Block Sequential Regularized Expectation Maximization Research Articles

Reduction of [68Ga]Ga-DOTA-TATE injected activity for digital PET/MR in comparison with analogue PET/CT

BackgroundNew digital detectors and block-sequential regularized expectation maximization (BSREM) reconstruction algorithm improve positron emission tomography (PET)/magnetic resonance (MR) image quality. The impact on image quality may differ from analogue PET/computed tomography (CT) protocol. The aim of this study is to determine the potential reduction of injected [68Ga]Ga-DOTA-TATE activity for digital PET/MR with BSREM reconstruction while maintaining at least equal image quality compared to the current analogue PET/CT protocol.MethodsNEMA IQ phantom data and 25 patients scheduled for a diagnostic PET/MR were included. According to our current protocol, 1.5 MBq [68Ga]Ga-DOTA-TATE per kilogram (kg) was injected. After 60 min, scans were acquired with 3 (≤ 70 kg) or 4 (> 70 kg) minutes per bedposition. PET/MR scans were reconstructed using BSREM and factors β 150, 300, 450 and 600. List mode data with reduced counts were reconstructed to simulate scans with 17%, 33%, 50% and 67% activity reduction. Image quality was measured quantitatively for PET/CT and PET/MR phantom and patient data. Experienced nuclear medicine physicians performed visual image quality scoring and lesion counting in the PET/MR patient data.ResultsPhantom analysis resulted in a possible injected activity reduction of 50% with factor β = 600. Quantitative analysis of patient images revealed a possible injected activity reduction of 67% with factor β = 600. Both with equal or improved image quality as compared to PET/CT. However, based on visual scoring a maximum activity reduction of 33% with factor β = 450 was acceptable, which was further limited by lesion detectability analysis to an injected activity reduction of 17% with factor β = 450.ConclusionA digital [68Ga]Ga-DOTA-TATE PET/MR together with BSREM using factor β = 450 result in 17% injected activity reduction with quantitative values at least similar to analogue PET/CT, without compromising on PET/MR visual image quality and lesion detectability.

Study of 68Ga Labelled PET/CT Scan Parameters Optimization

Purpose: Gallium-68 (68Ga) is increasingly used in nuclear medicine imaging for various conditions such as lymphoma and neuroendocrine tumors by labeling tracers like Prostate Specific Membrane Antigen (PSMA) and DOTA-TOC. However, compared to Fluorine-18 (18F) used in conventional nuclear medicine imaging, 68Ga has lower spatial resolution and relatively higher Signal to Background Ratio (SBR). Therefore, this study aimed to investigate the optimized parameters and reconstruction methods for PET/CT imaging using the 68Ga radiotracer through model-based image evaluation. Materials and Methods: Based on clinical images of 68Ga-PSMA PET/CT, a NEMA/IEC 2008 PET phantom model was prepared with a Hot vs Background (H/B) ratio of 10:1. Images were acquired for 9 minutes in list mode using DMIDR (GE, Milwaukee WI, USA). Subsequently, reconstructions were performed for 1 to 8 minutes using OS-EM (Ordered Subset Expectation Maximization) + TOF (Time of Flight) + Sharp IR (VPFX-S), and BSREM (Block Sequential Regularized Expectation Maximization) + TOF + Sharp IR (QCFX-S-400), followed by comparative evaluation. Based on the previous experimental results, images were reconstructed for BSREM + TOF + Sharp IR / 2 minutes (QCFX-S–2min) with varying β-strength values from 100 to 700. The image quality was evaluated using AMIDE (freeware, Ver.1.0.1) and Advanced Workstation (GE, USA). Results: Images reconstructed with QCFX-S-400 showed relatively higher values for SNR (Signal to Noise Ratio), CNR (Contrast to Noise Ratio), count, RC (Recovery Coefficient), and SUV (Standardized Uptake Value) compared to VPFX-S. SNR, CNR, and SUV exhibited the highest values at 2 minutes/bed acquisition time. RC showed the highest values for a 10 mm sphere at 2 minutes/bed acquisition time. For small spheres of 10 mm and 13 mm, an inverse relationship between β-strength increase and count was observed. SNR and CNR peaked at β-strength 400 and then decreased, while SUV and RC exhibited a normal distribution based on sphere size for β-strength values of 400 and above. Conclusion: Based on the experiments, PET/CT imaging using the 68Ga radiotracer yielded the most favorable quantitative and qualitative results with a 2 minutes/bed acquisition time and BSREM reconstruction, particularly when applying β-strength 400. The application of BSREM can enhance accurate quantification and image quality in 68Ga PET/CT imaging, and an optimization process tailored to each institution's imaging objectives appears necessary.

Reduced count pediatric whole-body 18F-FDG PET imaging reconstruction with a Bayesian penalized likelihood algorithm.

Advanced positron emission tomography (PET) image reconstruction methods promise to allow optimized PET/CT protocols with improved image quality,decreased administered activity and/or acquisition times. To evaluate the impact of reducing counts(simulating reduced acquisition time) in block sequential regularized expectation maximization (BSREM) reconstructed pediatric whole-body 18F-fluorodeoxyglucose (FDG) PET images, and to compare BSERM with ordered-subset expectation maximization (OSEM) reconstructed reduced-count images. Twenty children (16 male) underwent clinical whole-body 18F-FDG PET/CT examinations using a 25-cm axial field-of-view (FOV) digital PET/CT system at 90s per bed (s/bed) with BSREM reconstruction (β=700). Reduced count simulations with varied BSREM β levels were generated from list-mode data: 60s/bed, β=800; 50s/bed, β=900; 40s/bed, β=1000; and 30s/bed, β=1300. In addition, a single OSEM reconstruction was created at 60s/bed based on prior literature. Qualitative (Likert scores) and quantitative (standardized uptake value [SUV]) analyses were performed to evaluate image quality and quantitation across simulated reconstructions. The mean patient age was 9.0 ± 5.5 (SD) years, mean weight was 38.5 ± 24.5kg, and mean administered 18F-FDG activity was 4.5 ± 0.7 (SD) MBq/kg. Between BSREM reconstructions, no qualitative measure showed a significant difference versus the 90s/bed β=700 standard (all P>0.05). SUVmax values for lesions were significantly lower from 90s/bed, β=700 only at a simulated acquisition time of 30s/bed, β=1300 (P=0.001). In a side-by-side comparison of BSREM versus OSEM reconstructions, 40s/bed, β=1000 images were generally preferred over 60s/bed TOF OSEM images. In children who undergo whole-body 18F-FDG PET/CT on a 25-cm FOV digital PET/CT scanner, reductions in acquisition time or, by corollary, administered radiopharmaceutical activity of >50% from a clinical standard of 90s/bed may be possible while maintaining diagnostic quality when a BSREM reconstruction algorithm is used.

Influence of different time framings, reconstruction algorithms and post-processing methods on the quantification of myocardial blood flow from 13 N-NH3 PET images.

The aim was to investigate to what extent the quantification of myocardial blood flow (MBF) from dynamic 13 N-NH3 positron emission tomography(PET) images is affected by time frame schemes, time-of-flight (ToF), reconstruction algorithms, blood pool volume of interest (VOI) locations and compartment models in patients with suspected chronic coronary syndrome. A standard MBF value was determined from 25 patients' rest/stress 13 N-NH3 PET/CT images reconstructed with ordered subset expectation maximization (OSEM), 5 s time frame for the first frames without ToF, subsequently analyzed using a basal VOI and the deGrado compartment model. MBFs calculated using 2or 10 s for the first frames, ToF, block-sequential regularized expectation maximization (BSREM), apical or large VOI, Hutchins or Krivokapich compartment models were compared to MBFstandard in Bland-Altman plots (bias ± SD). Good agreement in global rest/stress MBF (mL/min/g) was found when changing the time frame scheme or reconstruction algorithm (MBFstandard vs. MBF2s : -0.02 ± 0.06; MBF10s : 0.01 ± 0.07; MBFBSREM : 0.01 ± 0.07), while a lower level of agreement was found when altering the other factors (MBFstandard vs.MBFToF : -0.07 ± 0.10; MBFapical VOI : -0.27 ± 0.25; MBFlarge VOI : -0.11 ± 0.10; MBFHutchins : -0.08 ± 0.10; MBFKrivokapich : -0.47 ± 0.50). Quantification of MBF from 13 N-NH3 PET images is more affected by choice of compartment models, ToF and blood pool VOIs than by different time frame schemes and reconstruction algorithms.

Quantitative 177Lu SPECT/CT imaging for personalized dosimetry using a ring-shaped CZT-based camera

BackgroundDosimetry after radiopharmaceutical therapy with 177Lu (177Lu-RPT) relies on quantitative SPECT/CT imaging, for which suitable reconstruction protocols are required. In this study, we characterized for the first time the quantitative performance of a ring-shaped CZT-based camera using two different reconstruction algorithms: an ordered subset expectation maximization (OSEM) and a block sequential regularized expectation maximization (BSREM) combined with noise reduction regularization. This study lays the foundations for the definition of a reconstruction protocol enabling accurate dosimetry for patients treated with 177Lu-RPT.MethodsA series of 177Lu-filled phantoms were acquired on a StarGuide™ (GE HealthCare), with energy and scatter windows centred at 208 (± 6%) keV and 185 (± 5%) keV, respectively. Images were reconstructed with the manufacturer implementations of OSEM (GE-OSEM) and BSREM (Q.Clear) algorithms, and various combinations of iterations and subsets. Additionally, the manufacturer-recommended Q.Clear-based reconstruction protocol was evaluated. Quantification accuracy, measured as the difference between the SPECT-based and the radionuclide calibrator-based activity, and noise were evaluated in a large cylinder. Recovery coefficients (RCs) and spatial resolution were assessed in a NEMA IEC phantom with sphere inserts. The reconstruction protocols considered suitable for clinical applications were tested on a cohort of patients treated with [177Lu]Lu-PSMA-I&T.ResultsThe accuracy of the activity from the cylinder, although affected by septal penetration, was < 10% for all reconstructions. Both algorithms featured improved spatial resolution and higher RCs with increasing updates at the cost of noise build-up, but Q.Clear outperformed GE-OSEM in reducing noise accumulation. When the reconstruction parameters were carefully selected, similar values for noise (~0.15), spatial resolution (~1 cm) and RCs were found, irrespective of the reconstruction algorithm. Analogue results were found in patients.ConclusionsAccurate activity quantification is possible when imaging 177Lu with StarGuide™. However, the impact of septal penetration requires further investigations. GE-OSEM is a valid alternative to the recommended Q.Clear reconstruction algorithm, featuring comparable performances assessed on phantoms and patients.

Fully automated computational measurement of noise in positron emission tomography

ObjectivesTo introduce an automated computational algorithm that estimates the global noise level across the whole imaging volume of PET datasets.Methods[18F]FDG PET images of 38 patients were reconstructed with simulated decreasing acquisition times (15–120 s) resulting in increasing noise levels, and with block sequential regularized expectation maximization with beta values of 450 and 600 (Q.Clear 450 and 600). One reader performed manual volume-of-interest (VOI) based noise measurements in liver and lung parenchyma and two readers graded subjective image quality as sufficient or insufficient. An automated computational noise measurement algorithm was developed and deployed on the whole imaging volume of each reconstruction, delivering a single value representing the global image noise (Global Noise Index, GNI). Manual noise measurement values and subjective image quality gradings were compared with the GNI.ResultsIrrespective of the absolute noise values, there was no significant difference between the GNI and manual liver measurements in terms of the distribution of noise values (p = 0.84 for Q.Clear 450, and p = 0.51 for Q.Clear 600). The GNI showed a fair to moderately strong correlation with manual noise measurements in liver parenchyma (r = 0.6 in Q.Clear 450, r = 0.54 in Q.Clear 600, all p < 0.001), and a fair correlation with manual noise measurements in lung parenchyma (r = 0.52 in Q.Clear 450, r = 0.33 in Q.Clear 600, all p < 0.001). Classification performance of the GNI for subjective image quality was AUC 0.898 for Q.Clear 450 and 0.919 for Q.Clear 600.ConclusionAn algorithm provides an accurate and meaningful estimation of the global noise level encountered in clinical PET imaging datasets.Clinical relevance statementAn automated computational approach that measures the global noise level of PET imaging datasets may facilitate quality standardization and benchmarking of clinical PET imaging within and across institutions.Key Points• Noise is an important quantitative marker that strongly impacts image quality of PET images.• An automated computational noise measurement algorithm provides an accurate and meaningful estimation of the global noise level encountered in clinical PET imaging datasets.• An automated computational approach that measures the global noise level of PET imaging datasets may facilitate quality standardization and benchmarking as well as protocol harmonization.

First clinical experience of a ring-configured cadmium zinc telluride camera: A comparative study versus conventional gamma camera systems.

A novel semiconductor cadmium zinc telluride (CZT) gamma camera system using a block sequential regularized expectation maximization (BSREM) reconstruction algorithm is now clinically available. Here we investigate how a multi-purpose ring-configurated CZT system can be safely applied in clinics and describe the initial optimization process. Seventy-six patients (bone-, cardiac- and lung scan) were scanned on a conventional gamma camera (planar and/or single-photon emission computed tomography [SPECT]/SPECT-CT) used in clinical routine and on the ring-configurated CZT camera Starguide (GE Healthcare). These data were used to validate and optimize the Starguide system for routine clinical use. Comparable image quality for the Starguide system, to that of the conventional gamma camera, was achieved for bone scan (4 min/bed position [BP] using a relative difference prior [RDP] with gamma 2 and beta 0.4, along with 10 iterations and 10 subsets), cardiac scan (8 min [stress] and 3 min 20 s [rest] using median root prior [MRP] with beta 0.07 non attenuation corrected and 0.008 attenuation corrected and 50 interations and 10 subsets for both stress and rest) and lung scan (10 min [vent] and 5 min [perf] using RDP with gamma 0.5 and beta 0.03 [vent] and 0.02 [perf] and 20 interations and 10 subsets for both vent and perf). It was possible to transition from a conventional gamma camera to the Starguide system as part of the clinical routine, with acceptable image quality. Images from the Starguide system were deemed to be at least as good as those from a conventional gamma camera.

Phantom and clinical evaluation of Block Sequential Regularized Expectation Maximization (BSREM) reconstruction algorithm in 68Ga-PSMA PET-CT studies.

In this study, we aimed to examine the effect of varying β-values in the block sequential regularized expectation maximization (BSREM) algorithm under differing lesion sizes to determine an optimal penalty factor for clinical application. The National Electrical Manufacturers Association phantom and 15 prostate cancer patients were injected with 68Ga-PSMA and scanned using a GE Discovery IQ PET/CT scanner. Images were reconstructed using ordered subset expectation maximization (OSEM) and BSREM with different β-values. Then, the background variability (BV), contrast recovery, signal-to-noise ratio, and lung residual error were measured from the phantom data, and the signal-to-background ratio (SBR) and contrast from the clinical data. The increment of BV using a β-value of 100 was 120.0%, and the decrement of BV using a β-value of 1000 was 40.5% compared to OSEM. As β decreased from 1000 to 100, the [Formula: see text] increased by 59.0% for a sphere with a diameter of 10mm and 26.4% for a sphere with a diameter of 37mm. Conversely, [Formula: see text] increased by 140.5% and 29.0% in the smallest and largest spheres, respectively. Furthermore, the Δ[Formula: see text] and Δ[Formula: see text] were - 41.1% and - 36.7%, respectively. In the clinical study, OSEM exhibited the lowest SBR and contrast. When the β-value was reduced from 500 to 100, the SBR and contrast increased by 69.7% and 71.8% in small and 35.6% and 33.0%, respectively, in large lesions. Moreover, the optimal β-value decreased as lesion size decreased. In conclusion, a β-value of 400 is optimal for small lesion reconstruction, while β-values of 600 and 500 are optimal for large lesions in phantom and clinical studies, respectively.

Automated F18-FDG PET/CT image quality assessment using deep neural networks on a latest 6-ring digital detector system

To evaluate whether a machine learning classifier can evaluate image quality of maximum intensity projection (MIP) images from F18-FDG-PET scans. A total of 400 MIP images from F18-FDG-PET with simulated decreasing acquisition time (120 s, 90 s, 60 s, 30 s and 15 s per bed-position) using block sequential regularized expectation maximization (BSREM) with a beta-value of 450 and 600 were created. A machine learning classifier was fed with 283 images rated “sufficient image quality” and 117 images rated “insufficient image quality”. The classification performance of the machine learning classifier was assessed by calculating sensitivity, specificity, and area under the receiver operating characteristics curve (AUC) using reader-based classification as the target. Classification performance of the machine learning classifier was AUC 0.978 for BSREM beta 450 and 0.967 for BSREM beta 600. The algorithm showed a sensitivity of 89% and 94% and a specificity of 94% and 94% for the reconstruction BSREM 450 and 600, respectively. Automated assessment of image quality from F18-FDG-PET images using a machine learning classifier provides equivalent performance to manual assessment by experienced radiologists.

Influence of image reconstruction on quantitative cardiac 15O-water positron emission tomography

BackgroundThe impact on quantitative 15O-water PET/CT of a wide range of different reconstruction settings, including regularized reconstruction by block-sequential regularized expectation maximization (BSREM), was investigated. MethodsTwenty clinical stress scans from patients referred for assessment of myocardial ischemia were included. Patients underwent a 4-min dynamic stress PET scan with 15O-water on a digital PET/CT scanner. Twenty-two reconstructions were generated from each scan and a clinical reconstruction was used as reference. Varied parameters were number of iterations, filter, exclusion of time-of-flight and point-spread function, and regularization parameter with BSREM. Analyses were performed in aQuant utilizing two different methods and resulting regional myocardial blood flow (MBF), perfusable tissue fraction (PTF), and transmural MBF (MBFt) values were evaluated. ResultsAcross the two analyses, correlations toward the reference reconstruction were strong for all parameters (ρ ≥ 0.83). Using automated analysis and the diagnostic threshold of hyperemic MBF at 2.3 mL⋅g−1⋅min−1, diagnosis was unchanged irrespective of reconstruction method in all patients except for one, where only four of the most extreme reconstruction methods resulted in a change of diagnosis. ConclusionThe low sensitivity of MBF values to reconstruction method and, as previously shown, scanner type and PET/CT misalignment, confirms that diagnostic hyperemic MBF cutoff values can be consistently used for 15O-water.

Optimization of Bayesian penalized likelihood reconstruction for 68Ga-prostate-specific membrane antigen-11 PET/computed tomography.

The objective of this study is to determine the optimal β value for clinical use in digital 68Ga-prostate-specific membrane antigen (PSMA-11) PET/computed tomography (CT) imaging. 68Ga PSMA PET/CT of 21 patients with prostate cancer were reconstructed using block-sequential regularized expectation maximization (β value of 400-1600) and ordered subsets expectation maximization. Nine independent blinded readers evaluated each reconstruction for overall image quality, noise level and lesion detectability. Maximum standardized uptake value (SUVmax) of the most intense lesion, liver SUVmean and liver SUVSD were recorded. Lesions were then subdivided according to uptake and size; the SUVmax of these lesions were analyzed. There is a statistically significant correlation between improvement in image quality and β value, with the best being β 1400. This trend was also seen in image noise (P < 0.001), with the least image noise reported with β 1400. Lesion detectability was not significantly different between the different β values (P = 0.6452). There was no statistically significant difference in SUVmax of the most intense lesion (P = 0.9966) and SUVmean of liver background between the different β values (P = 0.9999); however, the SUVSD of the liver background showed a clear trend, with the lowest with β 1400 (P = 0.0008). There was a decreasing trend observed in SUVmax when β values increased from 800 to 1400 for all four subgroups, and this decrease was greatest in small and low uptake lesions. Bayesian penalized likelihood reconstruction algorithms improve image quality without affecting lesion detectability. A β value of 1400 is optimal.

A Fast Convergent Ordered-Subsets Algorithm With Subiteration-Dependent Preconditioners for PET Image Reconstruction.

We investigated the imaging performance of a fast convergent ordered-subsets algorithm with subiteration-dependent preconditioners (SDPs) for positron emission tomography (PET) image reconstruction. In particular, we considered the use of SDP with the block sequential regularized expectation maximization (BSREM) approach with the relative difference prior (RDP) regularizer due to its prior clinical adaptation by vendors. Because the RDP regularization promotes smoothness in the reconstructed image, the directions of the gradients in smooth areas more accurately point toward the objective function's minimizer than those in variable areas. Motivated by this observation, two SDPs have been designed to increase iteration step-sizes in the smooth areas and reduce iteration step-sizes in the variable areas relative to a conventional expectation maximization preconditioner. The momentum technique used for convergence acceleration can be viewed as a special case of SDP. We have proved the global convergence of SDP-BSREM algorithms by assuming certain characteristics of the preconditioner. By means of numerical experiments using both simulated and clinical PET data, we have shown that the SDP-BSREM algorithms substantially improve the convergence rate, as compared to conventional BSREM and a vendor's implementation as Q.Clear. Specifically, SDP-BSREM algorithms converge 35%-50% faster in reaching the same objective function value than conventional BSREM and commercial Q.Clear algorithms. Moreover, we showed in phantoms with hot, cold and background regions that the SDP-BSREM algorithms approached the values of a highly converged reference image faster than conventional BSREM and commercial Q.Clear algorithms.

Impact of Bayesian penalized likelihood reconstruction on quantitative and qualitative aspects for pulmonary nodule detection in digital 2-[18F]FDG-PET/CT

To evaluate the impact of block sequential regularized expectation maximization (BSREM) reconstruction on quantitative and qualitative aspects of 2-[18F]FDG-avid pulmonary nodules compared to conventional ordered subset expectation maximization (OSEM) reconstruction method. Ninety-one patients with 144 2-[18F]FDG-avid pulmonary nodules (all ≤ 20 mm) undergoing PET/CT for oncological (re-)staging were retrospectively included. Quantitative parameters in BSREM and OSEM (including point spread function modelling) were measured, including maximum standardized uptake value (SUVmax). Nodule conspicuity in BSREM and OSEM images was evaluated by two readers. Wilcoxon matched pairs signed-rank test was used to compare quantitative and qualitative parameters in BSREM and OSEM. Pulmonary nodule SUVmax was significantly higher in BSREM images compared to OSEM images [BSREM 5.4 (1.2–20.7), OSEM 3.6 (0.7–17.4); p = 0.0001]. In a size-based analysis, the relative increase in SUVmax was more pronounced in smaller nodules (≤ 7 mm) as compared to larger nodules (8–10 mm, or > 10 mm). Lesion conspicuity was higher in BSREM than in OSEM (p < 0.0001). BSREM reconstruction results in a significant increase in SUVmax and a significantly improved conspicuity of small 2-[18F]FDG-avid pulmonary nodules compared to OSEM reconstruction. Digital 2-[18F]FDG-PET/CT reading may be enhanced with BSREM as small lesion conspicuity is improved.

BSREM for Brain Metastasis Detection with 18F-FDG-PET/CT in Lung Cancer Patients

The aim of the study was to analyze the use of block sequential regularized expectation maximization (BSREM) with different β-values for the detection of brain metastases in digital fluorine-18 labeled 2-deoxy-2-fluoro-D-glucose (18F-FDG) PET/CT in lung cancer patients. We retrospectively analyzed staging/restaging 18F-FDG PET/CT scans of 40 consecutive lung cancer patients with new brain metastases, confirmed by MRI. PET images were reconstructed using BSREM (β-values of 100, 200, 300, 400, 500, 600, 700) and OSEM. Two independent blinded readers (R1 and R2) evaluated each reconstruction using a 4-point scale for general image quality, noise, and lesion detectability. SUVmax of metastases, brain background, target-to-background ratio (TBR), and contrast recovery (CR) ratio were recorded for each reconstruction. Among all reconstruction techniques, differences in qualitative parameters were analyzed using non-parametric Friedman test, while differences in quantitative parameters were compared using analysis of variances for repeated measures. Cohen’s kappa (k) was used to measure inter-reader agreement. The overall detectability of brain metastases was highest for BSREM200 (R1: 2.83 ± 1.17; R2: 2.68 ± 1.32) and BSREM300 (R1: 2.78 ± 1.23; R2: 2.68 ± 1.36), followed by BSREM100, which had lower accuracy owing to noise. The highest median TBR was found for BSREM100 (R1: 2.19 ± 1.05; R2: 2.42 ± 1.08), followed by BSREM200 and BSREM300. Image quality ratings were significantly different among reconstructions (p < 0.001). The median quality score was higher for BSREM100-300, and both noise and metastases’ SUVmax decreased with increasing β-value. Inter-reader agreement was particularly high for the detectability of photopenic metastases and blurring (all k > 0.65). BSREM200 and BSREM300 yielded the best results for the detection of brain metastases, surpassing both BSREM400 and OSEM, typically used in clinical practice.

The effect of Q.Clear reconstruction on quantification and spatial resolution of 18F-FDG PET in simultaneous PET/MR

BackgroundQ.Clear is a block sequential regularized expectation maximization penalized-likelihood reconstruction algorithm for Positron Emission Tomography (PET). It has shown high potential in improving image reconstruction quality and quantification accuracy in PET/CT system. However, the evaluation of Q.Clear in PET/MR system, especially for clinical applications, is still rare. This study aimed to evaluate the impact of Q.Clear on the 18F-fluorodeoxyglucose (FDG) PET/MR system and to determine the optimal penalization factor β for clinical use.MethodsA PET National Electrical Manufacturers Association/ International Electrotechnical Commission (NEMA/IEC) phantom was scanned on GE SIGNA PET/MR, based on NEMA NU 2-2012 standard. Metrics including contrast recovery (CR), background variability (BV), signal-to-noise ratio (SNR) and spatial resolution were evaluated for phantom data. For clinical data, lesion SNR, signal to background ratio (SBR), noise level and visual scores were evaluated. PET images reconstructed from OSEM + TOF and Q.Clear were visually compared and statistically analyzed, where OSEM + TOF adopted point spread function as default procedure, and Q.Clear used different β values of 100, 200, 300, 400, 500, 800, 1100 and 1400.ResultsFor phantom data, as β value increased, CR and BV of all sizes of spheres decreased in general; images reconstructed from Q.Clear reached the peak SNR with β value of 400 and generally had better resolution than those from OSEM + TOF. For clinical data, compared with OSEM + TOF, Q.Clear with β value of 400 achieved 138% increment in median SNR (from 58.8 to 166.0), 59% increment in median SBR (from 4.2 to 6.8) and 38% decrement in median noise level (from 0.14 to 0.09). Based on visual assessment from two physicians, Q.Clear with β values ranging from 200 to 400 consistently achieved higher scores than OSEM + TOF, where β value of 400 was considered optimal.ConclusionsThe present study indicated that, on 18F-FDG PET/MR, Q.Clear reconstruction improved the image quality compared to OSEM + TOF. β value of 400 was optimal for Q.Clear reconstruction.

68Ga-PSMA11 PET/CT for biochemically recurrent prostate cancer: Influence of dual-time and PMT- vs SiPM-based detectors

Objectives68Ga-PSMA11 PET/CT is excellent for evaluating biochemically recurrent prostate cancer (BCR PC). Here, we compared the positivity rates of dual-time point imaging using a PET/CT scanner (DMI) with silicon photomultiplier (SiPM) detectors and a PET/CT scanner (D690) with photomultiplier tubes (PMT), in patients with BCR PC. MethodsFifty-eight patients were prospectively recruited and randomized to receive scans on DMI followed by D690 or vice-versa. Images from DMI were reconstructed using the block sequential regularized expectation maximization (BSREM) algorithm and images from D690 were reconstructed using ordered subset expectation maximization (OSEM), according to the vendor's recommendations. Two readers independently reviewed all images in randomized order, recorded the number and location of lesions, as well as standardized uptake value (SUV) measurements. ResultsTwenty-eight patients (group A) had DMI as first scanner followed by D690, while 30 patients (group B) underwent scans in reversed order. Mean PSA was 30±112.9 (range 0.3–600.66) ng/mL for group A and 41.5 ± 213.2 (range 0.21–1170) ng/mL for group B (P = 0.796). The positivity rate in group A was 78.6% (22/28 patients) vs. 73.3% (22/30 patients) in group B. Although the performance of the two scanners was equivalent on a per-patient basis, DMI identified 5 additional sites of suspected recurrent disease when used as first scanner. The second scan time point did not reveal additional abnormal uptake. ConclusionsThe delayed time point in 68Ga-PSMA11 PET/CT did not show a higher positivity rate. SiPM-based PET/CT identified additional lesions. Further studies with larger cohorts are needed to confirm these results.

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.

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.

Improved detection of in-transit metastases of malignant melanoma with BSREM reconstruction in digital [18F]FDG PET/CT

ObjectivesTo compare block sequential regularized expectation maximization (BSREM) and ordered subset expectation maximization (OSEM) for the detection of in-transit metastasis (ITM) of malignant melanoma in digital [18F]FDG PET/CT.MethodsWe retrospectively analyzed a cohort of 100 [18F]FDG PET/CT scans of melanoma patients with ITM, performed between May 2017 and January 2020. PET images were reconstructed with both OSEM and BSREM algorithms. SUVmax, target-to-background ratio (TBR), and metabolic tumor volume (MTV) were recorded for each ITM. Differences in PET parameters were analyzed with the Wilcoxon signed-rank test. Differences in image quality for different reconstructions were tested using the Man-Whitney U test.ResultsBSREM reconstruction led to the detection of 287 ITM (39% more than OSEM). PET parameters of ITM were significantly different between BSREM and OSEM reconstructions (p < 0.001). SUVmax and TBR were higher (76.5% and 77.7%, respectively) and MTV lower (49.5%) on BSREM. ITM missed with OSEM had significantly lower SUVmax (mean 2.03 vs. 3.84) and TBR (mean 1.18 vs. 2.22) and higher MTV (mean 2.92 vs. 1.01) on OSEM compared to BSREM (all p < 0.001).ConclusionsBSREM detects significantly more ITM than OSEM, owing to higher SUVmax, higher TBR, and less blurring. BSREM is particularly helpful in small and less avid lesions, which are more often missed with OSEM.Key Points• In melanoma patients, [18F]FDG PET/CT helps to detect in-transit metastases (ITM), and their detection is improved by using BSREM instead of OSEM reconstruction.• BSREM is particularly useful in small lesions.

Impact of image reconstruction method on dose distributions derived from 90Y PET images: phantom and liver radioembolization patient studies

PET images acquired after liver 90Y radioembolization therapies are typically very noisy, which significantly challenges both visualization and quantification of activity distributions. To improve their noise characteristics, regularized iterative reconstruction algorithms such as block sequential regularized expectation maximization (Q.Clear for GE Healthcare, USA) have been proposed. In this study, we aimed to investigate the effects which different reconstruction algorithms may have on patient images, with reconstruction parameters initially narrowed down using phantom studies. Moreover, we evaluated the impact of these reconstruction methods on voxel-based dose distribution in phantom and patient studies (lesions and healthy livers). The International Electrotechnical Commission (IEC)/NEMA phantom, containing six spheres, was filled with 90Y and imaged using a GE Discovery 690 PET/CT scanner with time-of-flight enabled. The images were reconstructed using Q.Clear (with β parameter ranging from 0 to 8000) and ordered subsets expectation maximization. The image quality and quantification accuracy were evaluated by computing the hot () and cold () contrast recovery coefficients, background variability (BV) and activity bias. Next, dose distributions and dose volume histograms were generated using MIM® software’s SurePlan LiverY90 toolbox. Subsequently, parameters optimized in these phantom studies were applied to five patient datasets. Dose parameters, such as D max, D mean, D 70, and V 100Gy , were estimated, and their variability for different reconstruction methods was investigated. Based on phantom studies, the β parameter values optimized for image quality and quantification accuracy were 2500 and 300, respectively. When all investigated reconstructions were applied to patient studies, D mean, D50, D 70, and V 100Gy showed coefficients of variation below 8%; whereas the variability of D max was up to 30% for both phantom and patient images. Although β = 300−1000 would provide accurate activity quantification for a region of interest, when considering activity/dose voxelized distribution, higher β value (e.g. 4000–5000) would provide the greatest accuracy for dose distributions. In this 90Y radioembolization PET/CT study, the β parameter in regularized iterative (Q.Clear) reconstruction was investigated for image quality, accurate quantification and dose distributions based on phantom experiments and then applied to patient studies. Our results indicate that more accurate dose distribution can be achieved from smoother PET images, reconstructed with larger β values than those yielding the best activity quantifications but noisy images. Most importantly, these results suggest that quantitative measures, which are commonly used in clinics, such as SUVmax or SUVpeak( equivalent of D max), should not be employed for 90Y PET images, since their values would highly depend on the image reconstruction.

Evaluation of block-sequential regularized expectation maximization reconstruction of 68Ga-DOTATOC, 18F-fluoride, and 11C-acetate whole-body examinations acquired on a digital time-of-flight PET/CT scanner

BackgroundBlock-sequential regularized expectation maximization (BSREM) is a fully convergent iterative image reconstruction algorithm. We hypothesize that tracers with different distribution patterns will result in different optimal settings for the BSREM algorithm. The aim of this study was to evaluate the image quality with variations in the applied β-value and acquisition time for three positron emission tomography (PET) tracers. NEMA image quality phantom measurements and clinical whole-body digital time-of-flight (TOF) PET/computed tomography (CT) examinations with 68Ga-DOTATOC (n = 13), 18F-fluoride (n = 10), and 11C-acetate (n = 13) were included. Each scan was reconstructed using BSREM with β-values of 133, 267, 400, and 533, and ordered subsets expectation maximization (OSEM; 3 iterations, 16 subsets, and 5-mm Gaussian post-processing filter). Both reconstruction methods included TOF and point spread function (PSF) recovery. Quantitative measures of noise, signal-to-noise ratio (SNR), and signal-to-background ratio (SBR) were analysed for various acquisition times per bed position (bp).ResultsThe highest β-value resulted in the lowest level of noise, which in turn resulted in the highest SNR and lowest SBR. Noise levels equal to or lower than those of OSEM were found with β-values equal to or higher than 400, 533, and 267 for 68Ga-DOTATOC, 18F-fluoride, and 11C-acetate, respectively. The specified β-ranges resulted in increased SNR at a minimum of 25% (P < 0.0001) and SBR at a maximum of 23% (P < 0.0001) as compared to OSEM. At a reduced acquisition time by 25% for 68Ga-DOTATOC and 18F-fluoride, and 67% for 11C-acetate, BSREM with β-values equal to or higher than 533 resulted in noise equal to or lower than that of OSEM at full acquisition duration (2 min/bp for 68Ga-DOTATOC and 18F-fluoride, 3 min/bp for 11C-acetate). The reduced acquisition time with β 533 resulted in increased SNR (16–26%, P < 0.003) and SBR (6–18%, P < 0.0001 (P = 0.07 for 11C-acetate)) compared to the full acquisition OSEM.ConclusionsWithin tracer-specific ranges of β-values, BSREM reconstruction resulted in increased SNR and SBR with respect to conventional OSEM reconstruction. Similar SNR, SBR, and noise levels could be attained with BSREM at relatively shorter acquisition times or, alternatively, lower administered dosages, compared to those attained with OSEM.