Evaluation of a Bayesian penalized likelihood reconstruction algorithm for low-count clinical 18F-FDG PET/CT

Y-90 PET/MR imaging optimization with a Bayesian penalized likelihood reconstruction algorithm

Positron Emission Tomography (PET) imaging after90 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 for90 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 β 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 β = 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 β parameter lowered RC, COV and BV, while CNR was maximized at β = 4000; further increase resulted in oversmoothing. For quantification purposes, β = 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 β 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.

Optimizing scan time and bayesian penalized likelihood reconstruction algorithm in copper-64 PET/CT imaging: a phantom study

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 parameterβwas 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, variousβvalues (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 increasingβ, peaking atβ= 550. The CNR for allβ, sphere sizes and tumor-to-background ratios (TBRs) satisfied the Rose criterion for image detectability (CNR > 5). BPL reconstructed images withβ= 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.

Impact of [11C]methionine PET with Bayesian penalized likelihood reconstruction on glioma grades based on new WHO 2021 classification

OBJECTIVE

The uptake of [11C]methionine in positron emission tomography (PET) imaging overlapped in earlier images of tumors. Bayesian penalized likelihood (BPL) reconstruction increases the quantitative values of tumors compared with conventional ordered subset-expectation maximization (OSEM). The present study aimed to grade glioma malignancy based on the new WHO 2021 classification using [11C]methionine PET images reconstructed using BPL.

METHODS

We categorized 32 gliomas in 28 patients as grades 2/3 (n = 15) and 4 (n = 17) based on the WHO 2021 classification. All [11C]methionine images were reconstructed using OSEM + time-of-flight (TOF) and BPL + TOF (β = 200). Maximum standardized uptake value (SUVmax) and tumor-to-normal tissue ratio (T/Nmax) were measured at each lesion.

RESULTS

The mean SUVmax was 4.65 and 4.93 in grade 2/3 and 6.38 and 7.11 in grade 4, and the mean T/Nmax was 7.08 and 7.22 in grade 2/3 and 9.30 and 10.19 in grade 4 for OSEM and BPL, respectively. The BPL significantly increased these values in grade 4 gliomas. The area under the receiver operator characteristic (ROC) curve (AUC) for SUVmax was the highest (0.792) using BPL.

CONCLUSIONS

The BPL increased mean SUVmax and mean T/Nmax in lesions with higher contrast such as grade 4 glioma. The discrimination power between grades 2/3 and 4 in SUVmax was also increased using [11C]methionine PET images reconstructed with BPL.

Phantom and clinical evaluation of the Bayesian penalised likelihood reconstruction algorithm Q.Clear without PSF correction in amyloid PET images

PURPOSE

Bayesian penalised likelihood (BPL) reconstruction, which incorporates point-spread-function (PSF) correction, provides higher signal-to-noise ratios and more accurate quantitation than conventional ordered subset expectation maximization (OSEM) reconstruction. However, applying PSF correction to brain PET imaging is controversial due to Gibbs artefacts that manifest as unpredicted cortical uptake enhancement. The present study aimed to validate whether BPL without PSF would be useful for amyloid PET imaging.

METHODS

Images were acquired from Hoffman 3D brain and cylindrical phantoms for phantom study and 71 patients administered with [18F]flutemetamol in clinical study using a Discovery MI. All images were reconstructed using OSEM, BPL with PSF correction, and BPL without PSF correction. Count profile, %contrast, recovery coefficients (RCs), and image noise were calculated from the images acquired from the phantoms. Amyloid β deposition in patients was visually assessed by two physicians and quantified based on the standardised uptake value ratio (SUVR).

RESULTS

The overestimated radioactivity in profile curves was eliminated using BPL without PSF correction. The %contrast and image noise decreased with increasing β values in phantom images. Image quality and RCs were better using BPL with, than without PSF correction or OSEM. An optimal β value of 600 was determined for BPL without PSF correction. Visual evaluation almost agreed perfectly (κ = 0.91-0.97), without depending on reconstruction methods. Composite SUVRs did not significantly differ between reconstruction methods.

CONCLUSION

Gibbs artefacts disappeared from phantom images using the BPL without PSF correction. Visual and quantitative evaluation of [18F]flutemetamol imaging was independent of the reconstruction method. The BPL without PSF correction could be the standard reconstruction method for amyloid PET imaging, despite being qualitatively inferior to BPL with PSF correction for [18F]flutemetamol amyloid PET imaging.

Feasibility of noise-reduction reconstruction technology based on non-local-mean principle in SiPM-PET/CT

Quantitative values of positron emission tomography (PET) images using non-local-mean in a silicon photomultiplier (SiPM)-PET/computed tomography (CT) system with phantom and clinical images. The evaluation was conducted on a National Electrical Manufacturers Association body phantom with micro-spheres (4, 5, 6, 8, 10, 13 mm) and clinical images using the SiPM-PET/CT system. The signal-to-background ratio of the phantom was set to 4, and all PET image data was obtained and reconstructed using three-dimensional ordered subset expectation maximization, time-of-flight, point-spread function, and a 4-mm Gaussian filter (GF) and clear adaptive low-noise method (CaLM) in mild, standard, and strong intensities. The evaluation included the standardized uptake value (SUV), percent contrast (QH), coefficient of variation of the background area (CVbackground) clinical imaging for SUV of lung nodules, liver signal-to-noise ratio (SNR), and visual evaluation. SUVmax for 8-mm sphere in phantom images at 2 min for GF and CaLM (mild, standard, strong) were 2.11, 2.32, 2.02, and 1.72; the QH, 8 mm was 27.33 %, 27.47 %, 21.81 %, and 16.09 %; and CVbackground was 12.78, 11.35, 7.86, and 4.71, respectively. CaLM demonstrated higher SUVmax in clinical images than GF for all lung nodule sizes. The average SUVmax for nodules with a diameter of ≤ 1 cm were 5.9 ± 2.4, 9.9 ± 4.9, 9.9 ± 5.0, and 9.9 ± 5.0 for GF and CaLM-mild, standard, and strong intensities, respectively. Liver SNRs were higher for CaLM (mild, standard, strong) compared to GF, with increasing CaLM intensity causing higher liver SNR. CaLM-mild and standard demonstrated suitability for diagnosis in visual evaluation.

Detection of Liver Lesions in Colorectal Cancer Patients Using 18F-FDG PET/CT Dual-Time-Point Scan Imaging

OBJECTIVE

The aim of this study was to evaluate the diagnostic performance of dual-time-point fluorine-18-fluorodeoxyglucose positron emission computed tomography/computed tomography (18F-FDG PET/CT) compared to conventional early imaging for detecting colorectal liver metastases (CRLM) in colorectal cancer (CRC) patients.

METHODS

One hundred twenty-four consecutive CRC patients underwent dual-time-point imaging scans on a retrospective basis. Histopathological confirmation and/or clinical follow-up were accepted as the gold standard. Standard uptake values (SUV), signal-to-noise ratio (SNR), retention index (RI), tumor-to-normal liver ratio (TNR), and lesion sizes were measured for early and delayed PET scans. The diagnostic performance of early and delayed images was calculated on a per-patient basis and compared using McNemar's test.

RESULTS

Among the 124 patients, 57 (46%) had CRLM, 6 (4.8%) had benign lesions, and 61 (49.2%) had no concerning lesions detected. Smaller CRLM lesions (<5 cm3) showed significantly higher uptake in the delayed scans relative to early imaging (p < 0.001). The SUV and TNR increased significantly in delayed imaging of all metastatic lesions (p < 0.001). The retention index of all CRLM was high (40.8%), especially for small lesions (54.8%). A total of 177 lesions in delayed images and 124 in standard early images were identified. In a per-patient analysis, delayed imaging had significantly higher sensitivity (100% vs. 87.7%) and specificity (91.0% vs. 94.0%) compared to early imaging (p-value = 0.04).

CONCLUSIONS

The detection of liver lesions using dual-time-point PET/CT scan improves the sensitivity and specificity for the detection of colorectal liver metastasis.

Optimization of Q.Clear reconstruction for dynamic 18F PET imaging

BACKGROUND

Q.Clear, a Bayesian penalized likelihood reconstruction algorithm, has shown high potential in improving quantitation accuracy in PET systems. The Q.Clear algorithm controls noise during the iterative reconstruction through a β penalization factor. This study aimed to determine the optimal β-factor for accurate quantitation of dynamic PET scans.

METHODS

A Flangeless Esser PET Phantom with eight hollow spheres (4-25 mm) was scanned on a GE Discovery MI PET/CT system. Data were reconstructed into five sets of variable acquisition times using Q.Clear with 18 different β-factors ranging from 100 to 3500. The recovery coefficient (RC), coefficient of variation (CVRC) and root-mean-square error (RMSERC) were evaluated for the phantom data. Two male patients with recurrent glioblastoma were scanned on the same scanner using 18F-PSMA-1007. Using an irreversible two-tissue compartment model, the area under curve (AUC) and the net influx rate Ki were calculated to assess the impact of different β-factors on the pharmacokinetic analysis of clinical PET brain data.

RESULTS

In general, RC and CVRC decreased with increasing β-factor in the phantom data. For small spheres (< 10 mm), and in particular for short acquisition times, low β-factors resulted in high variability and an overestimation of measured activity. Increasing the β-factor improves the variability, however at a cost of underestimating the measured activity. For the clinical data, AUC decreased and Ki increased with increased β-factor; a change in β-factor from 300 to 1000 resulted in a 25.5% increase in the Ki.

CONCLUSION

In a complex dynamic dataset with variable acquisition times, the optimal β-factor provides a balance between accuracy and precision. Based on our results, we suggest a β-factor of 300-500 for quantitation of small structures with dynamic PET imaging, while large structures may benefit from higher β-factors.

TRIAL REGISTRATION

Clinicaltrials.gov, NCT03951142. Registered 5 October 2019, https://clinicaltrials.gov/ct2/show/NCT03951142 . EudraCT no 2018-003229-27. Registered 26 February 2019, https://www.clinicaltrialsregister.eu/ctr-search/trial/2018-003229-27/NO .

The Pattern of Metastatic Breast Cancer: A Prospective Head-to-Head Comparison of [18F]FDG-PET/CT and CE-CT

The study aimed to compare the metastatic pattern of breast cancer and the intermodality proportion of agreement between [18F]FDG-PET/CT and CE-CT. Women with metastatic breast cancer (MBC) were enrolled prospectively and underwent a combined [18F]FDG-PET/CT and CE-CT scan to diagnose MBC. Experienced nuclear medicine and radiology physicians evaluated the scans blinded to the opposite scan results. Descriptive statistics were applied, and the intermodality proportion of agreement was used to compare [18F]FDG-PET/CT and CE-CT. In total, 76 women with verified MBC were enrolled in the study. The reported number of site-specific metastases for [18F]FDG-PET/CT vs. CE-CT was 53 (69.7%) vs. 44 (57.9%) for bone lesions, 31 (40.8%) vs. 43 (56.6%) for lung lesions, and 16 (21.1%) vs. 23 (30.3%) for liver lesions, respectively. The proportion of agreement between imaging modalities was 76.3% (95% CI 65.2-85.3) for bone lesions; 82.9% (95% CI 72.5-90.6) for liver lesions; 57.9% (95% CI 46.0-69.1) for lung lesions; and 59.2% (95% CI 47.3-70.4) for lymph nodes. In conclusion, bone and distant lymph node metastases were reported more often by [18F]FDG-PET/CT than CE-CT, while liver and lung metastases were reported more often by CE-CT than [18F]FDG-PET/CT. Agreement between scans was highest for bone and liver lesions and lowest for lymph node metastases.

3D-OSEM versus FORE + OSEM: Optimal Reconstruction Algorithm for FDG PET with a Short Acquisition Time

Objective  In this study, we investigated the optimal reconstruction algorithm in fluorodeoxyglucose (FDG) positron emission tomography (PET) with a short acquisition time. Materials and Methods  In the phantom study, six spheres filled with FDG solution (sphere size: 6.23-37 mm; radioactivity ratio of spheres to background = 8:1) and placed in a National Electrical Manufacturers Association phantom were evaluated. Image acquisition time was 15 to 180 seconds, and the obtained image data were reconstructed using each of the Fourier rebinning (FORE) + ordered subsets expectation-maximization (OSEM) and 3D-OSEM algorithms. In the clinical study, mid-abdominal images of 19 patients were evaluated using regions of interest placed on areas of low, intermediate, and high radioactivity. All obtained images were investigated visually, and quantitatively using maximum standardized uptake value (SUV) and coefficient of variation (CV). Results  In the phantom study, FORE + OSEM images with a short acquisition time had large CVs (poor image quality) but comparatively constant maximum SUVs. 3D-OSEM images showed comparatively constant CVs (good image quality) but significantly low maximum SUVs. The results of visual evaluation were well correlated with those of quantitative evaluation. Small spheres were obscured on 3D-OSEM images with short acquisition time, but image quality was not greatly deteriorated. The clinical and phantom studies yielded similar results. Conclusion  FDG PET images with a short acquisition time reconstructed by FORE + OSEM showed poorer image quality than by 3D-OSEM. However, images obtained with a short acquisition time and reconstructed with FORE + OSEM showed clearer FDG uptake and more useful than 3D-OSEM in the light of the detection of lesions.

A retrospective evaluation of Bayesian-penalized likelihood reconstruction for [15O]H2O myocardial perfusion imaging

BACKGROUND

New Block-Sequential-Regularized-Expectation-Maximization (BSREM) image reconstruction technique has been introduced for clinical use mainly for oncologic use. Accurate and quantitative image reconstruction is essential in myocardial perfusion imaging with positron emission tomography (PET) as it utilizes absolute quantitation of myocardial blood flow (MBF). The aim of the study was to evaluate BSREM reconstruction for quantitation in patients with suspected coronary artery disease (CAD).

METHODS AND RESULTS

We analyzed cardiac [15O]H2O PET studies of 177 patients evaluated for CAD. Differences between BSREM and Ordered-Subset-Expectation-Maximization with Time-Of-Flight (TOF) and Point-Spread-Function (PSF) modeling (OSEM-TOF-PSF) in terms of MBF, perfusable tissue fraction, and vascular volume fraction were measured. Classification of ischemia was assessed between the algorithms. OSEM-TOF-PSF and BSREM provided similar global stress MBF in patients with ischemia (1.84 ± 0.21 g⋅ml-1⋅min-1 vs 1.86 ± 0.21 g⋅ml-1⋅min-1) and no ischemia (3.26 ± 0.34 g⋅ml-1⋅min-1 vs 3.28 ± 0.34 g⋅ml-1⋅min-1). Global resting MBF was also similar (0.97 ± 0.12 g⋅ml-1⋅min-1 and 1.12 ± 0.06 g⋅ml-1⋅min-1). The largest mean relative difference in MBF values was 7%. Presence of myocardial ischemia was classified concordantly in 99% of patients using OSEM-TOF-PSF and BSREM reconstructions CONCLUSION: OSEM-TOF-PSF and BSREM image reconstructions produce similar MBF values and diagnosis of myocardial ischemia in patients undergoing [15O]H2O PET due to suspected obstructive coronary artery disease.

Rapid Whole-Body FDG PET/MRI in Oncology Patients: Utility of Combining Bayesian Penalised Likelihood PET Reconstruction and Abbreviated MRI

This study evaluated the diagnostic value of a rapid whole-body fluorodeoxyglucose (FDG) positron emission tomography (PET)/magnetic resonance imaging (MRI) approach, combining Bayesian penalised likelihood (BPL) PET with an optimised β value and abbreviated MRI (abb-MRI). The study compares the diagnostic performance of this approach with the standard PET/MRI that utilises ordered subsets expectation maximisation (OSEM) PET and standard MRI (std-MRI). The optimal β value was determined by evaluating the noise-equivalent count (NEC) phantom, background variability, contrast recovery, recovery coefficient, and visual scores (VS) for OSEM and BPL with β100-1000 at 2.5-, 1.5-, and 1.0-min scans, respectively. Clinical evaluations were conducted for NEC_patient, NEC_density, liver signal-to-noise ratio (SNR), lesion maximum standardised uptake value, lesion signal-to-background ratio, lesion SNR, and VS in 49 patients. The diagnostic performance of BPL/abb-MRI was retrospectively assessed for lesion detection and differentiation in 156 patients using VS. The optimal β values were β600 for a 1.5-min scan and β700 for a 1.0-min scan. BPL/abb-MRI at these β values was equivalent to OSEM/std-MRI for a 2.5-min scan. By combining BPL with optimal β and abb-MRI, rapid whole-body PET/MRI could be achieved in ≤1.5 min per bed position, while maintaining comparable diagnostic performance to standard PET/MRI.

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 novel figure of merit to investigate 68Ga PET/CT image quality based on patient weight and lesion size using Q.Clear reconstruction algorithm: A phantom study

INTRODUCTION

Q.Clear is a Bayesian penalised-likelihood algorithm that uses a β-value for positron emission tomography(PET)/computed tomography(CT) image reconstruction(IR). Our study proposes a novel figure of merit, named CRBV, to compare the Q.Clear performances using 68Ga PET/CT image with the ordered-subset-expectation-maximization(OSEM) algorithm and to identify the optimal β-values for these images using two phantoms mimicking normal and overweight patients.

METHODS

NEMA IQ phantom with or without a ring of water-filled plastic bags (NEMAstd and NEMAow, respectively) was acquired and reconstructed with OSEM and Q.Clear at various β-values and minutes/bed position(min/bp). Contrast recovery(CR), background variability(BV) and CRBV were calculated. Highest CRBV values were used to identify optimal β-value ranges.

RESULTS

Q.Clear with 250 ≤ β ≤ 800 improved CRBV compared to OSEM for all the investigated spheres and acquisition setups. Outside of this range, Q.Clear still outperformed OSEM with few exceptions depending on spheres diameters and phantoms(e.g.,β-value = 1600 for diameters ≤ 17 mm using the NEMAow phantom). Regarding the CRBV performance for IR optimization, for the 4 min/bp NEMAstd IR, β-values = 300 ÷ 350 allowed to simultaneously optimize all diameters(except for the 10 mm); for the NEMAow IR, β-values = 350 ÷ 500 were needed for diameters > 20 mm, while β-values = 200 ÷ 250 were selected for the remaining diameters. For the 2 min/bp, β-value = 500 was suitable for diameters > 17 mm in both NEMAstd and NEMAow IR, while for smaller diameters β-value = 200 and β-values = 250 ÷ 350 were obtained for NEMAstd and NEMAow, respectively.

CONCLUSION

Almost all tested β-values of Q.Clear improved the CRBV compared to OSEM. In both phantoms, simulating normal and over-weight patients, optimal β-values were found according to lesion sizes and investigated acquisition times.

Impact of γ factor in the penalty function of Bayesian penalized likelihood reconstruction (Q.Clear) to achieve high-resolution PET images

BACKGROUND

The Bayesian penalized likelihood PET reconstruction (BPL) algorithm, Q.Clear (GE Healthcare), has recently been clinically applied to clinical image reconstruction. The BPL includes a relative difference penalty (RDP) as a penalty function. The β value that controls the behavior of RDP determines the global strength of noise suppression, whereas the γ factor in RDP controls the degree of edge preservation. The present study aimed to assess the effects of various γ factors in RDP on the ability to detect sub-centimeter lesions.

METHODS

All PET data were acquired for 10 min using a Discovery MI PET/CT system (GE Healthcare). We used a NEMA IEC body phantom containing spheres with inner diameters of 10, 13, 17, 22, 28 and 37 mm and 4.0, 5.0, 6.2, 7.9, 10 and 13 mm. The target-to-background ratio of the phantom was 4:1, and the background activity concentration was 5.3 kBq/mL. We also evaluated cold spheres containing only non-radioactive water with the same background activity concentration. All images were reconstructed using BPL + time of flight (TOF). The ranges of β values and γ factors in BPL were 50-600 and 2-20, respectively. We reconstructed PET images using the Duetto toolbox for MATLAB software. We calculated the % hot contrast recovery coefficient (CRC_hot) of each hot sphere, the cold CRC (CRC_cold) of each cold sphere, the background variability (BV) and residual lung error (LE). We measured the full width at half maximum (FWHM) of the micro hollow hot spheres ≤ 13 mm to assess spatial resolution on the reconstructed PET images.

RESULTS

The CRC_hot and CRC_cold for different β values and γ factors depended on the size of the small spheres. The CRC_hot, CRC_cold and BV increased along with the γ factor. A 6.2-mm hot sphere was obvious in BPL as lower β values and higher γ factors, whereas γ factors ≥ 10 resulted in images with increased background noise. The FWHM became smaller when the γ factor increased.

CONCLUSION

High and low γ factors, respectively, preserved the edges of reconstructed PET images and promoted image smoothing. The BPL with a γ factor above the default value in Q.Clear (γ factor = 2) generated high-resolution PET images, although image noise slightly diverged. Optimizing the β value and the γ factor in BPL enabled the detection of lesions ≤ 6.2 mm.

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.

Determination of optimal regularization factor in Bayesian penalized likelihood reconstruction of brain PET images using [18 F]FDG and [11 C]PiB

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 (β) in BPL requires optimization according to the radiopharmaceutical tracer. The present study aimed to define the optimal β value in BPL required to diagnose Alzheimer disease from brain 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/CT 710 and reconstructed using OSEM + time-of-flight (TOF) under clinical conditions and BPL + TOF (β = 20-1,000). 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 β 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 β 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 β = 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 β = 150-1000 and 400-1,000 for [¹⁸ F]FDG and [¹¹ C]PiB, respectively. The phantom study restricted the ranges of β values to 100-300 and 300-500 for [¹⁸ F]FDG and [¹¹ C]PiB, respectively. The BPL scores for grey-white matter contrast and image noise, exceeded those of OSEM in [¹⁸ F]FDG and [¹¹ C]PiB images regardless of β values. Visual evaluation confirmed that the optimal β 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 SUVR due to full convergence, more rigorous noise control and edge preservation. The optimal β 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 β values in BPL for brain PET imaging. This article is protected by copyright. All rights reserved.

Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [11C]PHNO brain PET-MR studies

INTRODUCTION

Q.Clear is a Bayesian penalised likelihood (BPL) reconstruction algorithm available on General Electric (GE) Positron Emission Tomography (PET)-Computed Tomography (CT) and PET-Magnetic Resonance (MR) scanners. This algorithm is regulated by a β value which acts as a noise penalisation factor and yields improvements in signal to noise ratio (SNR) in clinical scans, and in contrast recovery and spatial resolution in phantom studies. However, its performance in human brain imaging studies remains to be evaluated in depth. This pilot study aims to investigate the impact of Q.Clear reconstruction methods using different β value versus ordered subset expectation maximization (OSEM) on brain kinetic modelling analysis of low count brain images acquired in the PET-MR.

METHODS

Six [¹¹C]PHNO PET-MR brain datasets were reconstructed with Q.Clear with β100-1000 (in increments of 100) and OSEM. The binding potential relative to non-displaceable volume (BP_ND) were obtained for the Substantia Nigra (SN), Striatum (St), Globus Pallidus (GP), Thalamus (Th), Caudate (Cd) and Putamen (Pt), using the MIAKAT™ software. Intraclass correlation coefficients (ICC), repeatability coefficients (RC), coefficients of variation (CV) and bias from Bland-Altman plots were reported. Statistical analysis was conducted using a 2-way ANOVA model with correction for multiple comparisons.

RESULTS

When comparing a standard OSEM reconstruction of 6 iterations/16 subsets and 5 mm filter with Q.Clear with different β values under low counts, the bias and RC were lower for Q.Clear with β100 for the SN (RC = 2.17), Th (RC = 0.08) and GP (RC = 0.22) and with β200 for the St (RC = 0.14), Cd (RC = 0.18)and Pt (RC = 0.10). The p-values in the 2-way ANOVA model corroborate these findings. ICC values obtained for Th, St, GP, Pt and Cd demonstrate good reliability (0.87, 0.99, 0.96, 0.99 and 0.96, respectively). For the SN, ICC values demonstrate poor reliability (0.43).

CONCLUSION

BP_ND results obtained from quantitative low count brain PET studies using [¹¹C]PHNO and reconstructed with Q.Clear with β < 400, which is the value used for clinical [¹⁸F]FDG whole-body studies, demonstrate the lowest bias versus the typical iterative reconstruction method OSEM.

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.

Optimization of pediatric FDG-PET/CT examinations based on physical indicators using the SiPM-PET/CT system

OBJECTIVE

This study aimed to investigate the appropriate Silicon photomultiplier -PET/CT acquisition and image reconstruction conditions for each age group.

METHODS

The original phantom was developed to reflect the thickness and width of the torso in each age group (neonates, 1-year-olds, 5-year-olds, 10-year-olds, 15-year-olds, and adults). The ratio of hot spheres to background radioactivity was 4:1, and the radioactivity concentration was adjusted according to the Japanese consensus guidelines for appropriate implementation of pediatric nuclear medicine examinations. We evaluated the root mean square error (RMSE) as an assessment/function of the standardized uptake value of each hot sphere, the background variability (N10 mm), the % contrast of the hot sphere (QH, 10 mm/N10 mm), and the noise equivalent counts to determine the optimal reconstruction parameters and the appropriate acquisition time.

RESULTS

The minimum RMSE was obtained by setting the half-width of the Gaussian filter to 0-2 mm for iteration 1 or 2 and to 2-4 mm for iteration 3 or more. The acquisition times that satisfied the image quality equivalent to 120 s acquisitions in the adult phantoms were 30 s in the neonatal and 1-year-old phantoms, 60 s in the 5- and 10-year-old phantoms, and 75 s in the 15-year-old phantoms.

CONCLUSION

This study demonstrated that good PET images could be obtained with short acquisition times when the examination is performed under appropriate reconstruction conditions.

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.

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.

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.

Evaluation of Quantitative Ga-68 PSMA PET/CT Repeatability of Recurrent Prostate Cancer Lesions Using Both OSEM and Bayesian Penalized Likelihood Reconstruction Algorithms

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.

Impact of the Noise Penalty Factor on Quantification in Bayesian Penalized Likelihood (Q.Clear) Reconstructions of 68Ga-PSMA PET/CT Scans

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 β of phantom scans, an intermediate β (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.

Performance evaluation of the Q.Clear reconstruction framework versus conventional reconstruction algorithms for quantitative brain PET-MR studies

Background

Q.Clear is a Bayesian penalized likelihood (BPL) reconstruction algorithm that presents improvements in signal-to-noise ratio (SNR) in clinical positron emission tomography (PET) scans. Brain studies in research require a reconstruction that provides a good spatial resolution and accentuates contrast features however, filtered back-projection (FBP) reconstruction is not available on GE SIGNA PET-Magnetic Resonance (PET-MR) and studies have been reconstructed with an ordered subset expectation maximization (OSEM) algorithm. This study aims to propose a strategy to approximate brain PET quantitative outcomes obtained from images reconstructed with Q.Clear versus traditional FBP and OSEM.

Methods

Contrast recovery and background variability were investigated with the National Electrical Manufacturers Association (NEMA) Image Quality (IQ) phantom. Resolution, axial uniformity and SNR were investigated using the Hoffman phantom. Both phantoms were scanned on a Siemens Biograph 6 TruePoint PET-Computed Tomography (CT) and a General Electric SIGNA PET-MR, for FBP, OSEM and Q.Clear. Differences between the metrics obtained with Q.Clear with different β values and FBP obtained on the PET-CT were determined.

Results

For in plane and axial resolution, Q.Clear with low β values presented the best results, whereas for SNR Q.Clear with higher β gave the best results. The uniformity results are greatly impacted by the β value, where β < 600 can yield worse uniformity results compared with the FBP reconstruction.

Conclusion

This study shows that Q.Clear improves contrast recovery and provides better resolution and SNR, in comparison to OSEM, on the PET-MR. When using low β values, Q.Clear can provide similar results to the ones obtained with traditional FBP reconstruction, suggesting it can be used for quantitative brain PET kinetic modelling studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-021-00386-3.

The clinical effectiveness of reconstructing 18F-sodium fluoride PET/CT bone using Bayesian penalized likelihood algorithm for evaluation of metastatic bone disease in obese patients

OBJECTIVE

A new Bayesian penalized likelihood reconstruction algorithm for positron emission tomography (PET) (Q.Clear) is now in clinical use for fludeoxyglucose (FDG) PET/CT. However, experience with non-FDG tracers and in special patient populations is limited. This pilot study aims to compare Q.Clear to standard PET reconstructions for ¹⁸F sodium fluoride (¹⁸F-NaF) PET in obese patients.

METHODS

30 whole body ¹⁸F-NaF PET/CT scans (10 patients with BMI 30-40 Kg/m² and 20 patients with BMI >40 Kg/m²) and a NEMA image quality phantom scans were analyzed using ordered subset expectation maximization (OSEM) and Q.Clear reconstructions methods with B400, 600, 800 and 1000. The images were assessed for overall image quality (IQ), noise level, background soft tissue, and lesion detectability, contrast recovery (CR), background variability (BV) and contrast-to-noise ratio (CNR) for both algorithms.

RESULTS

CNR for clinical cases was higher for Q.Clear than OSEM (p < 0.05). Mean CNR for OSEM was (21.62 ± 8.9), and for Q.Clear B400 (31.82 ± 14.6), B600 (35.54 ± 14.9), B800 (39.81 ± 16.1), and B1000 (40.9 ± 17.8). As the β value increased the CNR increased in all clinical cases. B600 was the preferred β value for reconstruction in obese patients. The phantom study showed Q.Clear reconstructions gave lower CR and lower BV than OSEM. The CNR for all spheres was significantly higher for Q.Clear (independent of β) than OSEM (p < 0.05), suggesting superiority of Q.Clear.

CONCLUSION

This pilot clinical study shows that Q.Clear reconstruction algorithm improves overall IQ of ¹⁸F-NaF PET in obese patients. Our clinical and phantom measurement results demonstrate improved CNR and reduced BV when using Q.Clear. A β value of 600 is preferred for reconstructing ¹⁸F-NaF PET/CT with Q.Clear in obese patients.

ADVANCES IN KNOWLEDGE

¹⁸F-NaF PET/CT is less susceptible to artifacts induced by body habitus. Bayesian penalized likelihood reconstruction with¹⁸F-NaF PET improves overall IQ in obese patients.

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

Background

The Bayesian penalized likelihood (BPL) algorithm Q.Clear (GE Healthcare) allows fully convergent iterative reconstruction that results in better image quality and quantitative accuracy, while limiting image noise. The present study aimed to optimize BPL reconstruction parameters for ¹⁸F-NaF PET/CT images and to determine the feasibility of ¹⁸F-NaF PET/CT image acquisition over shorter durations in clinical practice.

Methods

A custom-designed thoracic spine phantom consisting of several inserts, soft tissue, normal spine, and metastatic bone tumor, was scanned using a Discovery MI PET/CT scanner (GE Healthcare). The phantom allows optional adjustment of activity distribution, tumor size, and attenuation. We reconstructed PET images using OSEM + PSF + TOF (2 iterations, 17 subsets, and a 4-mm Gaussian filter), BPL + TOF (β = 200 to 700), and scan durations of 30–120 s. Signal-to-noise ratios (SNR), contrast, and coefficients of variance (CV) as image quality indicators were calculated, whereas the quantitative measures were recovery coefficients (RC) and RC linearity over a range of activity. We retrospectively analyzed images from five persons without bone metastases (male, n = 1; female, n = 4), then standardized uptake values (SUV), CV, and SNR at the 4th, 5th, and 6th thoracic vertebra were calculated in BPL + TOF (β = 400) images.

Results

The optimal reconstruction parameter of the BPL was β = 400 when images were acquired at 120 s/bed. At 90 s/bed, the BPL with a β value of 400 yielded 24% and 18% higher SNR and contrast, respectively, than OSEM (2 iterations; 120 s acquisitions). The BPL was superior to OSEM in terms of RC and the RC linearity over a range of activity, regardless of scan duration. The SUV_max were lower in BPL, than in OSEM. The CV and vertebral SNR in BPL were superior to those in OSEM.

Conclusions

The optimal reconstruction parameters of ¹⁸F-NaF PET/CT images acquired over different durations were determined. The BPL can reduce PET acquisition to 90 s/bed in ¹⁸F-NaF PET/CT imaging. Our results suggest that BPL (β = 400) on SiPM-based TOF PET/CT scanner maintained high image quality and quantitative accuracy even for shorter acquisition durations.

Optimization of injected 68Ga-PSMA activity based on list-mode phantom data and clinical validation

Optimization of injected gallium-68 (⁶⁸Ga) activity for ⁶⁸Ga-prostate-specific membrane antigen positron emission tomography/computed tomography (⁶⁸Ga-PSMA PET/CT) studies is relevant for image quality, radiation protection, and from an economic point of view. However, no clear guidelines are available for ⁶⁸Ga-PSMA studies. Therefore, a phantom study is performed to determine the highest coefficient of variation (COV) acceptable for reliable image interpretation and quantification.

To evaluate image interpretation, the relationship of COV and contrast-to-noise ratio (CNR) was studied. The CNR should remain larger than five, according to the Rose criterion. To evaluate image quantification, the effect of COV on the percentage difference (PD) between quantification results of two studies was analyzed. Comparison was done by calculating the PD of the SUV_max. The maximum allowable PD_SUVmax was set at 20%. The highest COV at which both criteria are still met is defined as COV_max. Of the NEMA Image Quality phantom, a 20 min/bed (2 bed positions) scan was acquired in list-mode PET (Philips Gemini TF PET/CT). The spheres to background activity ratio was approximately 9:1. To obtain images with different COV, lower activity was mimicked by reconstructions with acquisition times of 10 min/bed to 5 s/bed. Pairs of images were obtained by reconstruction of two non-overlapping parts of list-mode data.

For the 10-mm diameter sphere, a COV of 25% still meets the criteria of CNR_SUVmean ≥ 5 and PD_SUVmax ≤ 20%. This phantom scan was acquired with an acquisition time of 116 s and a background activity concentration of 0.71 MBq/kg. Translation to a clinical protocol results in a clinical activity regimen of 3.5 MBq/kg min at injection. To verify this activity regimen, 15 patients (6 MBq/kg min) with a total of 22 lesions are included. Additional reconstructions were made to mimic the proposed activity regimen. Based on the CNR_SUVmax, no lesions were missed with this proposed activity regimen.

For our institution, a clinical activity regimen of 3.5 MBq/kg min at injection is acceptable, which indicates that activity can be reduced by almost 50% compared with the current code of practice. Our proposed method could be used to obtain an objective activity regimen for other PET/CT systems and tracers.