Impact of acquisition time and penalizing factor in a block-sequential regularized expectation maximization reconstruction algorithm on a Si-photomultiplier-based PET-CT system for 18F-FDG

Abstract Background Lung cancer is one of the most common cancers in the world. Early detection and correct staging are fundamental for treatment and prognosis. Positron emission tomography with computed tomography (PET/CT) is recommended clinically. Silicon (Si) photomultiplier (PM)-based PET technology and new reconstruction algorithms are hoped to increase the detection of small lesions and enable earlier detection of pathologies including metastatic spread. The aim of this study was to compare the diagnostic performance of a SiPM-based PET/CT (including a new block-sequential regularization expectation maximization (BSREM) reconstruction algorithm) with a conventional PM-based PET/CT including a conventional ordered subset expectation maximization (OSEM) reconstruction algorithm. The focus was patients admitted for 18F-fluorodeoxyglucose (FDG) PET/CT for initial diagnosis and staging of suspected lung cancer. Patients were scanned on both a SiPM-based PET/CT (Discovery MI; GE Healthcare, Milwaukee, MI, USA) and a PM-based PET/CT (Discovery 690; GE Healthcare, Milwaukee, MI, USA). Standardized uptake values (SUV) and image interpretation were compared between the two systems. Image interpretations were further compared with histopathology when available. Results Seventeen patients referred for suspected lung cancer were included in our single injection, dual imaging study. No statically significant differences in SUVmax of suspected malignant primary tumours were found between the two PET/CT systems. SUVmax in suspected malignant intrathoracic lymph nodes was 10% higher on the SiPM-based system (p = 0.026). Good consistency (14/17 cases) between the PET/CT systems were found when comparing simplified TNM staging. The available histology results did not find any obvious differences between the systems. Conclusion In a clinical setting, the new SiPM-based PET/CT system with a new BSREM reconstruction algorithm provided a higher SUVmax for suspected lymph node metastases compared to the PM-based system. However, no improvement in lung cancer detection was seen.

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Optimization of PET reconstruction algorithm, SUV thresholding algorithm and PET acquisition time in clinical 11C-acetate PET/CT

PLoS ONE ◽

10.1371/journal.pone.0209169 ◽

2018 ◽

Vol 13 (12) ◽

pp. e0209169

Author(s):

Sara Strandberg ◽

Armin Hashemi ◽

Jan Axelsson ◽

Katrine Riklund

Keyword(s):

Reconstruction Algorithm ◽

Acquisition Time ◽

Pet Ct ◽

Pet Reconstruction ◽

Thresholding Algorithm

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PET/CT BiographTM Sensation 16

Nuklearmedizin ◽

10.1055/s-0038-1625926 ◽

2006 ◽

Vol 45 (03) ◽

pp. 126-133 ◽

Cited By ~ 18

Author(s):

Y. Bercier ◽

M. Schwaiger ◽

S. I. Ziegler ◽

M.-J. Martínez

Keyword(s):

Image Quality ◽

Count Rate ◽

Signal To Noise Ratio ◽

Reconstruction Algorithm ◽

Considerable Change ◽

Acquisition Time ◽

Average Increase ◽

Scatter Fraction ◽

Pet Ct ◽

Before And After

SummaryAim: The new PET/CT Biograph Sensation 16 (BS16) tomographs have faster detector electronics which allow a reduced timing coincidence window and an increased lower energy threshold (from 350 to 400 keV). This paper evaluates the performance of the BS16 PET scanner before and after the Pico-3D electronics upgrade. Methods: Four NEMA NU 2–2001 protocols, (i) spatial resolution, (ii) scatter fraction, count losses and random measurement, (iii) sensitivity, and (iv) image quality, have been performed. Results: A considerable change in both PET count-rate performance and image quality is observed after electronics upgrade. The new scatter fraction obtained using Pico-3D electronics showed a 14% decrease compared to that obtained with the previous electronics. At the typical patient background activity (5.3 kBq/ml), the new scatter fraction was approximately 0.42. The noise equivalent count-rate (RNEC) performance was also improved. The value at which the RNEC curve peaked, increased from 3.7·104s-1 at 14 kBq/ml to 6.4·104s-1 at 21 kBq/ml (2R-NEC rate). Likewise, the peak true count-rate value increased from 1.9·105s-1 at 22 kBq/ml to 3.4·105s-1 at 33 kBq/ml. An average increase of 45% in contrast was observed for hot spheres when using AW-OSEM (4ix8s) as the reconstruction algorithm. For cold spheres, the average increase was 12%. Conclusion: The performance of the PET scanners in the BS16 tomographs is improved by the optimization of the signal processing. The narrower energy and timing coincidence windows lead to a considerable increase of signal- to-noise ratio. The existing combination of fast detectors and adapted electronics in the BS16 tomographs allow imaging protocols with reduced acquisition time, providing higher patient throughput.

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The image quality, lesion detectability, and acquisition time of 18F-FDG total-body PET/CT in oncological patients

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-020-04823-w ◽

2020 ◽

Vol 47 (11) ◽

pp. 2507-2515 ◽

Cited By ~ 2

Author(s):

Yi-Qiu Zhang ◽

Peng-Cheng Hu ◽

Run-Ze Wu ◽

Yu-Shen Gu ◽

Shu-Guang Chen ◽

...

Keyword(s):

Image Quality ◽

Acquisition Time ◽

Lesion Detectability ◽

Oncological Patients ◽

Pet Ct ◽

18F Fdg ◽

Total Body

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Impact of penalizing factor in a block-sequential regularized expectation maximization reconstruction algorithm for 18F-fluorocholine PET-CT regarding image quality and interpretation

EJNMMI Physics ◽

10.1186/s40658-019-0242-2 ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 4

Author(s):

Mimmi Bjöersdorff ◽

Jenny Oddstig ◽

Nina Karindotter-Borgendahl ◽

Helén Almquist ◽

Sophia Zackrisson ◽

...

Keyword(s):

Image Quality ◽

Expectation Maximization ◽

Reconstruction Algorithm ◽

Pet Ct

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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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Comparison of Regularized Reconstruction and Ordered Subset Expectation Maximization Reconstruction in the Diagnostics of Prostate Cancer Using Digital Time-of-Flight 68Ga-PSMA-11 PET/CT Imaging

Diagnostics ◽

10.3390/diagnostics11040630 ◽

2021 ◽

Vol 11 (4) ◽

pp. 630

Author(s):

Olof Jonmarker ◽

Rimma Axelsson ◽

Ted Nilsson ◽

Stefan Gabrielson

Keyword(s):

Prostate Cancer ◽

Bone Metastasis ◽

Lymph Nodes ◽

Expectation Maximization ◽

Reconstruction Algorithm ◽

Lesion Detection ◽

Detection Methods ◽

Cohen’S Kappa ◽

Cohen's Kappa ◽

Pet Ct

In prostate cancer, the early detection of distant spread has been shown to be of importance. Prostate-specific membrane antigen (PSMA)-binding radionuclides in positron emission tomography (PET) is a promising method for precise disease staging. PET diagnostics depend on image reconstruction techniques, and ordered subset expectation maximization (OSEM) is the established standard. Block sequential regularized expectation maximization (BSREM) is a more recent reconstruction algorithm and may produce fewer equivocal findings and better lesion detection. Methods: 68Ga PSMA-11 PET/CT scans of patients with de novo or suspected recurrent prostate cancer were retrospectively reformatted using both the OSEM and BSREM algorithms. The lesions were counted and categorized by three radiologists. The intra-class correlation (ICC) and Cohen’s kappa for the inter-rater reliability were calculated. Results: Sixty-one patients were reviewed. BSREM identified slightly fewer lesions overall and fewer equivocal findings. ICC was excellent with regards to definitive lymph nodes and bone metastasis identification and poor with regards to equivocal metastasis irrespective of the reconstruction algorithm. The median Cohen’s kappa were 0.66, 0.74, 0.61 and 0.43 for OSEM and 0.61, 0.63, 0.66 and 0.53 for BSREM, with respect to the tumor, local lymph nodes, metastatic lymph nodes and bone metastasis detection, respectively. Conclusions: BSREM in the setting of 68Ga PMSA PET staging or restaging is comparable to OSEM.

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Exposure Stability of Quantitative Fluorine-18 Measurements On The Biograph Vision Quadra PET/CT System

10.21203/rs.3.rs-864693/v1 ◽

2021 ◽

Author(s):

George Amadeus Prenosil ◽

Michael Hentschel ◽

Thilo Weitzel ◽

Hasan Sari ◽

Kuangyu Shi ◽

...

Keyword(s):

High Sensitivity ◽

Acquisition Time ◽

Medicine Research ◽

Recovery Curves ◽

Pet Ct ◽

Measured Activity ◽

Applied Dose ◽

Ct Technology ◽

Ct System ◽

Frame Duration

Abstract Background: Our aim was to determine sets of reconstruction parameters for the Biograph Vision Quadra (Siemens Healthineers) PET/CT system that result in quantitative images compliant with the European Association of Nuclear Medicine Research Ltd. (EARL) criteria. Using the Biograph Vision 600 (Siemens Healthineers) PET/CT technology but extending the axial field of view to 106 cm, gives the Vision Quadra currently an around fivefold higher sensitivity over the Vision 600 with otherwise comparable spatial resolution. Therefore, we also investigated how the number of incident positron decays - i.e. exposure - affects EARL compliance. This will allow estimating a minimal acquisition time or a minimal applied dose in clinical scans while retaining data comparability. Methods: We measured activity recovery curves on a NEMA IEC body phantom filled with an aqueous 18 F solution and a sphere to background ratio of 10 to 1 according to the latest EARL guidelines. Reconstructing 3570 images with varying OSEM PSF iterations, post-reconstruction Gaussian filter full width at half maximum (FWHM), and varying exposure from 0.2 MDecays/ml (= 10 sec frame duration) to 59.2 MDecays/ml (= 1 h frame duration), allowed us to determine sets of parameters to achieve compliance with the current EARL 1 and EARL 2 standards. Recovery coefficients (RCs) were calculated for the metrics RC max , RC mean , and RC peak , and the respective recovery curves were analysed for monotonicity. Results: Using 6 iterations, 5 subsets and 7.8 mm Gauss filtering resulted in best EARL 1 compliance and recovery curve monotonicity in all analysed frames. Most robust EARL2 compliance and monotonicity was achieved with 4 iterations, 5 subsets, and 4.6 mm Gauss FWHM in frames with durations between 10 min and 30 sec. RC peak only impeded EARL2 compliance in the 10 sec frame. Conclusions: While EARL1 compliance proved to be robust over all exposure ranges, EARL2 compliance required exposures between 0.6 MDecays/ml to 11.5 MDecays/ml. The Biograph Vision Quadra’s high sensitivity makes frames as short as 10 sec feasible for comparable quantitative images. Lowering EARL2 RC max limits closer to unity would possibly even permit 10 sec EARL2 compliant frames.

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