image Model
Matulić, Tomislav
Seršić, Damir
Signal Processing
Machine Learning
In this paper, we provide a precise mathematical model of crystal-to-crystal response which is used to generate the white image - a necessary compensation model needed to overcome the physical limitations of the PET scanner. We present a closed-form solution, as well as several accurate approximations, due to the complexity of the exact mathematical expressions. We prove, experimentally and analytically, that the difference between the best approximations and real crystal-to-crystal response is insignificant. The obtained responses are used to generate the white image compensation model. It can be written as a single closed-form expression making it easy to implement in known reconstruction methods. The maximum likelihood expectation maximization (MLEM) algorithm is modified and our white image model is integrated into it. The modified MLEM algorithm is not based on the system matrix, rather it is based on ray-driven projections and back-projections. The compensation model provides all necessary information about the system. Finally, we check our approach on synthetic and real data. For the real-world acquisition, we use the Raytest ClearPET camera for small animals and the NEMA NU 4-2008 phantom. The proposed approach overperforms competitive, non-compensated reconstruction methods.
title Accurate 2D Reconstruction for PET Scanners based on the Analytical White Image Model
topic Signal Processing
Machine Learning
url https://arxiv.org/abs/2306.17652