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PURPOSE: Minimal misregistration of fused PET and MRI images can be achieved with simultaneous positron emission tomography/magnetic resonance imaging (PET/MRI). However, the acquisition of multiple MRI sequences during a single PET emission scan may impair fusion precision of each sequence. This study evaluated the diagnostic utility of time-synchronized PET/MRI using an MR active trigger and a Bayesian penalized likelihood reconstruction algorithm (BPL) to assess the locoregional extension of endometrial cancer. METHODS: Fifty-five patients with endometrial cancer who underwent pelvic 2-deoxy-2-[18F]fluoro-D-glucose PET/MRI were retrospectively evaluated. The PET emission time for the BPL reconstruction was determined by the MR active trigger of each MR sequence. The concordance rates of image interpretation with pathological T-staging, diagnostic performance for deep myometrial invasion (MI), and diagnostic confidence levels were evaluated by two readers and compared between time-synchronized, overlapping (conventional and simultaneous, but not time-synchronized), and sequential (not simultaneous) PET/MRI and MRI with diffusion-weighted imaging. Misregistration of the PET/MRI-fused images was determined by evaluating the differences in bladder dimensions. RESULTS: The T classification by time-synchronized PET/MRI was the most concordant with the pathological T classification for the two readers. Time-synchronized PET/MRI had a significantly higher diagnostic performance for deep MI and higher confidence level scores than overlapping PET/MRI for the novice reader (p = 0.033 and p = 0.038, respectively). The differences in bladder dimension on sequential PET/MRI were significantly larger than those on overlapping and time-synchronized PET/MRI (p <0.001). CONCLUSION: Time-synchronized PET/MRI is superior to conventional PET/MRI for assessing the locoregional extension of endometrial cancer.

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Positron Emission Tomography (PET) imaging after 90 Y liver radioembolization is used for both lesion identification and dosimetry. Bayesian penalized likelihood (BPL) reconstruction algorithms are an alternative to ordered subset expectation maximization (OSEM) with improved image quality and lesion detectability. The investigation of optimal parameters for 90 Y image reconstruction of Q.Clear, a commercial BPL algorithm developed by General Electric (GE), in PET/MR is a field of interest and the subject of this study. The NEMA phantom was filled at an 8:1 sphere-to-background ratio. Acquisitions were performed on a PET/MR scanner for clinically relevant activities between 0.7 and 3.3 MBq/ml. Reconstructions with Q.Clear were performed varying the ß penalty parameter between 20 and 6000, the acquisition time between 5 and 20 min and pixel size between 1.56 and 4.69 mm. OSEM reconstructions of 28 subsets with 2 and 4 iterations with and without Time-of-Flight (TOF) were compared to Q.Clear with ß = 4000. Recovery coefficients (RC), their coefficient of variation (COV), background variability (BV), contrast-to-noise ratio (CNR) and residual activity in the cold insert were evaluated. Increasing ß parameter lowered RC, COV and BV, while CNR was maximized at ß = 4000; further increase resulted in oversmoothing. For quantification purposes, ß = 1000-2000 could be more appropriate. Longer acquisition times resulted in larger CNR due to reduced image noise. Q.Clear reconstructions led to higher CNR than OSEM. A ß of 4000 was obtained for optimal image quality, although lower values could be considered for quantification purposes. An optimal acquisition time of 15 min was proposed considering its clinical use.

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BACKGROUND: PSMA PET/CT is a predictive and prognostic biomarker for determining response to [177Lu]Lu-PSMA-617 in patients with metastatic castration resistant prostate cancer (mCRPC). Thresholds defined to date may not be generalizable to newer image reconstruction algorithms. Bayesian penalized likelihood (BPL) reconstruction algorithm is a novel reconstruction algorithm that may improve contrast whilst preventing introduction of image noise. The aim of this study is to compare the quantitative parameters obtained using BPL and the Ordered Subset Expectation Maximization (OSEM) reconstruction algorithms. METHODS: Fifty consecutive patients with mCRPC who underwent [68Ga]Ga-PSMA-11 PET/CT using OSEM reconstruction to assess suitability for [177Lu]Lu-PSMA-617 therapy were selected. BPL algorithm was then used retrospectively to reconstruct the same PET raw data. Quantitative and volumetric measurements such as tumour standardised uptake value (SUV)max, SUVmean and Molecular Tumour Volume (MTV-PSMA) were calculated on both reconstruction methods. Results were compared (Bland-Altman, Pearson correlation coefficient) including subgroups with low and high-volume disease burdens (MTV-PSMA cut-off 40 mL). RESULTS: The SUVmax and SUVmean were higher, and MTV-PSMA was lower in the BPL reconstructed images compared to the OSEM group, with a mean difference of 8.4 (17.5%), 0.7 (8.2%) and - 21.5 mL (-3.4%), respectively. There was a strong correlation between the calculated SUVmax, SUVmean, and MTV-PSMA values in the OSEM and BPL reconstructed images (Pearson r values of 0.98, 0.99, and 1.0, respectively). No patients were reclassified from low to high volume disease or vice versa when switching from OSEM to BPL reconstruction. CONCLUSIONS: [68Ga]Ga-PSMA-11 PET/CT quantitative and volumetric parameters produced by BPL and OSEM reconstruction methods are strongly correlated. Differences are proportional and small for SUVmean, which is used as a predictive biomarker. Our study suggests that both reconstruction methods are acceptable without clinical impact on quantitative or volumetric findings. For longitudinal comparison, committing to the same reconstruction method would be preferred to ensure consistency.

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Objectives: The aim of this study was to evaluate Cu-64 PET phantom image quality using Bayesian Penalized Likelihood (BPL) and Ordered Subset Expectation Maximum with point-spread function modeling (OSEM-PSF) reconstruction algorithms. In the BPL, the regularization parameterßwas varied to identify the optimum value for image quality. In the OSEM-PSF, the effect of acquisition time was evaluated to assess the feasibility of shortened scan duration.Methods: A NEMA IEC PET body phantom was filled with known activities of water soluble Cu-64. The phantom was imaged on a PET/CT scanner and was reconstructed using BPL and OSEM-PSF algorithms. For the BPL reconstruction, variousßvalues (150, 250, 350, 450, and 550) were evaluated. For the OSEM-PSF algorithm, reconstructions were performed using list-mode data intervals ranging from 7.5 to 240 s. Image quality was assessed by evaluating the signal to noise ratio (SNR), contrast to noise ratio (CNR), and background variability (BV).Results: The SNR and CNR were higher in images reconstructed with BPL compared to OSEM-PSF. Both the SNR and CNR increased with increasingß, peaking atß= 550. The CNR for allß, sphere sizes and tumor-to-background ratios (TBRs) satisfied the Rose criterion for image detectability (CNR > 5). BPL reconstructed images withß= 550 demonstrated the highest improvement in image quality. For OSEM-PSF reconstructed images with list-mode data duration ≥ 120 s, the noise level and CNR were not significantly different from the baseline 240 s list-mode data duration.Conclusions: BPL reconstruction improved Cu-64 PET phantom image quality by increasing SNR and CNR relative to OSEM-PSF reconstruction. Additionally, this study demonstrated scan time can be reduced from 240 to 120 s when using OSEM-PSF reconstruction while maintaining similar image quality. This study provides baseline data that may guide future studies aimed to improve clinical Cu-64 imaging.

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Objetivos La escala de Deauville (DS) en la tomografía de emisión de positrones (PET) con [18F]fludeoxiglucosa ([18F]FDG) es un método semicuantitativo único para la evaluación del linfoma. Sin embargo, el tipo de algoritmo de reconstrucción empleado para el cálculo de los valores de captación estándar (max, medio y pico) podría afectar a la DS. Comparamos el algoritmo de reconstrucción de probabilidad penalizada bayesiano (BPL) con el de maximización de expectativas de subconjuntos ordenados (OSEM) respecto a los parámetros cuantitativos y en la DS en el linfoma. Investigamos el efecto del tamaño del ganglio linfático sobre la variación cuantitativa. Métodos Se reconstruyeron por separado los resultados de la PET sin procesar de 255 pacientes con linfoma utilizando la aplicación Q.Clear (General Electric Healthcare, Milwaukee, WI, EE. UU.), un algoritmo BPL, y la aplicación SharpIR (General Electric Healthcare, Milwaukee, WI, EE. UU.), un algoritmo OSEM. En ambas imágenes, para cada paciente, se valoró hígado, el pool sanguíneo mediastínico y los valores de captación estándar (SUV) (SUVmáx, SUVmedio y SUVpico) de un total de 487 lesiones seleccionadas. Se compararon DSmáx, DSmedio y DSpico. Resultados En nuestro estudio hubo un aumento significativo de la DS con el BPL (p<0,001) que pasó a una puntuación de 4 a 5 en 30 pacientes inicialmente catalogados como 1-2-3 mediante el algoritmo OSEM. Se observó que los valores cuantitativos de los ganglios linfáticos aumentaban de forma estadísticamente significativa con el BPL (p<0,001), mientras que la disminución de los valores de hígado fue notable respecto a las regiones de referencia (p<0,001). Además, la diferencia en los ganglios linfáticos se asoció de forma independiente con el tamaño de la lesión y fue considerablemente más pronunciada en las lesiones de pequeño tamaño (p<0,001) (AU)

Introduction and Objectives 18F-FDG PET with the Deauville score (DS) is a unique semiquantitative method for lymphoma. However, type of standard uptake values (max, mean, and peak) reconstruction algorithms could affect DS. We compared the Bayesian Penalized Likelihood reconstruction algorithm (BPL) with Ordered Subsets Expectation Maximization (OSEM) on quantitative parameters and DS in lymphoma. We investigated the effect of the size of the lymph node on quantitative variation. Patients and Methods Raw PET data of 255 lymphoma patients were reconstructed separately with Q.Clear (GE Healthcare), a BPL, and SharpIR (GE Healthcare), an OSEM algorithm. In both images, each patient's liver, mediastinal blood pool, and SUVs (SUVmax, SUVmean, and SUVpeak) of a total of 487 lesions selected from the patients were performed. DSmax, DSmean, and DSpeak were compared. Results In our study, DS increased significantly with BPL (p<0.001), and the DS increased to 4-5 in 30 patients evaluated as 1-2-3 with OSEM. It was found that the quantitative values of the lymph nodes increased statistically with BPL (p<0.001), and the liver from the reference regions were significantly decreased (p<0.001). In addition, difference in lymph node was independently associated with size of lesion and was significantly more pronounced in small lesions (p<0.001). The effects of BPL algorithm were more pronounced in SUVmax than in SUVmean and SUVpeak. DS-mean and DS-peak scores were less changed by BPL than DS-max. Conclusion Different reconstruction algorithms in FDG PET/CT affect the quantitative evaluation. That variation may affect the change in DS in lymphoma patients, thus affecting patient management (AU)

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INTRODUCTION AND OBJECTIVES: 18F-FDG PET with the Deauville score (DS) is a unique semiquantitative method for lymphoma. However, type of standard uptake values (max, mean, and peak) reconstruction algorithms could affect DS. We compared the Bayesian Penalized Likelihood reconstruction algorithm (BPL) with Ordered Subsets Expectation Maximization (OSEM) on quantitative parameters and DS in lymphoma. We investigated the effect of the size of the lymph node on quantitative variation. PATIENTS AND METHODS: Raw PET data of 255 lymphoma patients were reconstructed separately with Q.Clear (GE Healthcare), a BPL, and SharpIR (GE Healthcare), an OSEM algorithm. In both images, each patient's liver, mediastinal blood pool, and SUVs (SUVmax, SUVmean, and SUVpeak) of a total of 487 lesions selected from the patients were performed. DSmax, DSmean, and DSpeak were compared. RESULTS: In our study, DS increased significantly with BPL (p < 0.001), and the DS increased to 4-5 in thirty patients evaluated as 1-2-3 with OSEM. It was found that the quantitative values of the lymph nodes increased statistically with BPL (p < 0.001), and the liver from the reference regions were significantly decreased (p < 0.001). In addition, difference in lymph node was independently associated with size of lesion and was significantly more pronounced in small lesions (p < 0.001). The effects of BPL algorithm were more pronounced in SUVmax than in SUVmean and SUVpeak. DS-mean and DS-peak scores were less changed by BPL than DS-max. CONCLUSION: Different reconstruction algorithms in FDG PET/CT affect the quantitative evaluation. That variation may affect the change in DS in lymphoma patients, thus affecting patient management.

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BACKGROUND: Bayesian penalized likelihood (BPL) algorithm is an effective way to suppress noise in the process of positron emission tomography (PET) image reconstruction by incorporating a smooth penalty. The strength of the smooth penalty is controlled by the penalization factor. The aim was to investigate the impact of different penalization factors and acquisition times in a new BPL algorithm, HYPER Iterative, on the quality of 68Ga-DOTA-NOC PET/CT images. A phantom and 25 patients with neuroendocrine neoplasms who underwent 68Ga-DOTA-NOC PET/CT were included. The PET data were acquired in a list-mode with a digital PET/CT scanner and reconstructed by ordered subset expectation maximization (OSEM) and the HYPER Iterative algorithm with seven penalization factors between 0.03 and 0.5 for acquisitions of 2 and 3 min per bed position (m/b), both including time-of-flight and point of spread function recovery. The contrast recovery (CR), background variability (BV) and radioactivity concentration ratio (RCR) of the phantom; The SUVmean and coefficient of variation (CV) of the liver; and the SUVmax of the lesions were measured. Image quality was rated by two radiologists using a five-point Likert scale. RESULTS: The CR, BV, and RCR decreased with increasing penalization factors for four "hot" spheres, and the HYPER Iterative 2 m/b groups with penalization factors of 0.07 to 0.2 had equivalent CR and superior BV performance compared to the OSEM 3 m/b group. The liver SUVmean values were approximately equal in all reconstruction groups (range 5.95-5.97), and the liver CVs of the HYPER Iterative 2 m/b and 3 m/b groups with the penalization factors of 0.1 to 0.2 were equivalent to those of the OSEM 3 m/b group (p = 0.113-0.711 and p = 0.079-0.287, respectively), while the lesion SUVmax significantly increased by 19-22% and 25%, respectively (all p < 0.001). The highest qualitative score was attained at a penalization factor of 0.2 for the HYPER Iterative 2 m/b group (3.20 ± 0.52) and 3 m/b group (3.70 ± 0.36); those scores were comparable to or greater than that of the OSEM 3 m/b group (3.09 ± 0.36, p = 0.388 and p < 0.001, respectively). CONCLUSIONS: The HYPER Iterative algorithm with a penalization factor of 0.2 resulted in higher lesion contrast and lower image noise than OSEM for 68Ga-DOTA-NOC PET/CT, allowing the same image quality to be achieved with less injected radioactivity and a shorter acquisition time.

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BACKGROUND: To explore the feasibility of short-time-window Ki imaging using a population-based arterial input function (IF) and optimized Bayesian penalized likelihood (BPL) reconstruction as a practical alternative to long-time-window Ki imaging with an individual patient-based IF. Myocardial Ki images were generated from 73 dynamic 18F-FDG-PET/CT scans of 30 patients with cardiac sarcoidosis. For each dynamic scan, the Ki images were obtained using the IF from each individual patient and a long time window (10-60 min). In addition, Ki images were obtained using the normalized averaged population-based IF and BPL algorithms with different beta values (350, 700, and 1000) with a short time window (40-60 min). The visual quality of each image was visually rated using a 4-point scale (0, not visible; 1, poor; 2, moderate; and 3, good), and the Ki parameters (Ki-max, Ki-mean, Ki-volume) of positive myocardial lesions were measured independently by two readers. Wilcoxon's rank sum test, McNemar's test, or linear regression analysis were performed to assess the differences or relationships between two quantitative variables. RESULTS: Both readers similarly rated 51 scans as positive (scores = 1-3) and 22 scans as negative (score = 0) for all four Ki images. Among the three types of population-based IF Ki images, the proportion of images with scores of 3 was highest with a beta of 1000 (78.4 and 72.5%, respectively) and lowest with a beta of 350 (33.3 and 23.5%) for both readers (all p < 0.001). The coefficients of determination between the Ki parameters obtained with the individual patient-based IF and those obtained with the population-based IF were highest with a beta of 1000 for both readers (Ki-max, 0.91 and 0.92, respectively; Ki-mean, 0.91 and 0.92, respectively; Ki-volume, 0.75 and 0.60, respectively; and all p < 0.001). CONCLUSIONS: Short-time-window Ki images with a population-based IF reconstructed using the BPL algorithm and a high beta value were closely correlated with long-time-window Ki images generated with an individual patient-based IF. Short-time-window Ki images using a population-based IF and BPL reconstruction might represent practical alternatives to long-time-window Ki images generated using an individual patient-based IF.

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BACKGROUND: To investigate the influence of small voxel Bayesian penalized likelihood (SVB) reconstruction on small lesion detection compared to ordered subset expectation maximization (OSEM) reconstruction using a clinical trials network (CTN) chest phantom and the patients with 18F-FDG-avid small lung tumors, and determine the optimal penalty factor for the lesion depiction and quantification. METHODS: The CTN phantom was filled with 18F solution with a sphere-to-background ratio of 3.81:1. Twenty-four patients with 18F-FDG-avid lung lesions (diameter < 2 cm) were enrolled. Six groups of PET images were reconstructed: routine voxel OSEM (RVOSEM), small voxel OSEM (SVOSEM), and SVB reconstructions with four penalty factors: 0.6, 0.8, 0.9, and 1.0 (SVB0.6, SVB0.8, SVB0.9, and SVB1.0). The routine and small voxel sizes are 4 × 4 × 4 and 2 × 2 × 2 mm3. The recovery coefficient (RC) was calculated by dividing the measured activity by the injected activity of the hot spheres in the phantom study. The SUVmax, target-to-liver ratio (TLR), contrast-to-noise ratio (CNR), the volume of the lesions, and the image noise of the liver were measured and calculated in the patient study. Visual image quality of the patient image was scored by two radiologists using a 5-point scale. RESULTS: In the phantom study, SVB0.6, SVB0.8, and SVB0.9 achieved higher RCs than SVOSEM. The RC was higher in SVOSEM than RVOSEM and SVB1.0. In the patient study, the SUVmax, TLR, and visual image quality scores of SVB0.6 to SVB0.9 were higher than those of RVOSEM, while the image noise of SVB0.8 to SVB1.0 was equivalent to or lower than that of RVOSEM. All SVB groups had higher CNRs than RVOSEM, but there was no difference between RVOSEM and SVOSEM. The lesion volumes derived from SVB0.6 to SVB0.9 were accurate, but over-estimated by RVOSEM, SVOSEM, and SVB1.0, using the CT measurement as the standard reference. CONCLUSIONS: The SVB reconstruction improved lesion contrast, TLR, CNR, and volumetric quantification accuracy for small lesions compared to RVOSEM reconstruction without image noise degradation or the need of longer emission time. A penalty factor of 0.8-0.9 was optimal for SVB reconstruction for the small tumor detection with 18F-FDG PET/CT.

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PURPOSE: The Bayesian penalized likelihood (BPL) reconstruction algorithm, Q.Clear, can achieve a higher signal-to-noise ratio on images and more accurate quantitation than ordered subset-expectation maximization (OSEM). The reconstruction parameter (ß) in BPL requires optimization according to the radiopharmaceutical tracer. The present study aimed to define the optimal ß value in BPL required to diagnose Alzheimer disease from brain positron emission tomography (PET) images acquired using 18 F-fluoro-2-deoxy-D-glucose ([18 F]FDG) and 11 C-labeled Pittsburg compound B ([11 C]PiB). METHODS: Images generated from Hoffman 3D brain and cylindrical phantoms were acquired using a Discovery PET/computed tomography (CT) 710 and reconstructed using OSEM + time-of-flight (TOF) under clinical conditions and BPL + TOF (ß = 20-1000). Contrast was calculated from images generated by the Hoffman 3D brain phantom, and noise and uniformity were calculated from those generated by the cylindrical phantom. Five cognitively healthy controls and five patients with Alzheimer disease were assessed using [18 F]FDG and [11 C]PiB PET to validate the findings from the phantom study. The ß values were restricted by the findings of the phantom study, then one certified nuclear medicine physician and two certified nuclear medicine technologists visually determined optimal ß values by scoring the quality parameters of image contrast, image noise, cerebellar stability, and overall image quality of PET images from 1 (poor) to 5 (excellent). RESULTS: The contrast in BPL satisfied the Japanese Society of Nuclear Medicine (JSNM) criterion of ≥55% and exceeded that of OSEM at ranges of ß = 20-450 and 20-600 for [18 F]FDG and [11 C]PiB, respectively. The image noise in BPL satisfied the JSNM criterion of ≤15% and was below that in OSEM when ß = 150-1000 and 400-1000 for [18 F]FDG and [11 C]PiB, respectively. The phantom study restricted the ranges of ß values to 100-300 and 300-500 for [18 F]FDG and [11 C]PiB, respectively. The BPL scores for gray-white matter contrast and image noise, exceeded those of OSEM in [18 F]FDG and [11 C]PiB images regardless of ß values. Visual evaluation confirmed that the optimal ß values were 200 and 450 for [18 F]FDG and [11 C]PiB, respectively. CONCLUSIONS: The BPL achieved better image contrast and less image noise than OSEM, while maintaining quantitative standardized uptake value ratios (SUVR) due to full convergence, more rigorous noise control, and edge preservation. The optimal ß values for [18 F]FDG and [11 C]PiB brain PET were apparently 200 and 450, respectively. The present study provides useful information about how to determine optimal ß values in BPL for brain PET imaging.

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PURPOSE: This study evaluated the effects of new Bayesian penalized likelihood (BPL) reconstruction algorithm on visualization and quantification of upper abdominal malignant tumors in clinical FDG PET/CT examinations, comparing the results to those obtained by an ordered subset expectation maximization (OSEM) reconstruction algorithm. Metabolic tumor volume (MTV) and texture features (TFs), as well as SUV-related metrics, were evaluated to clarify the BPL effects on quantification. MATERIALS AND METHODS: A total of 153 upper abdominal lesions (82 liver metastatic and 71 pancreatic cancers) were included in this study. FDG PET/CT images were acquired with a GE Discovery 710 scanner equipped with a time-of-flight system. Images were reconstructed using OSEM and BPL (beta 700) algorithms. In 58 lesions <1.5 cm in greatest diameter (small-lesion group), visual image quality of each lesion was evaluated using a four-point scale. SUVmax was obtained for quantitative metrics. Visual scores and SUVmax were compared between OSEM and BPL images. In 95 lesions >2.0 cm in greatest diameter (larger-lesion group), SUVmax, SUVpeak, MTV, and six TFs were compared between OSEM and BPL images. In addition to the size-based analyses, an increase of SUVmax with BPL was evaluated according to the original SUVmax in OSEM images. RESULTS: In the small-lesion group, both visual score and SUVmax were significantly higher in the BPL than OSEM images. The increase in visual score was observed in 20 (34%) of all 58 lesions. In the larger-lesion group, no statistical difference was observed in SUVmax, SUVpeak, or MTV between OSEM and BPL images. BPL increased high gray-level zone emphasis and decreased low gray-level zone emphasis among six TFs compared to OSEM with statistical significance. No statistical differences were observed in other TFs. SUVmax-based analysis demonstrated that BPL increased and decreased SUVmax in lesions with low (<5) and high (>10) SUVmax in original OSEM images, respectively. CONCLUSION: This study demonstrated that BPL improved conspicuity of small or low-count upper abdominal malignant lesions in clinical FDG PET/CT examinations. Only two TFs represented significant differences between OSEM and BPL images of all quantitative metrics in larger lesions.

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RATIONALE: To formally determine the repeatability of Ga-68 PSMA lesion uptake in both relapsing and metastatic tumor. In addition, it was hypothesized that the BPL algorithm Q. Clear has the ability to lower SUV signal variability in the small lesions typically encountered in Ga-68 PSMA PET imaging of prostate cancer. METHODS: Patients with biochemical recurrence of prostate cancer were prospectively enrolled in this single center pilot test-retest study and underwent two Ga-68 PSMA PET/CT scans within 7.9 days on average. Lesions were classified as suspected local recurrence, lymph node metastases or bone metastases. Two datasets were generated: one standard PSF + OSEM and one with PSF + BPL reconstruction algorithm. For tumor lesions, SUVmax was determined. Repeatability was formally assessed using Bland-Altman analysis for both BPL and standard reconstruction. RESULTS: A total number of 65 PSMA-positive tumor lesions were found in 23 patients (range 1 to 12 lesions a patient). Overall repeatability in the 65 lesions was -1.5% ± 22.7% (SD) on standard reconstructions and -2.1% ± 29.1% (SD) on BPL reconstructions. Ga-68 PSMA SUVmax had upper and lower limits of agreement of +42.9% and -45.9% for standard reconstructions and +55.0% and -59.1% for BPL reconstructions, respectively (NS). Tumor SUVmax repeatability was dependent on lesion area, with smaller lesions exhibiting poorer repeatability on both standard and BPL reconstructions (F-test, p < 0.0001). CONCLUSION: A minimum response of 50% seems appropriate in this clinical situation. This is more than the recommended 30% for other radiotracers and clinical situations (PERCIST response criteria). BPL does not seem to lower signal variability in these cases.

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Functional imaging with 68Ga prostate-specific membrane antigen (PSMA) and positron emission tomography (PET) can fulfill an important role in treatment selection and adjustment in prostate cancer. This article focusses on quantitative assessment of 68Ga-PSMA-PET. The effect of various parameters on standardized uptake values (SUVs) is explored, and an optimal Bayesian penalized likelihood (BPL) reconstruction is suggested. PET acquisitions of two phantoms consisting of a background compartment and spheres with diameter 4 mm to 37 mm, both filled with solutions of 68Ga in water, were performed with a GE Discovery 710 PET/CT scanner. Recovery coefficients (RCs) in multiple reconstructions with varying noise penalty factors and acquisition times were determined and analyzed. Apparent recovery coefficients of spheres with a diameter smaller than 17 mm were significantly lower than those of spheres with a diameter of 17 mm and bigger (p < 0.001) for a tumor-to-background (T/B) ratio of 10:1 and a scan time of 10 min per bed position. With a T/B ratio of 10:1, the four largest spheres exhibit significantly higher RCs than those with a T/B ratio of 20:1 (p < 0.0001). For spheres with a diameter of 8 mm and less, alignment with the voxel grid potentially affects the RC. Evaluation of PET/CT scans using (semi-)quantitative measures such as SUVs should be performed with great caution, as SUVs are influenced by scanning and reconstruction parameters. Based on the evaluation of multiple reconstructions with different ß of phantom scans, an intermediate ß (600) is suggested as the optimal value for the reconstruction of clinical 68Ga-PSMA PET/CT scans, considering that both detectability and reproducibility are relevant.

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PURPOSE: This study aims to provide a detailed investigation on the noise penalization factor in Bayesian penalized likelihood (BPL)-based algorithm, with the utilization of partial volume effect correction (PVC), so as to offer the suitable beta value and optimum standardized uptake value (SUV) parameters in clinical practice for small pulmonary nodules. METHODS: A National Electrical Manufacturers Association (NEMA) image-quality phantom was scanned and images were reconstructed using BPL with beta values ranged from 100 to 1000. The recovery coefficient (RC), contrast recovery (CR), and background variability (BV) were measured to assess the quantification accuracy and image quality. In the clinical assessment, lesions were categorized into sub-centimeter (<10 mm, n = 7) group and medium size (10-30 mm, n = 16) group. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured to evaluate the image quality and lesion detectability. With PVC was performed, the impact of beta values on SUVs (SUVmax, SUVmean, SUVpeak) of small pulmonary nodules was evaluated. Subjective image analysis was performed by two experienced readers. RESULTS: With the increasing of beta values, RC, CR, and BV decreased gradually in the phantom work. In the clinical study, SNR and CNR of both groups increased with the beta values (P < 0.001), although the sub-centimeter group showed increases after the beta value reached over 700. In addition, highly significant negative correlations were observed between SUVs and beta values for both lesion-size groups before the PVC (P < 0.001 for all). After the PVC, SUVpeak measured from the sub-centimeter group was no significantly different among different beta values (P = 0.830). CONCLUSION: Our study suggests using SUVpeak as the quantification parameter with PVC performed to mitigate the effects of beta regularization. Beta values between 300 and 400 were preferred for pulmonary nodules smaller than 30 mm.

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OBJECTIVE: To evaluate the value of Bayesian penalized likelihood (BPL) reconstruction for improving lesion conspicuity of malignant lung tumors on 18F-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography computed tomography (PET/CT) as compared with the ordered subset expectation maximization (OSEM) reconstruction incorporating time-of-flight (TOF) model and point-spread-function (PSF) correction. METHODS: Twenty-nine patients with primary or metastatic lung cancers who underwent 18F-FDG PET/CT were retrospectively studied. PET images were reconstructed with OSEM + TOF, OSEM + TOF + PSF, and BPL with noise penalty strength ß-value of 200, 400, 600, and 800. The signal-to-noise ratio (SNR) was determined in normal liver parenchyma. Lung lesion conspicuity was evaluated in 50 lung lesions by using a 4-point scale (0, no visible; 1, poor; 2, good; 3, excellent conspicuity). Two observers were independently asked to choose the most preferred reconstruction for detecting the lung lesions on a per-patient level. The maximum standardized uptake value (SUVmax) was measured in each of the 50 lung lesions. RESULTS: Liver SNR on the images reconstructed by BPL with ß-value of 600 and 800 (17.8 ± 3.7 and 22.5 ± 4.6, respectively) was significantly higher than that by OSEM + TOF + PSF (15.0 ± 3.4, p < 0.0001). BPL with ß-value of 600 was chosen most frequently as the preferred reconstruction algorithm for lung lesion assessment by both observers. The conspicuity score of the lung lesions < 10 mm in diameter on images reconstructed by BPL with ß-value of 600 was significantly greater than that with OSEM + TOF + PSF (2.2 ± 0.8 vs 1.6 ± 0.9, p < 0.0001), while the conspicuity score of the lesions ≥ 10 mm in diameter was not significantly different between BPL with ß-value of 600 and OSEM + TOF + PSF. The mean SUVmax was increased by BPL with ß-value of 600 for the lung lesions with < 10 mm in diameter, compared to OSEM + TOF + PSF (3.4 ± 3.1 to 4.2 ± 3.5, p = 0.001). In contrast, BPL with ß-value of 600 did not provide increased SUVmax for the lesions ≥ 10 mm in diameter. CONCLUSION: BPL reconstruction significantly improves the detection of small inconspicuous malignant tumors in the lung, improving the diagnostic performance of PET/CT.

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OBJECTIVE. The purpose of this study was to characterize the Bayesian penalized likelihood (BPL) reconstruction algorithm in comparison with an ordered subset expectation maximization (OSEM) reconstruction algorithm and to determine its optimal penalization factor (expressed as a beta value) for clinical use. MATERIALS AND METHODS. FDG PET/CT scans of 46 patients with lung cancer were reconstructed using OSEM and BPL with beta values of 200, 300, 400, 500, and 1000. The liver signal-to-noise ratio, mean standardized uptake value (SUVmean) of the liver, and maximum standardized uptake value (SUVmax) and SUVmean of the cancers were measured. Tumors were categorized into three size groups, and the percentage difference in the tumor SUVmax between OSEM and BPL with a beta value of 200 as well as the percentage difference in the SUVmax between BPL with a beta value of 200 and BPL with a beta value of 1000 were calculated. Image quality was assessed by visual scoring. RESULTS. BPL showed a significantly higher liver signal-to-noise ratio than OSEM, except for BPL with a beta value of 200. The liver SUVmean showed no statistical difference among all algorithms. The SUVmax and SUVmean of tumors decreased as the beta value increased. BPL with a beta value of 200 produced a significantly higher tumor SUVmax than did OSEM (p < 0.01), and BPL with a beta value of 400, 500, or 1000 produced a significantly lower tumor SUVmax than did OSEM (p < 0.01). Visual analysis showed the highest and lowest scores for BPL with beta values of 500 and 200, respectively. In the small size group, the percentage difference in the SUVmax between OSEM and BPL with a beta value of 200 and the percentage difference in the SUVmax between BPL with a beta value of 200 and BPL with a beta value of 1000 were significantly larger than that in the other size groups (p < 0.01). CONCLUSION. The BPL algorithm improves image quality without compromising image quantification. A beta value of 500 appeared to be optimal in this study. Smaller tumors were more influenced by BPL.

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BACKGROUND: Recently, a Bayesian penalized likelihood (BPL) reconstruction algorithm was introduced for a commercial PET/CT with the potential to improve image quality. We compared the performance of this BPL algorithm with conventional reconstruction algorithms under realistic clinical conditions such as daily practiced at many European sites, i.e. low 18F-FDG dose and short acquisition times. RESULTS: To study the performance of the BPL algorithm, regular clinical 18F-FDG whole body PET scans were made. In addition, two types of phantoms were scanned with 4-37 mm sized spheres filled with 18F-FDG at sphere-to-background ratios of 10-to-1, 4-to-1, and 2-to-1. Images were reconstructed using standard ordered-subset expectation maximization (OSEM), OSEM with point spread function (PSF), and the BPL algorithm using ß-values of 450, 550 and 700. To quantify the image quality, the lesion detectability, activity recovery, and the coefficient of variation (COV) within a single bed position (BP) were determined. We found that when applying the BPL algorithm both smaller lesions in clinical studies as well as spheres in phantom studies can be detected more easily due to a higher SUV recovery, especially for higher contrast ratios. Under standard clinical scanning conditions, i.e. low number of counts, the COV is higher for the BPL (ß=450) than the OSEM+PSF algorithm. Increase of the ß-value to 550 or 700 results in a COV comparable to OSEM+PSF, however, at the cost of contrast, though still better than OSEM+PSF. At the edges of the axial field of view (FOV) where BPs overlap, COV can increase to levels at which bands become visible in clinical images, related to the lower local axial sensitivity of the PET/CT, which is due to the limited bed overlap of 23% such as advised by the manufacturer. CONCLUSIONS: The BPL algorithm performs better than the standard OSEM+PSF algorithm on small lesion detectability, SUV recovery, and noise suppression. Increase of the percentage of bed overlap, time per BP, administered activity, or the ß-value, all have a direct positive impact on image quality, though the latter with some loss of small lesion detectability. Thus, BPL algorithms are very interesting for improving image quality, especially in small lesion detectability.

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INTRODUCTION: The aim of this study was to evaluate the behavior of a penalized-likelihood image reconstruction method (Q.Clear) under different count statistics and lesion-to-background ratios (LBR) on a BGO scanner, in order to obtain an optimum penalization factor (ß value) to study and optimize for different acquisition protocols and clinical goals. METHODS: Both phantom and patient images were evaluated. Data from an image quality phantom were acquired using different Lesion-to-Background ratios and acquisition times. Then, each series of the phantom was reconstructed using ß values between 50 and 500, at intervals of 50. Hot and cold contrasts were obtained, as well as background variability and contrast-to-noise ratio (CNR). Fifteen 18 F-FDG patients (five brain scans and 10 torso acquisitions) were acquired and reconstructed using the same ß values as in the phantom reconstructions. From each lesion in the torso acquisition, noise, contrast, and signal-to-noise ratio (SNR) were computed. Image quality was assessed by two different nuclear medicine physicians. Additionally, the behaviors of 12 different textural indices were studied over 20 different lesions. RESULTS: Q.Clear quantification and optimization in patient studies depends on the activity concentration as well as on the lesion size. In the studied range, an increase on ß is translated in a decrease in lesion contrast and noise. The net product is an overall increase in the SNR, presenting a tendency to a steady value similar to the CNR in phantom data. As the activity concentration or the sphere size increase the optimal ß increases, similar results are obtained from clinical data. From the subjective quality assessment, the optimal ß value for torso scans is in a range between 300 and 400, and from 100 to 200 for brain scans. For the recommended torso ß values, texture indices present coefficients of variation below 10%. CONCLUSIONS: Our phantom and patients demonstrate that improvement of CNR and SNR of Q.Clear algorithm which depends on the studied conditions and the penalization factor. Using the Q.Clear reconstruction algorithm in a BGO scanner, a ß value of 350 and 200 appears to be the optimal value for 18F-FDG oncology and brain PET/CT, respectively.

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OBJECTIVE: A study was performed to compare background liver signal-to-noise ratio (SNR) and visually assessed image quality of clinical PET/CT studies from the same PET acquisition data reconstructed by Bayesian penalized likelihood (BPL) and ordered subset expectation maximization (OSEM) over a range of patient weights. MATERIALS AND METHODS: The effect of a BPL PET reconstruction algorithm on liver SNR and visually assessed image quality over a range of patient weights (41-196 kg; n = 108) was retrospectively compared with standard-of-care OSEM reconstruction on the same PET acquisition data after IV administration of 18F-FDG (4 MBq/kg). RESULTS: BPL showed no significant change (p > 0.05) in liver SNR with increasing weight and body mass index (BMI), whereas OSEM showed increasing noise with increasing weight and BMI. The liver SNR was significantly higher using BPL than a standard OSEM reconstruction (p < 0.0002 for all BMI groups). Visually assessed image quality declined at a greater rate with increasing weight and BMI in the OSEM images than with BPL images. CONCLUSION: BPL provides a more consistent visually assessed image quality and liver background SNR than does OSEM, with the greatest benefit for the heaviest patients.

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Imaging on a γ-camera with 90Y after selective internal radiotherapy (SIRT) may allow for verification of treatment delivery but suffers relatively poor spatial resolution and imprecise dosimetry calculation. 90Y PET/CT imaging is possible on 3-dimensional, time-of-flight machines; however, images are usually poor because of low count statistics and noise. A new PET reconstruction software using a Bayesian penalized likelihood (BPL) reconstruction algorithm (termed Q.Clear) was investigated using phantom and patient scans to optimize the reconstruction for post-SIRT imaging and clarify whether BPL leads to an improvement in clinical image quality using 90Y. Methods: Phantom studies over an activity range of 0.5-4.2 GBq were performed to assess the contrast recovery, background variability, and contrast-to-noise ratio for a range of BPL and ordered-subset expectation maximization (OSEM) reconstructions on a PET/CT scanner. Patient images after SIRT were reconstructed using the same parameters and were scored and ranked on the basis of image quality, as assessed by visual evaluation, with the corresponding SPECT/CT Bremsstrahlung images by 2 experienced radiologists. Results: Contrast-to-noise ratio was significantly better in BPL reconstructions when compared with OSEM in phantom studies. The patient-derived BPL and matching Bremsstrahlung images scored higher than OSEM reconstructions when scored by radiologists. BPL with a ß value of 4,000 was ranked the highest of all images. Deadtime was apparent in the system above a total phantom activity of 3.3 GBq. Conclusion: BPL with a ß value of 4,000 is the optimal image reconstruction in PET/CT for confident radiologic reading when compared with other reconstruction parameters for 90Y imaging after SIRT imaging. Activity in the field of view should be below 3.3 GBq at the time of PET imaging to avoid deadtime losses for this scanner.

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