Penalized Likelihood Estimation of Gamma Distributed Response Variable via Corrected Solution of Regression Coefficients

A Gamma distributed response is subjected to regression penalized likelihood estimations of Least Absolute Shrinkage and Selection Operator (LASSO) and Minimax Concave Penalty via Generalized Linear Models (GLMs). The Gamma related disturbance controls the influence of skewness and spread in the corrected path solutions of the regression coefficients.

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

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.

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

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.

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

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.

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

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.

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

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.