Optimization of Image Reconstruction for
            90
            Y Selective Internal Radiotherapy on a Lutetium Yttrium Orthosilicate PET/CT System Using a Bayesian Penalized Likelihood Reconstruction Algorithm

Abstract The aim of this study was to investigate the optimization of spatial resolution and image reconstruction parameters related to image quality in an iterative reconstruction algorithm for the small-animal MetisTM PET/CT system. We used a homemade Derenzo phantom to evaluate the image quality by visual assessment, signal-to-noise ratio, contrast, coefficient of variation, and contrast-to-noise ratio of the 0.8 mm hot rods of 8 slices in the centre of the phantom PET images. A healthy mouse study was performed to analyze the influence of optimal reconstruction parameters and Gaussian post-filter FWHM. In the phantom study, the best image quality was obtained by placing the phantom at one end, keeping the central axis parallel to X-axis of the system, selecting iterations between 30 and 40, with a reconstruction voxel of 0.314 mm and a Gaussian post-filter FWHM of 1.57 mm. The optimization of spatial resolution can reach 0.6-mm. In the animal study, it was suitable to choose a voxel size of 0.472-mm, iterations between 30 and 40, and 2.36-mm Gaussian post-filter FWHM. Our results indicate that optimal imaging conditions and reconstruction parameters are necessary to obtain high-resolution images and quantitative accuracy, especially for the high-precision identification of tiny lesions.

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Impact of a Bayesian penalized likelihood reconstruction algorithm on image quality in novel digital PET/CT: clinical implications for the assessment of lung tumors

EJNMMI Physics ◽

10.1186/s40658-018-0223-x ◽

2018 ◽

Vol 5 (1) ◽

Cited By ~ 18

Author(s):

Michael Messerli ◽

Paul Stolzmann ◽

Michèle Egger-Sigg ◽

Josephine Trinckauf ◽

Stefano D’Aguanno ◽

...

Keyword(s):

Image Quality ◽

Penalized Likelihood ◽

Reconstruction Algorithm ◽

Lung Tumors ◽

Clinical Implications ◽

Pet Ct ◽

Digital Pet

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Evaluation of 68Ga-PSMA PET/CT Images Acquired with a Reduced Scan Time Duration in Prostate Cancer Patients using the Digital Biograph Vision

10.21203/rs.3.rs-30754/v1 ◽

2020 ◽

Author(s):

Manuel Weber ◽

Regina Hofferber ◽

Ken Herrmann ◽

Wolfgang Peter Fendler ◽

Maurizio Conti ◽

...

Keyword(s):

Prostate Cancer ◽

Image Reconstruction ◽

Diagnostic Performance ◽

Reconstruction Algorithm ◽

Reconstruction Algorithms ◽

Time Duration ◽

Acquisition Protocol ◽

Scan Time ◽

Psma Pet ◽

Pet Ct

Abstract Aim 68Ga-PSMA PET/CT allows for a superior detection of prostate cancer (PC) tissue, especially in context of a low tumor burden. Digital PET/CT bears the potential of reducing scan time duration / administered tracer activity due to, for instance, its higher sensitivity and improved time coincidence resolution. It might thereby expand 68Ga-PSMA PET/CT that is currently limited by 68Ge/68Ga-generator yield. Our aim was to clinically evaluate the influence of a reduced scan time duration in combination with different image reconstruction algorithms on the diagnostic performance. Methods Twenty PC patients (11 for biochemical recurrence, 5 for initial staging, 4 for metastatic disease) sequentially underwent 68Ga-PSMA PET/CT on a digital Siemens Biograph Vision. PET data were collected in continuous-bed-motion mode with a scan time duration of approximately 17 min (reference acquisition protocol) and 5 min (reduced acquisition protocol). 4 iterative reconstruction algorithms were applied using a time-of-flight (TOF) approach alone or combined with point-spread-function (PSF) correction, each with 2 or 4 iterations. To evaluate the diagnostic performance, the following metrics were chosen: (a) per-region detectability, (b) the tumor maximum and peak standardized uptake values (SUVmax and SUVpeak) and (c) image noise using the liver’s activity distribution. Results Overall, 98% of regions (91% of affected regions) were correctly classified in the reduced acquisition protocol independent of the image reconstruction algorithm. Two nodal lesions (each ≤ 4 mm) were not identified (leading to downstaging in 1/20 cases). Mean absolute percentage deviation of SUVmax (SUVpeak) was approximately 9% (6%) for each reconstruction algorithm. The mean image noise increased from 13–21% (4 iterations) and from 10–15% (2 iterations) for PSF + TOF and TOF images. Conclusions High agreement at 3.5-fold reduction of scan time in terms of per-region detection (98% of regions) and image quantification (mean deviation ≤ 10%) was demonstrated; however, small lesions can be missed in about 10% of patients leading to downstaging (T1N0M0 instead of T1N1M0) in 5% of patients. Our results suggest that a reduction of scan time duration or administered 68Ga-PSMA activities can be considered in metastatic patients, where missing small lesions would not impact patient management.

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

EJNMMI Physics ◽

10.1186/s40658-019-0264-9 ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 6

Author(s):

Paulo R. R. V. Caribé ◽

M. Koole ◽

Yves D’Asseler ◽

B. Van Den Broeck ◽

S. Vandenberghe

Keyword(s):

Noise Reduction ◽

Expectation Maximization ◽

Time Of Flight ◽

Penalized Likelihood ◽

Reconstruction Algorithm ◽

Patient Data ◽

Noise Levels ◽

Patient Study ◽

Phantom Data ◽

Pet Ct

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 β-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 β = 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 β = 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.

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A fast image reconstruction algorithm based on penalized-likelihood estimate

Medical Engineering & Physics ◽

10.1016/j.medengphy.2005.02.004 ◽

2005 ◽

Vol 27 (8) ◽

pp. 679-686 ◽

Cited By ~ 6

Author(s):

Jinhua Sheng ◽

Lei Ying

Keyword(s):

Image Reconstruction ◽

Penalized Likelihood ◽

Reconstruction Algorithm ◽

Likelihood Estimate ◽

Image Reconstruction Algorithm

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Evaluation of [68Ga]Ga-PSMA PET/CT Images Acquired with a Reduced Scan Time Duration in Prostate Cancer Patients using the Digital Biograph Vision

10.21203/rs.3.rs-30754/v3 ◽

2020 ◽

Author(s):

Manuel Weber ◽

Walter Jentzen ◽

Regina Hofferber ◽

Ken Herrmann ◽

Wolfgang Peter Fendler ◽

...

Keyword(s):

Prostate Cancer ◽

Image Reconstruction ◽

Cancer Patients ◽

Diagnostic Performance ◽

Reconstruction Algorithm ◽

Reconstruction Algorithms ◽

Time Duration ◽

Acquisition Protocol ◽

Scan Time ◽

Pet Ct

Abstract Aim: [68Ga]Ga-PSMA-11 PET/CT allows for a superior detection of prostate cancer tissue, especially in the context of a low tumor burden. Digital PET/CT bears the potential of reducing scan time duration / administered tracer activity due to, for instance, its higher sensitivity and improved time coincidence resolution. It might thereby expand [68Ga]Ga-PSMA-11 PET/CT that is currently limited by 68Ge/68Ga-generator yield. Our aim was to clinically evaluate the influence of a reduced scan time duration in combination with different image reconstruction algorithms on the diagnostic performance.Methods: Twenty prostate cancer patients (11 for biochemical recurrence, 5 for initial staging, 4 for metastatic disease) sequentially underwent [68Ga]Ga-PSMA-11 PET/CT on a digital Siemens Biograph Vision. PET data were collected in continuous-bed-motion mode with a mean scan time duration of 16.7 min (reference acquisition protocol) and 4.6 min (reduced acquisition protocol). 4 iterative reconstruction algorithms were applied using a time-of-flight (TOF) approach alone or combined with point-spread-function (PSF) correction, each with 2 or 4 iterations. To evaluate the diagnostic performance, the following metrics were chosen: (a) per-region detectability, (b) the tumor maximum and peak standardized uptake values (SUVmax and SUVpeak) and (c) image noise using the liver’s activity distribution.Results: Overall, 98% of regions (91% of affected regions) were correctly classified in the reduced acquisition protocol independent of the image reconstruction algorithm. Two nodal lesions (each ≤4 mm) were not identified (leading to downstaging in 1/20 cases). Mean absolute percentage deviation of SUVmax (SUVpeak) was approximately 9% (6%) for each reconstruction algorithm. The mean image noise increased from 13% to 21% (4 iterations) and from 10% to 15% (2 iterations) for PSF+TOF and TOF images.Conclusions: High agreement at 3.5-fold reduction of scan time in terms of per-region detection (98 % of regions) and image quantification (mean deviation ≤ 10 %) was demonstrated; however, small lesions can be missed in about 10% of patients leading to downstaging (T1N0M0 instead of T1N1M0) in 5 % of patients. Our results suggest that a reduction of scan time duration or administered [68Ga]Ga-PSMA-11 activities can be considered in metastatic patients, where missing small lesions would not impact patient management. Limitations include the small and heterogeneous sample size and the lack of follow-up.

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