Can a penalized-likelihood estimation algorithm be used to reduce the injected dose or the acquisition time in 68Ga-DOTATATE PET/CT studies?

Abstract Background: Image quality and quantitative accuracy of Positron Emission Tomography (PET) depend on several factors such as uptake time, scanner characteristics and image reconstruction methods. Ordered subset expectation maximization (OSEM) is considered the gold standard for image reconstruction. Penalized-likelihood estimation (PL) algorithms have been recently developed for PET reconstruction to improve quantitation accuracy while maintaining or even improving image quality. In PL algorithms a regularization parameter β controls the penalization of relative differences between neighboring pixels and determines image characteristics. In the present study, we aim to compare the performance of Q.Clear (PL algorithm, GE Healthcare) and OSEM (3 iterations, 8 subsets, 6 mm post-processing filter) for 68Ga-DOTATATE (68Ga-DOTA) PET studies, both visually and quantitatively.Thirty consecutive whole-body 68Ga-DOTA studies were included. The data were acquired in list mode and were reconstructed using 3D OSEM and Q.Clear with various values of β, and various acquisition times per bed position (bp), thus generating images with reduced injected dose (1.5 min/bp: β=300-1100; 1.0 min/bp: β=600-1400 and 0.5 min/bp: β=800-2200). An additional analysis adding β values up to 1500, 1700 and 3000 for 1.5, 1.0 and 0.5 min/bp, respectively, was performed for a random sample of 8 studies. Evaluation was performed using a phantom and clinical data. Two experienced nuclear medicine physicians blinded to the variables assessed the image quality visually.Results: Clinical images reconstructed with Q.Clear, set at 1.5, 1.0 min/bp and 0.5 min/bp using β = 1100, 1300, 3000 respectively, resulted in images with noise equivalence to 3D OSEM (1.5 min/bp) with a mean increase in SUVmax of 14%, 13% and 4%, an increase in SNR of 30%, 24% and 10%, and in SBR of 13%, 13% and 2%, respectively. Visual assessment yielded similar results for β values of 1300-1500 and 1500-1700 for 1.5 and 1.0 min/bp, respectively although for 0.5 min/bp there was no significant improvement compared to OSEM.Conclusion: 68Ga-DOTA reconstructions with Q.Clear, 1.5 and 1.0 min/bp resulted in increased tumor SUVmax and in improved SNR and SBR at a similar level of noise compared to 3D OSEM. Q.Clear with β =1500-1700 enables one-third reduction of acquisition time or injected dose, with similar image quality compared to 3D OSEM.

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Can a penalized-likelihood estimation algorithm be used to reduce the injected dose or the acquisition time in 68Ga-DOTATATE PET/CT studies?

10.21203/rs.3.rs-34588/v2 ◽

2020 ◽

Author(s):

Alexandre Chicheportiche ◽

Elinor Goshen ◽

Jeremy Godefroy ◽

Simona Grozinsky-Glasberg ◽

Kira Oleinikov ◽

...

Keyword(s):

Image Reconstruction ◽

Image Quality ◽

Regularization Parameter ◽

Penalized Likelihood ◽

Likelihood Estimation ◽

Whole Body ◽

Acquisition Time ◽

Clinical Images ◽

Penalized Likelihood Estimation ◽

Reconstruction Methods

Abstract Background: Image quality and quantitative accuracy of Positron Emission Tomography (PET) depend on several factors such as uptake time, scanner characteristics and image reconstruction methods. Ordered subset expectation maximization (OSEM) is considered the gold standard for image reconstruction. Penalized-likelihood estimation (PL) algorithms have been recently developed for PET reconstruction to improve quantitation accuracy while maintaining or even improving image quality. In PL algorithms a regularization parameter β controls the penalization of relative differences between neighboring pixels and determines image characteristics. In the present study, we aim to compare the performance of Q.Clear (PL algorithm, GE Healthcare) and OSEM (3 iterations, 8 subsets, 6 mm post-processing filter) for 68Ga-DOTATATE (68Ga-DOTA) PET studies, both visually and quantitatively.Thirty consecutive whole-body 68Ga-DOTA studies were included. The data were acquired in list mode and were reconstructed using 3D OSEM and Q.Clear with various values of β, and various acquisition times per bed position (bp), thus generating images with reduced injected dose (1.5 min/bp: β=300-1100; 1.0 min/bp: β=600-1400 and 0.5 min/bp: β=800-2200). An additional analysis adding β values up to 1500, 1700 and 300 for 1.5, 1.0 and 0.5 min/bp, respectively, was performed for a random sample of 8 studies. Evaluation was performed using a phantom and clinical data. Two experienced nuclear medicine physicians blinded to the variables assessed the image quality visually.Results: Clinical images reconstructed with Q.Clear, set at 1.5, 1.0 min/bp and 0.5 min/bp using β = 1100, 1300, 3000 respectively, resulted in images with noise equivalence to 3D OSEM (1.5 min/bp) with a mean increase in SUVmax of 14%, 13% and 4%, an increase in SNR of 30%, 24% and 10%, and in SBR of 13%, 13% and 2%, respectively. Visual assessment yielded similar results for β values of 1300-1500 and 1500-1700 for 1.5 and 1.0 min/bp, respectively although for 0.5 min/bp there was no significant improvement compared to OSEM. Conclusion: 68Ga-DOTA reconstructions with Q.Clear, 1.5 and 1.0 min/bp resulted in increased tumor SUVmax and in improved SNR and SBR at a similar level of noise compared to 3D OSEM. Q.Clear with β =1500-1700 enables one-third reduction of acquisition time or injected dose, with similar image quality compared to 3D OSEM.

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Can a penalized-likelihood estimation algorithm be used to reduce the injected dose or the acquisition time in 68Ga-DOTATATE PET/CT studies?

EJNMMI Physics ◽

10.1186/s40658-021-00359-6 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Alexandre Chicheportiche ◽

Elinor Goshen ◽

Jeremy Godefroy ◽

Simona Grozinsky-Glasberg ◽

Kira Oleinikov ◽

...

Keyword(s):

Image Reconstruction ◽

Image Quality ◽

Regularization Parameter ◽

Penalized Likelihood ◽

Likelihood Estimation ◽

Whole Body ◽

Acquisition Time ◽

Clinical Images ◽

Penalized Likelihood Estimation ◽

Reconstruction Methods

Abstract Background Image quality and quantitative accuracy of positron emission tomography (PET) depend on several factors such as uptake time, scanner characteristics and image reconstruction methods. Ordered subset expectation maximization (OSEM) is considered the gold standard for image reconstruction. Penalized-likelihood estimation (PL) algorithms have been recently developed for PET reconstruction to improve quantitation accuracy while maintaining or even improving image quality. In PL algorithms, a regularization parameter β controls the penalization of relative differences between neighboring pixels and determines image characteristics. In the present study, we aim to compare the performance of Q.Clear (PL algorithm, GE Healthcare) and OSEM (3 iterations, 8 subsets, 6-mm post-processing filter) for 68Ga-DOTATATE (68Ga-DOTA) PET studies, both visually and quantitatively. Thirty consecutive whole-body 68Ga-DOTA studies were included. The data were acquired in list mode and were reconstructed using 3D OSEM and Q.Clear with various values of β and various acquisition times per bed position (bp), thus generating images with reduced injected dose (1.5 min/bp: β = 300–1100; 1.0 min/bp: β = 600–1400 and 0.5 min/bp: β = 800–2200). An additional analysis adding β values up to 1500, 1700 and 3000 for 1.5, 1.0 and 0.5 min/bp, respectively, was performed for a random sample of 8 studies. Evaluation was performed using a phantom and clinical data. Two experienced nuclear medicine physicians blinded to the variables assessed the image quality visually. Results Clinical images reconstructed with Q.Clear, set at 1.5, 1.0 and 0.5 min/bp using β = 1100, 1300 and 3000, respectively, resulted in images with noise equivalence to 3D OSEM (1.5 min/bp) with a mean increase in SUVmax of 14%, 13% and 4%, an increase in SNR of 30%, 24% and 10%, and an increase in SBR of 13%, 13% and 2%. Visual assessment yielded similar results for β values of 1100–1400 and 1300–1600 for 1.5 and 1.0 min/bp, respectively, although for 0.5 min/bp there was no significant improvement compared to OSEM. Conclusion 68Ga-DOTA reconstructions with Q.Clear, 1.5 and 1.0 min/bp, resulted in increased tumor SUVmax and in improved SNR and SBR at a similar level of noise compared to 3D OSEM. Q.Clear with β = 1300–1600 enables one-third reduction of acquisition time or injected dose, with similar image quality compared to 3D OSEM.

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Can a penalized-likelihood estimation algorithm be used to reduce the injected dose or the acquisition time in 68Ga-DOTATATE PET/CT studies?

10.21203/rs.3.rs-34588/v4 ◽

2021 ◽

Author(s):

Alexandre Chicheportiche ◽

Elinor Goshen ◽

Jeremy Godefroy ◽

Simona Grozinsky-Glasberg ◽

Kira Oleinikov ◽

...

Keyword(s):

Image Reconstruction ◽

Image Quality ◽

Regularization Parameter ◽

Penalized Likelihood ◽

Likelihood Estimation ◽

Whole Body ◽

Acquisition Time ◽

Clinical Images ◽

Penalized Likelihood Estimation ◽

Reconstruction Methods

Abstract Background: Image quality and quantitative accuracy of Positron Emission Tomography (PET) depend on several factors such as uptake time, scanner characteristics and image reconstruction methods. Ordered subset expectation maximization (OSEM) is considered the gold standard for image reconstruction. Penalized-likelihood estimation (PL) algorithms have been recently developed for PET reconstruction to improve quantitation accuracy while maintaining or even improving image quality. In PL algorithms a regularization parameter β controls the penalization of relative differences between neighboring pixels and determines image characteristics. In the present study, we aim to compare the performance of Q.Clear (PL algorithm, GE Healthcare) and OSEM (3 iterations, 8 subsets, 6 mm post-processing filter) for 68Ga-DOTATATE (68Ga-DOTA) PET studies, both visually and quantitatively.Thirty consecutive whole-body 68Ga-DOTA studies were included. The data were acquired in list mode and were reconstructed using 3D OSEM and Q.Clear with various values of β, and various acquisition times per bed position (bp), thus generating images with reduced injected dose (1.5 min/bp: β=300-1100; 1.0 min/bp: β=600-1400 and 0.5 min/bp: β=800-2200). An additional analysis adding β values up to 1500, 1700 and 3000 for 1.5, 1.0 and 0.5 min/bp, respectively, was performed for a random sample of 8 studies. Evaluation was performed using a phantom and clinical data. Two experienced nuclear medicine physicians blinded to the variables assessed the image quality visually.Results: Clinical images reconstructed with Q.Clear, set at 1.5, 1.0 min/bp and 0.5 min/bp using β = 1100, 1300, 3000 respectively, resulted in images with noise equivalence to 3D OSEM (1.5 min/bp) with a mean increase in SUVmax of 14%, 13% and 4%, an increase in SNR of 30%, 24% and 10%, and in SBR of 13%, 13% and 2%, respectively. Visual assessment yielded similar results for β values of 1100-1400 and 1300-1600 for 1.5 and 1.0 min/bp, respectively although for 0.5 min/bp there was no significant improvement compared to OSEM.Conclusion: 68Ga-DOTA reconstructions with Q.Clear, 1.5 and 1.0 min/bp resulted in increased tumor SUVmax and in improved SNR and SBR at a similar level of noise compared to 3D OSEM. Q.Clear with β =1300-1600 enables one-third reduction of acquisition time or injected dose, with similar image quality compared to 3D OSEM.

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Can a penalized-likelihood estimation algorithm be used to reduce the injected dose or the acquisition time in 68Ga-DOTATATE PET/CT studies?

10.21203/rs.3.rs-34588/v1 ◽

2020 ◽

Author(s):

Alexandre Chicheportiche ◽

Elinor Goshen ◽

Jeremy Godefroy ◽

Simona Grozinsky-Glasberg ◽

Kira Oleinikov ◽

...

Keyword(s):

Image Reconstruction ◽

Image Quality ◽

Regularization Parameter ◽

Penalized Likelihood ◽

Likelihood Estimation ◽

Whole Body ◽

Acquisition Time ◽

Clinical Images ◽

Penalized Likelihood Estimation ◽

Reconstruction Methods

Abstract Background: Both image quality and quantitative accuracy of PET depend on several factors such as uptake time, scanner characteristics and image reconstruction methods. Ordered subset expectation maximization (OSEM) is considered today the gold standard for image reconstruction. Penalized-likelihood estimation (PL) algorithms have been recently developed for PET reconstruction to improve quantitation accuracy while maintaining or even improving image quality. In the present study, we aim to compare the performance of a PL algorithm (Q.Clear, GE Healthcare) and 3D OSEM for 68Ga-DOTATATE (68Ga-DOTA) PET studies, both visually and quantitatively. Thirty consecutive whole-body 68Ga-DOTA studies were included. The data were acquired in list mode and reconstructed using 3D OSEM and Q.Clear with various values of the regularization parameter β, and various acquisition times per bed position (bp), thus generating images with reduced injected dose (1.5 min/bp: β=300-1100; 1.0 min/bp: β=600-1300 and 0.5 min/bp: β=800-2200). Evaluation was performed using a phantom and clinical data. Finally, two experienced nuclear medicine physicians blinded to the variables assessed the image quality visually. Results: Clinical images reconstructed with Q.Clear , set at 1.5 and 1.0 min/bp using β = 1100 and 1300 respectively, resulted in images with noise equivalence to 3D OSEM (1.5 min/bp) with a mean increase in SUVmax of 14 % and 11%, in SNR of 18% and 10%, and in SBR of 14% and 12%, respectively. Reconstruction using 0.5 min/bp and β = 2200 resulted in SUVmax, SNR and SBR with a relative difference < 1%. Visual assessment yielded similar results with mean scores for Q.Clear (1.5, 1.0 and 0.5 min/bp) vs 3D OSEM (1.5 min/bp) of 3.58 vs 3.38, 3.64 vs 3.47 and 3.60 vs 3.61, respectively. Conclusion: 68Ga-DOTA reconstructions with Q.Clear, 1.5 and 1.0 min/bp resulted in increased tumor SUVmax and in improved SNR and SBR at a similar level of noise compared to 3D OSEM. Q.Clear with β =1300 enabled a one-third reduction of acquisition time or injected dose, with similar image quality compared to 3D OSEM.

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Task-based detectability in CT image reconstruction by filtered backprojection and penalized likelihood estimation

Medical Physics ◽

10.1118/1.4883816 ◽

2014 ◽

Vol 41 (8Part1) ◽

pp. 081902 ◽

Cited By ~ 48

Author(s):

Grace J. Gang ◽

J. Webster Stayman ◽

Wojciech Zbijewski ◽

Jeffrey H. Siewerdsen

Keyword(s):

Image Reconstruction ◽

Penalized Likelihood ◽

Likelihood Estimation ◽

Ct Image ◽

Filtered Backprojection ◽

Penalized Likelihood Estimation ◽

Ct Image Reconstruction

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Efficiency for Regularization Parameter Selection in Penalized Likelihood Estimation of Misspecified Models

Journal of the American Statistical Association ◽

10.1080/01621459.2013.801775 ◽

2013 ◽

Vol 108 (503) ◽

pp. 1031-1043 ◽

Cited By ~ 20

Author(s):

Cheryl J. Flynn ◽

Clifford M. Hurvich ◽

Jeffrey S. Simonoff

Keyword(s):

Regularization Parameter ◽

Penalized Likelihood ◽

Likelihood Estimation ◽

Parameter Selection ◽

Misspecified Models ◽

Penalized Likelihood Estimation ◽

Regularization Parameter Selection

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Improved image reconstruction of 89Zr-immunoPET studies using a Bayesian penalized likelihood reconstruction algorithm

EJNMMI Physics ◽

10.1186/s40658-021-00352-z ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Julian Kirchner ◽

Joseph A. O’Donoghue ◽

Anton S. Becker ◽

Gary A. Ulaner

Keyword(s):

Image Reconstruction ◽

Image Quality ◽

Signal To Noise Ratio ◽

Penalized Likelihood ◽

Reconstruction Algorithm ◽

Normal Liver ◽

Whole Body ◽

List Mode ◽

Improve Image Quality ◽

Administered Activity

Abstract Purpose The aim of this study was to evaluate the use of a Bayesian penalized likelihood reconstruction algorithm (Q.Clear) for 89Zr-immunoPET image reconstruction and its potential to improve image quality and reduce the administered activity of 89Zr-immunoPET tracers. Methods Eight 89Zr-immunoPET whole-body PET/CT scans from three 89Zr-immunoPET clinical trials were selected for analysis. On average, patients were imaged 6.3 days (range 5.0–8.0 days) after administration of 69 MBq (range 65–76 MBq) of [89Zr]Zr-DFO-daratumumab, [89Zr]Zr-DFO-pertuzumab, or [89Zr]Zr-DFO-trastuzumab. List-mode PET data was retrospectively reconstructed using Q.Clear with incremental β-values from 150 to 7200, as well as standard ordered-subset expectation maximization (OSEM) reconstruction (2-iterations, 16-subsets, a 6.4-mm Gaussian transaxial filter, “heavy” z-axis filtering and all manufacturers’ corrections active). Reduced activities were simulated by discarding 50% and 75% of original counts in each list mode stream. All reconstructed PET images were scored for image quality and lesion detectability using a 5-point scale. SUVmax for normal liver and sites of disease and liver signal-to-noise ratio were measured. Results Q.Clear reconstructions with β = 3600 provided the highest scores for image quality. Images reconstructed with β-values of 3600 or 5200 using only 50% or 25% of the original counts provided comparable or better image quality scores than standard OSEM reconstruction images using 100% of counts. Conclusion The Bayesian penalized likelihood reconstruction algorithm Q.Clear improved the quality of 89Zr-immunoPET images. This could be used in future studies to improve image quality and/or decrease the administered activity of 89Zr-immunoPET tracers.

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