Validation of a computational chain from PET Monte Carlo simulations to reconstructed images
(2022) In Heliyon 8(4).- Abstract
The study aimed to create a pipeline from Monte Carlo simulated projections of a Gate PET system to reconstructed images. The PET system was modelled after the GE Discovery MI (DMI) PET/CT, and the simulated projections were reconstructed with the stand-alone reconstruction software CASToR. Attenuation correction, normalisation calibration, random estimation, and scatter estimation for the simulations were computed with in-house programs. The pipeline was compared in both projection and image space with data acquired on a clinical DMI and reconstructed with GE's off-line PET reconstruction software (PET Toolbox) and CASToR. The simulated and measured data were compared for the number of prompt coincidences, scatter fraction, contrast... (More)
The study aimed to create a pipeline from Monte Carlo simulated projections of a Gate PET system to reconstructed images. The PET system was modelled after the GE Discovery MI (DMI) PET/CT, and the simulated projections were reconstructed with the stand-alone reconstruction software CASToR. Attenuation correction, normalisation calibration, random estimation, and scatter estimation for the simulations were computed with in-house programs. The pipeline was compared in both projection and image space with data acquired on a clinical DMI and reconstructed with GE's off-line PET reconstruction software (PET Toolbox) and CASToR. The simulated and measured data were compared for the number of prompt coincidences, scatter fraction, contrast recovery coefficient (CRC), signal-to-noise ratio (SNR), background variability, residual lung error, and image profiles. A slight discrepancy was noted in the projection space, but good agreements were generally achieved in image space between simulated and measured data. The CRC was found to be 81 % for Gate – CASToR, 84 % for GE – CASToR, and 84 % for GE - PET Toolbox for the largest sphere of the NEMA image quality (IQ) phantom, and the SNR was found to be 98 for Gate – CASToR, 91 for GE – CASToR, and 93 for GE – PET Toolbox. Profiles drawn over the spheres for the NEMA IQ phantom and the Data Spectrum (DS) phantom show a good match between measurement and simulation. The results indicate feasibility to utilise the pipeline as a tool for off-line simulation-based studies. A complete pipeline introduces possibilities to study the impact of single parameters in the whole chain from simulation to reconstructed images.
(Less)
- author
- Kalaitzidis, Philip LU ; Gustafsson, Johan LU ; Hindorf, Cecilia LU and Ljungberg, Michael LU
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- CASToR, Gate, Monte Carlo simulation, Positron emission tomography, Tomographic reconstruction
- in
- Heliyon
- volume
- 8
- issue
- 4
- article number
- e09316
- publisher
- Elsevier
- external identifiers
-
- scopus:85129521352
- pmid:35520630
- ISSN
- 2405-8440
- DOI
- 10.1016/j.heliyon.2022.e09316
- language
- English
- LU publication?
- yes
- id
- bc830c7e-8455-468e-8ae4-89d5e5819499
- date added to LUP
- 2022-07-07 14:11:36
- date last changed
- 2024-06-27 15:32:09
@article{bc830c7e-8455-468e-8ae4-89d5e5819499,
abstract = {{<p>The study aimed to create a pipeline from Monte Carlo simulated projections of a Gate PET system to reconstructed images. The PET system was modelled after the GE Discovery MI (DMI) PET/CT, and the simulated projections were reconstructed with the stand-alone reconstruction software CASToR. Attenuation correction, normalisation calibration, random estimation, and scatter estimation for the simulations were computed with in-house programs. The pipeline was compared in both projection and image space with data acquired on a clinical DMI and reconstructed with GE's off-line PET reconstruction software (PET Toolbox) and CASToR. The simulated and measured data were compared for the number of prompt coincidences, scatter fraction, contrast recovery coefficient (CRC), signal-to-noise ratio (SNR), background variability, residual lung error, and image profiles. A slight discrepancy was noted in the projection space, but good agreements were generally achieved in image space between simulated and measured data. The CRC was found to be 81 % for Gate – CASToR, 84 % for GE – CASToR, and 84 % for GE - PET Toolbox for the largest sphere of the NEMA image quality (IQ) phantom, and the SNR was found to be 98 for Gate – CASToR, 91 for GE – CASToR, and 93 for GE – PET Toolbox. Profiles drawn over the spheres for the NEMA IQ phantom and the Data Spectrum (DS) phantom show a good match between measurement and simulation. The results indicate feasibility to utilise the pipeline as a tool for off-line simulation-based studies. A complete pipeline introduces possibilities to study the impact of single parameters in the whole chain from simulation to reconstructed images.</p>}},
author = {{Kalaitzidis, Philip and Gustafsson, Johan and Hindorf, Cecilia and Ljungberg, Michael}},
issn = {{2405-8440}},
keywords = {{CASToR; Gate; Monte Carlo simulation; Positron emission tomography; Tomographic reconstruction}},
language = {{eng}},
number = {{4}},
publisher = {{Elsevier}},
series = {{Heliyon}},
title = {{Validation of a computational chain from PET Monte Carlo simulations to reconstructed images}},
url = {{http://dx.doi.org/10.1016/j.heliyon.2022.e09316}},
doi = {{10.1016/j.heliyon.2022.e09316}},
volume = {{8}},
year = {{2022}},
}