Evaluating image reconstruction methods in improving effective parameters on image quality in IRI-microPET

2014 ◽  
Vol 57 (2) ◽  
pp. 218-221 ◽  
Author(s):  
S. Z. Islami rad ◽  
M. Shamsaei Zafarghandi ◽  
R. Gholipour Peyvandi ◽  
M. Ghannadi Maragheh
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Effective Parameters ◽  
Reconstruction Methods

Evaluating Image Reconstruction Methods in Improving Effective Parameters on Image Quality in IRI-micro PET

2014 ◽  
Vol 2014 (2) ◽  
pp. 131-134
Author(s):  
S. Z. Islami rad ◽  
M. Shamsaei Zafarghandi ◽  
R. Gholipour Peyvandi ◽  
M. Ghannadi Maragheh
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Effective Parameters ◽  
Reconstruction Methods

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

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.

scholarly journals Image Quality Evaluation of New Image Reconstruction Methods Applying the Iterative Reconstruction

2012 ◽  
Vol 68 (4) ◽  
pp. 404-412 ◽  
Author(s):  
Tadanori Takata ◽  
Katsuhiro Ichikawa ◽  
Hiroyuki Hayashi ◽  
Wataru Mitsui ◽  
Keita Sakuta ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Iterative Reconstruction ◽  
Quality Evaluation ◽  
Image Quality Evaluation ◽  
Reconstruction Methods

scholarly journals Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
N. Koonjoo ◽  
B. Zhu ◽  
G. Cody Bagnall ◽  
D. Bhutto ◽  
M. S. Rosen
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Plant Root ◽  
Signal Acquisition ◽  
Neural Network Approach ◽  
Low Field ◽  
Reconstruction Methods ◽  
Reconstruction Performance ◽  
Low Field Mri ◽  
The Impact

AbstractRecent years have seen a resurgence of interest in inexpensive low magnetic field (< 0.3 T) MRI systems mainly due to advances in magnet, coil and gradient set designs. Most of these advances have focused on improving hardware and signal acquisition strategies, and far less on the use of advanced image reconstruction methods to improve attainable image quality at low field. We describe here the use of our end-to-end deep neural network approach (AUTOMAP) to improve the image quality of highly noise-corrupted low-field MRI data. We compare the performance of this approach to two additional state-of-the-art denoising pipelines. We find that AUTOMAP improves image reconstruction of data acquired on two very different low-field MRI systems: human brain data acquired at 6.5 mT, and plant root data acquired at 47 mT, demonstrating SNR gains above Fourier reconstruction by factors of 1.5- to 4.5-fold, and 3-fold, respectively. In these applications, AUTOMAP outperformed two different contemporary image-based denoising algorithms, and suppressed noise-like spike artifacts in the reconstructed images. The impact of domain-specific training corpora on the reconstruction performance is discussed. The AUTOMAP approach to image reconstruction will enable significant image quality improvements at low-field, especially in highly noise-corrupted environments.

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

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.

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

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 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.

A feasibility study of realizing low-dose abdominal CT using deep learning image reconstruction algorithm

2021 ◽  
pp. 1-12
Author(s):  
Lu-Lu Li ◽  
Huang Wang ◽  
Jian Song ◽  
Jin Shang ◽  
Xiao-Ying Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Radiation Dose ◽  
Low Dose ◽  
Standard Dose ◽  
Image Noise ◽  
Arterial Phase ◽  
Abdominal Ct ◽  
Reconstruction Methods

OBJECTIVES: To explore the feasibility of achieving diagnostic images in low-dose abdominal CT using a Deep Learning Image Reconstruction (DLIR) algorithm. METHODS: Prospectively enrolled 47 patients requiring contrast-enhanced abdominal CT scans. The late-arterial phase scan was added and acquired using lower-dose mode (tube current range, 175–545 mA; 80 kVp for patients with BMI ≤24 kg/m2 and 100 kVp for patients with BMI >  24 kg/m2) and reconstructed with DLIR at medium setting (DLIR-M) and high setting (DLIR-H), ASIR-V at 0% (FBP), 40% and 80% strength. Both the quantitative measurement and qualitative analysis of the five types of reconstruction methods were compared. In addition, radiation dose and image quality between the early-arterial phase ASIR-V images using standard-dose and the late-arterial phase DLIR images using low-dose were compared. RESULTS: For the late-arterial phase, all five reconstructions had similar CT value (P >  0.05). DLIR-H, DLIR-M and ASIR-V80% images significantly reduced the image noise and improved the image contrast noise ratio, compared with the standard ASIR-V40% images (P <  0.05). ASIR-V80% images had undesirable image characteristics with obvious “waxy” artifacts, while DLIR-H images maintained high spatial resolution and had the highest subjective image quality. Compared with the early-arterial scans, the late-arterial phase scans significantly reduced the radiation dose (P <  0.05), while the DLIR-H images exhibited lower image noise and good display of the specific image details of lesions. CONCLUSIONS: DLIR algorithm improves image quality under low-dose scan condition and may be used to reduce the radiation dose without adversely affecting the image quality.

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

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.

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

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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