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Noise reduction using a Bayesian penalized-likelihood reconstruction algorithm on a time-of-flight PET-CT scanner

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Abstract
Purpose: Q.Clear is a block sequential regularized expectation maximization (BSREM) penalized-likelihood reconstruction algorithm for PET. It tries to improve image quality by controlling noise amplification during image reconstruction. In this study, the noise properties of this BSREM were compared to the ordered-subset expectation maximization (OSEM) algorithm for both phantom and patient data acquired on a state-of-the-art PET/CT. Methods: The NEMA IQ phantom and a whole-body patient study were acquired on a GE DMI 3-rings system in list mode and different datasets with varying noise levels were generated. Phantom data was evaluated using four different contrast ratios. These were reconstructed using BSREM with different beta-factors of 300-3000 and with a clinical setting used for OSEM including point spread function (PSF) and time-of-flight (TOF) information. Contrast recovery (CR), background noise levels (coefficient of variation, COV), and contrast-to-noise ratio (CNR) were used to determine the performance in the phantom data. Findings based on the phantom data were compared with clinical data. For the patient study, the SUV ratio, metabolic active tumor volumes (MATVs), and the signal-to-noise ratio (SNR) were evaluated using the liver as the background region. Results: Based on the phantom data for the same count statistics, BSREM resulted in higher CR and CNR and lower COV than OSEM. The CR of OSEM matches to the CR of BSREM with beta = 750 at high count statistics for 8:1. A similar trend was observed for the ratios 6:1 and 4:1. A dependence on sphere size, counting statistics, and contrast ratio was confirmed by the CNR of the ratio 2:1. BSREM with beta = 750 for 2.5 and 1.0 min acquisition has comparable COV to the 10 and 5.0 min acquisitions using OSEM. This resulted in a noise reduction by a factor of 2-4 when using BSREM instead of OSEM. For the patient data, a similar trend was observed, and SNR was reduced by at least a factor of 2 while preserving contrast. Conclusion: The BSREM reconstruction algorithm allowed a noise reduction without a loss of contrast by a factor of 2-4 compared to OSEM reconstructions for all data evaluated. This reduction can be used to lower the injected dose or shorten the acquisition time.
Keywords
Penalized-likelihood reconstruction, BSREM, Q.Clear, OSEM, PET, IMAGE-RECONSTRUCTION, ORDERED SUBSETS

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MLA
De Vasconcelos Caribé, Paulo Rauli Rafeson, et al. “Noise Reduction Using a Bayesian Penalized-Likelihood Reconstruction Algorithm on a Time-of-Flight PET-CT Scanner.” EJNMMI PHYSICS, vol. 6, 2019.
APA
De Vasconcelos Caribé, P. R. R., Koole, M., D’Asseler, Y., Van den Broeck, B., & Vandenberghe, S. (2019). Noise reduction using a Bayesian penalized-likelihood reconstruction algorithm on a time-of-flight PET-CT scanner. EJNMMI PHYSICS, 6.
Chicago author-date
De Vasconcelos Caribé, Paulo Rauli Rafeson, M Koole, Yves D’Asseler, Bliede Van den Broeck, and Stefaan Vandenberghe. 2019. “Noise Reduction Using a Bayesian Penalized-Likelihood Reconstruction Algorithm on a Time-of-Flight PET-CT Scanner.” EJNMMI PHYSICS 6.
Chicago author-date (all authors)
De Vasconcelos Caribé, Paulo Rauli Rafeson, M Koole, Yves D’Asseler, Bliede Van den Broeck, and Stefaan Vandenberghe. 2019. “Noise Reduction Using a Bayesian Penalized-Likelihood Reconstruction Algorithm on a Time-of-Flight PET-CT Scanner.” EJNMMI PHYSICS 6.
Vancouver
1.
De Vasconcelos Caribé PRR, Koole M, D’Asseler Y, Van den Broeck B, Vandenberghe S. Noise reduction using a Bayesian penalized-likelihood reconstruction algorithm on a time-of-flight PET-CT scanner. EJNMMI PHYSICS. 2019;6.
IEEE
[1]
P. R. R. De Vasconcelos Caribé, M. Koole, Y. D’Asseler, B. Van den Broeck, and S. Vandenberghe, “Noise reduction using a Bayesian penalized-likelihood reconstruction algorithm on a time-of-flight PET-CT scanner,” EJNMMI PHYSICS, vol. 6, 2019.
@article{8639480,
  abstract     = {Purpose: Q.Clear is a block sequential regularized expectation maximization (BSREM) penalized-likelihood reconstruction algorithm for PET. It tries to improve image quality by controlling noise amplification during image reconstruction. In this study, the noise properties of this BSREM were compared to the ordered-subset expectation maximization (OSEM) algorithm for both phantom and patient data acquired on a state-of-the-art PET/CT. 
Methods: The NEMA IQ phantom and a whole-body patient study were acquired on a GE DMI 3-rings system in list mode and different datasets with varying noise levels were generated. Phantom data was evaluated using four different contrast ratios. These were reconstructed using BSREM with different beta-factors of 300-3000 and with a clinical setting used for OSEM including point spread function (PSF) and time-of-flight (TOF) information. Contrast recovery (CR), background noise levels (coefficient of variation, COV), and contrast-to-noise ratio (CNR) were used to determine the performance in the phantom data. Findings based on the phantom data were compared with clinical data. For the patient study, the SUV ratio, metabolic active tumor volumes (MATVs), and the signal-to-noise ratio (SNR) were evaluated using the liver as the background region. 
Results: Based on the phantom data for the same count statistics, BSREM resulted in higher CR and CNR and lower COV than OSEM. The CR of OSEM matches to the CR of BSREM with beta = 750 at high count statistics for 8:1. A similar trend was observed for the ratios 6:1 and 4:1. A dependence on sphere size, counting statistics, and contrast ratio was confirmed by the CNR of the ratio 2:1. BSREM with beta = 750 for 2.5 and 1.0 min acquisition has comparable COV to the 10 and 5.0 min acquisitions using OSEM. This resulted in a noise reduction by a factor of 2-4 when using BSREM instead of OSEM. For the patient data, a similar trend was observed, and SNR was reduced by at least a factor of 2 while preserving contrast. 
Conclusion: The BSREM reconstruction algorithm allowed a noise reduction without a loss of contrast by a factor of 2-4 compared to OSEM reconstructions for all data evaluated. This reduction can be used to lower the injected dose or shorten the acquisition time.},
  articleno    = {22},
  author       = {De Vasconcelos Caribé, Paulo Rauli Rafeson and Koole, M and D'Asseler, Yves and Van den Broeck, Bliede and Vandenberghe, Stefaan},
  issn         = {2197-7364},
  journal      = {EJNMMI PHYSICS},
  keywords     = {Penalized-likelihood reconstruction,BSREM,Q.Clear,OSEM,PET,IMAGE-RECONSTRUCTION,ORDERED SUBSETS},
  language     = {eng},
  pages        = {14},
  title        = {Noise reduction using a Bayesian penalized-likelihood reconstruction algorithm on a time-of-flight PET-CT scanner},
  url          = {http://dx.doi.org/10.1186/s40658-019-0264-9},
  volume       = {6},
  year         = {2019},
}

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