Does a novel penalized likelihood reconstruction of 18F-FDG PET-CT improve signal-to-background in colorectal liver metastases?

β1600 Q.Clear Digital Reconstruction of [68Ga]Ga-DOTANOC PET/CT Improves Image Quality in NET Patients

Background: Image reconstruction is crucial for improving overall image quality and diagnostic accuracy. Q.Clear is a novel reconstruction algorithm that reduces image noise. The aim of the present study is to assess the preferred Q.Clear β-level for digital [68Ga]Ga-DOTANOC PET/CT reconstruction vs. standard reconstruction (STD) for both overall scan and single-lesion visualization. Methods: Inclusion criteria: (1) patients with/suspected neuroendocrine tumors included in a prospective observational monocentric study between September 2019 and January 2022; (2) [68Ga]Ga-DOTANOC digital PET/CT and contrast-enhanced-CT (ceCT) performed at our center at the same time. Images were reconstructed with STD and with Q.Clear β-levels 800, 1000, and 1600. Scans were blindly reviewed by three nuclear-medicine experts: the preferred β-level reconstruction was independently chosen for the visual quality of both the overall scan and the most avid target lesion < 1 cm (t) and >1 cm (T). PET/CT results were compared to ceCT. Semiquantitative analysis was performed (STD vs. β1600) in T and t concordant at both PET/CT and ceCT. Subgroup analysis was also performed in patients presenting discordant t. Results: Overall, 52 patients were included. β1600 reconstruction was considered superior over the others for both overall scan quality and single-lesion detection in all cases. The only significantly different (p < 0.001) parameters between β1600 and STD were signal-to-noise liver ratio and standard deviation of the liver background. Lesion-dependent parameters were not significantly different in concordant T (n = 37) and t (n = 10). Among 26 discordant t, when PET was positive, all findings were confirmed as malignant. Conclusions: β1600 Q.Clear reconstruction for [68Ga]Ga-DOTANOC imaging is feasible and improves image quality for both overall and small-lesion assessment.

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.

A review of digital PET-CT technology: Comparing performance parameters in SiPM integrated digital PET-CT systems

OBJECTIVE

The objective of this study was to perform a narrative review of digital Positron emission tomography-computed tomography (PET-CT) scanners, focussing on the current development in the technology of optimized crystal size and design, the time of flight (ToF) resolution, sensitivity, and axial field of view (AFOV).

KEY FINDINGS

It was observed that significant developments were carried out on the optimization of scintillation crystal size which results in the improvement of spatial resolution. such developments include the upgrade in the AFOV after the integration of SiPM technology, which results in dynamic parametric imaging acquisition in PET and sensitivity boost. The improvement in ToF resolution and the better ToF resolution values, which result in a boost in adequate sensitivity and signal-to-noise ratio (SNR). Other upgrades include the use of the smallest crystal size of 2.76 × 2.76 mm, and the use of the lowest ToF resolution of 214 ps. The use of the largest AFOV of 194 cm with the highest observed NEMA sensitivity of 225 cps/kBq for the total body PET-CT system.

CONCLUSION

Digital PET-CT systems offer various advantages such as a reduction in radiation dose from injected radiopharmaceuticals doses and the overall PET acquisition time with an improved diagnostic certainty. This is because of the better performance of the SiPM detector. Digital PET-CT also has added benefits of the dynamic acquisition and Patlak modeling capabilities into routine clinical practice with the advancement in higher AFOV PET systems.

IMPLICATION

This will help the users choose the best system during the evaluation of the PET-CT for purchase in clinical and research applications. This review will further help in teaching the latest technology and developments in PET-CT systems.

Total variation regularized expectation maximization reconstruction improves 68Ga-FAPI-04 PET/CT image quality as compared to ordered subset expectation maximization reconstruction

BACKGROUND

This study aimed to investigate improvements in 68Ga-FAPI PET/CT image quality due to using total variation regularized expectation maximization (TVR-EM) and ordered subset expectation maximization (OS-EM) reconstruction.

METHODS

Data from a total of 24 patients were retrospectively analyzed in this study. Positron emission tomography (PET) images were reconstructed using OS-EM and TVR-EM for 2 and 3 minutes-per-bed (min/bed) acquisition. The SUVmean of a region-of-interest on the liver, image noise, signal-to-noise ratios (SNR), the SUVmax of the lesions and the tumour-to-background ratios (TBR) were measured and compared between the 2 methods. Subjective image qualities were evaluated by two experienced radiologists using a 5-point score scale (5-excellent, 1-poor).

RESULTS

In total, 132 lesions were analyzed. The image noise in TVR-EM reconstruction groups was lower than in the OS-EM groups (all P<0.05). The SNR, SUVmax of lesions and the TBR were higher for the TVR-EM reconstruction groups compared to OS-EM groups (all P<0.05). Also, the SUVmax of the lesions in the TVR-EM groups increased by at least 12% compared to OS-EM 3 min/bed group. The SUVmax for small (<10 mm) and large lesions (>10 mm) in the TVR-EM 2 min/bed group were significantly larger compared to the OS-EM 3 min/bed groups (all P<0.05). The highest image quality score resulted from the TVR-EM 3 min/bed group with a penalization factor of 0.25 (3.92±0.19).

CONCLUSIONS

TVR-EM reduces image noise and improved the SNR, SUVmax and TBR of the lesions. It also enables fast acquisition without compromising image quality compared to standard OS-EM.

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.

Bayesian penalized likelihood PET reconstruction impact on quantitative metrics in diffuse large B-cell lymphoma

Evaluate the quantitative, subjective (Deauville score [DS]) and reader agreement differences between standard ordered subset expectation maximization (OSEM) and Bayesian penalized likelihood (BPL) positron emission tomography (PET) reconstruction methods. A retrospective review of 104 F-18 fluorodeoxyglucose PET/computed tomography (CT) exams among 52 patients with diffuse large B-cell lymphoma. An unblinded radiologist moderator reviewed both BPL and OSEM PET/CT exams. Four blinded radiologists then reviewed the annotated cases to provide a visual DS for each annotated lesion. Significant (P < .001) differences in BPL and OSEM PET methods were identified with greater standard uptake value (SUV) maximum and SUV mean for BPL. The DS was altered in 25% of cases when BPL and OSEM were reviewed by the same radiologist. Interobserver DS agreement was higher for OSEM (>1 cm lesion = 0.89 and ≤1 cm lesion = 0.84) compared to BPL (>1 cm lesion = 0.85 and ≤1 cm lesion = 0.81). Among the 4 readers, average intraobserver visual DS agreement between OSEM and BPL was 0.67 for lesions >1cm and 0.4 for lesions ≤1 cm. F-18 Fluorodeoxyglucose PET/CT of diffuse large B-cell lymphoma reconstructed with BPL has higher SUV values, altered DSs and reader agreement when compared to OSEM. This report finds volumetric PET measurements such as metabolic tumor volume to be similar between BPL and OSEM PET reconstructions. Efforts such as adoption of European Association Research Ltd accreditation should be made to harmonize PET data with an aim at balancing the need for harmonization and sensitivity for lesion detection.

The frequency of change in five-point scale score with a Bayesian penalised likelihood PET reconstruction algorithm on interim FDG PET-CT and its potential implications for therapy decisions in Hodgkin's lymphoma

AIM

To assess the effect of a Bayesian penalised likelihood (BPL) reconstruction algorithm on the five-point scale (5-PS) score, response categorisation, and potential implications for therapy decisions after interim 2-[¹⁸F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)-computed tomography (CT) (iPET-CT) to guide treatment in classical Hodgkin's lymphoma (HL).

MATERIALS AND METHODS

The present study included new patients with HL undergoing iPET-CT from 2014-2019 after two cycles of doxorubicin (Adriamycin), bleomycin, vincristine, and dacarbazine (ABVD). Two reporters categorised response using the 5-PS and measured maximum standardised uptake values (SUV_max) of the most avid tumour residuum, mediastinal blood pool, and normal liver with ordered subset expected maximisation (OSEM) and BPL reconstructions.

RESULTS

Eighty-one iPET-CT examinations were reviewed. Compared with OSEM, BPL increased the 5-PS score by a single score in 18/81 (22.2%) patients. The frequency of potential treatment intensification by changing a score of 3-4 was 13.6% (11/81) and represented 25% (11/44) of patients with a score of 3 on OSEM. All 11 patients remained in remission without a change in therapy (mean 63 months) except one who required second-line treatment for refractory disease. Median SUV_max of tumour residuum was significantly higher with BPL compared with OSEM (2.7 versus 2.4, p<<0.0001), whilst liver SUV_max was significantly lower for both reporters (up to 6.6%, p<0.0001).

CONCLUSION

BPL PET reconstruction increased the 5-PS score on iPET-CT in 22% of HL patients and can potentially result in unnecessary treatment escalation in over half of these patients.

Assessing the accuracy of [18F]PSMA-1007 PET/CT for primary staging of lymph node metastases in intermediate- and high-risk prostate cancer patients

Background

[¹⁸F]PSMA-1007 is a promising tracer for integrated positron emission tomography and computed tomography (PET/CT).

Objective

Our aim was to assess the diagnostic accuracy of [¹⁸F]PSMA-1007 PET/CT for primary staging of lymph node metastasis before robotic-assisted laparoscopy (RALP) with extended lymph node dissection (ePLND).

Design, Setting and Participants

The study was a retrospective cohort in a tertiary referral center. Men with prostate cancer that underwent surgical treatment for intermediate- or high-risk prostate cancer between May 2019 and August 2021 were included.

Interventions

[¹⁸F]PSMA-1007 PET/CT for initial staging followed by RALP and ePLND.

Outcome measurements and statistical analyses

Sensitivity and specificity were calculated both for the entire cohort and for patients with lymph node metastasis ≥ 3 mm. Positive (PPV) and negative (NPV) predictive values were calculated.

Results and limitations

Among 104 patients included in the analyses, 26 patients had lymph node metastasis based on pathology reporting and metastases were ≥ 3 mm in size in 13 of the cases (50%). In the entire cohort, the sensitivity and specificity of [¹⁸F]PSMA-1007 were 26.9% (95% confidence interval (CI); 11.6–47.8) and 96.2% (95% CI; 89.2–99.2), respectively. The sensitivity and specificity of [¹⁸F]PSMA-1007 to detect a lymph node metastasis ≥ 3 mm on PET/CT were 53.8% (95% CI; 25.1–80.8) and 96.7% (95% CI; 90.7–99.3), respectively. PPV was 70% and NPV 93.6%.

Conclusions

In primary staging of intermediate- and high-risk prostate cancer, [¹⁸F]PSMA-1007 PET/CT is highly specific for prediction of lymph node metastases, but the sensitivity for detection of metastases smaller than 3 mm is limited. Based on our results, [¹⁸F]PSMA-1007 PET/CT cannot completely replace ePLND.

Patient summary

This study investigated the use of an imaging method based on a prostate antigen-specific radiopharmaceutical tracer to detect lymph node prostate cancer metastasis. We found that it is unreliable to discover small metastasis.

Local bone metabolism during the consolidation process of spinal interbody fusion

INTRODUCTION

Although computed tomography (CT) can identify the presence of eventual bony bridges following lumbar interbody fusion (LIF) surgery, it does not provide information on the ongoing formation process of new bony structures. ¹⁸F sodium fluoride (¹⁸F-NaF) positron emission tomography (PET) could be used as complementary modality to add information on the bone metabolism at the fusion site. However, it remains unknown how bone metabolism in the operated segment changes early after surgery in uncompromised situations. This study aimed to quantify the changes in local bone metabolism during consolidation of LIF.

MATERIALS AND METHODS

Six skeletally mature sheep underwent LIF surgery. ¹⁸F-NaF PET/CT scanning was performed 6 and 12 weeks postoperatively to quantify the bone volume and metabolism in the operated segment. Bone metabolism was expressed as a function of bone volume.

RESULTS

Early in the fusion process, bone metabolism was increased at the endplates of the operated vertebrae. In a next phase, bone metabolism increased in the center of the interbody region, peaked, and declined to an equilibrium state. During the entire postoperative time period of 12 weeks, bone metabolism in the interbody region was higher than that of a reference site in the spinal column.

CONCLUSION

Following LIF surgery, there is a rapid increase in bone metabolism at the vertebral endplates that develops towards the center of the interbody region. Knowing the local bone metabolism during uncompromised consolidation of spinal interbody fusion might enable identification of impaired bone formation early after LIF surgery using ¹⁸F-NaF PET/CT scanning.

Effect of Bayesian penalty likelihood algorithm on 18F-FDG PET/CT image of lymphoma

OBJECTIVE

Recently, a new Bayesian penalty likelihood (BPL) reconstruction algorithm has been applied in PET, which is expected to provide better image resolution than the widely used ordered subset expectation maximization (OSEM). The purpose of this study is to compare the differences between these two algorithms in terms of image quality and effects on clinical diagnostics and quantification of lymphoma.

METHODS

A total of 246 FDG-positive lesions in 70 patients with lymphoma were retrospectively analyzed by using BPL and OSEM + time-of-flight + point spread function algorithms. Visual analysis was used to evaluate the effects of different reconstruction algorithms on clinical image quality and diagnostic certainty. Quantitative analysis was used to compare the differences between pathology and lesion size.

RESULTS

There were significant differences in lesion-related SUVmax, total-lesion-glycolysis (TLG), and signal-to-background ratio (SBR) (P < 0.01). The variation Δ SUVmax% and Δ SBR% caused by the two reconstruction algorithms were negatively correlated with tumor diameter, while Δ MTV% and Δ TLG% were positively correlated with tumor diameter. In the grouped analysis based on pathology, there were significant differences in lesion SUVmax, lesion SUVmean, and SBR. In non-Hodgkin's lymphoma (diffuse large B cells and follicular lymphoma), diversities were significantly found in SUVmax, SUVmean, SBR, and TLG of the lesions (P < 0.05). According to the grouped analysis based on lesion size, for lesions smaller than 1 cm and 2 cm, there was a significant difference in SUVmean, SUVmax, SBR, and MTV, but not in lesions larger than or equal to 2 cm (P > 0.05), and the liver background SUVmean (P > 0.05) remained unchanged.

CONCLUSION

BPL reconstruction algorithm could effectively improve clinical image quality and diagnostic certainty. In quantitative analysis, there were no significant differences among different pathological groups, but there were significant diversities in lesion sizes. Especially for small lesions, lesion SUVmax increased and SBR was significantly improved, which may better assist in the diagnosis of small lesions of lymphoma.

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.

Advances in PET-CT technology: An update

This article reviews the current evolution and future directions in PET-CT technology focusing on three areas: time of flight, image reconstruction, and data-driven gating. Image reconstruction is considered with advances in point spread function modelling, Bayesian penalised likelihood reconstruction, and artificial intelligence approaches. Data-driven gating is examined with reference to respiratory motion, cardiac motion, and head motion. For each of these technological advancements, theory will be briefly discussed, benefits of their use in routine practice will be detailed and potential future developments will be discussed. Representative clinical cases will be presented, demonstrating the huge opportunities given to the PET community by hardware and software advances in PET technology when it comes to lesion detection, disease characterization, accurate quantitation and quicker scans. Through this review, hospitals are encouraged to embrace, evaluate and appropriately implement the wide range of new PET technologies that are available now or in the near future, for the improvement of patient care.

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.

Computational approaches to detect small lesions in 18 F-FDG PET/CT scans

Purpose

When physicians interpret ¹⁸F‐FDG PET/CT scans, they rely on their subjective visual impression of the presence of small lesions, the criteria for which may vary among readers. Our investigation used physical phantom scans to evaluate whether image texture analysis metrics reliably correspond to visual criteria used to identify lesions and accurately differentiate background regions from sub‐centimeter simulated lesions.

Methods

Routinely collected quality assurance test data were processed retrospectively for 65 different ¹⁸F‐FDG PET scans performed of standardized phantoms on eight different PET/CT systems. Phantoms included 8‐, 12‐, 16‐, and 25‐mm diameter cylinders embedded in a cylindrical water bath, prepared with 2.5:1 activity‐to‐background ratio emulating typical whole‐body PET protocols. Voxel values in cylinder regions and background regions were sampled to compute several classes of image metrics. Two experienced physicists, blinded to quantified image metrics and to each other's readings, independently graded cylinder visibility on a 5‐level scale (0 = definitely not visible to 4 = definitely visible).

Results

The three largest cylinders were visible in 100% of cases with a mean visibility score of 3.3 ± 1.2, while the smallest 8‐mm cylinder was visible in 58% of cases with a significantly lower mean visibility score of 1.5±1.1 (P < 0.0001). By ROC analysis, the polynomial‐fit signal‐to‐noise ratio was the most accurate at discriminating 8‐mm cylinders from the background, with accuracy greater than visual detection (93% ± 2% versus 76% ± 4%, P = 0.0001), and better sensitivity (94% versus 58%, P < 0.0001).

Conclusion

Image texture analysis metrics are more sensitive than visual impressions for detecting sub‐centimeter simulated lesions. Therefore, image texture analysis metrics are potentially clinically useful for ¹⁸F‐FDG PET/CT studies.

Application of a Flexible PET Scanner Combined with 3 T MRI Using Non-local Means Reconstruction: Qualitative and Quantitative Comparison with Whole-Body PET/CT

PURPOSE

Flexible positron emission tomography (fxPET) employing a non-local means reconstruction algorithm was designed to fit existing magnetic resonance imaging (MRI) systems. We aimed to compare the qualitative and quantitative performance of fxPET among fxPET with MR-based attenuation correction (MRAC), fxPET with CT-based attenuation correction (CTAC) using CT as a part of WB PET/CT, and whole-body (WB) PET/CT.

PROCEDURES

Sixteen patients with suspected head and neck cancer underwent 2-deoxy-2-[¹⁸F]fluoro-D-glucose WB PET/CT scans, followed by fxPET and 3 T MRI scans. Phantom data were compared among the three datasets. For registration accuracy, we measured the distance between the center of the tumor determined by fxPET and that in MRI. We compared image quality, detection rates, and quantitative values including maximal standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and tumor-to-muscle ratio (TMR) among the three datasets.

RESULTS

The phantom data in fxPET, except the percent contrast recoveries of 17-mm and 22-mm hot spheres, were inferior to those in WB PET/CT. The mean registration accuracy was 4.4 mm between fxPET using MRAC and MRI. The image quality was comparable between two fxPET datasets, but significantly inferior to WB PET/CT (p < 0.0001). In contrast, detection rates were comparable among the three datasets. SUVmax was significantly higher, and MTV and TLG were significantly lower in the two fxPET datasets compared with the WB PET/CT dataset (p < 0.005). There were no significant differences in SUVmax, MTV, and TLG between the two fxPET datasets or in TMR among the three datasets. All quantitative values had significantly positive correlations.

CONCLUSIONS

Compared with WB PET/CT, the phantom data and image quality were inferior in fxPET. However, the results of the detection rates and quantitative values suggested the clinical feasibility of fxPET.

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

Background

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

Methods

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

Results

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

Conclusion

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

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

Purpose

This study evaluated the effects of new Bayesian penalized likelihood (BPL) reconstruction algorithm on visualization and quantification of upper abdominal malignant tumors in clinical FDG PET/CT examinations, comparing the results to those obtained by an ordered subset expectation maximization (OSEM) reconstruction algorithm. Metabolic tumor volume (MTV) and texture features (TFs), as well as SUV-related metrics, were evaluated to clarify the BPL effects on quantification.

Materials and Methods

A total of 153 upper abdominal lesions (82 liver metastatic and 71 pancreatic cancers) were included in this study. FDG PET/CT images were acquired with a GE Discovery 710 scanner equipped with a time-of-flight system. Images were reconstructed using OSEM and BPL (beta 700) algorithms. In 58 lesions <1.5 cm in greatest diameter (small-lesion group), visual image quality of each lesion was evaluated using a four-point scale. SUVmax was obtained for quantitative metrics. Visual scores and SUVmax were compared between OSEM and BPL images. In 95 lesions >2.0 cm in greatest diameter (larger-lesion group), SUVmax, SUVpeak, MTV, and six TFs were compared between OSEM and BPL images. In addition to the size-based analyses, an increase of SUVmax with BPL was evaluated according to the original SUVmax in OSEM images.

Results

In the small-lesion group, both visual score and SUVmax were significantly higher in the BPL than OSEM images. The increase in visual score was observed in 20 (34%) of all 58 lesions. In the larger-lesion group, no statistical difference was observed in SUVmax, SUVpeak, or MTV between OSEM and BPL images. BPL increased high gray-level zone emphasis and decreased low gray-level zone emphasis among six TFs compared to OSEM with statistical significance. No statistical differences were observed in other TFs. SUVmax-based analysis demonstrated that BPL increased and decreased SUVmax in lesions with low (<5) and high (>10) SUVmax in original OSEM images, respectively.

Conclusion

This study demonstrated that BPL improved conspicuity of small or low-count upper abdominal malignant lesions in clinical FDG PET/CT examinations. Only two TFs represented significant differences between OSEM and BPL images of all quantitative metrics in larger lesions.

A dedicated paediatric [18F]FDG PET/CT dosage regimen

BACKGROUND

The role of 2-[¹⁸F]fluoro-2-deoxy-D-glucose ([¹⁸F]FDG) positron emission tomography/computed tomography (PET/CT) in children is still expanding. Dedicated paediatric dosage regimens are needed to keep the radiation dose as low as reasonably achievable and reduce the risk of radiation-induced carcinogenesis. The aim of this study is to investigate the relation between patient-dependent parameters and [¹⁸F]FDG PET image quality in order to propose a dedicated paediatric dose regimen.

METHODS

In this retrospective analysis, 102 children and 85 adults were included that underwent a diagnostic [¹⁸F]FDG PET/CT scan. The image quality of the PET scans was measured by the signal-to-noise ratio (SNR) in the liver. The SNR liver was normalized (SNRnorm) for administered activity and acquisition time to apply curve fitting with body weight, body length, body mass index, body weight/body length and body surface area. Curve fitting was performed with two power fits, a nonlinear two-parameter model α p^-d and a linear single-parameter model α p^-0.5. The fit parameters of the preferred model were combined with a user preferred SNR to obtain at least moderate or good image quality for the dosage regimen proposal.

RESULTS

Body weight demonstrated the highest coefficient of determination for the nonlinear (R² = 0.81) and linear (R² = 0.80) models. The nonlinear model was preferred by the Akaike's corrected information criterion. We decided to use a SNR of 6.5, based on the expert opinion of three nuclear medicine physicians. Comparison with the quadratic adult protocol confirmed the need for different dosage regimens for both patient groups. In this study, the amount of administered activity can be considerably reduced in comparison with the current paediatric guidelines.

CONCLUSION

Body weight has the strongest relation with [¹⁸F]FDG PET image quality in children. The proposed nonlinear dosage regimen based on body mass will provide a constant and clinical sufficient image quality with a significant reduction of the effective dose compared to the current guidelines. A dedicated paediatric dosage regimen is necessary, as a universal dosing regimen for paediatric and adult is not feasible.

[Influence of Reconstruction FOV and Matrix Size on the Quantitative Accuracy of FDG-PET: Comparison between OSEM and Bayesian Penalized Likelihood]

PURPOSE

Field of view (FOV) and matrix size determine the pixel size of positron emission tomography (PET) images; however, the effect of any variation in these parameters on the quantitative accuracy is unclear. The FOV and matrix size of PET images are adjusted as per each clinical objective. Therefore, this study aimed to evaluate the quantitative accuracy of PET images under different FOV and matrix sizes.

METHOD

A National Electrical Manufacturers Association (NEMA) body phantom set was filled with ¹⁸F-FDG solution, and imaging data were acquired for 30 min. Images were reconstructed using ordered subset expectation maximization (OSEM) and Bayesian penalized likelihood (BPL), both of which were combined with point spread function (PSF) and time of flight (TOF). In each reconstruction method, the image parameters were set to the following: FOV, 20-70 cm; matrix size, 128×128 to 384×384; and pixel size, 1-3 mm. The images were evaluated by physical assessment of the recovery coefficient (RC) and maximum standardized uptake value ratio (SUV_{max ratio}).

RESULT

The RC of OSEM images was not affected by changes in FOV, whereas the RC of BPL images decreased in small spheres, when FOV was 20 and 30 cm. The SUV_{max ratio} of the OSEM images was not affected by the difference in pixel size. However, the SUV_{max ratio} of BPL images degraded in the 1-mm pixel size; this influence was observed only when the FOV was changed. Conclusion: BPL images reconstructed using a small FOV might degrade the quantitative accuracy of small spheres.

Phantom and clinical assessment of small pulmonary nodules using Q.Clear reconstruction on a silicon-photomultiplier-based time-of-flight PET/CT system

To evaluate the quantification accuracy of different positron emission tomography-computed tomography (PET/CT) reconstruction algorithms, we measured the recovery coefficient (RC) and contrast recovery (CR) in phantom studies. The results played a guiding role in the partial-volume-effect correction (PVC) for following clinical evaluations. The PET images were reconstructed with four different methods: ordered subsets expectation maximization (OSEM), OSEM with time-of-flight (TOF), OSEM with TOF and point spread function (PSF), and Bayesian penalized likelihood (BPL, known as Q.Clear in the PET/CT of GE Healthcare). In clinical studies, SUVmax and SUVmean (the maximum and mean of the standardized uptake values, SUVs) of 75 small pulmonary nodules (sub-centimeter group: < 10 mm and medium-size group: 10-25 mm) were measured from 26 patients. Results show that Q.Clear produced higher RC and CR values, which can improve quantification accuracy compared with other methods (P < 0.05), except for the RC of 37 mm sphere (P > 0.05). The SUVs of sub-centimeter fludeoxyglucose (FDG)-avid pulmonary nodules with Q.Clear illustrated highly significant differences from those reconstructed with other algorithms (P < 0.001). After performing the PVC, highly significant differences (P < 0.001) still existed in the SUVmean measured by Q.Clear comparing with those measured by the other algorithms. Our results suggest that the Q.Clear reconstruction algorithm improved the quantification accuracy towards the true uptake, which potentially promotes the diagnostic confidence and treatment response evaluations with PET/CT imaging, especially for the sub-centimeter pulmonary nodules. For small lesions, PVC is essential.

Post-reconstruction enhancement of [18F]FDG PET images with a convolutional neural network

BACKGROUND

The aim of the study was to develop and test an artificial intelligence (AI)-based method to improve the quality of [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) images.

METHODS

A convolutional neural network (CNN) was trained by using pairs of excellent (acquisition time of 6 min/bed position) and standard (acquisition time of 1.5 min/bed position) or sub-standard (acquisition time of 1 min/bed position) images from 72 patients. A test group of 25 patients was used to validate the CNN qualitatively and quantitatively with 5 different image sets per patient: 4 min/bed position, 1.5 min/bed position with and without CNN, and 1 min/bed position with and without CNN.

RESULTS

Difference in hotspot maximum or peak standardized uptake value between the standard 1.5 min and 1.5 min CNN images fell short of significance. Coefficient of variation, the noise level, was lower in the CNN-enhanced images compared with standard 1 min and 1.5 min images. Physicians ranked the 1.5 min CNN and the 4 min images highest regarding image quality (noise and contrast) and the standard 1 min images lowest.

CONCLUSIONS

AI can enhance [18F]FDG-PET images to reduce noise and increase contrast compared with standard images whilst keeping SUVmax/peak stability. There were significant differences in scoring between the 1.5 min and 1.5 min CNN image sets in all comparisons, the latter had higher scores in noise and contrast. Furthermore, difference in SUVmax and SUVpeak fell short of significance for that pair. The improved image quality can potentially be used either to provide better images to the nuclear medicine physicians or to reduce acquisition time/administered activity.

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.

Comparison of Regularized Reconstruction and Ordered Subset Expectation Maximization Reconstruction in the Diagnostics of Prostate Cancer Using Digital Time-of-Flight 68Ga-PSMA-11 PET/CT Imaging

In prostate cancer, the early detection of distant spread has been shown to be of importance. Prostate-specific membrane antigen (PSMA)-binding radionuclides in positron emission tomography (PET) is a promising method for precise disease staging. PET diagnostics depend on image reconstruction techniques, and ordered subset expectation maximization (OSEM) is the established standard. Block sequential regularized expectation maximization (BSREM) is a more recent reconstruction algorithm and may produce fewer equivocal findings and better lesion detection. Methods: ⁶⁸Ga PSMA-11 PET/CT scans of patients with de novo or suspected recurrent prostate cancer were retrospectively reformatted using both the OSEM and BSREM algorithms. The lesions were counted and categorized by three radiologists. The intra-class correlation (ICC) and Cohen’s kappa for the inter-rater reliability were calculated. Results: Sixty-one patients were reviewed. BSREM identified slightly fewer lesions overall and fewer equivocal findings. ICC was excellent with regards to definitive lymph nodes and bone metastasis identification and poor with regards to equivocal metastasis irrespective of the reconstruction algorithm. The median Cohen’s kappa were 0.66, 0.74, 0.61 and 0.43 for OSEM and 0.61, 0.63, 0.66 and 0.53 for BSREM, with respect to the tumor, local lymph nodes, metastatic lymph nodes and bone metastasis detection, respectively. Conclusions: BSREM in the setting of ⁶⁸Ga PMSA PET staging or restaging is comparable to OSEM.

Does the beta regularization parameter of bayesian penalized likelihood reconstruction always affect the quantification accuracy and image quality of positron emission tomography computed tomography?

Purpose

This study aims to provide a detailed investigation on the noise penalization factor in Bayesian penalized likelihood (BPL)‐based algorithm, with the utilization of partial volume effect correction (PVC), so as to offer the suitable beta value and optimum standardized uptake value (SUV) parameters in clinical practice for small pulmonary nodules.

Methods

A National Electrical Manufacturers Association (NEMA) image‐quality phantom was scanned and images were reconstructed using BPL with beta values ranged from 100 to 1000. The recovery coefficient (RC), contrast recovery (CR), and background variability (BV) were measured to assess the quantification accuracy and image quality. In the clinical assessment, lesions were categorized into sub‐centimeter (<10 mm, n = 7) group and medium size (10–30 mm, n = 16) group. Signal‐to‐noise ratio (SNR) and contrast‐to‐noise ratio (CNR) were measured to evaluate the image quality and lesion detectability. With PVC was performed, the impact of beta values on SUVs (SUVmax, SUVmean, SUVpeak) of small pulmonary nodules was evaluated. Subjective image analysis was performed by two experienced readers.

Results

With the increasing of beta values, RC, CR, and BV decreased gradually in the phantom work. In the clinical study, SNR and CNR of both groups increased with the beta values (P < 0.001), although the sub‐centimeter group showed increases after the beta value reached over 700. In addition, highly significant negative correlations were observed between SUVs and beta values for both lesion‐size groups before the PVC (P < 0.001 for all). After the PVC, SUVpeak measured from the sub‐centimeter group was no significantly different among different beta values (P = 0.830).

Conclusion

Our study suggests using SUVpeak as the quantification parameter with PVC performed to mitigate the effects of beta regularization. Beta values between 300 and 400 were preferred for pulmonary nodules smaller than 30 mm.

Improved image reconstruction of 89Zr-immunoPET studies using a Bayesian penalized likelihood reconstruction algorithm

Purpose

The aim of this study was to evaluate the use of a Bayesian penalized likelihood reconstruction algorithm (Q.Clear) for ⁸⁹Zr-immunoPET image reconstruction and its potential to improve image quality and reduce the administered activity of ⁸⁹Zr-immunoPET tracers.

Methods

Eight ⁸⁹Zr-immunoPET whole-body PET/CT scans from three ⁸⁹Zr-immunoPET clinical trials were selected for analysis. On average, patients were imaged 6.3 days (range 5.0–8.0 days) after administration of 69 MBq (range 65–76 MBq) of [⁸⁹Zr]Zr-DFO-daratumumab, [⁸⁹Zr]Zr-DFO-pertuzumab, or [⁸⁹Zr]Zr-DFO-trastuzumab. List-mode PET data was retrospectively reconstructed using Q.Clear with incremental β-values from 150 to 7200, as well as standard ordered-subset expectation maximization (OSEM) reconstruction (2-iterations, 16-subsets, a 6.4-mm Gaussian transaxial filter, “heavy” z-axis filtering and all manufacturers’ corrections active). Reduced activities were simulated by discarding 50% and 75% of original counts in each list mode stream. All reconstructed PET images were scored for image quality and lesion detectability using a 5-point scale. SUV_max for normal liver and sites of disease and liver signal-to-noise ratio were measured.

Results

Q.Clear reconstructions with β = 3600 provided the highest scores for image quality. Images reconstructed with β-values of 3600 or 5200 using only 50% or 25% of the original counts provided comparable or better image quality scores than standard OSEM reconstruction images using 100% of counts.

Conclusion

The Bayesian penalized likelihood reconstruction algorithm Q.Clear improved the quality of ⁸⁹Zr-immunoPET images. This could be used in future studies to improve image quality and/or decrease the administered activity of ⁸⁹Zr-immunoPET tracers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-021-00352-z.

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.

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

Background

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

Purpose

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

Methods

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

Results

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

Conclusion

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

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

Background

Block-sequential regularized expectation maximization (BSREM) is a fully convergent iterative image reconstruction algorithm. We hypothesize that tracers with different distribution patterns will result in different optimal settings for the BSREM algorithm. The aim of this study was to evaluate the image quality with variations in the applied β-value and acquisition time for three positron emission tomography (PET) tracers. NEMA image quality phantom measurements and clinical whole-body digital time-of-flight (TOF) PET/computed tomography (CT) examinations with ⁶⁸Ga-DOTATOC (n = 13), ¹⁸F-fluoride (n = 10), and ¹¹C-acetate (n = 13) were included. Each scan was reconstructed using BSREM with β-values of 133, 267, 400, and 533, and ordered subsets expectation maximization (OSEM; 3 iterations, 16 subsets, and 5-mm Gaussian post-processing filter). Both reconstruction methods included TOF and point spread function (PSF) recovery. Quantitative measures of noise, signal-to-noise ratio (SNR), and signal-to-background ratio (SBR) were analysed for various acquisition times per bed position (bp).

Results

The highest β-value resulted in the lowest level of noise, which in turn resulted in the highest SNR and lowest SBR. Noise levels equal to or lower than those of OSEM were found with β-values equal to or higher than 400, 533, and 267 for ⁶⁸Ga-DOTATOC, ¹⁸F-fluoride, and ¹¹C-acetate, respectively. The specified β-ranges resulted in increased SNR at a minimum of 25% (P < 0.0001) and SBR at a maximum of 23% (P < 0.0001) as compared to OSEM. At a reduced acquisition time by 25% for ⁶⁸Ga-DOTATOC and ¹⁸F-fluoride, and 67% for ¹¹C-acetate, BSREM with β-values equal to or higher than 533 resulted in noise equal to or lower than that of OSEM at full acquisition duration (2 min/bp for ⁶⁸Ga-DOTATOC and ¹⁸F-fluoride, 3 min/bp for ¹¹C-acetate). The reduced acquisition time with β 533 resulted in increased SNR (16–26%, P < 0.003) and SBR (6–18%, P < 0.0001 (P = 0.07 for ¹¹C-acetate)) compared to the full acquisition OSEM.

Conclusions

Within tracer-specific ranges of β-values, BSREM reconstruction resulted in increased SNR and SBR with respect to conventional OSEM reconstruction. Similar SNR, SBR, and noise levels could be attained with BSREM at relatively shorter acquisition times or, alternatively, lower administered dosages, compared to those attained with OSEM.

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

Purpose

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

Materials and methods

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

Results

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

Conclusion

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

Trial registration

ClinicalTrials.gov, NCT02490696. Registered 1 June 2015

Optimization of [18F]PSMA-1007 PET-CT using regularized reconstruction in patients with prostate cancer

BACKGROUND

Prostate-specific membrane antigen (PSMA) radiotracers such as [¹⁸F]PSMA-1007 used with positron emission tomography-computed tomography (PET-CT) is promising for initial staging and detection of recurrent disease in prostate cancer patients. The block-sequential regularization expectation maximization algorithm (BSREM) is a new PET reconstruction algorithm, which provides higher image contrast while also reducing noise. The aim of the present study was to evaluate the influence of different acquisition times and different noise-suppressing factors in BSREM (β values) in [¹⁸F]PSMA-1007 PET-CT regarding quantitative data as well as a visual image quality assessment. We included 35 patients referred for clinical [¹⁸F]PSMA-1007 PET-CT. Four megabecquerels per kilogramme were administered and imaging was performed after 120 min. Eighty-four image series per patient were created with combinations of acquisition times of 1-4 min/bed position and β values of 300-1400. The noise level in normal tissue and the contrast-to-noise ratio (CNR) of pathological uptakes versus the local background were calculated. Image quality was assessed by experienced nuclear medicine physicians.

RESULTS

The noise level in the liver, spleen, and muscle was higher for low β values and low acquisition times (written as activity time products (ATs = administered activity × acquisition time)) and was minimized at maximum AT (16 MBq/kg min) and maximum β (1400). There was only a small decrease above AT 10. The median CNR increased slowly with AT from approximately 6 to 12 and was substantially lower at AT 4 and higher at AT 14-16. At AT 4-6, many images were regarded as being of unacceptable quality. For AT 8, β values of 700-900 were considered of acceptable quality.

CONCLUSIONS

An AT of 8 (for example as in our study, 4 MB/kg with an acquisition time of 2 min) with a β value of 700 performs well regarding noise level, CNR, and visual image quality assessment.

Evaluation of PET quantitation accuracy among multiple discovery IQ PET/CT systems via NEMA image quality test

Introduction

Quantitative imaging biomarkers are becoming usual in oncology for assessing therapy response. The harmonization of image quantitation reporting has become of utmost importance due to the multi-center trials increase. The NEMA image quality test is often considered for the evaluation of quantitation and is more accurate with a radioactive solid phantom that reduces variability. The goal of this project is to determine the level of variability among imaging centers if acquisition and imaging protocol parameters are left to the center’s preference while all other parameters are fixed including the scanner type.

Methods

A NEMA-IQ phantom filled with radioactive ⁶⁸Ge solid resin was imaged in five clinical sites throughout Europe. Sites reconstructed data with OSEM and BSREM algorithms applying the sites’ clinical parameters. Images were analyzed according with the NEMA-NU2-2012 standard using the manufacturer-provided NEMA tools to calculate contrast recovery (CR) and background variability (BV) for each sphere and the lung error (LE) estimation. In addition, a ¹⁸F-filled NEMA-IQ phantom was also evaluated to obtain a gauge for variability among centers when the sites were provided with identical specific instructions for acquisition and reconstruction protocol (the aggregate of data from 12 additional sites is presented).

Results

The data using the ⁶⁸Ge solid phantom showed no statistical differences among different sites, proving a very good reproducibility among the PET center models even if dispersion of data is higher with OSEM compared to BSREM. Furthermore, BSREM shows better CR and comparable BV, while LE is slightly reduced. Two centers exhibit significant differences in CR and BV values for the ¹⁸F NEMA NU2-2012 experiments; these outlier results are explained.

Conclusion

The same PET system type from the various sites produced similar quantitative results, despite allowing each site to choose their clinical protocols with no restriction on data acquisition and reconstruction parameters. BSREM leads to lower dispersion of quantitative data among different sites. A solid radioactive phantom may be recommended to qualify the sites to perform quantitative imaging.

The value of Bayesian penalized likelihood reconstruction for improving lesion conspicuity of malignant lung tumors on 18F-FDG PET/CT: comparison with ordered subset expectation maximization reconstruction incorporating time-of-flight model and point spread function correction

OBJECTIVE

To evaluate the value of Bayesian penalized likelihood (BPL) reconstruction for improving lesion conspicuity of malignant lung tumors on ¹⁸F-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography computed tomography (PET/CT) as compared with the ordered subset expectation maximization (OSEM) reconstruction incorporating time-of-flight (TOF) model and point-spread-function (PSF) correction.

METHODS

Twenty-nine patients with primary or metastatic lung cancers who underwent ¹⁸F-FDG PET/CT were retrospectively studied. PET images were reconstructed with OSEM + TOF, OSEM + TOF + PSF, and BPL with noise penalty strength β-value of 200, 400, 600, and 800. The signal-to-noise ratio (SNR) was determined in normal liver parenchyma. Lung lesion conspicuity was evaluated in 50 lung lesions by using a 4-point scale (0, no visible; 1, poor; 2, good; 3, excellent conspicuity). Two observers were independently asked to choose the most preferred reconstruction for detecting the lung lesions on a per-patient level. The maximum standardized uptake value (SUV_max) was measured in each of the 50 lung lesions.

RESULTS

Liver SNR on the images reconstructed by BPL with β-value of 600 and 800 (17.8 ± 3.7 and 22.5 ± 4.6, respectively) was significantly higher than that by OSEM + TOF + PSF (15.0 ± 3.4, p < 0.0001). BPL with β-value of 600 was chosen most frequently as the preferred reconstruction algorithm for lung lesion assessment by both observers. The conspicuity score of the lung lesions < 10 mm in diameter on images reconstructed by BPL with β-value of 600 was significantly greater than that with OSEM + TOF + PSF (2.2 ± 0.8 vs 1.6 ± 0.9, p < 0.0001), while the conspicuity score of the lesions ≥ 10 mm in diameter was not significantly different between BPL with β-value of 600 and OSEM + TOF + PSF. The mean SUV_max was increased by BPL with β-value of 600 for the lung lesions with < 10 mm in diameter, compared to OSEM + TOF + PSF (3.4 ± 3.1 to 4.2 ± 3.5, p = 0.001). In contrast, BPL with β-value of 600 did not provide increased SUV_max for the lesions  ≥ 10 mm in diameter.

CONCLUSION

BPL reconstruction significantly improves the detection of small inconspicuous malignant tumors in the lung, improving the diagnostic performance of PET/CT.

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

Background

Recently, a Bayesian penalized likelihood (BPL) reconstruction algorithm was introduced for a commercial PET/CT with the potential to improve image quality. We compared the performance of this BPL algorithm with conventional reconstruction algorithms under realistic clinical conditions such as daily practiced at many European sites, i.e. low ¹⁸F-FDG dose and short acquisition times.

Results

To study the performance of the BPL algorithm, regular clinical ¹⁸F-FDG whole body PET scans were made. In addition, two types of phantoms were scanned with 4-37 mm sized spheres filled with ¹⁸F-FDG at sphere-to-background ratios of 10-to-1, 4-to-1, and 2-to-1. Images were reconstructed using standard ordered-subset expectation maximization (OSEM), OSEM with point spread function (PSF), and the BPL algorithm using β-values of 450, 550 and 700. To quantify the image quality, the lesion detectability, activity recovery, and the coefficient of variation (COV) within a single bed position (BP) were determined. We found that when applying the BPL algorithm both smaller lesions in clinical studies as well as spheres in phantom studies can be detected more easily due to a higher SUV recovery, especially for higher contrast ratios. Under standard clinical scanning conditions, i.e. low number of counts, the COV is higher for the BPL (β=450) than the OSEM+PSF algorithm. Increase of the β-value to 550 or 700 results in a COV comparable to OSEM+PSF, however, at the cost of contrast, though still better than OSEM+PSF. At the edges of the axial field of view (FOV) where BPs overlap, COV can increase to levels at which bands become visible in clinical images, related to the lower local axial sensitivity of the PET/CT, which is due to the limited bed overlap of 23% such as advised by the manufacturer.

Conclusions

The BPL algorithm performs better than the standard OSEM+PSF algorithm on small lesion detectability, SUV recovery, and noise suppression. Increase of the percentage of bed overlap, time per BP, administered activity, or the β-value, all have a direct positive impact on image quality, though the latter with some loss of small lesion detectability. Thus, BPL algorithms are very interesting for improving image quality, especially in small lesion detectability.

Noise reduction using a Bayesian penalized-likelihood reconstruction algorithm on a time-of-flight PET-CT scanner

PURPOSE

Q.Clear is a block sequential regularized expectation maximization (BSREM) penalized-likelihood reconstruction algorithm for PET. It tries to improve image quality by controlling noise amplification during image reconstruction. In this study, the noise properties of this BSREM were compared to the ordered-subset expectation maximization (OSEM) algorithm for both phantom and patient data acquired on a state-of-the-art PET/CT.

METHODS

The NEMA IQ phantom and a whole-body patient study were acquired on a GE DMI 3-rings system in list mode and different datasets with varying noise levels were generated. Phantom data was evaluated using four different contrast ratios. These were reconstructed using BSREM with different β-factors of 300-3000 and with a clinical setting used for OSEM including point spread function (PSF) and time-of-flight (TOF) information. Contrast recovery (CR), background noise levels (coefficient of variation, COV), and contrast-to-noise ratio (CNR) were used to determine the performance in the phantom data. Findings based on the phantom data were compared with clinical data. For the patient study, the SUV ratio, metabolic active tumor volumes (MATVs), and the signal-to-noise ratio (SNR) were evaluated using the liver as the background region.

RESULTS

Based on the phantom data for the same count statistics, BSREM resulted in higher CR and CNR and lower COV than OSEM. The CR of OSEM matches to the CR of BSREM with β = 750 at high count statistics for 8:1. A similar trend was observed for the ratios 6:1 and 4:1. A dependence on sphere size, counting statistics, and contrast ratio was confirmed by the CNR of the ratio 2:1. BSREM with β = 750 for 2.5 and 1.0 min acquisition has comparable COV to the 10 and 5.0 min acquisitions using OSEM. This resulted in a noise reduction by a factor of 2-4 when using BSREM instead of OSEM. For the patient data, a similar trend was observed, and SNR was reduced by at least a factor of 2 while preserving contrast.

CONCLUSION

The BSREM reconstruction algorithm allowed a noise reduction without a loss of contrast by a factor of 2-4 compared to OSEM reconstructions for all data evaluated. This reduction can be used to lower the injected dose or shorten the acquisition time.

Image quality and detectability in Siemens Biograph PET/MRI and PET/CT systems-a phantom study

BACKGROUND

The technology of modern positron emission tomography (PET) systems continuously improving, and with it the possibility to detect smaller lesions. Since first introduced in 2010, the number of hybrid PET/magnetic resonance imaging (MRI) systems worldwide is constantly increasing. It is therefore important to assess and compare the image quality, in terms of detectability, between the PET/MRI and the well-established PET/computed tomography (CT) systems. For this purpose, a PET image quality phantom (Esser) with hot spheres, ranging from 4 to 20 mm in diameter, was prepared with fluorodeoxyglucose and sphere-to-background activity concentrations of 8:1 and 4:1, to mimic clinical conditions. The phantom was scanned on a PET/MRI and a PET/CT system for both concentrations to obtain contrast recovery coefficients (CRCs) and contrast-to-noise ratios (CNRs), for a range of reconstruction settings. The detectability of the spheres was scored by three human observers for both systems and concentrations and all reconstructions. Furthermore, the impact of acquisition time on CNR and observer detectability was investigated.

RESULTS

Reconstructions applying point-spread-function modeling (and time-of-flight for the PET/CT) yielded the highest CRC and CNR in general, and PET/CT demonstrated slightly higher values than PET/MRI for most sphere sizes. CNR was dependent on reconstruction settings and was maximized for 2 iterations, a pixel size of less than 2 mm and a 4 mm Gaussian filter. Acquisition times of 97 s (PET/MRI) and 150 s (PET/CT) resulted in similar total net true counts. For these acquisition times, the smallest detected spheres by the human observers in the 8:1 activity concentration was the 6-mm sphere with PET/MRI (CNR = 5.6) and the 5-mm sphere with PET/CT (CNR = 5.5). With an acquisition time of 180 s, the 5-mm sphere was also detected with PET/MRI (CNR = 5.8). The 8-mm sphere was the smallest detected sphere in the 4:1 activity concentration for both systems.

CONCLUSION

In this experimental study, similar detectability was found for the PET/MRI and the PET/CT, although for an increased acquisition time for the PET/MRI.

The effect of a novel Bayesian penalised likelihood PET reconstruction algorithm on the assessment of malignancy risk in solitary pulmonary nodules according to the British Thoracic Society guidelines

PURPOSE

British Thoracic Society (BTS) guidelines advocate using FDG PET-CT with the Herder model to estimate malignancy risk in solitary pulmonary nodules (SPNs). Qualitative and semi-quantitative assessment of SPN uptake is based upon analysis of Ordered Subset Expected Maximisation (OSEM) PET images. Our aim was to assess the effect of a Bayesian Penalised Likelihood (BPL) PET reconstruction on the assessment of SPN FDG uptake and estimation of malignancy risk (Herder score).

METHODS

Subjects with SPNs who underwent FDG PET-CT between 2014-2017, with histological confirmation of malignancy or histological/imaging follow-up confirmation of benignity were included. Two blinded readers independently classified SPN uptake on both OSEM and BPL (BTS score; 1 = none; 2 = ≤ mediastinal blood pool (MBP); 3 = >MBP but ≤ 2x liver; 4 = >2x liver), with resultant calculation of the Herder score (%) for both reconstructions.

RESULTS

97 subjects with 75 (77%) malignant SPNs were included. BPL increased the BTS score in 25 (26%) SPNs; 9 SPNs (7 malignant) increased from BTS score 2 to 3, 16 (13 malignant) from BTS score 3 to 4, with a mean Herder score increase of 18 ± 22%. The mean Herder score for all SPNs with BPL was higher than OSEM (73 ± 29 vs 68 ± 32%, p = 0.001). There was no difference in Herder model diagnostic performance between BPL and OSEM, with similar areas under the curve (0.84 vs 0.83, p = 0.39).

CONCLUSION

BPL increases the Herder score in 26% of SPNs compared to OSEM but does not alter the diagnostic performance of the Herder model.

Impact of acquisition time and penalizing factor in a block-sequential regularized expectation maximization reconstruction algorithm on a Si-photomultiplier-based PET-CT system for 18F-FDG

Background

Block-sequential regularized expectation maximization (BSREM), commercially Q. Clear (GE Healthcare, Milwaukee, WI, USA), is a reconstruction algorithm that allows for a fully convergent iterative reconstruction leading to higher image contrast compared to conventional reconstruction algorithms, while also limiting noise. The noise penalization factor β controls the trade-off between noise level and resolution and can be adjusted by the user. The aim was to evaluate the influence of different β values for different activity time products (ATs = administered activity × acquisition time) in whole-body ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET-CT) regarding quantitative data, interpretation, and quality assessment of the images.

Twenty-five patients with known or suspected malignancies, referred for clinical ¹⁸F-FDG PET-CT examinations acquired on a silicon photomultiplier PET-CT scanner, were included. The data were reconstructed using BSREM with β values of 100–700 and ATs of 4–16 MBq/kg × min/bed (acquisition times of 1, 1.5, 2, 3, and 4 min/bed). Noise level, lesion SUV_max, and lesion SUV_peak were calculated. Image quality and lesion detectability were assessed by four nuclear medicine physicians for acquisition times of 1.0 and 1.5 min/bed position.

Results

The noise level decreased with increasing β values and ATs. Lesion SUV_max varied considerably between different β values and ATs, whereas SUV_peak was more stable. For an AT of 6 (in our case 1.5 min/bed), the best image quality was obtained with a β of 600 and the best lesion detectability with a β of 500. AT of 4 generated poor-quality images and false positive uptakes due to noise.

Conclusions

For oncologic whole-body ¹⁸F-FDG examinations on a SiPM-based PET-CT, we propose using an AT of 6 (i.e., 4 MBq/kg and 1.5 min/bed) reconstructed with BSREM using a β value of 500–600 in order to ensure image quality and lesion detection rate as well as a high patient throughput. We do not recommend using AT < 6 since the risk of false positive uptakes due to noise increases.

Performance Evaluation of a Newly Developed MR-Compatible Mobile PET Scanner with Two Detector Layouts

PURPOSE

A mobile positron emission tomography (PET) scanner called flexible PET (fxPET), designed to fit existing magnetic resonance imaging (MRI) or computed tomography (CT) system, has been developed. The purpose of this study was to evaluate the image quality, lesion detection rate, and quantitative values of fxPET compared with conventional bismuth germanium oxide (BGO)-based PET/CT without time-of-flight capability.

PROCEDURES

Fifty-nine patients underwent whole-body (WB) PET/CT scans approximately 1 h after injection of 2-deoxy-2-[¹⁸F]fluoro-D-glucose, followed by the fxPET scans with detectors located above and below the patients (layout A) and with detectors closer to the patients (layout B). Two readers assessed the image quality using a 4-point grade for each layout and reached a consensus. We evaluated the differences and/or correlations between fxPET and WB PET/CT, including the lesion detection rates, the standardized uptake value (SUV), the metabolic tumor volume (MTV), the total lesion glycolysis (TLG), the tumor-to-normal liver ratio (TLR), and the background liver signal-to-noise ratio (SNR).

RESULTS

The image quality of layout B was better than layout A (p < 0.0001). Of 184 lesions, the detection rate of layout B was significantly higher than WB PET/CT (p = 0.041), while the detection rate of layout A was comparable to WB PET/CT. The SUVmax/mean/peak were larger, and the MTVs were smaller in fxPET than WB PET/CT, especially in the lesions smaller than 2 cm (p < 0.01). The SUVmax/mean/peak, the MTVs and the TLGs of fxPET had significant positive correlations with WB PET/CT (p < 0.0001). The TLRs were significantly larger (p < 0.0001), but the background SNRs were significantly lower in fxPET than WB PET/CT (p < 0.05).

CONCLUSIONS

The fxPET system yielded reasonable image quality and quantitative accuracy. Bringing the detectors closer to the patient yielded improved results.

Optimising quantitative 90Y PET imaging: an investigation into the effects of scan length and Bayesian penalised likelihood reconstruction

Background

Positron emission tomography (PET) imaging of ⁹⁰Y following selective internal radiation therapy (SIRT) is possible, but image quality is poor, and therefore, accurate quantification and dosimetry are challenging. This study aimed to quantitatively optimise ⁹⁰Y PET imaging using a new Bayesian penalised likelihood (BPL) reconstruction algorithm (Q.Clear, GE Healthcare). The length of time per bed was also investigated to study its impact on quantification accuracy.

Methods

A NEMA IQ phantom with an 8:1 sphere-to-background ratio was scanned overnight on a GE Discovery 710 PET/CT scanner. Datasets were rebinned into varying lengths of time (5–60 min); the 15-min rebins were reconstructed using BPL reconstruction with a range of noise penalisation weighting factors (beta values). The metrics of contrast recovery (CR), background variability (BV), and recovered activity percentage (RAP) were calculated in order to identify the optimum beta value. Reconstructions were then carried out on the rest of the timing datasets using the optimised beta value; the same metrics were used to assess the quantification accuracy of the reconstructed images.

Results

A beta value of 1000 produced the highest CR and RAP (76% and 73%, 37 mm sphere) without overly accentuating the noise (BV) in the image. There was no statistically significant increase (p < 0.05) in either the CR or RAP for scan times of > 15 min. For the 5-min acquisitions, there was a statistically significant decrease in RAP (28 mm sphere, p < 0.01) when compared to the 15-min acquisition.

Conclusion

Our results indicate that an acquisition length of 15 min and beta value of 1000 (when using Q.Clear reconstruction) are optimum for quantitative ⁹⁰Y PET imaging. Increasing the acquisition time to more than 15 min reduces the image noise but has no significant impact on image quantification.

Electronic supplementary material

The online version of this article (10.1186/s13550-019-0512-y) contains supplementary material, which is available to authorized users.

How Do the More Recent Reconstruction Algorithms Affect the Interpretation Criteria of PET/CT Images?

Purpose

Recently, a new Bayesian Penalized Likelihood (BPL) Reconstruction Algorithm was introduced by GE Healthcare, Q.Clear; it promises to provide better PET image resolution compared to the widely used Ordered Subset Expectation Maximization (OSEM). The aim of this study is to compare the performance of these two algorithms on several types of findings, in terms of image quality, lesion detectability, sensitivity, and specificity.

Methods

Between September 6th 2017 and July 31st 2018, 663 whole body 18F-FDG PET/CT scans were performed at the Nuclear Medicine Department of S. Martino Hospital (Belluno, Italy). Based on the availability of clinical/radiological follow-up data, 240 scans were retrospectively reviewed. For each scan, a hypermetabolic finding was selected, reporting both for OSEM and Q.Clear: SUVmax and SUVmean values of the finding, the liver and the background close to the finding; size of the finding; percentage variations of SUVmax and SUVmean. Each finding was subsequently correlated with clinical and radiological follow-up, to define its benign/malignant nature.

Results

Overall, Q.Clear improved the SUV values in each scan, especially in small findings (< 10 mm), high SUVmax values (≥ 10), and medium/low backgrounds. Furthermore, Q.Clear amplifies the signal of hypermetabolic findings without modifying the background signal, which leads to an increase in signal-to-noise ratio, improving overall image quality. Finally, Q.Clear did not affect PET sensitivity or specificity, in terms of number of reported findings and characterization of their nature.

Conclusions

Q.Clear is an iterative algorithm that improves significantly the quality of PET images compared to OSEM, increasing the SUVmax of findings (in particular for small findings) and the signal-to-noise ratio. However, due to the intrinsic characteristics of this algorithm, it will be necessary to adapt and/or modify the current interpretative criteria based of quantitative evaluation, to avoid an overestimation of the disease burden.

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

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

Impact of penalizing factor in a block-sequential regularized expectation maximization reconstruction algorithm for 18F-fluorocholine PET-CT regarding image quality and interpretation

BACKGROUND

Recently, the block-sequential regularized expectation maximization (BSREM) reconstruction algorithm was commercially introduced (Q.Clear, GE Healthcare, Milwaukee, WI, USA). However, the combination of noise-penalizing factor (β), acquisition time, and administered activity for optimal image quality has not been established for ¹⁸F-fluorocholine (FCH). The aim was to compare image quality and diagnostic performance of different reconstruction protocols for patients with prostate cancer being examined with ¹⁸F-FCH on a silicon photomultiplier-based PET-CT. Thirteen patients were included, injected with 4 MBq/kg, and images were acquired after 1 h. Images were reconstructed with frame durations of 1.0, 1.5, and 2.0 min using β of 150, 200, 300, 400, 500, and 550. An ordered subset expectation maximization (OSEM) reconstruction with a frame duration of 2.0 min was used for comparison. Images were quantitatively analyzed regarding standardized uptake values (SUV) in metastatic lymph nodes, local background, and muscle to obtain contrast-to-noise ratios (CNR) as well as the noise level in muscle. Images were analyzed regarding image quality and number of metastatic lymph nodes by two nuclear medicine physicians.

RESULTS

The highest median CNR was found for BSREM with a β of 300 and a frame duration of 2.0 min. The OSEM reconstruction had the lowest median CNR. Both the noise level and lesion SUV_max decreased with increasing β. For a frame duration of 1.5 min, the median quality score was highest for β 400-500, and for a frame duration of 2.0 min the score was highest for β 300-500. There was no statistically significant difference in the number of suspected lymph node metastases between the different image series for one of the physicians, and for the other physician the number of lymph nodes differed only for one combination of image series.

CONCLUSIONS

To achieve acceptable image quality at 4 MBq/kg ¹⁸F-FCH, we propose using a β of 400-550 with a frame duration of 1.5 min. The lower β should be used if a high CNR is desired and the higher if a low noise level is important.

Phantom, clinical, and texture indices evaluation and optimization of a penalized-likelihood image reconstruction method (Q.Clear) on a BGO PET/CT scanner

INTRODUCTION

The aim of this study was to evaluate the behavior of a penalized-likelihood image reconstruction method (Q.Clear) under different count statistics and lesion-to-background ratios (LBR) on a BGO scanner, in order to obtain an optimum penalization factor (β value) to study and optimize for different acquisition protocols and clinical goals.

METHODS

Both phantom and patient images were evaluated. Data from an image quality phantom were acquired using different Lesion-to-Background ratios and acquisition times. Then, each series of the phantom was reconstructed using β values between 50 and 500, at intervals of 50. Hot and cold contrasts were obtained, as well as background variability and contrast-to-noise ratio (CNR). Fifteen ¹⁸ F-FDG patients (five brain scans and 10 torso acquisitions) were acquired and reconstructed using the same β values as in the phantom reconstructions. From each lesion in the torso acquisition, noise, contrast, and signal-to-noise ratio (SNR) were computed. Image quality was assessed by two different nuclear medicine physicians. Additionally, the behaviors of 12 different textural indices were studied over 20 different lesions.

RESULTS

Q.Clear quantification and optimization in patient studies depends on the activity concentration as well as on the lesion size. In the studied range, an increase on β is translated in a decrease in lesion contrast and noise. The net product is an overall increase in the SNR, presenting a tendency to a steady value similar to the CNR in phantom data. As the activity concentration or the sphere size increase the optimal β increases, similar results are obtained from clinical data. From the subjective quality assessment, the optimal β value for torso scans is in a range between 300 and 400, and from 100 to 200 for brain scans. For the recommended torso β values, texture indices present coefficients of variation below 10%.

CONCLUSIONS

Our phantom and patients demonstrate that improvement of CNR and SNR of Q.Clear algorithm which depends on the studied conditions and the penalization factor. Using the Q.Clear reconstruction algorithm in a BGO scanner, a β value of 350 and 200 appears to be the optimal value for 18F-FDG oncology and brain PET/CT, respectively.

A clinical evaluation of the impact of the Bayesian penalized likelihood reconstruction algorithm on PET FDG metrics

PURPOSE

The aim of this study was to evaluate the impact of using the Bayesian penalized likelihood (BPL) algorithm on a bismuth germanium oxide positron emission tomography (PET)/computed tomography (CT) system for F-FDG PET/CT exams in case of low injected activity and scan duration.

MATERIALS AND METHODS

F-FDG respiratory gated PET/CT performed on 102 cancer patients, injected with ∼2 MBq/kg of F-FDG, were reconstructed using two algorithms: ordered subset expectation maximization (OSEM) and BPL. The signal-to-noise ratio (SNR) was calculated as the ratio of mean standard uptake value (SUV) over the standard deviation in a reference volume defined automatically in the liver. The peak SUV and volumes were also measured in lesions larger than 2 cm thanks to the automated segmentation method.

RESULTS

On 85 respiratory gated patients, the median SNR was significantly higher with BPL (P<0.0001) and it is even better when the BMI of the patient increases (odds ratio=1.26).For the 55 lesions, BPL significantly increased the SUVpeak [difference: (-0.5; 1.4), median=0.4, P<0.0001] compared with OSEM in 83.6% of the cases. With BPL, the volume was lower in 61.8% of the cases compared with OSEM, but this was not statistically significant.

CONCLUSION

The BPL algorithm improves the image quality and lesion contrast and appears to be particularly appropriate for patients with a high BMI as it improves the SNR. However, it will be important for patient follow-up or multicenter studies to use the same algorithm and preferably BPL.

BSREM Reconstruction for Improved Detection of In-Transit Metastases With Digital FDG-PET/CT in Patients With Malignant Melanoma

Block sequential regularized expectation maximization (BSREM) is a Bayesian penalized-likelihood reconstruction algorithm for PET, which reaches full convergence without the detriment of deteriorating the image quality by noise. Therefore, BSREM might have implications particularly for the detection of small lesions, which may be beneficial in melanoma patients. Our case of a 70-year-old man with metastasized malignant melanoma illustrates the impact of such a novel iterative PET reconstruction algorithm. Whereas the lymph node metastases are seen with the latest generation ordered subset expectation maximization reconstruction, the in-transit metastases are identified straightforward only with BSREM reconstruction.

Bayesian penalized-likelihood reconstruction algorithm suppresses edge artifacts in PET reconstruction based on point-spread-function

PURPOSE

The Bayesian penalized-likelihood reconstruction algorithm (BPL), Q.Clear, uses relative difference penalty as a regularization function to control image noise and the degree of edge-preservation in PET images. The present study aimed to determine the effects of suppression on edge artifacts due to point-spread-function (PSF) correction using a Q.Clear.

METHODS

Spheres of a cylindrical phantom contained a background of 5.3 kBq/mL of [¹⁸F]FDG and sphere-to-background ratios (SBR) of 16, 8, 4 and 2. The background also contained water and spheres containing 21.2 kBq/mL of [¹⁸F]FDG as non-background. All data were acquired using a Discovery PET/CT 710 and were reconstructed using three-dimensional ordered-subset expectation maximization with time-of-flight (TOF) and PSF correction (3D-OSEM), and Q.Clear with TOF (BPL). We investigated β-values of 200-800 using BPL. The PET images were analyzed using visual assessment and profile curves, edge variability and contrast recovery coefficients were measured.

RESULTS

The 38- and 27-mm spheres were surrounded by higher radioactivity concentration when reconstructed with 3D-OSEM as opposed to BPL, which suppressed edge artifacts. Images of 10-mm spheres had sharper overshoot at high SBR and non-background when reconstructed with BPL. Although contrast recovery coefficients of 10-mm spheres in BPL decreased as a function of increasing β, higher penalty parameter decreased the overshoot.

CONCLUSIONS

BPL is a feasible method for the suppression of edge artifacts of PSF correction, although this depends on SBR and sphere size. Overshoot associated with BPL caused overestimation in small spheres at high SBR. Higher penalty parameter in BPL can suppress overshoot more effectively.