Consistent oncological PET/CT image quality on a high-performance scanner was achieved from an analysis of the relations existing between dose regimen, patient habitus, acquisition, and processing techniques. The proposed methodology may be used by PET/CT centers to develop protocols to standardize PET/CT imaging procedures and achieve better patient management and cost-effective operations.

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Oncological F-FDG PET/CT acquisition and reconstruction protocols need to be optimized for both quantitative and detection tasks. To date, most studies have focused on either quantification or noise, leading to quantitative harmonization guidelines or appropriate noise levels. We developed and evaluated protocols that provide harmonized quantitation with optimal amount of noise as a function of acquisition parameters and body mass. Multiple image acquisitions ( = 17) of the IEC/NEMA PET image quality phantom were performed with variable counting statistics. Phantom images were reconstructed with OSEM3D and PSF reconstructions for harmonized CRCmax quantification. The lowest counting statistics that resulted in compliance with EANM recommendations for CRCmax and CRCmax variability were used as optimization metrics. Image noise in the liver of 48 typical oncological F-FDG PET/CT studies was analysed with OSEM3D and PSF harmonized reconstructions. 164 additionalF-FDG PET/CT reconstructed list mode images were also evaluated to derive analytical expressions that predict image quality and noise variability. Phantom to subject translational analysis was used to derive optimized acquisition and reconstruction protocols. For harmonized quantitation levels, PSF reconstructions yielded decreased noise and lower CRCmax variability compared with regular OSEM3D reconstructions, suggesting they could enable a decreased activity regimen for matched performance. A PSF reconstruction with 7mm post-filter can provide harmonized quantification performance and acceptable image noise levels with injected activity, duration, and mass settings of 260 MBq.s/kg acquisition parameter at scan time. Similarly, the OSEM3D with 5mm post filter can provide similar performance with 401 MBq.s/kg.

Positron emission tomography (PET) imaging allows for measurement of activity concentrations of a given radiotracer in vivo. The quantitative capabilities of PET imaging are particularly important in the context of monitoring response to treatment, where quantitative changes in tracer uptake could be used as a biomarker of treatment response. Reconstruction algorithms and settings have a significant impact on PET quantification. In this work we introduce a novel harmonization methodology requiring only a simple cylindrical phantom and show that it can match the performance of more complex harmonization approaches based on phantoms with spherical inserts. Resolution and noise measurements from cylindrical phantoms are used to simulate the spherical inserts from NEMA image quality phantoms. An optimization algorithm was used to find the optimal smoothing filters for the simulated NEMA phantom images to identify those that best harmonized the PET scanners. Our methodology was tested on seven different PET models from two manufacturers installed at five institutions. Our methodology is able to predict contrast recovery coefficients (CRCs) from NEMA phantoms with errors within  ±5.2% for CRCmax and  ±3.7% for CRCmean (limits of agreement  =  95%). After applying the proposed harmonization protocol, all the CRC values were within the tolerances from EANM. Quantitative harmonization in compliance with the EARL FDG-PET/CT accreditation program is achieved in a simpler way, without the need of NEMA phantoms. This may lead to simplified scanner harmonization workflows more accessible to smaller institutions.

Image quality in positron emission tomography (PET) is limited by the number of detected photons. Heavier patients present higher photon attenuation levels, thus increasing image noise. In this work, we propose a new method that uses the combined patient attenuation/system matrix together with a tracer uptake prediction model to optimize scan times for different bed positions in whole body scans. Our main goal is to achieve consistent noise levels across patients and anatomical regions. We propose to optimize scan times for individual bed positions, for patients of any size, based on the scanner sensitivity and patient-specific attenuation. Variable scan times for every bed position were determined by combining the system matrix, derived from the computed tomography (CT) and the scanner-specific geometric sensitivity profiles, and estimations of the global tracer uptake for each patient. The method was validated with anthropomorphic phantoms and whole-body patient 18F-FDG PET/CT scans, where variable and fixed times were compared. Phantom experiments showed that the proposed method was successful in keeping noise level constant for different attenuation setups. In real patients, image noise variability was reduced to less than one-half compared with conventional fixed-time scans at the expense of a four-fold increase in scan times between the biggest and smallest patients. Our method can homogenize image quality not only across patients of different sizes but also across different bed positions of the same patient.

Purpose. To investigate image intensity histograms as a potential source of useful imaging biomarkers in both a clinical example of detecting immune-related colitis (irColitis) in 18 F-FDG PET/CT images of immunotherapy patients and an idealized case of classifying digital reference objects (DRO). Methods. Retrospective analysis of bowel 18 F-FDG uptake in N=40 patients receiving immune checkpoint inhibitors was conducted. A CNN trained to segment the bowel was used to generate the histogram of bowel 18 F-FDG uptake, and percentiles of the histogram were considered as potential metrics for detecting inflammation associated with irColitis. A model of the colon was also considered using cylindrical DRO. Classification of DRO with different intensity distributions was undertaken under varying geometry and noise settings. Results. The most predictive biomarker of irColitis was the 95th percentile of the bowel SUV histogram (SUV 95% ). Patients later diagnosed with irColitis had a significantly higher increase in SUV 95% from baseline to first on-treatment PET than patients who did not experience irColitis (p=0.02). An increase in SUV 95% > + 40% separated pre-irColitis change from normal variability with a sensitivity of 75% and specificity of 88%. Furthermore, histogram percentiles were ideal metrics for classifying 'hot center' and 'cold center' DRO, and were robust to varying DRO geometry and noise, and to the presence of spoiler volumes unrelated to the detection task. Conclusions. The 95th percentile of the bowel SUV histogram was the optimal metric for detecting irColitis on 18 F-FDG PET/CT. Image intensity histograms are a promising source of imaging biomarkers for clinical tasks.

Patients with metastatic melanoma often receive 18F-FDG PET/CT scans on different scanners throughout their monitoring period. In this study, we quantified the impact of scanner harmonization on longitudinal changes in PET standardized uptake values using various harmonization and normalization methods, including an anthropomorphic PET phantom. Twenty metastatic melanoma patients received at least two FDG PET/CT scans, each on two different scanners with an average of 4 months (range: 2–8) between. Scans from a General Electric (GE) Discovery 710 PET CT−1 were harmonized to the GE Discovery VCT using image reconstruction settings matching recovery coefficients in an anthropomorphic phantom with bone equivalent inserts and wall-less synthetic lesions. In patient images, SUVmax was measured for each melanoma lesion and time-point. Lesions were classified as progressing, stable, or responding based on pre-defined threshold of ±30% change in SUVmax. For comparison, harmonization was also performed using simpler methods, including harmonization using a NEMA phantom, post-reconstruction filtering, reference region normalization of SUVmax, and use of SUVpeak instead of SUVmax. In the 20 patients, 90 lesions across two time-points were available for treatment response assessment. Treatment response classification changed in 47% (42/90) of cases after harmonization with anthropomorphic phantom. Before harmonization, 37% (33/90) of the lesions were classified as stable (changing less than 30% between two time-points), while the fraction of stable lesions increased to 58% (52/90) after harmonization. Harmonization with the NEMA phantom agreed with harmonization with the anthropomorphic phantom in 91% (82/90) of cases. Post-reconstruction filtering agreed with anthropomorphic phantom-based harmonization in 83% (75/90) cases. The utilization of reference regions for normalization or SUVpeak was unable to correct for changes as identified by the anthropomorphic phantom-based harmonization. Overall, PET scanner harmonization has a major impact on individual lesion treatment response classification in metastatic melanoma patients. Harmonization using the NEMA phantom yielded similar results to harmonization using anthropomorphic phantom, while the only acceptable post-reconstruction technique was post-reconstruction filtering. Phantom-based harmonization is therefore strongly recommended when comparing lesion uptake across time-points when the images have been acquired on different PET scanners.

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