Evaluation and Optimization of a New PET Reconstruction Algorithm, Bayesian Penalized Likelihood Reconstruction, for Lung Cancer Assessment According to Lesion Size

Optimizing time-of-flight of PET/CT image quality via penalty β value in Bayesian penalized likelihood reconstruction algorithm

INTRODUCTION

Optimizing the image quality of Positron Emission Tomography/Computed Tomography (PET/CT) systems is crucial for effective monitoring, diagnosis, and treatment planning in oncology. This study evaluates the impact of time-of-flight (TOF) on PET/CT performance, focusing on varying penalty β values within Q. Clear reconstruction algorithm.

METHODS

The study measured signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) using the Discovery MI PET/CT scanner and NEMA IQ phantom filled with the radiotracer fluorodeoxyglucose (18F-FDG). PET/CT scans were performed with and without TOF using β values of 100, 500, 1000, 1500, 2000, and 3000. Pixel intensity values were measured using ImageJ software, and SNR and CNR were calculated.

RESULTS

Results indicated that increasing β values improved SNR and CNR for both non-TOF and TOF images. At a β value of 100, SNR and CNR increased across all sphere sizes (10 mm, 13 mm, 17 mm, 22 mm, 28 mm, 37 mm) when comparing non-TOF and TOF images. However, β values of 500 or higher led to decreased SNR and CNR, particularly in larger spheres (22 mm, 28 mm, 37 mm), when TOF was utilized.

CONCLUSION

These findings underscore the importance of optimizing β values and employing TOF reconstruction in PET/CT scans to achieve the highest possible image quality.

IMPLICATIONS FOR PRACTICE

In clinical practice, practitioners should adjust β values in accordance with routine protocols, considering the size of the target region and the use of TOF reconstruction.

Steroids May Be Associated With Extensive Skeletal Muscle Uptake of 18 F-FDG

PURPOSE

The possibility of steroid administration inducing the extensive skeletal muscle uptake (ESMU) of FDG in PET scans was investigated.

METHODS

From 8923 consecutive 18 F-FDG PET/CT scans taken at our hospital, 23 scans (15 patients) met adult age and ESMU-positive inclusion criteria. Among the 15 patients, 13 with both ESMU-positive and -negative scans were examined for association with steroid administration.

RESULTS

Extensive skeletal muscle uptake was associated with a history of steroid administration ( χ2 test: P = 0.001). Notably, 20 ESMU-positive scans and 11 ESMU-negative scans were significantly different, with 0 to 95 days (median, 18.5 days) and 0 to 708 days (median, 319.0 days) since the last steroid administration, respectively (Mann-Whitney U test, P = 0.003). A significant correlation was observed between mean skeletal muscle SUV max and the number of days since the last steroid administration (Spearman rank correlation coefficient, ρ = -0.501, P = 0.004). Specifically, the degree of ESMU tended to decrease over time, after steroid administration. From multiple regression analysis, the number of days since the last steroid administration was significantly associated with mean SUV max ( P = 0.007), but the blood glucose level was not significant ( P = 0.204), revealing that the number of days since the last steroid administration was an independent risk factor. Multicollinearity was low (the variance inflation factor was 1.007 for both the number of days since the last steroid administration and blood glucose levels).

CONCLUSIONS

Steroid administration within months before PET may be one cause of ESMU.

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

OBJECTIVE

The objective of this study is to determine the optimal β value for clinical use in digital 68 Ga-prostate-specific membrane antigen (PSMA-11) PET/computed tomography (CT) imaging.

METHODS

68 Ga 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 SUV SD were recorded. Lesions were then subdivided according to uptake and size; the SUVmax of these lesions were analyzed.

RESULTS

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 SUV SD 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.

CONCLUSION

Bayesian penalized likelihood reconstruction algorithms improve image quality without affecting lesion detectability. A β value of 1400 is optimal.

Comparison of Image Quality and Quantification Parameters between Q.Clear and OSEM Reconstruction Methods on FDG-PET/CT Images in Patients with Metastatic Breast Cancer

We compared the image quality and quantification parameters through bayesian penalized likelihood reconstruction algorithm (Q.Clear) and ordered subset expectation maximization (OSEM) algorithm for 2-[18F]FDG-PET/CT scans performed for response monitoring in patients with metastatic breast cancer in prospective setting. We included 37 metastatic breast cancer patients diagnosed and monitored with 2-[18F]FDG-PET/CT at Odense University Hospital (Denmark). A total of 100 scans were analyzed blinded toward Q.Clear and OSEM reconstruction algorithms regarding image quality parameters (noise, sharpness, contrast, diagnostic confidence, artefacts, and blotchy appearance) using a five-point scale. The hottest lesion was selected in scans with measurable disease, considering the same volume of interest in both reconstruction methods. SULpeak (g/mL) and SUVmax (g/mL) were compared for the same hottest lesion. There was no significant difference regarding noise, diagnostic confidence, and artefacts within reconstruction methods; Q.Clear had significantly better sharpness (p < 0.001) and contrast (p = 0.001) than the OSEM reconstruction, while the OSEM reconstruction had significantly less blotchy appearance compared with Q.Clear reconstruction (p < 0.001). Quantitative analysis on 75/100 scans indicated that Q.Clear reconstruction had significantly higher SULpeak (5.33 ± 2.8 vs. 4.85 ± 2.5, p < 0.001) and SUVmax (8.27 ± 4.8 vs. 6.90 ± 3.8, p < 0.001) compared with OSEM reconstruction. In conclusion, Q.Clear reconstruction revealed better sharpness, better contrast, higher SUVmax, and higher SULpeak, while OSEM reconstruction had less blotchy appearance.

A new upper limit for effective dose in patient administered with 18F-FDG for PET/CT whole-body imaging with diagnostic CT parameters

INTRODUCTION

The aim of present study is to estimate effective dose in patient undergoing ¹⁸F-FDG for whole body PET/CT imaging with diagnostic CT parameters and identify the lowest achievable total effective dose.

METHOD

A total of 2247 PET/CT patients with normal glucose level underwent ¹⁸F-FDG-whole body imaging procedures. The ¹⁸F-FDG dose of 3.7MBq per kg of patient weight administered via intravenous infusion. For CT parameters, kilovoltage of 140keV and current of 40 mAs were used for all studies. All the acquired images collected retrospectively and the effective dose was calculated for each patient using algorithm adapted from ICRP Publication 106, modified for patient weight and patient blood volume. The estimated effective doses were evaluated for patients' body weight and BMI.

RESULTS

The mean of total effective dose and standard deviation is approximately 15.08(4.52) mSv using ICRP algorithm. 56% of total patient has normal BMI and their average total effective dose is 13.6mSv. Underweight patients' effective dose can be as low as 9.6mSv even using diagnostic CT protocols.

CONCLUSION

The effective dose of PET/CT procedure in present study is one of the lowest although using diagnostic parameters for CT acquisition compared to published data worldwide. This is due to the improved sensitivity of PET and complex reconstruction technique that maintains the image quality. A significant association between body weight, BMI and effective dose is reported in present study. Therefore, it is suggested that attention must be given for underweight and ideal BMI patients while prescribing FDG activity and CT imaging parameters in order to minimize the effective dose. The effective dose reported in present study can be considered as an upper limit for effective dose in PET/CT patients with normal BMI. This upper limit can be treated as a standard limit when optimizing imaging parameters, developing algorithm for image reconstruction and prescribing activity for patients. This practice could fulfill ALARA principle that could reduce cancer risk.

Impact of framing scheme optimization and smoking status on binding potential analysis in dynamic PET with [11C]ABP688

BACKGROUND

For positron emission tomography (PET) ligands, such as [¹¹C]ABP688, to be able to provide more evidence about the glutamatergic hypothesis in schizophrenia (SZ), quantification bias during dynamic PET studies and its propagation into the estimated values of non-displaceable binding potential (BP_ND) must be addressed. This would enable more accurate quantification during bolus + infusion (BI) neuroreceptor studies and further our understanding of neurological diseases. Previous studies have shown BP_ND-related biases can often occur due to overestimated cerebellum activity (reference region). This work investigates whether an alternative framing scheme can minimize quantification biases propagated into BP_ND, whether confounders, such as smoking status, need to be controlled for during the study, and what the consequences for the data interpretation following analysis are. A group of healthy controls (HC) and a group of SZ patients (balanced and unbalanced number of smokers) were investigated with [¹¹C]ABP688 and a BI protocol. Possible differences in BP_ND quantification as a function of smoking status were tested with constant 5 min ('Const 5 min') and constant true counts ('Const Trues') framing schemes. In order to find biomarkers for SZ, the differences in smoking effects were compared between groups. The normalized BP_ND and the balanced number of smokers and non-smokers for both framing schemes were evaluated.

RESULTS

When applying F-tests to the 'Const 5 min' framing scheme, effect sizes (η²p) and brain regions which showed significant effects fluctuated considerably with F = 50.106 ± 54.948 (9.389 to 112.607), P-values 0.005 to < 0.001 and η²p = 0.514 ± 0.282 (0.238 to 0.801). Conversely, when the 'Const Trues' framing scheme was applied, the results showed much smaller fluctuations with F = 78.038 ± 8.975 (86.450 to 68.590), P < 0.001 for all conditions and η²p = 0.730 ± 0.017 (0.742 to 0.710), and regions with significant effects were more robustly reproduced. Further, differences, which would indicate false positive identifications between HC and SZ groups in five brain regions when using the 'Const 5 min' framing scheme, were not observed with the 'Const Trues' framing.

CONCLUSIONS

Based on an [¹¹C]ABP688 PET study in SZ patients, the results show that non-consistent BP_ND outcomes can be propagated by the framing scheme and that potential bias can be minimized using 'Const Trues' framing.

Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction

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 SUV_max, 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 SUV_max, 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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-022-00451-5.

Influences on PET Quantification and Interpretation

Various factors have been identified that influence quantitative accuracy and image interpretation in positron emission tomography (PET). Through the continuous introduction of new PET technology-both imaging hardware and reconstruction software-into clinical care, we now find ourselves in a transition period in which traditional and new technologies coexist. The effects on the clinical value of PET imaging and its interpretation in routine clinical practice require careful reevaluation. In this review, we provide a comprehensive summary of important factors influencing quantification and interpretation with a focus on recent developments in PET technology. Finally, we discuss the relationship between quantitative accuracy and subjective image interpretation.

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

Background

Q.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.

Methods

A 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.

Results

For 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.

Conclusions

The 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.

Changes of [18F]FDG-PET/CT quantitative parameters in tumor lesions by the Bayesian penalized-likelihood PET reconstruction algorithm and its influencing factors

Background

To compare the changes in quantitative parameters and the size and degree of ¹⁸F-fluorodeoxyglucose ([¹⁸F]FDG) uptake of malignant tumor lesions between Bayesian penalized-likelihood (BPL) and non-BPL reconstruction algorithms.

Methods

Positron emission tomography/computed tomography images of 86 malignant tumor lesions were reconstructed using the algorithms of ordered subset expectation maximization (OSEM), OSEM + time of flight (TOF), OSEM + TOF + point spread function (PSF), and BPL. [¹⁸F]FDG parameters of maximum standardized uptake value (SUVmax), SUVmean, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and signal-to-background ratio (SBR) of these lesions were measured. Quantitative parameters between the different reconstruction algorithms were compared, and correlations between parameter variation and lesion size or the degree of [¹⁸F]FDG uptake were analyzed.

Results

After BPL reconstruction, SUVmax, SUVmean, and SBR were significantly increased, MTV was significantly decreased. The difference values of %ΔSUVmax, %ΔSUVmean, %ΔSBR, and the absolute value of %ΔMTV between BPL and OSEM + TOF were 40.00%, 38.50%, 33.60%, and 33.20%, respectively, which were significantly higher than those between BPL and OSEM + TOF + PSF. Similar results were observed in the comparison of OSEM and OSEM + TOF + PSF with BPL. The %ΔSUVmax, %ΔSUVmean, and %ΔSBR were all significantly negatively correlated with the size and degree of [¹⁸F]FDG uptake in the lesions, whereas significant positive correlations were observed for %ΔMTV and %ΔTLG.

Conclusion

The BPL reconstruction algorithm significantly increased SUVmax, SUVmean, and SBR and decreased MTV of tumor lesions, especially in small or relatively hypometabolic lesions.

Effects of New Bayesian Penalized Likelihood Reconstruction Algorithm on Visualization and Quantification of Upper Abdominal Malignant Tumors in Clinical FDG PET/CT Examinations

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.

Cumulative radiation doses from recurrent PET/CT examinations

Positron emission tomography (PET–CT) is an essential imaging modality for the management of various diseases. Increasing numbers of PET–CT examinations are carried out across the world and deliver benefits to patients; however, there are concerns about the cumulative radiation doses from these examinations in patients. Compared to the radiation exposure delivered by CT, there have been few reports on the frequency of patients with a cumulative effective radiation dose of ≥100 mSv from repeated PET–CT examinations. The emerging dose tracking system facilitates surveys on patient cumulative doses by PET–CT because it can easily wrap up exposure doses of PET radiopharmaceuticals and CT. Regardless of the use of a dose tracking system, implementation of justification for PET–CT examinations and utilisation of dose reduction measures are key issues in coping with the cumulative dose in patients. Despite all the advantages of PET/MRI such as eliminating radiation exposure from CT and providing good tissue contrast in MRI, it is expensive and cannot be introduced at every facility; thus, it is still necessary to utilise PET–CT with radiation reduction measures in most clinical situations.

FDG-PET/CT for pretherapeutic lymph node staging in non-small cell lung cancer: A tailored approach to the ESTS/ESMO guideline workflow

OBJECTIVES

In patients with NSCLC, current ESTS and ESMO guidelines recommend invasive lymph node (LN) staging with EBUS-TBNA even if FDG-PET/CT is negative for mediastinal LNs if at least one of three risk factors is present (cN1, non-peripheral primary or primary >3 cm). Modified workflows to avoid unnecessary invasive procedures were evaluated.

MATERIALS AND METHODS

Monocentric retrospective analysis of pretherapeutic FDG-PET/CT in 247 patients with NSCLC (62 % male; age, 68 [43-88] years) using an analog or digital PET/CT scanner. PET windowing was standardized. LNs were positive if 'LN uptake > mediastinal blood pool' or short axis >10 mm. Surgery or EBUS-TBNA served as reference for diagnostic accuracy per LN station. In all patients with negative mediastinal LNs by PET/CT, LN histology from surgery was available.

RESULTS

Among 700 L N stations analyzed, 180 were malignant. Sensitivity and specificity of PET/CT per LN station were 93 % and 71 %. Following current guidelines, 76 patients with mediastinal negative PET/CT required confirmatory invasive staging. Only 5/76 patients had unexpected pN2 (all had adenocarcinoma). In a modified approach, confirmatory invasive staging was confined to patients with mediastinal negative PET/CT who showed all three risk factors. Using this modification, EBUS-TBNA could have been omitted in 62 (82 %) of the 76 patients who required EBUS-TBNA based on current recommendation. Among these 62 patients, only one patient had unsuspected pN2 (single-level) while the remaining 61 of 62 omitted EBUS-TBNA were deemed unnecessary because mediastinal LNs were confirmed to be negative. No multi-level pN2 would have been missed.

CONCLUSION

In the current analysis, 82 % of EBUS-TBNA procedures in patients with mediastinal negative PET/CT could have been omitted by modifying the current guideline workflow as proposed (i.e., restricting EBUS-TBNA in patients with cN0/1 to those with all three risk factors). This was consistent with different PET/CT scanners. Prospective confirmation is required.

Bias evaluation and reduction in 3D OP-OSEM reconstruction in dynamic equilibrium PET studies with 11C-labeled for binding potential analysis

Iterative image reconstruction is widely used in positron emission tomography. However, it is known to contribute to quantitation bias and is particularly pronounced during dynamic studies with ¹¹C-labeled radiotracers where count rates become low towards the end of the acquisition. As the strength of the quantitation bias depends on the counts in the reconstructed frame, it can differ from frame to frame of the acquisition. This is especially relevant in the case of neuro-receptor studies with simultaneous PET/MR when a bolus-infusion protocol is applied to allow the comparison of pre- and post-task effects. Here, count dependent changes in quantitation bias may interfere with task changes. We evaluated the impact of different framing schemes on quantitation bias and its propagation into binding potential (BP) using a phantom decay study with ¹¹C and 3D OP-OSEM. Further, we propose a framing scheme that keeps the true counts per frame constant over the acquisition time as constant framing schemes and conventional increasing framing schemes are unlikely to achieve stable bias values during the acquisition time range. For a constant framing scheme with 5 minutes frames, the BP bias was 7.13±2.01% (10.8% to 3.8%) compared to 5.63±2.85% (7.8% to 4.0%) for conventional increasing framing schemes. Using the proposed constant true counts framing scheme, a stabilization of the BP bias was achieved at 2.56±3.92% (3.5% to 1.7%). The change in BP bias was further studied by evaluating the linear slope during the acquisition time interval. The lowest slope values were observed in the constant true counts framing scheme. The constant true counts framing scheme was effective for BP bias stabilization at relevant activity and time ranges. The mean BP bias under these conditions was 2.56±3.92%, which represents the lower limit for the detection of changes in BP during equilibrium and is especially important in the case of cognitive tasks where the expected changes are low.

Impact of the Q.Clear reconstruction algorithm on the interpretation of PET/CT images in patients with lymphoma

Background

Q.Clear is a new Bayesian penalized-likelihood PET reconstruction algorithm. It has been documented that Q.Clear increases the SUVmax values of different malignant lesions.

Purpose

SUVmax values are crucial for the interpretation of PET/CT images in patients with lymphoma, particularly when the early and final responses to treatment are evaluated. The aim of the study was to systematically analyse the impact of the use of Q.Clear on the interpretation of PET/CT in patients with lymphoma.

Methods

A total of 280 ¹⁸F-FDG PET/CT scans in patients with lymphoma were performed for staging (sPET), for early treatment response (iPET), after the end of treatment (ePET) and when a relapse of lymphoma was suspected (rPET). Scans were separately reconstructed with two algorithms, Q.Clear and OSEM, and further compared.

Results

The stage of lymphoma was concordantly diagnosed in 69/70 patients with both algorithms on sPET. Discordant assessment of the Deauville score (p < 0.001) was found in 11 cases (15.7%) of 70 iPET scans and in 11 cases of 70 ePET scans. An upgrade from a negative to a positive scan by Q.Clear occurred in 3 cases (4.3%) of iPET scans and 7 cases (10.0%) of ePET scans. The results of all 70 rPET scans were concordant. The SUVmax values of the target lymphoma lesions measured with Q.Clear were higher than those measured with OSEM in 88.8% of scans.

Conclusion

Although the Q.Clear algorithm may alter the interpretations of PET/CT in only a small proportion of patients, we recommend using standard OSEM reconstruction for the assessment of treatment response.

Clinical evaluation of reconstruction and acquisition time for pediatric 18F-FDG brain PET using digital PET/CT

BACKGROUND

¹⁸F-2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) plays an important role in the diagnosis, evaluation and treatment of childhood epilepsy. The selection of appropriate acquisition and reconstruction parameters, however, can be challenging with the introduction of advanced hardware and software functionalities.

OBJECTIVE

To quantify the diagnostic performance of a block-sequential regularized expectation maximization (BSREM) tool and reduced effective counts in brain PET/CT for pediatric epilepsy patients on a digital silicon photomultiplier system.

MATERIALS AND METHODS

We included 400 sets of brain PET/CT images from 25 pediatric patients (0.5-16 years old) in this retrospective study. Patient images were reconstructed with conventional iterative techniques or BSREM with varied penalization factor (β), at varied acquisition time (45 s, 90 s, 180 s, 300 s) to simulate reduced count density. Two pediatric nuclear medicine physicians reviewed images in random order - blinded to patient, reconstruction method and imaging time - and scored technical quality (noise, spatial resolution, artifacts), clinical quality (image quality of the cortex, basal ganglia and thalamus) and overall diagnostic satisfaction on a 5-point scale.

RESULTS

Reconstruction with BSREM improved quality and clinical scores across all count levels, with the greatest benefits in low-count conditions. Image quality scores were greatest at 300-s acquisition times with β=500 (overall; noise; artifacts; image quality of the cortex, basal ganglia and thalamus) or β=200 (spatial resolution). No statistically significant difference in the highest graded reconstruction was observed between imaging at 180 s and 300 s with an appropriately implemented penalization factor (β=350-500), indicating that a reduction in dose or acquisition time is feasible without reduction in diagnostic satisfaction.

CONCLUSION

Clinical evaluation of pediatric ¹⁸F-FDG brain PET image quality was shown to be diagnostic at reductions of count density by 40% using BSREM with a penalization factor of β=350-500. This can be accomplished while maintaining confidence of achieving a diagnostic-quality image.

Impact of the Bayesian penalized likelihood algorithm (Q.Clear®) in comparison with the OSEM reconstruction on low contrast PET hypoxic images

Purpose

To determine the impact of the Bayesian penalized likelihood (BPL) reconstruction algorithm in comparison to OSEM on hypoxia PET/CT images of NSCLC using ¹⁸F-MIZO and ¹⁸F-FAZA.

Materials and methods

Images of low-contrasted (SBR = 3) micro-spheres of Jaszczak phantom were acquired. Twenty patients with lung neoplasia were included. Each patient benefitted from ¹⁸F-MISO and/or ¹⁸F-FAZA PET/CT exams, reconstructed with OSEM and BPL. Lesion was considered as hypoxic if the lesion SUV_max > 1.4. A blind evaluation of lesion detectability and image quality was performed on a set of 78 randomized BPL and OSEM images by 10 nuclear physicians. SUV_max, SUV_mean, and hypoxic volumes using 3 thresholding approaches were measured and compared for each reconstruction.

Results

The phantom and patient datasets showed a significant increase of quantitative parameters using BPL compared to OSEM but had no impact on detectability. The optimal beta parameter determined by the phantom analysis was β350. Regarding patient data, there was no clear trend of image quality improvement using BPL. There was no correlation between SUV_max increase with BPL and either SUV or hypoxic volume from the initial OSEM reconstruction. Hypoxic volume obtained by a SUV > 1.4 thresholding was not impacted by the BPL reconstruction parameter.

Conclusion

BPL allows a significant increase in quantitative parameters and contrast without significantly improving the lesion detectability or image quality. The variation in hypoxic volume by BPL depends on the method used but SUV > 1.4 thresholding seems to be the more robust method, not impacted by the reconstruction method (BPL or OSEM).

Trial registration

ClinicalTrials.gov, NCT02490696. Registered 1 June 2015

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

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 (11.6 vs 10.3 [p = 0.00278], 7.0 vs 6.7 [p = 0.077] and 33.9 vs 26.7 [p < 0.00001, respectively). Mean background uptake did not differ significantly between Q.Clear and OSEM (0.42 vs 0.39, p = 0.07).

Conclusions

In 18F-PSMA-1007 PET/CT, Q.Clear SUVs and TBR tend to be higher (regardless of lesion localisation), especially for small and highly avid lesions. Increase in SUVs is also higher for lesions with high tracer uptake. Still, Q.Clear does not affect 18F-PSMA-1007 PET/CT specificity and sensitivity.