Phantom, clinical, and texture indices evaluation and optimization of a penalized‐likelihood image reconstruction method (Q.Clear) on a <scp>BGO PET</scp>/<scp>CT</scp> scanner

Reynés-Llompart, Gabriel; Gámez-Cenzano, Cristina; Conejero, J.L. Vercher; Sabaté‐Llobera, Aida; Calvo, Nahum; Martí-Climent, Josep M.

doi:10.1002/mp.12986

Cited by 37 publications

(53 citation statements)

References 30 publications

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“…4). This result was comparable with previous findings of oncologic PET imaging using 18 F-fluciclovine (300) [29], 68 Ga-prostate-specific membrane antigen (400-550) [31], and 18 F-FDG (350-400) [12,15,32]. Figure 4 shows that BPL achieved higher contrast and lower image noise than OSEM.…”

Section: Discussionsupporting

confidence: 90%

“…The highest SNR 10 mm were at β values of 400-500. A trade-off needs to be reached between the two conflicting performance parameters of high contrast and low image noise for determining an optimal β value [25,32]. The balance of contrast and CV was optimal when a β value was 400 in the present study, in which the β value was independent of the acquisition duration at ≥ 45 s (Fig.…”

Section: Discussionmentioning

confidence: 71%

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Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for 18F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom

et al. 2020

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Background The Bayesian penalized likelihood (BPL) algorithm Q.Clear (GE Healthcare) allows fully convergent iterative reconstruction that results in better image quality and quantitative accuracy, while limiting image noise. The present study aimed to optimize BPL reconstruction parameters for 18F-NaF PET/CT images and to determine the feasibility of 18F-NaF PET/CT image acquisition over shorter durations in clinical practice. Methods A custom-designed thoracic spine phantom consisting of several inserts, soft tissue, normal spine, and metastatic bone tumor, was scanned using a Discovery MI PET/CT scanner (GE Healthcare). The phantom allows optional adjustment of activity distribution, tumor size, and attenuation. We reconstructed PET images using OSEM + PSF + TOF (2 iterations, 17 subsets, and a 4-mm Gaussian filter), BPL + TOF (β = 200 to 700), and scan durations of 30–120 s. Signal-to-noise ratios (SNR), contrast, and coefficients of variance (CV) as image quality indicators were calculated, whereas the quantitative measures were recovery coefficients (RC) and RC linearity over a range of activity. We retrospectively analyzed images from five persons without bone metastases (male, n = 1; female, n = 4), then standardized uptake values (SUV), CV, and SNR at the 4th, 5th, and 6th thoracic vertebra were calculated in BPL + TOF (β = 400) images. Results The optimal reconstruction parameter of the BPL was β = 400 when images were acquired at 120 s/bed. At 90 s/bed, the BPL with a β value of 400 yielded 24% and 18% higher SNR and contrast, respectively, than OSEM (2 iterations; 120 s acquisitions). The BPL was superior to OSEM in terms of RC and the RC linearity over a range of activity, regardless of scan duration. The SUVmax were lower in BPL, than in OSEM. The CV and vertebral SNR in BPL were superior to those in OSEM. Conclusions The optimal reconstruction parameters of 18F-NaF PET/CT images acquired over different durations were determined. The BPL can reduce PET acquisition to 90 s/bed in 18F-NaF PET/CT imaging. Our results suggest that BPL (β = 400) on SiPM-based TOF PET/CT scanner maintained high image quality and quantitative accuracy even for shorter acquisition durations.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 71%

Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for 18F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom

et al. 2020

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“…The optimal beta values depend on the type of radiopharmaceutical and the anatomical region scanned. In 18F-FDG PET imaging, the optimal beta for torso scans is between 300 and 400 and between 100 and 200 for brain scans [4]. The optimal beta values for some prostate cancer-targeting tracers have been reported as: 400-550 for 68Ga-PSMA, 400-550 for 18F-fluorocholine and 300 for 18F-flucyclovine [5,11,12].…”

Section: Discussionmentioning

confidence: 99%

“…SUV values measured in Q.Clear should therefore be more accurate and image resolution should be better when compared to OSEM algorithm. The penalty function is controlled by the penalisation factor termed beta which is the only user-dependent variable to the algorithm [1,[4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Ordered subset expectation maximisation vs Bayesian penalised likelihood reconstruction algorithm in 18F-PSMA-1007 PET/CT

et al. 2020

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Background The aim of the study was to compare widely used ordered subset expectation maximisation (OSEM) algorithm with a new Bayesian penalised likelihood (BPL) Q.Clear algorithm in 18F-PSMA-1007 PET/CT. Methods We retrospectively assessed 25 18F-PSMA-1007 PET/CT scans with both OSEM and Q.Clear reconstructions available. Each scan was independently reported by two physicians both in OSEM and Q.Clear. SUVmax, SUVmean and tumour-to-background ratio (TBR) of each lesion were measured. Reports were also compared for their final conclusions and the number and localisation of lesions. Results In both reconstructions the same 87 lesions were reported. Mean SUVmax, SUVmean and TBR were higher for Q.Clear than OSEM (7.01 vs 6.53 [p = 0.052], 4.16 vs 3.84 [p = 0.036] and 20.2 vs 16.8 [p < 0.00001], respectively). Small lesions (< 10 mm) had statistically significant higher SUVmax, SUVmean and TBR in Q.Clear than OSEM (5.37 vs 4.79 [p = 0.032], 3.08 vs 2.70 [p = 0.04] and 15.5 vs 12.5 [p = 0.00214], respectively). For lesions ≥ 10 mm, no significant differences were observed. Findings with higher tracer avidity (SUVmax ≥ 5) tended to have higher SUVmax, SUVmean and TBR values in Q.Clear (11.

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“…GLCM texture analysis has been used in a number of radiomics studies to identify disease states by CT, by PET, and by PET/CT . Image texture analysis approaches also have been applied to optimize choices of tomographic reconstruction approaches and parameters …”

Section: Discussionmentioning

confidence: 99%

Texture analysis for automated evaluation of Jaszczak phantom SPECT system tests

2018

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Purpose: Routine quarterly quality assurance (QA) assessment of single photon emission computed tomography (SPECT) systems includes analysis of multipurpose phantoms containing spheres and rods of various sizes. When evaluated by accreditation agencies, criteria applied to assess image quality are largely subjective. Determining a quantified image characteristic metric that emulates human reader impressions of image quality could be quite useful. Our investigation was conducted to ascertain whether image texture analysis metrics, such as those applied to PET scans to detect neoplasms, could prove helpful in linking qualitative statements of phantom sphere and rod visibility to quantified parameters. Because it is not obvious whether it is preferable to submit reconstructions to accrediting agencies performed using typical clinical (CLIN) protocol processing parameters or to follow agencies' filtered backprojection (FBP) suggestions, we applied texture analysis metrics to determine the degree to which these choices affect equipment capability assessment. Methods and materials: Data were processed retrospectively for 125 different Tc-99 m SPECT scans of standardized phantoms for 14 rotating Anger detector systems as part of routine quarterly QA. Algorithms were written to compute several classes of image metrics: quantile curve metrics, image texture analysis gray-level co-occurrence matrix (GLCM) metrics, contrast metrics, and count histogram metrics. For qualitative image scores, two experienced physicists independently graded sphere and rod visibility on a 5-level scale and assigned dichotomous visibility scores, without knowledge of quantified texture analysis metrics or each other's readings. The same phantom was used to collect 15 additional data sets with two dual-detector SPECT/CT systems, reconstructed both by FBP parameters that have been suggested by accrediting agencies and by manufacturers' default settings for CLIN SPECT/CT bone imaging protocols by ordered subsets expectation maximization (OSEM), incorporating attenuation correction using the CT scan. Image characteristics metrics were compared for FBP and CLIN reconstructions. Results: For spheres, the metric with the strongest rank correlation with 5-level scale readings was the quantile curve slope (q = 0.83, P < 0.0001), while for rods it was GLCM Energy normalized to the maximum GLCM Energy value (EnergyNorm) (q = À0.88, P < 0.0001). Compared to dichotomous readings, the metric with the highest ROC area under curve (AUC) for spheres was the quantile curve slopes (AUC = 96 AE 1%, sensitivity = 91%, specificity = 90%), and for rods was Energy-Norm (AUC = 98 AE 1%, sensitivity = 92%, specificity = 95%). Image contrast was higher for all sphere sizes and rod EnergyNorm was lower for sectors of intermediate-sized rods for FBP compared to CLIN reconstructions, in agreement with more rods judged to be visible from FBP than CLIN reconstructions (47% vs 33%, P = 0.002). Conclusions: When preparing to submit quality assurance images of standardized phanto...

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Phantom, clinical, and texture indices evaluation and optimization of a penalized‐likelihood image reconstruction method (Q.Clear) on a BGO PET/CT scanner

Cited by 37 publications

References 30 publications

Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for 18F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom

Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for 18F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom

Ordered subset expectation maximisation vs Bayesian penalised likelihood reconstruction algorithm in 18F-PSMA-1007 PET/CT

Texture analysis for automated evaluation of Jaszczak phantom SPECT system tests

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