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

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.

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

BACKGROUND

New digital detectors and block-sequential regularized expectation maximization (BSREM) reconstruction algorithm improve positron emission tomography (PET)/magnetic resonance (MR) image quality. The impact on image quality may differ from analogue PET/computed tomography (CT) protocol. The aim of this study is to determine the potential reduction of injected [68Ga]Ga-DOTA-TATE activity for digital PET/MR with BSREM reconstruction while maintaining at least equal image quality compared to the current analogue PET/CT protocol.

METHODS

NEMA IQ phantom data and 25 patients scheduled for a diagnostic PET/MR were included. According to our current protocol, 1.5 MBq [68Ga]Ga-DOTA-TATE per kilogram (kg) was injected. After 60 min, scans were acquired with 3 (≤ 70 kg) or 4 (> 70 kg) minutes per bedposition. PET/MR scans were reconstructed using BSREM and factors β 150, 300, 450 and 600. List mode data with reduced counts were reconstructed to simulate scans with 17%, 33%, 50% and 67% activity reduction. Image quality was measured quantitatively for PET/CT and PET/MR phantom and patient data. Experienced nuclear medicine physicians performed visual image quality scoring and lesion counting in the PET/MR patient data.

RESULTS

Phantom analysis resulted in a possible injected activity reduction of 50% with factor β = 600. Quantitative analysis of patient images revealed a possible injected activity reduction of 67% with factor β = 600. Both with equal or improved image quality as compared to PET/CT. However, based on visual scoring a maximum activity reduction of 33% with factor β = 450 was acceptable, which was further limited by lesion detectability analysis to an injected activity reduction of 17% with factor β = 450.

CONCLUSION

A digital [68Ga]Ga-DOTA-TATE PET/MR together with BSREM using factor β = 450 result in 17% injected activity reduction with quantitative values at least similar to analogue PET/CT, without compromising on PET/MR visual image quality and lesion detectability.

The effects of various penalty parameter values in Q.Clear algorithm for rectal cancer detection on 18F-FDG images using a BGO-based PET/CT scanner: a phantom and clinical study

BACKGROUND

The Q.Clear algorithm is a fully convergent iterative image reconstruction technique. We hypothesize that different PET/CT scanners with distinct crystal properties will require different optimal settings for the Q.Clear algorithm. Many studies have investigated the improvement of the Q.Clear reconstruction algorithm on PET/CT scanner with LYSO crystals and SiPM detectors. We propose an optimum penalization factor (β) for the detection of rectal cancer and its metastases using a BGO-based detector PET/CT system which obtained via accurate and comprehensive phantom and clinical studies.

METHODS

18F-FDG PET-CT scans were acquired from NEMA phantom with lesion-to-background ratio (LBR) of 2:1, 4:1, 8:1, and 15 patients with rectal cancer. Clinical lesions were classified into two size groups. OSEM and Q.Clear (β value of 100-500) reconstruction was applied. In Q.Clear, background variability (BV), contrast recovery (CR), signal-to-noise ratio (SNR), SUVmax, and signal-to-background ratio (SBR) were evaluated and compared to OSEM.

RESULTS

OSEM had 11.5-18.6% higher BV than Q.Clear using β value of 500. Conversely, RC from OSEM to Q.Clear using β value of 500 decreased by 3.3-7.7% for a sphere with a diameter of 10 mm and 2.5-5.1% for a sphere with a diameter of 37 mm. Furthermore, the increment of contrast using a β value of 500 was 5.2-8.1% in the smallest spheres compared to OSEM. When the β value was increased from 100 to 500, the SNR increased by 49.1% and 30.8% in the smallest and largest spheres at LBR 2:1, respectively. At LBR of 8:1, the relative difference of SNR between β value of 100 and 500 was 43.7% and 44.0% in the smallest and largest spheres, respectively. In the clinical study, as β increased from 100 to 500, the SUVmax decreased by 47.7% in small and 31.1% in large lesions. OSEM demonstrated the least SUVmax, SBR, and contrast. The decrement of SBR and contrast using OSEM were 13.6% and 12.9% in small and 4.2% and 3.4%, respectively, in large lesions.

CONCLUSIONS

Implementing Q.Clear enhances quantitative accuracies through a fully convergent voxel-based image approach, employing a penalization factor. In the BGO-based scanner, the optimal β value for small lesions ranges from 200 for LBR 2:1 to 300 for LBR 8:1. For large lesions, the optimal β value is between 400 for LBR 2:1 and 500 for LBR 8:1. We recommended β value of 300 for small lesions and β value of 500 for large lesions in clinical study.

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

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

Quantitative Assessment of Myocardial Ischemia With Positron Emission Tomography

Recent advances in positron emission tomography (PET) technology and reconstruction techniques have now made quantitative assessment using cardiac PET readily available in most cardiac PET imaging centers. Multiple PET myocardial perfusion imaging (MPI) radiopharmaceuticals are available for quantitative examination of myocardial ischemia, with each having distinct convenience and accuracy profile. Important properties of these radiopharmaceuticals ( 15 O-water, 13 N-ammonia, 82 Rb, 11 C-acetate, and 18 F-flurpiridaz) including radionuclide half-life, mean positron range in tissue, and the relationship between kinetic parameters and myocardial blood flow (MBF) are presented. Absolute quantification of MBF requires PET MPI to be performed with protocols that allow the generation of dynamic multiframes of reconstructed data. Using a tissue compartment model, the rate constant that governs the rate of PET MPI radiopharmaceutical extraction from the blood plasma to myocardial tissue is calculated. Then, this rate constant ( K1 ) is converted to MBF using an established extraction formula for each radiopharmaceutical. As most of the modern PET scanners acquire the data only in list mode, techniques of processing the list-mode data into dynamic multiframes are also reviewed. Finally, the impact of modern PET technologies such as PET/CT, PET/MR, total-body PET, machine learning/deep learning on comprehensive and quantitative assessment of myocardial ischemia is briefly described in this review.

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.

Analysis of image quality by regulating beta function of BSREM reconstruction algorithm and comparison with conventional reconstructions in carcinoma breast studies of PET CT with BGO detector

BACKGROUND

The study aimed to evaluate the beta penalization factor of the BSREM reconstruction algorithm on a five-ring BGO-based PET CT system and compared it with conventional reconstructions.

METHODS

Retrospective study involves 30 breast cancer patient data of 18F-fluorodeoxyglucose ( 18 F-FDG) PET CT for reconstruction with OSEM, OSEM + PSF, and BSREM under variable β factors ranging from 200 to 600 in the steps of 50. Liver noise, lesion SUVmax, SBR, and SNR for each reconstruction were calculated. Quantitative parameters of each beta factor of BSREM were compared with OSEM and OSEM + PSF, using the Wilcoxon sign rank test with Bonferroni correction, a value of P < 0.002 was considered statistically significant. Visual scoring by two readers was also evaluated.

RESULTS

Thirty lesions of mean size 1.91 ± 0.58 cm range (0.7-3.6 cm) were identified. Liver noise and SBR were reduced, whereas SNR was increased with an increasing β value of BSREM. In comparison with OSEM, liver noise was not significantly different from β200 and β250. SNR of OSEM was significantly lower than any other β factors and SBR of β factor less than 500 was significantly higher than OSEM. In comparison with OSEM + PSF, liver noise was not significantly different from β400 and β350-500 do not show a significant difference in SNR and SBR compared with OSEM + PSF. β350 scored highest under visual scoring with a moderate agreement.

CONCLUSION

The study quantitatively indicates the optimum beta range of β250-450 and the qualitative evaluation indicates that β350 is an optimum beta factor of BSREM in breast cancer cases for 18 F-FDG WB-PET CT.

Positron Range Corrections and Denoising Techniques for Gallium-68 PET Imaging: A Literature Review

Gallium-68 (68Ga) is characterized by relatively high positron energy compared to Fluorine-18 (18F), causing substantial image quality degradation. Furthermore, the presence of statistical noise can further degrade image quality. The aim of this literature review is to identify the recently developed positron range correction techniques for 68Ga, as well as noise reduction methods to enhance the image quality of low count 68Ga PET imaging. The search engines PubMed and Scopus were employed, and we limited our research to published results from January 2010 until 1 August 2022. Positron range correction was achieved by using either deblurring or deep learning approaches. The proposed techniques improved the image quality and, in some cases, achieved an image quality comparable to 18F PET. However, none of these techniques was validated in clinical studies. PET denoising for 68Ga-labeled radiotracers was reported using either reconstruction-based techniques or deep learning approaches. It was demonstrated that both approaches can substantially enhance the image quality by reducing the noise levels of low count 68Ga PET imaging. The combination of 68Ga-specific positron range correction techniques and image denoising approaches may enable the application of low-count, high-quality 68Ga PET imaging in a clinical setting.

Phantom-based acquisition time and image reconstruction parameter optimisation for oncologic FDG PET/CT examinations using a digital system

Background

New-generation silicon-photomultiplier (SiPM)-based PET/CT systems exhibit an improved lesion detectability and image quality due to a higher detector sensitivity. Consequently, the acquisition time can be reduced while maintaining diagnostic quality. The aim of this study was to determine the lowest ¹⁸F-FDG PET acquisition time without loss of diagnostic information and to optimise image reconstruction parameters (image reconstruction algorithm, number of iterations, voxel size, Gaussian filter) by phantom imaging. Moreover, patient data are evaluated to confirm the phantom results.

Methods

Three phantoms were used: a soft-tissue tumour phantom, a bone-lung tumour phantom, and a resolution phantom. Phantom conditions (lesion sizes from 6.5 mm to 28.8 mm in diameter, lesion activity concentration of 15 kBq/mL, and signal-to-background ratio of 5:1) were derived from patient data. PET data were acquired on an SiPM-based Biograph Vision PET/CT system for 10 min in list-mode format and resampled into time frames from 30 to 300 s in 30-s increments to simulate different acquisition times. Different image reconstructions with varying iterations, voxel sizes, and Gaussian filters were probed. Contrast-to-noise-ratio (CNR), maximum, and peak signal were evaluated using the 10-min acquisition time image as reference. A threshold CNR value ≥ 5 and a maximum (peak) deviation of ± 20% were considered acceptable. 20 patient data sets were evaluated regarding lesion quantification as well as agreement and correlation between reduced and full acquisition time standard uptake values (assessed by Pearson correlation coefficient, intraclass correlation coefficient, Bland–Altman analyses, and Krippendorff’s alpha).

Results

An acquisition time of 60 s per bed position yielded acceptable detectability and quantification results for clinically relevant phantom lesions ≥ 9.7 mm in diameter using OSEM-TOF or OSEM-TOF+PSF image reconstruction, a 4-mm Gaussian filter, and a 1.65 × 1.65 x 2.00-mm³ or 3.30 × 3.30 x 3.00-mm³ voxel size. Correlation and agreement of patient lesion quantification between full and reduced acquisition times were excellent.

Conclusion

A threefold reduction in acquisition time is possible. Patients might benefit from more comfortable examinations or reduced radiation exposure, if instead of the acquisition time the applied activity is reduced.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09993-4.

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

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

The Impact of Total Variation Regularized Expectation Maximization Reconstruction on 68Ga-DOTA-TATE PET/CT Images in Patients With Neuroendocrine Tumor

Objective

The aim of this study was to investigate the effects of the total variation regularized expectation maximization (TVREM) reconstruction on improving 68Ga-DOTA-TATE PET/CT images compared to the ordered subset expectation maximization (OSEM) reconstruction.

Method

A total of 17 patients with neuroendocrine tumors who underwent clinical 68Ga-DOTA-TATE PET/CT were involved in this study retrospectively. The PET images were acquired with either 3 min-per-bed (min/bed) acquisition time and reconstructed with OSEM (2 iterations, 20 subsets, and a 3.2-mm Gaussian filter) and TVREM (seven penalization factors = 0.01, 0.07, 0.14, 0.21, 0.28, 0.35, and 0.42) for 2 and 3 min-per-bed (min/bed) acquisition time using list-mode. The SUVmean of the liver, background variability (BV), signal-to-noise ratios (SNR), SUVmax of the lesions and tumor-to-background ratios (TBR) were measured. The mean percentage difference in the SNR and TBR between TVREM with difference penalization factors and OSEM was calculated. Qualitative image quality was evaluated by two experienced radiologists using a 5-point score scale (5-excellent, 1-poor).

Results

In total, 63 lesions were analyzed in this study. The SUVmean of the liver did not differ significantly between TVREM and OSEM. The BV of all TVREM groups was lower than OSEM groups (all p &lt; 0.05), and the BV of TVREM 2 min/bed group with penalization factor of 0.21 was considered comparable to OSEM 3 min/bed group (p = 0.010 and 0.006). The SNR, SUVmax and TBR were higher for all TVREM groups compared to OSEM groups (all p &lt; 0.05). The mean percentage difference in the SNR and TBR was larger for small lesions (&lt;10 mm) than that for medium (≥10 mm but &lt; 20 mm) and large lesions (≥20 mm). The highest image quality score was given to TVREM 2 min/bed group with penalization factor of 0.21 (3.77 ± 0.26) and TVREM 3 min/bed group with penalization factor of 0.35 (3.77 ± 0.26).

Conclusion

TVREM could reduce image noise, improve the SNR, SUVmax and TBR of the lesions, and has the potential to preserves the image quality with shorter acquisition time.

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.

Digital PET vs Analog PET: Clinical Implications?

Positron emission tomography (PET) is a functional imaging technique introduced in 1970s. Over the years, PET was used alone but is in 2000 when the first hybrid PET/CT device was clinically introduced. Since then, PET has continuously been marked by technological developments, being the most recent one the introduction of silicon photomultipliers (SiPMs) as an alternative to standard photomultiplier tubes used in analog PET/CT systems. SiPMs, the basis for the so called digital PET/CT systems, are smaller than standard photomultiplier tubes (enabling higher spatial resolution) and provide up to 100% coverage of the crystal area, as well as high sensitivity, low noise, and fast timing resolution. SiPMs in combination with optimized acquisition and reconstruction parameters improve the localization of the annihilation events, provide high definition PET images, and offer higher sensitivity and higher diagnostic performance. This article summarizes the evidence about the superior performance of the state of the art digital PET and highlights its potential clinical implications. Digital PET opens new perspectives in the quantification and characterization of small lesions, which are mostly undetectable using analog PET systems, potentially changing patient management and improving outcomes in oncological and non-oncological diseases. Moreover, digital PET offers the possibility to reduce radiation dose and scan times which may facilitate the implementation of PET to address unmet clinical needs.

Can Q.Clear reconstruction be used to improve [68 Ga]Ga-DOTANOC PET/CT image quality in overweight NEN patients?

AIM/INTRODUCTION

Digital PET/CT allows Q.Clear image reconstruction with different Beta (β) levels. However, no definitive standard β level for [68 Ga]Ga-DOTANOC PET/CT has been established yet. As patient's body mass index (BMI) can affect image quality, the aim of the study was to visually and semi-quantitatively assess different β levels compared to standard OSEM in overweight patients.

MATERIALS AND METHODS

Inclusion criteria: (1) patients with NEN included in a prospective CE-approved electronic archive; (2) [68 Ga]Ga-DOTANOC PET/CT performed on a digital tomograph between September2019/March2021; (3) BMI ≥ 25. Images were acquired following EANM guidelines and reconstructed with OSEM and Q.Clear with three β levels (800, 1000, 1600). Scans were independently reviewed by three expert readers, unaware of clinical data, who independently chose the preferred β level reconstruction for visual overall image quality. Semi-quantitative analysis was performed on each scan: SUVmax of the highest uptake lesion (SUVmax-T), liver background SUVmean (SUVmean-L), SUVmax-T/SUVmean-L, Signal-to-noise ratio for both liver (LSNR) and the highest uptake lesion (SNR-T), Contrast-to-noise ratio (CNR).

RESULTS

Overall, 75 patients (median age: 63 years old [23-87]) were included: pre-obesity sub-group (25 ≤ BMI < 30, n = 50) and obesity sub-group (BMI ≥ 30, n = 25). PET/CT was positive for disease in 45/75 (60.0%) cases (14 obese and 31 pre-obese patients). Agreement among readers' visual rating was high (Fleiss κ = 0.88) and the β1600 was preferred in most cases (in 96% of obese patients and in 53.3% of pre-obese cases). OSEM was considered visually equal to β1600 in 44.7% of pre-obese cases and in 4% of obese patients. In a minority of pre-obese cases, OSEM was preferred (2%). In the whole population, CNR, SNR-T and LSNR were significantly different (p < 0.001) between OSEM and β1600, conversely to SUVmean-L (not significant). These results were also confirmed when calculated separately for the pre-obesity and obesity sub-groups β800 and β1000 were always rated inferior.

CONCLUSIONS

Q.Clear is a new technology for PET/CT image reconstruction that can be used to increase CNR and SNR-T, to subsequently optimise overall image quality as compared to standard OSEM. Our preliminary data on [68 Ga]Ga-DOTANOC PET/CT demonstrate that in overweight NEN patients, β1600 is preferable over β800 and β1000. Further studies are warranted to validate these results in lesions of different anatomical region and size; moreover, currently employed interpretative PET positivity criteria should be adjusted to the new reconstruction method.

Digital PET and detection of recurrent prostate cancer: what have we gained, and what is still missing?

Defined by the TIME magazine 'medical invention of the year 2000,' positron emission computed tomography (PET/CT) has experienced impressive improvements in technology and clinical applications over time. In recent years, silicon photomultipliers (SiPMs) detectors, characterized by excellent intrinsic time resolution and high photon-detection efficiency, have been introduced as an alternative to the classic photomultiplier tubes (PMTs), thus moving the field of PET technology forward and leading to the so-called digital PET/CT (dPET/CT). On the other side, the radiopharmaceutical ⁶⁸Ga-PSMA-11, approved by the Food and Drug Administration in December 2020, proved to strongly impact prostate cancer (PCa) diagnosis and management. In the study under evaluation, Alberts et al. retrospectively compared the performance of dPET/CT and PMTs-based PET/CT, namely analogue PET/CT (aPET/CT), in two cohorts, each one including 65 patients undergoing PET/CT with ⁶⁸Ga-PSMA-11 for suspected recurrent PCa. The authors found that dPET/CT presented a higher detection rate of pathological lesions with respect to aPET/CT. Of note, dPET/CT's higher sensitivity results are associated with an increased true-positive rate and high inter-reader agreement. This report underscores how innovative PET/CT instrumentation, by utilizing novel radiopharmaceuticals targeting specific metabolic/molecular signatures expressed by PCa, may represent a successful weapon in uro-oncology.

Impact of segmentation and discretization on radiomic features in 68Ga-DOTA-TOC PET/CT images of neuroendocrine tumor

OBJECTIVE

To identify the impact of segmentation methods and intensity discretization on radiomic features (RFs) extraction from 68Ga-DOTA-TOC PET images in patients with neuroendocrine tumors.

METHODS

Forty-nine patients were retrospectively analyzed. Tumor contouring was performed manually by four different operators and with a semi-automatic edge-based segmentation (SAEB) algorithm. Three SUVmax fixed thresholds (20, 30, 40%) were applied. Fifty-one RFs were extracted applying two different intensity rescale factors for gray-level discretization: one absolute (AR60 = SUV from 0 to 60) and one relative (RR = min-max of the VOI SUV). Dice similarity coefficient (DSC) was calculated to quantify segmentation agreement between different segmentation methods. The impact of segmentation and discretization on RFs was assessed by intra-class correlation coefficients (ICC) and the coefficient of variance (COVL). The RFs' correlation with volume and SUVmax was analyzed by calculating Pearson's correlation coefficients.

RESULTS

DSC mean value was 0.75 ± 0.11 (0.45-0.92) between SAEB and operators and 0.78 ± 0.09 (0.36-0.97), among the four manual segmentations. The study showed high robustness (ICC > 0.9): (a) in 64.7% of RFs for segmentation methods using AR60, improved by applying SUVmax threshold of 40% (86.5%); (b) in 50.9% of RFs for different SUVmax thresholds using AR60; and (c) in 37% of RFs for discretization settings using different segmentation methods. Several RFs were not correlated with volume and SUVmax.

CONCLUSIONS

RFs robustness to manual segmentation resulted higher in NET 68Ga-DOTA-TOC images compared to 18F-FDG PET/CT images. Forty percent SUVmax thresholds yield superior RFs stability among operators, however leading to a possible loss of biological information. SAEB segmentation appears to be an optimal alternative to manual segmentation, but further validations are needed. Finally, discretization settings highly impacted on RFs robustness and should always be stated.

Can a penalized-likelihood estimation algorithm be used to reduce the injected dose or the acquisition time in 68Ga-DOTATATE PET/CT studies?

Background

Image quality and quantitative accuracy of positron emission tomography (PET) depend on several factors such as uptake time, scanner characteristics and image reconstruction methods. Ordered subset expectation maximization (OSEM) is considered the gold standard for image reconstruction. Penalized-likelihood estimation (PL) algorithms have been recently developed for PET reconstruction to improve quantitation accuracy while maintaining or even improving image quality. In PL algorithms, a regularization parameter β controls the penalization of relative differences between neighboring pixels and determines image characteristics. In the present study, we aim to compare the performance of Q.Clear (PL algorithm, GE Healthcare) and OSEM (3 iterations, 8 subsets, 6-mm post-processing filter) for ⁶⁸Ga-DOTATATE (⁶⁸Ga-DOTA) PET studies, both visually and quantitatively.

Thirty consecutive whole-body ⁶⁸Ga-DOTA studies were included. The data were acquired in list mode and were reconstructed using 3D OSEM and Q.Clear with various values of β and various acquisition times per bed position (bp), thus generating images with reduced injected dose (1.5 min/bp: β = 300–1100; 1.0 min/bp: β = 600–1400 and 0.5 min/bp: β = 800–2200). An additional analysis adding β values up to 1500, 1700 and 3000 for 1.5, 1.0 and 0.5 min/bp, respectively, was performed for a random sample of 8 studies. Evaluation was performed using a phantom and clinical data. Two experienced nuclear medicine physicians blinded to the variables assessed the image quality visually.

Results

Clinical images reconstructed with Q.Clear, set at 1.5, 1.0 and 0.5 min/bp using β = 1100, 1300 and 3000, respectively, resulted in images with noise equivalence to 3D OSEM (1.5 min/bp) with a mean increase in SUV_max of 14%, 13% and 4%, an increase in SNR of 30%, 24% and 10%, and an increase in SBR of 13%, 13% and 2%. Visual assessment yielded similar results for β values of 1100–1400 and 1300–1600 for 1.5 and 1.0 min/bp, respectively, although for 0.5 min/bp there was no significant improvement compared to OSEM.

Conclusion

⁶⁸Ga-DOTA reconstructions with Q.Clear, 1.5 and 1.0 min/bp, resulted in increased tumor SUV_max and in improved SNR and SBR at a similar level of noise compared to 3D OSEM. Q.Clear with β = 1300–1600 enables one-third reduction of acquisition time or injected dose, with similar image quality compared to 3D OSEM.

Image reconstruction methods affect software-aided assessment of pathologies of [18F]flutemetamol and [18F]FDG brain-PET examinations in patients with neurodegenerative diseases

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[¹⁸F]Flutemetamol and [¹⁸F]FDG image reconstruction.

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Software-aided assessment of neurodegenerative disease patients.

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New developments in brain PET image reconstruction affect quantitative measures.

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Evaluation of SUVR and z-score measures.

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Normalizing to pons and whole brain induced greater absolute differences between reconstructions.

Purpose

To assess how some of the new developments in brain positron emission tomography (PET) image reconstruction affect quantitative measures and software-aided assessment of pathology in patients with neurodegenerative diseases.

Methods

PET data were grouped into four cohorts: prodromal Alzheimer’s disease patients and controls receiving [¹⁸F]flutemetamol, and neurodegenerative disease patients and controls receiving [¹⁸F]FDG PET scans. Reconstructed images were obtained by ordered-subsets expectation maximization (OSEM; 3 iterations (i), 16/34 subsets (s), 3/5-mm filter, ±time-of-flight (TOF), ±point-spread function (PSF)) and block-sequential regularized expectation maximization (BSREM; TOF, PSF, β-value 75–300). Standardized uptake value ratios (SUVR) and z-scores were calculated (CortexID Suite, GE Healthcare) using cerebellar gray matter, pons, whole cerebellum and whole brain as reference regions.

Results

In controls, comparable results to the normal database were obtained with OSEM 3i/16 s 5-mm reconstruction. TOF, PSF and BSREM either increased or decreased the relative uptake difference to the normal subjects’ database within the software, depending on the tracer and chosen reference area, i.e. resulting in increased absolute z-scores. Normalizing to pons and whole brain for [¹⁸F]flutemetamol and [¹⁸F]FDG, respectively, increased absolute differences between reconstructions methods compared to normalizing to cerebellar gray matter and whole cerebellum when applying TOF, PSF and BSREM.

Conclusions

Software-aided assessment of patient pathologies should be used with caution when employing other image reconstruction methods than those used for acquisition of the normal database.