Feasibility of state of the art PET/CT systems performance harmonisation

Impact of reconstruction algorithms at different sphere-to-background ratios on PET quantification: A phantom study

Using National Electrical Manufacturers Association (NEMA) phantom, the behavior of four distinct Positron Emission Tomography/Computed Tomography (PET/CT) reconstruction algorithms was investigated. These reconstruction algorithms were (Ordered Subset Expectation Maximization (OSEM), OSEM+ (Point Spread Function) PSF, OSEM + Time of Flight (TOF), and OSEM + TOF + PSF), and the focus was on sphere sizes and SBRs using recovery coefficients as a quantitation method. The obtained results demonstrated the significant effect of TOF on Gibbs artifact and Partial Volume Effect (PVE) at various Sphere-to-Background Ratios (SBRs). TOF-based algorithms improved quantification accuracy and mitigated the influence of Gibbs artifact, particularly at higher SBRs. Compared to PSF algorithm, TOF- based algorithms effectively mitigated the impact of PVE on small-sized spheres and less dependent on SBRs. In terms of Standardized Uptake Value (SUV) quantification, SUVmean was better when utilizing TOF-based algorithms at lower SBRs, whereas SUVmax at higher SBRs. The combination of TOF and PSF produced a promising outcomes in quantifying and detecting a small-sized spheres across various SBRs, ultimately resulting in a more reliable and precise diagnostic information.

Effects of a deep learning-based image quality enhancement method on a digital-BGO PET/CT system for 18F-FDG whole-body examination

BACKGROUND

The digital-BGO PET/CT system, Omni Legend 32, incorporates modified block sequential regularized expectation maximization (BSREM) image reconstruction and a deep learning-based time-of-flight (TOF)-like image quality enhancement process called Precision DL (PDL). The present study aimed to define the fundamental characteristics of PDL using phantom and clinical images.

METHODS

A NEMA IEC body phantom was scanned using the Omni Legend 32 PET/CT system. All PET/CT images were acquired over 60 and 90 s per bed position, with a 384 × 384 matrix. Phantom images were reconstructed using OSEM + PSF and BSREM at β values of 100-1,000, combined with low (LPDL), medium (MPDL), and high (HPDL) PDL. We evaluated contrast recovery, background variability, and the contrast-to-noise ratio (CNR) of a 10 mm hot sphere. Thirty clinical whole-body 18F-FDG PET/CT examinations were included. Clinical images were reconstructed using OSEM + PSF and BSREM at β values of 200, 300, 400, 500, and 600, determined based on findings from the phantom study, combined with the three PDL models. Noise levels, mean SUV (SUVmean), and the signal-to-noise ratio (SNR) of the liver as well as signal-to-background ratios (SBR) and maximum SUV (SUVmax) of lesions were evaluated. Two blinded readers evaluated visual image quality and rated several aspects to complement the analysis.

RESULTS

Contrast recovery and background variability decreased as the β value increased. This trend was consistent even when PDL processing was added to BSREM. Increased strength of the PDL models led to higher CNR. Noise levels decreased as a function of increasing β values in BSREM, resulting in a higher SNR, but lower SBR. Combining PDL with BSREM resulted in all β values producing better results in terms of noise, SBR, and SNR than OSEM + PSF. As the PDL increased (LPDL < MPDL < HPDL), noise levels, SBR, and SNR became higher. The β values of 400, 200, 300, and 300 for BSREM, LPDL, MPDL, and HPDL, respectively, resulted in noise equivalent to OSEM + PSF but significantly increased the SUVmax (9%, 15%, 18%, and 27%), SBR (16%, 17%, 20%, and 32%), and SNR (17%, 19%, 31%, and 36%), respectively. The visual evaluation of image quality yielded similar scores across BSREM + PDL reconstructions, although BSREM with β = 600 combined with MPDL delivered the best overall image quality and total mean score.

CONCLUSION

The combination of BSREM and PDL significantly enhanced the SUVmax of lesions and image quality compared with OSEM + PSF. A combination of BSREM at β values of 500-600 and MPDL is recommended for oncological whole-body PET/CT imaging when using PDL on the Omni Legend.

Improving image reconstruction to quantify dynamic whole-body PET/CT: Q.Clear versus OSEM

BACKGROUND

The introduction of PET systems featuring increased count rate sensitivity has resulted in the development of dynamic whole-body PET acquisition protocols to assess 18F-FDG uptake rate ( K i ) using 18F-FDG PET/CT. However, in short-axis field-of-view (SAFOV) PET/CT systems, multiple bed positions are required per time frame to achieve whole-body coverage. This results in high noise levels, requiring higher 18F-FDG activity administration and, consequently, increased patient radiation dose. Bayesian penalized-likelihood PET reconstruction (e.g. Q.Clear, GE Healthcare) has been shown to effectively suppress image noise compared to standard reconstruction techniques. This study investigated the impact of Bayesian penalized-likelihood reconstruction on dynamic whole-body 18F-FDG PET quantification.

METHODS

Dynamic whole-body 18F-FDG PET/CT data (SAFOV PET Discovery MI 5R, GE Healthcare) of healthy volunteers and one lung cancer patient, consisting of a ten-minute dynamic scan of the thoracic region followed by six whole-body passes, were reconstructed with Q.Clear and Ordered Subset Expectation Maximization (OSEM) according to EARL 2 standards. Image noise in the measured time-activity-curves (TAC) was determined for the myocardium, hamstring, liver, subcutaneous adipose tissue and lung lesion for both reconstruction methods. K i values were calculated using Patlak analysis. Finally, bootstrapping was used to investigate the effect of image noise levels on K i values (bias and precision) as a function of magnitude of K i and volume-of-interest (VOI) size for both computationally simulated TACs ( K i  = 1.0-50.0·10-3·ml·cm-3·min-1) and the measured TACs.

RESULTS

Compared to OSEM, Q.Clear showed 40-55% lower noise levels for all tissue types (p < 0.05). For the measured TACs no systematic bias in K i with either reconstruction method was observed. K i precision decreased with decreasing VOI size, with that of Q.Clear being superior compared to OSEM for small VOIs of 0.56 cm3 in all tissues (p < 0.05), with the largest difference in relative precision for small values of K i . The simulated TACs corroborated these results, with Q.Clear providing the best precision for small values of K i and small VOIs in all tissues.

CONCLUSION

Q.Clear reconstruction of dynamic whole-body PET/CT data yields more precise K i values, especially for small values of K i and smaller VOIs, compared to standard OSEM. This precision improvement shows Q.Clear's potential to better detect and characterize small lesion metabolic activity in oncology and allows for lower administered activity dosage.

Temporal Validation of an FDG-PET-Radiomic Model for Distant-Relapse-Free-Survival After Radio-Chemotherapy for Pancreatic Adenocarcinoma

Background/Objectives: Pancreatic cancer is a very aggressive disease with a poor prognosis, even when diagnosed at an early stage. This study aimed to validate and refine a radiomic-based [18F]FDG-PET model to predict distant relapse-free survival (DRFS) in patients with unresectable locally advanced pancreatic cancer (LAPC). Methods: A Cox regression model incorporating two radiomic features (RFs) and cancer stage (III vs. IV) was temporally validated using a larger cohort (215 patients treated between 2005-2022). Patients received concurrent chemoradiotherapy with capecitabine and hypo-fractionated Intensity Modulated Radiotherapy (IMRT). Data were split into training (145 patients, 2005-2017) and validation (70 patients, 2017-2022) groups. Seventy-eight RFs were extracted, harmonized, and analyzed using machine learning to develop refined models. Results: The model incorporating Statistical-Percentile10, Morphological-ComShift, and stage demonstrated moderate predictive accuracy (training: C-index = 0.632; validation: C-index = 0.590). When simplified to include only Statistical-Percentile10, performance improved slightly in the validation group (C-index = 0.601). Adding GLSZM3D-grayLevelVariance to Statistical-Percentile10, while excluding Morphological-ComShift, further enhanced accuracy (training: C-index = 0.654; validation: C-index = 0.623). Despite these refinements, all versions showed similar moderate ability to stratify patients into risk classes. Conclusions: [18F]FDG-PET radiomic features are robust predictors of DRFS after chemoradiotherapy in LAPC. Despite moderate performance, these models hold promise for patient risk stratification. Further validation with external cohorts is ongoing.

Towards harmonized holmium-166 SPECT image quality for dosimetry: a multi-center, multi-vendor study

BACKGROUND

Reliable dosimetry based on SPECT/CT imaging is essential to achieve personalized 166Ho-radioembolization treatment planning and evaluation. This study quantitatively evaluates multiple acquisition and reconstruction protocols for 166Ho-SPECT imaging based on data from five Dutch hospitals. We aim to recommend an imaging protocol which harmonizes 166Ho-SPECT images for reproducible and accurate dosimetry in a multi-scanner and multi-center setting.

METHODS

Cylindrical and NEMA IEC phantoms, filled with 166Ho-chloride, were imaged using seven SPECT/CT scanners from two vendors (GE HealthCare and Siemens Healthineers). Data were acquired with a photopeak window centered at 81 keV. Two adjacent scatter windows, and one upper scatter window at 118 keV were used for triple-energy window (TEW) and dual-energy window (DEW) scatter correction, respectively. The TEW and DEW reconstructions used vendor-specific software. Additionally, a vendor-neutral software package with Monte Carlo (MC) scatter correction (Hermes Medical Solutions) was used to study the influence of scanner hardware on the image quality. System sensitivity was measured in projection data of the cylindrical phantom. The axial uniformity in the cylindrical phantom was used to characterize the impact of the scatter correction method. The image quality was evaluated by the coefficient of variation (COV; noise), the contrast recovery coefficients (CRCs) and contrast-to-noise ratios (CNRs).

RESULTS

TEW scatter correction resulted in superior uniformity and higher CRCs compared to the DEW (CRC for the largest sphere over all scanners, mean ± SD (range): TEW 0.54 ± 0.07 (0.36-0.65), DEW 0.44 ± 0.04 (0.34-0.51)). DEW resulted in lower noise levels compared to TEW (16% lower on average). The DEW and TEW images resulted in comparable CNRs. The system sensitivities and the vendor-neutral image reconstructions demonstrated differences in hardware between the two vendors, most likely due to the characteristics of the vendor-specific medium energy collimator.

CONCLUSION

This study demonstrates that TEW scatter correction increases the accuracy of 166Ho-SPECT images compared to DEW, and we henceforth recommend adopting this method in the clinical 166Ho-dosimetry workflow. Scanner hardware has a substantial impact on the characteristics of the acquired data, and identical reconstruction settings will therefore not automatically lead to harmonized image quality.

[18F]FDG administered activity reduction capabilities of a 32-cm axial field-of-view solid-state digital bismuth germanium oxide PET/CT system while maintaining EARL compliance

PURPOSE

To assess the lower [18F]FDG limit in administered activity and/or scan time reduction capabilities of a digital-BGO 32-cm axial field-of-view PET system while being compliant with current and updated EANM Research Ltd Fluorine-18 accreditation specifications (EARL1 and EARL2).

METHODS

EARL1 and EARL2 compliance of the digital-BGO system (Omni Legend 32 cm) was tested for several reconstructions, including those that apply precision deep learning-based image enhancement (PDL) as postprocessing, using the calibration QC and NEMA IEC phantom measurements. The image quality QC scan was repeated every hour for 7 h, with each subsequent hour representing a lower administered activity, and reconstructed for various times per bed position, i.e. 30, 60, 120, 180, and 300 s. For each of the image quality QC images, coefficient of variation (COV) of the background compartment, and mean, maximum and peak activity concentration recovery coefficients (RCmean, RCmax and RCpeak) of differently-sized spheres were calculated and compared to current and updated EARL accreditation specifications.

RESULTS

When we apply 1 min per bed position for PET acquisition, [18F]FDG administration can be reduced by a factor of ∼ 4 for EARL1, by a factor of ∼ 8 for EARL2 (2 mm voxels) and by a factor of ∼ 4 for EARL2 (4 mm voxels) using both standard reconstructions and PDL post-processing compared to current EANM recommendations for [18F]FDG administration (7 MBqminbed-1kg-1).

CONCLUSIONS

Reduction in [18F]FDG administered activity is possible by at least a factor 4 for 1 min/bed with the Omni Legend 32 cm PET/CT while maintaining EARL1 and EARL2 compliance.

Optimization of Reconstruction Parameters for Discovery 710 Positron Emission Tomography/Computed Tomography

Aim

This study aimed to optimize the quantitative aspects of (18F) fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) imaging by investigating the impact of various reconstruction parameters on the recovery coefficients (RCs) using the NEMA image quality phantom. Specifically, the study aims to assess how different matrix sizes, iterations, subsets, and Gaussian postfilters affect the accuracy of standardized uptake value (SUV) quantification in (18F) FDG PET/CT imaging.

Materials and Methods

The study utilized the "Vue Point FX + Sharp IR" algorithm for PET image reconstruction, incorporating 3D-ordered subset expectation maximization (3D-OSEM), time-of-flight, and point spread function technologies. Various reconstruction parameters were explored, including two distinct matrix sizes, multiple iterations, subsets, and a wide range of Gaussian postfilters. The investigation focused on the impact of these parameters on RCs using the NEMA image quality phantom.

Results

The results of the study indicated that for accurate SUV quantification in spheres ≥17 mm, the 256 × 256 matrix size and mean SUV should be employed. Conversely, for spheres ≤13 mm, maximum SUV was found to be more suitable. The choice of postfiltering value was shown to have a significant impact on SUV quantification accuracy, particularly for small-sized spheres. In addition, a larger matrix size was found to partially mitigate the effects of Gibbs artifact and slightly enhance SUV quantification for the spheres of various sizes.

Conclusion

This study highlights the critical importance of optimizing PET reconstruction parameters in accordance with the guidelines set by European Association of Nuclear Medicine/EARL. By optimizing these parameters, the accuracy and reliability of SUV quantification in (18F) FDG PET imaging can be significantly enhanced, especially for small-sized spheres. This underscores the necessity of carefully considering reconstruction parameters to ensure precise and reliable quantitative measurements in PET/CT imaging.

Pharmacokinetic analysis and simplified uptake measures for tumour lesion [18F]F-AraG PET imaging in patients with non-small cell lung cancer

INTRODUCTION

The novel positron emission tomography (PET) imaging tracer, [18F]F-AraG, targets activated T-cells, offering a potential means to improve our understanding of immune-oncological processes. The aim of this study was to determine the optimal pharmacokinetic model to quantify tumour lesion [18F]F-AraG uptake in patients with non-small cell lung cancer (NSCLC), and to validate simplified measures at different time intervals against the pharmacokinetic uptake parameter.

METHODS

Ten patients with early-stage NSCLC and three patients with advanced NSCLC underwent a dynamic PET scan of minimal 60 min. Venous and/or arterial blood sampling was obtained at maximum seven time points. Tumour lesion time activity curves and metabolite-corrected input functions were analysed using single-tissue reversible (1T2k), two-tissue irreversible (2T3k) and two-tissue reversible (2T4k) plasma input models. Simplified uptake measures, such as standardised uptake value (SUV) and tumour-to-blood (TBR) or tumour-to-plasma ratio (TPR), were evaluated for different time intervals.

RESULTS

Whole-blood and plasma radioactivity concentrations showed rapid clearance of [18F]F-AraG. Metabolite analysis revealed a low rate of metabolism, at 70 min p.i., on average, 79% (SD = 9.8%) of the total radioactivity found in blood corresponded to intact [18F]F-AraG. The time activity curves were best fitted by the 2T3k model. Strong positive correlations were found for SUV (body weight (BW), lean body mass (LBM) or body surface area (BSA) corrected), TBR and TPR for any time interval between 20 and 70 min p.i. against the 2T3k-derived Ki. The correlation of TBR at 60-70 min p.i. with 2T3K-derived Ki (r (df = 20) = 0.87, p < 0.01), was stronger than for SUVBW (r (df = 20) = 0.80, p < 0.01).

CONCLUSION

Tumour lesion [18F]F-AraG uptake in patients with NSCLC is characterised by a 2T3k model. TBR and TPR show most potential for simplified quantification of tumour lesion [18F]F-AraG uptake in patients with NSCLC.

High concordance of PET-CT treatment response evaluation according to PERCIST 1.0 when comparing images reconstructed with OSEM vs. BSREM

BACKGROUND

Positron emission tomography (PET) response criteria in solid tumours (PERCIST 1.0) is a systematic assessment of therapy response with fluorine 18 fluorodeoxyglucose ([18F]FDG) PET. The ordered subset expectation maximization (OSEM) and block sequential regularized expectation maximization (BSREM) reconstruction algorithms are both commonly used. The impact of using OSEM or BSREM on PERCIST 1.0 is not fully determined. This study aimed to compare treatment response evaluation between the reconstructions OSEM and BSREM according to PERCIST 1.0.

METHODS

The highest peak standardized uptake value corrected for lean body mass (SULpeak) in a lesion were measured in PET-CT scans from 90 patients with metastatic breast cancer or malignant melanoma, who had undergone two [18F]FDG PET with computer tomography (CT) scans for treatment evaluation purpose. SUL measurements in the reference organ were also calculated. All scans were reconstructed with OSEM and BSREM. Bland Altman plots and Spearman correlation coefficient were used for comparing measurements obtained from OSEM and BSREM images. Patients were categorized as a complete metabolic response (CMR), partial metabolic response (PMR), stable metabolic disease (SMD), or progressive metabolic disease (PMD).

RESULTS

The mean standard deviation (SD) of SULmean in the reference organ was slightly lower for BSREM images, resulting in a slightly lower threshold. Lesion SULpeak was slightly higher for BSREM images compared with OSEM images. Only 5 out of 90 patients had different response evaluations for the different reconstructions.

CONCLUSION

PERCIST 1.0 serves as a robust response evaluation criterion and is only marginally dependent on the reconstruction used.

Phantom test procedures for a new neuro-oncological amino acid PET tracer: [18F]fluciclovine

OBJECTIVE

Amino acid positron emission tomography (PET) examinations using anti-1-amino-3-[18F]-fluorocyclobutane-1-carboxylic acid ([18F]FACBC) were allowed for routine clinical use in July 2024. However, phantom test procedures for [18F]FACBC reconstruction parameters have not yet been established. The present study aimed to establish new phantom test procedures for [18F]FACBC brain PET imaging to determine optimal reconstruction parameters.

METHODS

Background (BG) activity as well as hot sphere and target-to-background ratios (TBRs) of [18F]FACBC were estimated based on brain activity and tumor-to-normal tissue ratios (TNR) in a Japanese clinical trial of [18F]FACBC. Phantom experiments proceeded under [18F]FACBC or L-[methyl-11C]-methionine ([11C]MET) conditions. The number of iterations and the Gaussian filter parameters were determined from the reconstruction parameters %contrastmean and coefficients of variation (CVs) in ordered subset expectation maximization (OSEM) and time-of-flight (TOF) with or without point-spread-function (PSF) correction.

RESULTS

The amounts of activity in the hot spheres and BG were 1.1 and 5.5 kBq/mL, respectively, and the TBR was 5.0 at the start of acquisition. The %contrastmean of all hot spheres was higher with [18F]FACBC than [11C]MET, and %contrastmean converged between 4 and 6 iterations in hot spheres with diameters < 10 mm. We used four iterations for OSEM + TOF and five for OSEM + TOF + PSF correction for [18F]FACBC and [11C]MET images. The CV was higher for [18F]FACBC than [11C]MET. The optimal sizes of Gaussian filters for OSEM + TOF and OSEM + TOF + PSF correction of image reconstruction were 5 mm for [18F]FACBC, and 4 and 3 mm, respectively, for [11C]MET images.

CONCLUSIONS

We estimated phantom activity and TBR based on brain activity in a Japanese clinical trial and established new phantom test procedures for [18F]FACBC. We recommend that the optimal reconstruction parameters for [18F]FACBC should be set to the same number of iterations as [11C]MET and that the FWHM of Gaussian filter should have a few mm higher than [11C]MET to reduce image noise from brain normal tissue.

Investigating the Impact of Voxel Size and Postfiltering on Quantitative Analysis of Positron Emission Tomography/Computed Tomography: A Phantom Study

Aim

This study aims to investigate the influence of voxel size and postfiltering on the quantification of standardized uptake value (SUV) in positron emission tomography/computed tomography (PET/CT) images.

Materials and Methods

National Electrical Manufacturers Association phantom with the spheres of different sizes were utilized to simulate the lesions. The phantom was scanned using a PET/CT scanner, and the acquired images were reconstructed using two different matrix sizes, (192 × 192) and (256 × 256), and a wide range of postfiltering values.

Results

The findings demonstrated that postfiltering significantly affected SUV measurements. The changes in postfiltering values can result in overestimation or underestimation of SUV values, highlighting the importance of carefully selecting appropriate filters. Increasing the matrix size improved SUVmax and SUVmean values, particularly for small-sized spheres. Smaller voxel reconstructions slightly reduced partial volume effects and partially enhanced SUV quantification.

Conclusions

Careful consideration of postfiltering values and matrix size selection can lead to better SUV quantification. These findings emphasize the need to optimize the reconstruction parameters to enhance the clinical utility of PET/CT in detecting and evaluating malignant lesions.

Clinical Performance Comparison of a Long Versus a Short Axial Field-of-View PET/CT Using EARL-Compliant Reconstructions

PURPOSE

To ensure comparable PET/CT image quality between or within centres, clinical inter-system performance comparisons following European Association of Nuclear Medicine Research Ltd. (EARL) guidelines is required. In this work the performance of the long axial field-of-view Biograph Vision Quadra is compared to its predecessor, the short axial field-of-view Biograph Vision.

PROCEDURES

To this aim, patients with suspected tumour lesions received a single weight-based (3 MBq/kg) 2-deoxy-2-[18F]fluoro-D-glucose injection and underwent routine clinical ( ∼ 15 min) scans on the Vision and 3-min scans on the Quadra in listmode in balanced order. Image quality (IQ), image noise (IN), and tumour demarcation (TD) were assessed visually by four nuclear medicine physicians using a 5-point Likert scale and semiquantitative analysis was performed using standardised uptake values (SUVs). Inter-reader agreement was tested using Wilcoxon's signed rank test and the SUVs were statistically compared using a paired t-test.

RESULTS

Twenty patients (mean age, 60 years ± 8.8 [standard deviation], 16 male) were enrolled. Inter-reader agreement ranged from good to very good for IQ and IN (0.62 ≤ W ≤ 0.81), and fair for TD (0.29 ≤ W ≤ 0.39). Furthermore, a significant difference was found for TD (p = 0.015) between the systems, showing improved TD for the Quadra.

CONCLUSION

This study demonstrates that the Quadra can be used in routine clinical practice with multiple PET/CT systems or in multicentre studies. This system provides comparable diagnostic image quality and semiquantitative accuracy, improved TD, and has the advantage of shorter scan durations.

The impact of introducing deep learning based [18F]FDG PET denoising on EORTC and PERCIST therapeutic response assessments in digital PET/CT

BACKGROUND

[18F]FDG PET denoising by SubtlePET™ using deep learning artificial intelligence (AI) was previously found to induce slight modifications in lesion and reference organs' quantification and in lesion detection. As a next step, we aimed to evaluate its clinical impact on [18F]FDG PET solid tumour treatment response assessments, while comparing "standard PET" to "AI denoised half-duration PET" ("AI PET") during follow-up.

RESULTS

110 patients referred for baseline and follow-up standard digital [18F]FDG PET/CT were prospectively included. "Standard" EORTC and, if applicable, PERCIST response classifications by 2 readers between baseline standard PET1 and follow-up standard PET2 as a "gold standard" were compared to "mixed" classifications between standard PET1 and AI PET2 (group 1; n = 64), or between AI PET1 and standard PET2 (group 2; n = 46). Separate classifications were established using either standardized uptake values from ultra-high definition PET with or without AI denoising (simplified to "UHD") or EANM research limited v2 (EARL2)-compliant values (by Gaussian filtering in standard PET and using the same filter in AI PET). Overall, pooling both study groups, in 11/110 (10%) patients at least one EORTCUHD or EARL2 or PERCISTUHD or EARL2 mixed vs. standard classification was discordant, with 369/397 (93%) concordant classifications, unweighted Cohen's kappa = 0.86 (95% CI: 0.78-0.94). These modified mixed vs. standard classifications could have impacted management in 2% of patients.

CONCLUSIONS

Although comparing similar PET images is preferable for therapy response assessment, the comparison between a standard [18F]FDG PET and an AI denoised half-duration PET is feasible and seems clinically satisfactory.

QUALIPAED-A retrospective quality control study evaluating pediatric long axial field-of-view low-dose FDG-PET/CT

Introduction

Pediatric patients have an increased risk of radiation-induced malignancies due to their ongoing development and long remaining life span. Thus, optimization of PET protocols is an important task in pediatric nuclear medicine. Long axial field-of-view (LAFOV) PET/CT has shown a significant increase in sensitivity, which provides an ideal opportunity for reduction of injected tracer activity in the pediatric population. In this study we aim to evaluate the clinical performance of a 2-[18F]FDG-tracer reduction from 3 MBq/kg to 1.5 MBq/kg on the Biograph Vision Quadra LAFOV PET/CT.

Materials and methods

The first 50 pediatric patients referred for clinical whole-body PET/CT with 1.5 MBq/kg 2-[18F]FDG, were included. A standard pediatric protocol was applied. Five reconstructions were created with various time, filter and iteration settings. Image noise was computed as coefficient-of-variance (COV = SD/mean standardized-uptake-value) calculated from a spherical 20-50 mm (diameter) liver volume-of-interest. Sets of reconstructions were reviewed by one nuclear medicine physicians, who reported image lesions on a pre-defined list of sites. Paired comparison analysis was performed with significance at PB < 0.05 (Bonferroni corrected).

Results

All reconstructions, except one, achieved a COVmean (0.08-0.15) equal to or lower than current clinical acceptable values (COVref ≤ 0.15). Image noise significantly improved with increasing acquisition time, lowering iterations (i) from 6i to 4i (both with five subsets) and when applying a 2 mm Gauss filter (PB < 0.001). Significant difference in lesion detection was seen from 150s to 300s and from 150s to 600s (PB = 0.006-0.007). 99% of all lesions rated as malignant could be found on the 150s reconstruction, while 100% was found on the 300s, when compared to the 600s reconstruction.

Conclusion

Injected activity and scan time can be reduced to 1.5 MBq/kg 2-[18F]FDG with 5 min acquisition time on LAFOV PET/CT, while maintaining clinical performance in the pediatric population. These results can help limit radiation exposure to patients and personnel as well as shorten total scan time, which can help increase patient comfort, lessen the need for sedation and provide individually tailored scans.

Validation of image-derived input function using a long axial field of view PET/CT scanner for two different tracers

BACKGROUND

Accurate image-derived input function (IDIF) from highly sensitive large axial field of view (LAFOV) PET/CT scanners could avoid the need of invasive blood sampling for kinetic modelling. The aim is to validate the use of IDIF for two kinds of tracers, 3 different IDIF locations and 9 different reconstruction settings.

METHODS

Eight [18F]FDG and 10 [18F]DPA-714 scans were acquired respectively during 70 and 60 min on the Vision Quadra PET/CT system. PET images were reconstructed using various reconstruction settings. IDIFs were taken from ascending aorta (AA), descending aorta (DA), and left ventricular cavity (LV). The calibration factor (CF) extracted from the comparison between the IDIFs and the manual blood samples as reference was used for IDIFs accuracy and precision assessment. To illustrate the effect of various calibrated-IDIFs on Patlak linearization for [18F]FDG and Logan linearization for [18F]DPA-714, the same target time-activity curves were applied for each calibrated-IDIF.

RESULTS

For [18F]FDG, the accuracy and precision of the IDIFs were high (mean CF ≥ 0.82, SD ≤ 0.06). Compared to the striatum influx (Ki) extracted using calibrated AA IDIF with the updated European Association of Nuclear Medicine Research Ltd. standard reconstruction (EARL2), Ki mean differences were < 2% using the other calibrated IDIFs. For [18F]DPA714, high accuracy of the IDIFs was observed (mean CF ≥ 0.86) except using absolute scatter correction, DA and LV (respectively mean CF = 0.68, 0.47 and 0.44). However, the precision of the AA IDIFs was low (SD ≥ 0.10). Compared to the distribution volume (VT) in a frontal region obtained using calibrated continuous arterial sampler input function as reference, VT mean differences were small using calibrated AA IDIFs (for example VT mean difference = -5.3% using EARL2), but higher using calibrated DA and LV IDIFs (respectively + 12.5% and + 19.1%).

CONCLUSIONS

For [18F]FDG, IDIF do not need calibration against manual blood samples. For [18F]DPA-714, AA IDIF can replace continuous arterial sampling for simplified kinetic quantification but only with calibration against arterial blood samples. The accuracy and precision of IDIF from LAFOV PET/CT system depend on tracer, reconstruction settings and IDIF VOI locations, warranting careful optimization.

A novel model-based equation for size dependent mean recovery coefficients for spheres and other shapes

BACKGROUND

In NM-imaging, theoretical curves for the recovery coefficient (RC) of the signal maximum and mean are known for spheres and cubes, if a 3D Gaussian PSF is assumed. The RC of the maximum is also known for cylinders. For these and other shapes empirical equations with one or two fit-parameters have been utilized.

METHODS

An equation for the RC for large objects of arbitrary shape is derived and generalized into an empirical equation for smaller objects, which is verified by numerical simulations. The proposed equation is compared to published results on SPECT kidney phantom measurements and to PET measurements on the NEMA IEC PET body phantom with six spheres.

RESULTS

The signal loss (1-RC) for large spheres is inversely proportional to the radius, where the slope is proportional to the FWHM of the spatial resolution. For non-spherical shapes the generalized instead of the volume equivalent radius should be utilized. For smaller objects, an equation with one added empirical fit-parameter is presented. It is demonstrated that the EANM-guidelines' two-parameter logistic function results in a poor fit if the theoretical slope and inverse proportionality are forced and it gives a suboptimal fit when both parameters are fitted.

CONCLUSIONS

A novel model-based equation for the mean RC-curve is derived. It can be used for arbitrary shapes as long as the sphericity is taken into account and it is accurate down to RC = 10 %. One parameter is directly related to the spatial resolution, while the other is a shape depending fit-parameter.

Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives

Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.

Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data

Nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) in combination with computed tomography (CT) are established imaging modalities in clinical practice, particularly for oncological problems. Due to a multitude of manufacturers, different measurement protocols, local demographic or clinical workflow variations as well as various available reconstruction and analysis software, very heterogeneous datasets are generated. This review article examines the current state of interoperability and harmonisation of image data and related clinical data in the field of nuclear medicine. Various approaches and standards to improve data compatibility and integration are discussed. These include, for example, structured clinical history, standardisation of image acquisition and reconstruction as well as standardised preparation of image data for evaluation. Approaches to improve data acquisition, storage and analysis will be presented. Furthermore, approaches are presented to prepare the datasets in such a way that they become usable for projects applying artificial intelligence (AI) (machine learning, deep learning, etc.). This review article concludes with an outlook on future developments and trends related to AI in nuclear medicine, including a brief research of commercial solutions.

Quantitative and clinical implications of the EARL2 versus EARL1 [18F]FDG PET-CT performance standards in head and neck squamous cell carcinoma

BACKGROUND

The EANM Research Ltd. (EARL) guidelines give recommendations for harmonization of [18F]FDG PET-CT image acquisition and reconstruction, aiming to ensure reproducibility of quantitative data between PET scanners. Recent technological advancements in PET-CT imaging resulted in an updated version of the EARL guidelines (EARL2). The aim of this study is to compare quantitative [18F]FDG uptake metrics of the primary tumor and lymph nodes in patients with head and neck squamous cell carcinoma (HNSCC) on EARL2 versus EARL1 reconstructed images and to describe clinical implications for nodal staging and treatment.

METHODS

Forty-nine consecutive patients with HNSCC were included. For all, both EARL1 and EARL2 images were reconstructed from a singular [18F]FDG PET-CT scan. Primary tumors and non-necrotic lymph nodes ≥ 5 mm were delineated on CT-scan. In the quantitative analysis, maximum standardized uptake values (SUVmax) and standardized uptake ratios (SURmax, i.e., SUVmax normalized to cervical spinal cord uptake) were calculated for all lesions on EARL1 and EARL2 reconstructions. Metabolic tumor volume (MTV) and total lesion glycolysis were compared between EARL1 and EARL2 using different segmentation methods (adaptive threshold; SUV2.5/3.5/4.5; SUR2.5/3.5/4.5; MAX40%/50%). In the qualitative analysis, each lymph node was scored independently by two nuclear medicine physicians on both EARL1 and EARL2 images on different occasions using a 4-point scale.

RESULTS

There was a significant increase in SUVmax (16.5%) and SURmax (9.6%) of primary tumor and lymph nodes on EARL2 versus EARL1 imaging (p < 0.001). The proportional difference of both SUVmax and SURmax between EARL2 and EARL1 decreased with increasing tumor volume (p < 0.001). Absolute differences in MTVs between both reconstructions were small (< 1.0 cm3), independent of the segmentation method. MTVs decreased on EARL2 using relative threshold methods (adaptive threshold; MAX40%/50%) and increased using static SUV or SUR thresholds. With visual scoring of lymph nodes 38% (11/29) of nodes with score 2 on EARL1 were upstaged to score 3 on EARL2, which resulted in an alteration of nodal stage in 18% (6/33) of the patients.

CONCLUSIONS

Using the EARL2 method for PET image reconstruction resulted in higher SUVmax and SURmax compared to EARL1, with nodal upstaging in a significant number of patients.

Potential Clinical Impact of LAFOV PET/CT: A Systematic Evaluation of Image Quality and Lesion Detection

We performed a systematic evaluation of the diagnostic performance of LAFOV PET/CT with increasing acquisition time. The first 100 oncologic adult patients referred for 3 MBq/kg 2-[18F]fluoro-2-deoxy-D-glucose PET/CT on the Siemens Biograph Vision Quadra were included. A standard imaging protocol of 10 min was used and scans were reconstructed at 30 s, 60 s, 90 s, 180 s, 300 s, and 600 s. Paired comparisons of quantitative image noise, qualitative image quality, lesion detection, and lesion classification were performed. Image noise (n = 50, 34 women) was acceptable according to the current standard of care (coefficient-of-varianceref < 0.15) after 90 s and improved significantly with increasing acquisition time (PB < 0.001). The same was seen in observer rankings (PB < 0.001). Lesion detection (n = 100, 74 women) improved significantly from 30 s to 90 s (PB < 0.001), 90 s to 180 s (PB = 0.001), and 90 s to 300 s (PB = 0.002), while lesion classification improved from 90 s to 180 s (PB < 0.001), 180 s to 300 s (PB = 0.021), and 90 s to 300 s (PB < 0.001). We observed improved image quality, lesion detection, and lesion classification with increasing acquisition time while maintaining a total scan time of less than 5 min, which demonstrates a potential clinical benefit. Based on these results we recommend a standard imaging acquisition protocol for LAFOV PET/CT of minimum 180 s to maximum 300 s after injection of 3 MBq/kg 2-[18F]fluoro-2-deoxy-D-glucose.

PET imaging and quantification of small animals using a clinical SiPM-based camera

BACKGROUND

Small-animal PET imaging is an important tool in preclinical oncology. This study evaluated the ability of a clinical SiPM-PET camera to image several rats simultaneously and to perform quantification data analysis.

METHODS

Intrinsic spatial resolution was measured using 18F line sources, and image quality was assessed using a NEMA NU 4-2018 phantom. Quantification was evaluated using a fillable micro-hollow sphere phantom containing 4 spheres of different sizes (ranging from 3.95 to 7.86 mm). Recovery coefficients were computed for the maximum (Amax) and the mean (A50) pixel values measured on a 50% isocontour drawn on each sphere. Measurements were performed first with the phantom placed in the centre of the field of view and then in the off-centre position with the presence of three scattering sources to simulate the acquisition of four animals simultaneously. Quantification accuracy was finally validated using four 3D-printed phantoms mimicking rats with four subcutaneous tumours each. All experiments were performed for both 18F and 68Ga radionuclides.

RESULTS

Radial spatial resolutions measured using the PSF reconstruction algorithm were 1.80 mm and 1.78 mm for centred and off-centred acquisitions, respectively. Spill-overs in air and water and uniformity computed with the NEMA phantom centred in the FOV were 0.05, 0.1 and 5.55% for 18F and 0.08, 0.12 and 2.81% for 68Ga, respectively. Recovery coefficients calculated with the 18F-filled micro-hollow sphere phantom for each sphere varied from 0.51 to 1.43 for Amax and from 0.40 to 1.01 for A50. These values decreased from 0.28 to 0.92 for Amax and from 0.22 to 0.66 for A50 for 68 Ga acquisition. The results were not significantly different when imaging phantoms in the off-centre position with 3 scattering sources. Measurements performed with the four 3D-printed phantoms showed a good correlation between theoretical and measured activity in simulated tumours, with r2 values of 0.99 and 0.97 obtained for 18F and 68Ga, respectively.

CONCLUSION

We found that the clinical SiPM-based PET system was close to that obtained with a dedicated small-animal PET device. This study showed the ability of such a system to image four rats simultaneously and to perform quantification analysis for radionuclides commonly used in oncology.

Variability in PET image quality and quantification measured with a permanently filled 68Ge-phantom: a multi-center study

BACKGROUND

This study evaluated, as a snapshot, the variability in quantification and image quality (IQ) of the clinically utilized PET [¹⁸F]FDG whole-body protocols in Finland using a NEMA/IEC IQ phantom permanently filled with ⁶⁸Ge.

METHODS

The phantom was imaged on 14 PET-CT scanners, including a variety of models from two major vendors. The variability of the recovery coefficients (RC_max, RC_mean and RC_peak) of the hot spheres as well as percent background variability (PBV), coefficient of variation of the background (COV_BG) and accuracy of corrections (AOC) were studied using images from clinical and standardized protocols with 20 repeated measurements. The ranges of the RCs were also compared to the limits of the EARL ¹⁸F standards 2 accreditation (EARL2). The impact of image noise on these parameters was studied using averaged images (AVIs).

RESULTS

The largest variability in RC values of the routine protocols was found for the RC_max with a range of 68% and with 10% intra-scanner variability, decreasing to 36% when excluding protocols with suspected cross-calibration failure or without point-spread-function (PSF) correction. The RC ranges of individual hot spheres in routine or standardized protocols or AVIs fulfilled the EARL2 ranges with two minor exceptions, but fulfilling the exact EARL2 limits for all hot spheres was variable. RC_peak was less dependent on averaging and reconstruction parameters than RC_max and RC_mean. The PBV, COV_BG and AOC varied between 2.3-11.8%, 9.6-17.8% and 4.8-32.0%, respectively, for the routine protocols. The RC ranges, PBV and COV_BG were decreased when using AVIs. With AOC, when excluding routine protocols without PSF correction, the maximum value dropped to 15.5%.

CONCLUSION

The maximum variability of the RC values for the [¹⁸F]FDG whole-body protocols was about 60%. The RC ranges of properly cross-calibrated scanners with PSF correction fitted to the EARL2 RC ranges for individual sphere sizes, but fulfilling the exact RC limits would have needed further optimization. RC_peak was the most robust RC measure. Besides COV_BG, also RCs and PVB were sensitive to image noise.

A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [18F]FDG-PET/CT parameters

BACKGROUND

In patients with non-small cell lung cancer (NSCLC), accuracy of [18F]FDG-PET/CT for pretherapeutic lymph node (LN) staging is limited by false positive findings. Our aim was to evaluate machine learning with routinely obtainable variables to improve accuracy over standard visual image assessment.

METHODS

Monocentric retrospective analysis of pretherapeutic [18F]FDG-PET/CT in 491 consecutive patients with NSCLC using an analog PET/CT scanner (training + test cohort, n = 385) or digital scanner (validation, n = 106). Forty clinical variables, tumor characteristics, and image variables (e.g., primary tumor and LN SUVmax and size) were collected. Different combinations of machine learning methods for feature selection and classification of N0/1 vs. N2/3 disease were compared. Ten-fold nested cross-validation was used to derive the mean area under the ROC curve of the ten test folds ("test AUC") and AUC in the validation cohort. Reference standard was the final N stage from interdisciplinary consensus (histological results for N2/3 LNs in 96%).

RESULTS

N2/3 disease was present in 190 patients (39%; training + test, 37%; validation, 46%; p = 0.09). A gradient boosting classifier (GBM) with 10 features was selected as the final model based on test AUC of 0.91 (95% confidence interval, 0.87-0.94). Validation AUC was 0.94 (0.89-0.98). At a target sensitivity of approx. 90%, test/validation accuracy of the GBM was 0.78/0.87. This was significantly higher than the accuracy based on "mediastinal LN uptake > mediastinum" (0.7/0.75; each p < 0.05) or combined PET/CT criteria (PET positive and/or LN short axis diameter > 10 mm; 0.68/0.75; each p < 0.001). Harmonization of PET images between the two scanners affected SUVmax and visual assessment of the LNs but did not diminish the AUC of the GBM.

CONCLUSIONS

A machine learning model based on routinely available variables from [18F]FDG-PET/CT improved accuracy in mediastinal LN staging compared to established visual assessment criteria. A web application implementing this model was made available.

Characterization of the partial volume effect along the axial field-of-view of the Biograph Vision Quadra total-body PET/CT system for multiple isotopes

BACKGROUND

Total-body PET scanners with axial field of views (FOVs) longer than 1 m enable new applications to study multiple organs (e.g., the brain-gut-axis) simultaneously. As the spatial resolution and the associated partial volume effect (PVE) can vary significantly along the FOV, detailed knowledge of the contrast recovery coefficients (CRCs) is a prerequisite for image analysis and interpretation of quantitative results. The aim of this study was to determine the CRCs, as well as voxel noise, for multiple isotopes throughout the 1.06 m axial FOV of the Biograph Vision Quadra PET/CT system (Siemens Healthineers).

MATERIALS AND METHODS

Cylindrical phantoms equipped with three different sphere sizes (inner diameters 7.86 mm, 28 and 37 mm) were utilized for the PVE evaluation. The 7.86 mm sphere was filled with F-18 (8:1 and 4:1), Ga-68 (8:1) and Zr-89 (8:1). The 28 mm and 37 mm spheres were filled with F-18 (8:1). Background concentration in the respective phantoms was of ~ 3 kBq/ml. The phantoms were measured at multiple positions in the FOV (axial: 0, 10, 20, 30, 40 and 50 cm, transaxial: 0, 10, 20 cm). The data were reconstructed with the standard clinical protocol, including PSF correction and TOF information with up to 10 iterations for maximum ring differences (MRDs) of 85 and 322; CRCs, as well as voxel noise levels, were determined for each position.

RESULTS

F-18 CRCs (SBR 8:1 and 4:1) of the 7.86 mm sphere decreased up to 18% from the center FOV (cFOV) toward the transaxial edge and increased up to 17% toward the axial edge. Noise levels were below 15% for the default clinical reconstruction parameters. The larger spheres exhibited a similar pattern. Zr-89 revealed ~ 10% lower CRCs than F-18 but larger noise (9.1% (F-18), 19.1% (Zr-89); iteration 4, cFOV) for the default reconstruction. Zr-89 noise levels in the cFOV significantly decreased (~ 28%) when reconstructing the data with MRD322 compared with MRD85 along with a slight decrease in CRC values. Ga-68 exhibited the lowest CRCs for the three isotopes and noise characteristics comparable to those of F-18.

CONCLUSIONS

Distinct differences in the PVE within the FOV were detected for clinically relevant isotopes F-18, Ga-68 and Zr-89, as well as for different sphere sizes. Depending on the positions inside the FOV, the sphere-to-background ratios, count statistics and isotope used, this can result in an up to 50% difference between CRCs. Hence, these changes in PVE can significantly affect the quantitative analysis of patient data. MRD322 resulted in slightly lower CRC values, especially in the center FOV, whereas the voxel noise significantly decreased compared with MRD85.

PSMA PET/CT: joint EANM procedure guideline/SNMMI procedure standard for prostate cancer imaging 2.0

Here we aim to provide updated guidance and standards for the indication, acquisition, and interpretation of PSMA PET/CT for prostate cancer imaging. Procedures and characteristics are reported for a variety of available PSMA small radioligands. Different scenarios for the clinical use of PSMA-ligand PET/CT are discussed. This document provides clinicians and technicians with the best available evidence, to support the implementation of PSMA PET/CT imaging in research and routine practice.

Reduction of emission time for [68Ga]Ga-PSMA PET/CT using the digital biograph vision: a phantom study

BACKGROUND

The aim of this phantom study was to optimize the [⁶⁸Ga]Ga-PSMA PET/CT examination in terms of scan time duration and image reconstruction parameters, in combination with PSF and TOF modelling, in a digital Biograph Vision PET/CT scanner.

METHODS

Three types of phantoms were used: 1) soft-tissue tumor phantom consisting of six spheres mounted in a torso phantom; 2) bone-lung tumor phantom; 3) resolution phantom. Phantom inserts were filled with activity concentrations (ACs) that were derived from clinical data. Phantom data were acquired in list-mode at one bed position. Images with emission data ranging from 30 to 210 s in 30-s increments were reconstructed from a reference image acquired with 3.5-min emission. Iterative image reconstruction (OSEM), point-spread-function (PSF) and time-of-flight (TOF) options were applied using different iterations, Gaussian filters, and voxel sizes. The criteria for image quality was lesion detectability and lesion quantification, evaluated as contrast-to-noise ratio (CNR) and maximum AC (peak AC), respectively. A threshold value of CNR above 6 and percentage maximum AC (peak AC) deviation range of ±20% of the reference image were considered acceptable. The proposed single-bed scan time reduction was projected to a whole-body examination (patient validation scan) using the continuous-bed-motion mode.

RESULTS

Sphere and background ACs of 20 kBq/mL and 1 kBq/mL were selected, respectively. The optimized single-bed scan time was approximately 60 s using OSEM-TOF or OSEM-TOF+PSF (four iterations, 4.0-mm Gaussian filter and almost isotropic voxel size of 3.0-mm side length), resulting in a PET spatial resolution of 6.3 mm for OSEM-TOF and 5.5 mm for OSEM-TOF+PSF. In the patient validation, the maximum percentage difference in lesion quantification between standard and optimized protocol (whole-body scan time of 15 vs. 5 min) was below 19%.

CONCLUSIONS

A reduction of single-bed and whole-body scan time for [⁶⁸Ga]Ga-PSMA PET/CT compared to current recommended clinical acquisition protocols is postulated. Clinical studies are warranted to validate the applicability of this protocol.

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.

EANM guidelines for PET-CT and PET-MR routine quality control

We present guidelines by the European Association of Nuclear Medicine (EANM) for routine quality control (QC) of PET-CT and PET-MR systems. These guidelines are partially based on the current EANM guidelines for routine quality control of Nuclear Medicine instrumentation but focus more on the inherent multimodal aspect of the current, state-of-the-art PET-CT and PET-MR scanners. We briefly discuss the regulatory context put forward by the International Electrotechnical Commission (IEC) and European Commission (EC) and consider relevant guidelines and recommendations by other societies and professional organizations. As such, a comprehensive overview of recommended quality control procedures is provided to ensure the optimal operational status of a PET system, integrated with either a CT or MR system. In doing so, we also discuss the rationale of the different tests, advice on the frequency of each test and present the relevant MR and CT tests for an integrated system. In addition, we recommend a scheme of preventive actions to avoid QC tests from drifting out of the predefined range of acceptable performance values such that an optimal performance of the PET system is maintained for routine clinical use.

NaF-PET Imaging of Atherosclerosis Burden

The method of 18F-sodium fluoride (NaF) positron emission tomography/computed tomography (PET/CT) of atherosclerosis was introduced 12 years ago. This approach is particularly interesting because it demonstrates microcalcification as an incipient sign of atherosclerosis before the development of arterial wall macrocalcification detectable by CT. However, this method has not yet found its place in the clinical routine. The more exact association between NaF uptake and future arterial calcification is not fully understood, and it remains unclear to what extent NaF-PET may replace or significantly improve clinical cardiovascular risk scoring. The first 10 years of publications in the field were characterized by heterogeneity at multiple levels, and it is not clear how the method may contribute to triage and management of patients with atherosclerosis, including monitoring effects of anti-atherosclerosis intervention. The present review summarizes findings from the recent 2¾ years including the ability of NaF-PET imaging to assess disease progress and evaluate response to treatment. Despite valuable new information, pertinent questions remain unanswered, not least due to a pronounced lack of standardization within the field and of well-designed long-term studies illuminating the natural history of atherosclerosis and effects of intervention.

A review of harmonization strategies for quantitative PET

PET can reveal in vivo biological processes at the molecular level. PET-derived quantitative values have been used as a surrogate marker for clinical decision-making in numerous clinical studies and trials. However, quantitative values in PET are variable depending on technical, biological, and physical factors. The variability may have a significant impact on a study outcome. Appropriate scanner calibration and quality control, standardization of imaging protocols, and any necessary harmonization strategies are essential to make use of PET as a biomarker with low bias and variability. This review summarizes benefits, limitations, and remaining challenges for harmonization of quantitative PET, including whole-body PET in oncology, brain PET in neurology, PET/MR, and non-¹⁸F PET imaging. This review is expected to facilitate harmonization of quantitative PET and to promote the contribution of PET-derived biomarkers to research and development in medicine.

Reproducibility of [18F]FDG PET/CT liver SUV as reference or normalisation factor

INTRODUCTION

Although visual and quantitative assessments of [18F]FDG PET/CT studies typically rely on liver uptake value as a reference or normalisation factor, consensus or consistency in measuring [18F]FDG uptake is lacking. Therefore, we evaluate the variation of several liver standardised uptake value (SUV) measurements in lymphoma [18F]FDG PET/CT studies using different uptake metrics.

METHODS

PET/CT scans from 34 lymphoma patients were used to calculate SUVmaxliver, SUVpeakliver and SUVmeanliver as a function of (1) volume-of-interest (VOI) size, (2) location, (3) imaging time point and (4) as a function of total metabolic tumour volume (MTV). The impact of reconstruction protocol on liver uptake is studied on 15 baseline lymphoma patient scans. The effect of noise on liver SUV was assessed using full and 25% count images of 15 lymphoma scans.

RESULTS

Generally, SUVmaxliver and SUVpeakliver were 38% and 16% higher compared to SUVmeanliver. SUVmaxliver and SUVpeakliver increased up to 31% and 15% with VOI size while SUVmeanliver remained unchanged with the lowest variability for the largest VOI size. Liver uptake metrics were not affected by VOI location. Compared to baseline, liver uptake metrics were 15-18% and 9-18% higher at interim and EoT PET, respectively. SUVliver decreased with larger total MTVs. SUVmaxliver and SUVpeakliver were affected by reconstruction protocol up to 62%. SUVmax and SUVpeak moved 22% and 11% upward between full and 25% count images.

CONCLUSION

SUVmeanliver was most robust against VOI size, location, reconstruction protocol and image noise level, and is thus the most reproducible metric for liver uptake. The commonly recommended 3 cm diameter spherical VOI-based SUVmeanliver values were only slightly more variable than those seen with larger VOI sizes and are sufficient for SUVmeanliver measurements in future studies.

TRIAL REGISTRATION

EudraCT: 2006-005,174-42, 01-08-2008.

PET/CT morphology and cardiac conduction disorders help discriminate primary cardiac lymphoma from primary cardiac sarcoma

BACKGROUND

Primary cardiac lymphoma (PCL) and primary cardiac sarcoma (PCS) are similar in clinical presentation but differ in management and outcomes. We aim to explore the role of PET morphology and clinical characteristics in distinguishing PCL from PCS.

METHODS

Pretreatment 18F-FDG PET/CT and contrast-enhanced CT were performed in PCL (n = 14) and PCS (n = 15) patients. Patient demographics, overall survival, and progression-free survival were reviewed. PET/CT morphological and metabolic features were extracted. Specifically, R_Kurtosis, a PET-morphology parameter reflecting the tumor expansion within the heart, was calculated.

RESULTS

Compared with PCS, PCL occurred at an older age, resulted in more cardiac dysfunctions and arrhythmias, and showed higher glucometabolism (SUVmax, SUVpeak, SUVmean, MTV, and TLG). Curative treatments improved survival for PCL but not for PCS. Multivariable logistic regression identified R_Kurtosis (OR = 27.025, P = .007) and cardiac conduction disorders (OR = 37.732, P = .016) independently predictive of PCL, and classification and regression tree analysis stratified patients into three subgroups: R_Kurtosis ≥ 0.044 (probability of PCL 88.9%), R_Kurtosis < 0.044 with conduction disorders (80.0%), and R_Kurtosis < 0.044 without conduction disorders (13.3%).

CONCLUSION

PET-derived tumor expansion pattern (R_Kurtosis) and cardiac conduction disorders were helpful in distinguishing PCL from PCS, which might assist the clinical management.

Harmonization Strategies in Multicenter MRI-Based Radiomics

Radiomics analysis is a powerful tool aiming to provide diagnostic and prognostic patient information directly from images that are decoded into handcrafted features, comprising descriptors of shape, size and textural patterns. Although radiomics is gaining momentum since it holds great promise for accelerating digital diagnostics, it is susceptible to bias and variation due to numerous inter-patient factors (e.g., patient age and gender) as well as inter-scanner ones (different protocol acquisition depending on the scanner center). A variety of image and feature based harmonization methods has been developed to compensate for these effects; however, to the best of our knowledge, none of these techniques has been established as the most effective in the analysis pipeline so far. To this end, this review provides an overview of the challenges in optimizing radiomics analysis, and a concise summary of the most relevant harmonization techniques, aiming to provide a thorough guide to the radiomics harmonization process.

Mitigation of noise-induced bias of PET radiomic features

Introduction

One major challenge in PET radiomics is its sensitivity to noise. Low signal-to-noise ratio (SNR) affects not only the precision but also the accuracy of quantitative metrics extracted from the images resulting in noise-induced bias. This phantom study aims to identify the radiomic features that are robust to noise in terms of precision and accuracy and to explore some methods that might help to correct noise-induced bias.

Methods

A phantom containing three ¹⁸F-FDG filled 3D printed inserts, reflecting heterogeneous tracer uptake and realistic tumor shapes, was used in the study. The three different phantom inserts were filled and scanned with three different tumor-to-background ratios, simulating a total of nine different tumors. From the 40-minute list-mode data, ten frames each for 5 s, 10 s, 30 s, and 120 s frame duration were reconstructed to generate images with different noise levels. Under these noise conditions, the precision and accuracy of the radiomic features were analyzed using intraclass correlation coefficient (ICC) and similarity distance metric (SDM) respectively. Based on the ICC and SDM values, the radiomic features were categorized into four groups: poor, moderate, good, and excellent precision and accuracy. A “difference image” created by subtracting two statistically equivalent replicate images was used to develop a model to correct the noise-induced bias. Several regression methods (e.g., linear, exponential, sigmoid, and power-law) were tested. The best fitting model was chosen based on Akaike information criteria.

Results

Several radiomic features derived from low SNR images have high repeatability, with 68% of radiomic features having ICC ≥ 0.9 for images with a frame duration of 5 s. However, most features show a systematic bias that correlates with the increase in noise level. Out of 143 features with noise-induced bias, the SDM values were improved based on a regression model (53 features to excellent and 67 to good) indicating that the noise-induced bias of these features can be, at least partially, corrected.

Conclusion

To have a predictive value, radiomic features should reflect tumor characteristics and be minimally affected by noise. The present study has shown that it is possible to correct for noise-induced bias, at least in a subset of the features, using a regression model based on the local image noise estimates.

Convolutional neural networks for automatic image quality control and EARL compliance of PET images

Background

Machine learning studies require a large number of images often obtained on different PET scanners. When merging these images, the use of harmonized images following EARL-standards is essential. However, when including retrospective images, EARL accreditation might not have been in place. The aim of this study was to develop a convolutional neural network (CNN) that can identify retrospectively if an image is EARL compliant and if it is meeting older or newer EARL-standards.

Materials and methods

96 PET images acquired on three PET/CT systems were included in the study. All images were reconstructed with the locally clinically preferred, EARL1, and EARL2 compliant reconstruction protocols. After image pre-processing, one CNN was trained to separate clinical and EARL compliant reconstructions. A second CNN was optimized to identify EARL1 and EARL2 compliant images. The accuracy of both CNNs was assessed using fivefold cross-validation. The CNNs were validated on 24 images acquired on a PET scanner not included in the training data. To assess the impact of image noise on the CNN decision, the 24 images were reconstructed with different scan durations.

Results

In the cross-validation, the first CNN classified all images correctly. When identifying EARL1 and EARL2 compliant images, the second CNN identified 100% EARL1 compliant and 85% EARL2 compliant images correctly. The accuracy in the independent dataset was comparable to the cross-validation accuracy. The scan duration had almost no impact on the results.

Conclusion

The two CNNs trained in this study can be used to retrospectively include images in a multi-center setting by, e.g., adding additional smoothing. This method is especially important for machine learning studies where the harmonization of images from different PET systems is essential.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-022-00468-w.

Harmonization based on quantitative analysis of standardized uptake value variations across PET/CT scanners: a multicenter phantom study

OBJECTIVES

This study aimed to measure standardized uptake value (SUV) variations across different PET/computed tomography (CT) scanners to harmonize quantification across systems.

METHODS

We acquired images using the National Electrical Manufacturers Association International Electrotechnical Commission phantom from three PET/CT scanners operated using routine imaging protocols at each site. The SUVs of lesions were assessed in the presence of reference values by a digital reference object (DRO) and recommendations by the European Association of Nuclear Medicine (EANM/EARL) to measure inter-site variations. For harmonization, Gaussian filters with tuned full width at half maximum (FWHM) values were applied to images to minimize differences in SUVs between reference and images. Inter-site variation of SUVs was evaluated in both pre- and postharmonization situations. Test-retest analysis was also carried out to evaluate repeatability.

RESULTS

SUVs from different scanners became significantly more consistent, and inter-site differences decreased for SUVmean, SUVmax and SUVpeak from 17.3, 20.7, and 15.5% to 4.8, 4.7, and 2.7%, respectively, by harmonization (P values <0.05 for all). The values for contrast-to-noise ratio in the smallest lesion of the phantom verified preservation of image quality following harmonization (>2.8%).

CONCLUSIONS

Harmonization significantly lowered variations in SUV measurements across different PET/CT scanners, improving reproducibility while preserving image quality.

Artificial intelligence-based PET denoising could allow a two-fold reduction in [18F]FDG PET acquisition time in digital PET/CT

Purpose

We investigated whether artificial intelligence (AI)-based denoising halves PET acquisition time in digital PET/CT.

Methods

One hundred ninety-five patients referred for [¹⁸F]FDG PET/CT were prospectively included. Body PET acquisitions were performed in list mode. Original “PET90” (90 s/bed position) was compared to reconstructed ½-duration PET (45 s/bed position) with and without AI-denoising, “PET45AI and PET45”. Denoising was performed by SubtlePET™ using deep convolutional neural networks. Visual global image quality (IQ) 3-point scores and lesion detectability were evaluated. Lesion maximal and peak standardized uptake values using lean body mass (SUL_max and SUL_peak), metabolic volumes (MV), and liver SUL_mean were measured, including both standard and EARL₁ (European Association of Nuclear Medicine Research Ltd) compliant SUL. Lesion-to-liver SUL ratios (LLR) and liver coefficients of variation (CV_liv) were calculated.

Results

PET45 showed mediocre IQ (scored poor in 8% and moderate in 68%) and lesion concordance rate with PET90 (88.7%). In PET45AI, IQ scores were similar to PET90 (P = 0.80), good in 92% and moderate in 8% for both. The lesion concordance rate between PET90 and PET45AI was 836/856 (97.7%), with 7 lesions (0.8%) only detected in PET90 and 13 (1.5%) exclusively in PET45AI. Lesion EARL₁ SUL_peak was not significantly different between both PET (P = 0.09). Lesion standard SUL_peak, standard and EARL1 SUL_max, LLR and CV_liv were lower in PET45AI than in PET90 (P < 0.0001), while lesion MV and liver SUL_mean were higher (P < 0.0001). Good to excellent intraclass correlation coefficients (ICC) between PET90 and PET45AI were observed for lesion SUL and MV (ICC ≥ 0.97) and for liver SUL_mean (ICC ≥ 0.87).

Conclusion

AI allows [¹⁸F]FDG PET duration in digital PET/CT to be halved, while restoring degraded ½-duration PET image quality. Future multicentric studies, including other PET radiopharmaceuticals, are warranted.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-022-05800-1.

Implications of the Harmonization of [18F]FDG-PET/CT Imaging for Response Assessment of Treatment in Radiotherapy Planning

The purpose of this work is to present useful recommendations for the use of [18F]FDG-PET/CT imaging in radiotherapy planning and monitoring under different versions of EARL accreditation for harmonization of PET devices. A proof-of-concept experiment designed on an anthropomorphic phantom was carried out to establish the most suitable interpolation methods of the PET images in the different steps of the planning procedure. Based on PET/CT images obtained by using these optimal interpolations for the old EARL accreditation (EARL1) and for the new one (EARL2), the treatment plannings of representative actual clinical cases were calculated, and the clinical implications of the resulting differences were analyzed. As expected, EARL2 provided smaller volumes with higher resolution than EARL1. The increase in the size of the reconstructed volumes with EARL1 accreditation caused high doses in the organs at risk and in the regions adjacent to the target volumes. EARL2 accreditation allowed an improvement in the accuracy of the PET imaging precision, allowing more personalized radiotherapy. This work provides recommendations for those centers that intend to benefit from the new accreditation, EARL2, and can help build confidence of those that must continue working under the EARL1 accreditation.

EARL compliance measurements on the biograph vision Quadra PET/CT system with a long axial field of view

Background

Our aim was to determine sets of reconstruction parameters for the Biograph Vision Quadra (Siemens Healthineers) PET/CT system that result in quantitative images compliant with the European Association of Nuclear Medicine Research Ltd. (EARL) criteria. Using the Biograph Vision 600 (Siemens Healthineers) PET/CT technology but extending the axial field of view to 106 cm, gives the Vision Quadra currently an around fivefold higher sensitivity over the Vision 600 with otherwise comparable spatial resolution. Therefore, we also investigated how the number of incident positron decays—i.e., exposure—affects EARL compliance. This will allow estimating a minimal acquisition time or a minimal applied dose in clinical scans while retaining data comparability.

Methods

We measured activity recovery curves on a NEMA IEC body phantom filled with an aqueous ¹⁸F solution and a sphere to background ratio of 10–1 according to the latest EARL guidelines. Reconstructing 3570 image sets with varying OSEM PSF iterations, post-reconstruction Gaussian filter full width at half maximum (FWHM), and varying exposure from 59 kDecays/ml (= 3 s frame duration) to 59.2 MDecays/ml (= 1 h), allowed us to determine sets of parameters to achieve compliance with the current EARL 1 and EARL 2 standards. Recovery coefficients (RCs) were calculated for the metrics RC_max, RC_mean, and RC_peak, and the respective recovery curves were analyzed for monotonicity. The background’s coefficient of variation (COV) was also calculated.

Results

Using 6 iterations, 5 subsets and 7.8 mm Gauss filtering resulted in optimal EARL1 compliance and recovery curve monotonicity in all analyzed frames, except in the 3 s frames. Most robust EARL2 compliance and monotonicity were achieved with 2 iterations, 5 subsets, and 3.6 mm Gauss FWHM in frames with durations between 30 s and 10 min. RC_peak only impeded EARL2 compliance in the 10 s and 3 s frames.

Conclusions

While EARL1 compliance was robust over most exposure ranges, EARL2 compliance required exposures between 1.2 MDecays/ml to 11.5 MDecays/ml. The Biograph Vision Quadra’s high sensitivity makes frames as short as 10 s feasible for comparable quantitative images. Lowering EARL2 RC_max limits closer to unity would possibly even permit shorter frames.

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

Background

To investigate the influence of small voxel Bayesian penalized likelihood (SVB) reconstruction on small lesion detection compared to ordered subset expectation maximization (OSEM) reconstruction using a clinical trials network (CTN) chest phantom and the patients with ¹⁸F-FDG-avid small lung tumors, and determine the optimal penalty factor for the lesion depiction and quantification.

Methods

The CTN phantom was filled with ¹⁸F solution with a sphere-to-background ratio of 3.81:1. Twenty-four patients with ¹⁸F-FDG-avid lung lesions (diameter < 2 cm) were enrolled. Six groups of PET images were reconstructed: routine voxel OSEM (RVOSEM), small voxel OSEM (SVOSEM), and SVB reconstructions with four penalty factors: 0.6, 0.8, 0.9, and 1.0 (SVB0.6, SVB0.8, SVB0.9, and SVB1.0). The routine and small voxel sizes are 4 × 4 × 4 and 2 × 2 × 2 mm³. The recovery coefficient (RC) was calculated by dividing the measured activity by the injected activity of the hot spheres in the phantom study. The SUV_max, target-to-liver ratio (TLR), contrast-to-noise ratio (CNR), the volume of the lesions, and the image noise of the liver were measured and calculated in the patient study. Visual image quality of the patient image was scored by two radiologists using a 5-point scale.

Results

In the phantom study, SVB0.6, SVB0.8, and SVB0.9 achieved higher RCs than SVOSEM. The RC was higher in SVOSEM than RVOSEM and SVB1.0. In the patient study, the SUV_max, TLR, and visual image quality scores of SVB0.6 to SVB0.9 were higher than those of RVOSEM, while the image noise of SVB0.8 to SVB1.0 was equivalent to or lower than that of RVOSEM. All SVB groups had higher CNRs than RVOSEM, but there was no difference between RVOSEM and SVOSEM. The lesion volumes derived from SVB0.6 to SVB0.9 were accurate, but over-estimated by RVOSEM, SVOSEM, and SVB1.0, using the CT measurement as the standard reference.

Conclusions

The SVB reconstruction improved lesion contrast, TLR, CNR, and volumetric quantification accuracy for small lesions compared to RVOSEM reconstruction without image noise degradation or the need of longer emission time. A penalty factor of 0.8–0.9 was optimal for SVB reconstruction for the small tumor detection with ¹⁸F-FDG PET/CT.

Supplementary Information

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

Influences on PET Quantification and Interpretation

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

Quantitative evaluation of PSMA PET imaging using a realistic anthropomorphic phantom and shell-less radioactive epoxy lesions

BACKGROUND

Positron emission tomography (PET) with prostate specific membrane antigen (PSMA) have shown superior performance in detecting metastatic prostate cancers. Relative to [18F]fluorodeoxyglucose ([18F]FDG) PET images, PSMA PET images tend to visualize significantly higher-contrast focal lesions. We aim to evaluate segmentation and reconstruction algorithms in this emerging context. Specifically, Bayesian or maximum a posteriori (MAP) image reconstruction, compared to standard ordered subsets expectation maximization (OSEM) reconstruction, has received significant interest for its potential to reach convergence with minimal noise amplifications. However, few phantom studies have evaluated the quantitative accuracy of such reconstructions for high contrast, small lesions (sub-10 mm) that are typically observed in PSMA images. In this study, we cast 3 mm-16-mm spheres using epoxy resin infused with a long half-life positron emitter (sodium-22; 22Na) to simulate prostate cancer metastasis. The anthropomorphic Probe-IQ phantom, which features a liver, bladder, lungs, and ureters, was used to model relevant anatomy. Dynamic PET acquisitions were acquired and images were reconstructed with OSEM (varying subsets and iterations) and BSREM (varying β parameters), and the effects on lesion quantitation were evaluated.

RESULTS

The 22Na lesions were scanned against an aqueous solution containing fluorine-18 (18F) as the background. Regions-of-interest were drawn with MIM Software using 40% fixed threshold (40% FT) and a gradient segmentation algorithm (MIM's PET Edge+). Recovery coefficients (RCs) (max, mean, peak, and newly defined "apex"), metabolic tumour volume (MTV), and total tumour uptake (TTU) were calculated for each sphere. SUVpeak and SUVapex had the most consistent RCs for different lesion-to-background ratios and reconstruction parameters. The gradient-based segmentation algorithm was more accurate than 40% FT for determining MTV and TTU, particularly for lesions [Formula: see text] 6 mm in diameter (R2 = 0.979-0.996 vs. R2 = 0.115-0.527, respectively).

CONCLUSION

An anthropomorphic phantom was used to evaluate quantitation for PSMA PET imaging of metastatic prostate cancer lesions. BSREM with β = 200-400 and OSEM with 2-5 iterations resulted in the most accurate and robust measurements of SUVmean, MTV, and TTU for imaging conditions in 18F-PSMA PET/CT images. SUVapex, a hybrid metric of SUVmax and SUVpeak, was proposed for robust, accurate, and segmentation-free quantitation of lesions for PSMA PET.

New standards for phantom image quality and SUV harmonization range for multicenter oncology PET studies

Not only visual interpretation for lesion detection, staging, and characterization, but also quantitative treatment response assessment are key roles for ¹⁸F-FDG PET in oncology. In multicenter oncology PET studies, image quality standardization and SUV harmonization are essential to obtain reliable study outcomes. Standards for image quality and SUV harmonization range should be regularly updated according to progress in scanner performance. Accordingly, the first aim of this study was to propose new image quality reference levels to ensure small lesion detectability. The second aim was to propose a new SUV harmonization range and an image noise criterion to minimize the inter-scanner and intra-scanner SUV variabilities. We collected a total of 37 patterns of images from 23 recent PET/CT scanner models using the NEMA NU2 image quality phantom. PET images with various acquisition durations of 30-300 s and 1800 s were analyzed visually and quantitatively to derive visual detectability scores of the 10-mm-diameter hot sphere, noise-equivalent count (NEC_phantom), 10-mm sphere contrast (Q_H,10 mm), background variability (N_10 mm), contrast-to-noise ratio (Q_H,10 mm/N_10 mm), image noise level (CV_BG), and SUVmax and SUVpeak for hot spheres (10-37 mm diameters). We calculated a reference level for each image quality metric, so that the 10-mm sphere can be visually detected. The SUV harmonization range and the image noise criterion were proposed with consideration of overshoot due to point-spread function (PSF) reconstruction. We proposed image quality reference levels as follows: Q_H,10 mm/N_10 mm ≥ 2.5 and CV_BG ≤ 14.1%. The 10th-90th percentiles in the SUV distributions were defined as the new SUV harmonization range. CV_BG ≤ 10% was proposed as the image noise criterion, because the intra-scanner SUV variability significantly depended on CV_BG. We proposed new image quality reference levels to ensure small lesion detectability. A new SUV harmonization range (in which PSF reconstruction is applicable) and the image noise criterion were also proposed for minimizing the SUV variabilities. Our proposed new standards will facilitate image quality standardization and SUV harmonization of multicenter oncology PET studies. The reliability of multicenter oncology PET studies will be improved by satisfying the new standards.

Absolute Quantification in Diagnostic SPECT/CT: The Phantom Premise

The application of absolute quantification in SPECT/CT has seen increased interest in the context of radionuclide therapies where patient-specific dosimetry is a requirement within the European Union (EU) legislation. However, the translation of this technique to diagnostic nuclear medicine outside this setting is rather slow. Clinical research has, in some examples, already shown an association between imaging metrics and clinical diagnosis, but the applications, in general, lack proper validation because of the absence of a ground truth measurement. Meanwhile, additive manufacturing or 3D printing has seen rapid improvements, increasing its uptake in medical imaging. Three-dimensional printed phantoms have already made a significant impact on quantitative imaging, a trend that is likely to increase in the future. In this review, we summarize the data of recent literature to underpin our premise that the validation of diagnostic applications in nuclear medicine using application-specific phantoms is within reach given the current state-of-the-art in additive manufacturing or 3D printing.

An optimized imaging protocol for [99mTc]Tc-DPD scintigraphy and SPECT/CT quantification in cardiac transthyretin (ATTR) amyloidosis

BACKGROUND

In [99mTc]Tc-DPD scintigraphy for myocardial ATTR amyloidosis, planar images 3 hour p.i. and SPECT/CT acquisition in L-mode are recommended. This study investigated if earlier planar images (1 hour p.i.) are beneficial and if SPECT/CT acquisition should be preferred in H-mode (180° detector angle) or L-mode (90°).

METHODS

In SPECT/CT phantom measurements (NaI cameras, N = 2; CZT, N = 1), peak contrast recovery (CRpeak) was derived from sphere inserts or myocardial insert (cardiac phantom; signal-to-background ratio [SBR], 10:1 or 5:1). In 25 positive and 38 negative patients (reference: endomyocardial biopsy or clinical diagnosis), Perugini scores and heart-to-contralateral (H/CL) count ratios were derived from planar images 1 hour and 3 hour p.i.

RESULTS

In phantom measurements, accuracy of myocardial CRpeak at SBR 10:1 (H-mode, 0.95-0.99) and reproducibility at 5:1 (H-mode, 1.02-1.14) was comparable for H-mode and L-mode. However, L-mode showed higher variability of background counts and sphere CRpeak throughout the field of view than H-mode. In patients, sensitivity/specificity were ≥ 95% for H/CL ratios at both time points and visual scoring 3 hour. At 1 hour, visual scores showed specificity of 89% and reduced reader's confidence.

CONCLUSIONS

Early DPD images provided no additional value for visual scoring or H/CL ratios. In SPECT/CT, H-mode is preferred over L-mode, especially if quantification is applied apart from the myocardium.

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.