New PET technologies:performance, image quality, and clinical implications

The most specific and sensitive imaging modality for visualizing and measuring human (patho)physiology in vivo is Positron Emission Tomography (PET). PET is a firmly established biomedical imaging modality with applications in routine clinical diagnostic imaging, but also in research, including clinical trials. Over the past years, PET technology development brought new innovative PET systems to the commercial market: silicon photomultiplier (SiPM)-based or ‘digital’ PET systems, and large axial field-of-view or ‘total body’ PET systems. This thesis describes the technical performance characteristics of these new PET technologies and, in addition, associated optimization of image quality and activity administration is reported. Furthermore, clinical implications and future perspectives regarding these innovations in the field of nuclear medicine and molecular imaging and other medical disciplines are discussed

Long Axial Field-of-View PET for Ultra-Low-Dose Imaging of Non-Hodgkin Lymphoma during Pregnancy

Generally, positron emission tomography imaging is not often performed in the case of pregnant patients. The careful weighing of the risks of radiation exposure to the fetus and benefits for cancer staging and the swift onset of treatment for the mother complicates decision making in clinical practice. In oncology, the most commonly used PET radiotracer is 2-deoxy-2-[fluorine-18] fluoro-D-glucose (18F-FDG), a glucose analog which has established roles in the daily routines for, among other applications, initial diagnosis, staging, (radiation) therapy planning, and response monitoring. The introduction of long axial Field-of-View (LAFOV) PET systems allows for PET imaging with a reduced level of injected 18F-FDG activity while maintaining the image quality. Here, we discuss the first reported case of a pregnant patient diagnosed with follicular lymphoma using LAFOV PET imaging for the staging and therapy selection. The acquired PET images show diagnostic quality images with clearly distinguishable areas of lymphadenopathy, even with only 34 MBq of injected 18F-FDG activity, leading to a considerable decrease in the level of radiation exposure to the fetus

Clinically feasible semi-automatic workflows for measuring metabolically active tumour volume in metastatic melanoma

PURPOSE: Metabolically active tumour volume (MATV) is a potential quantitative positron emission tomography (PET) imaging biomarker in melanoma. Accumulating data indicate that low MATV may predict increased chance of response to immunotherapy and overall survival. However, metastatic melanoma can present with numerous (small) tumour lesions, making manual tumour segmentation time-consuming. The aim of this study was to evaluate multiple semi-automatic segmentation workflows to determine reliability and reproducibility of MATV measurements in patients with metastatic melanoma. METHODS: An existing cohort of 64 adult patients with histologically proven metastatic melanoma was used in this study. 18F-FDG PET/CT diagnostic baseline images were acquired using a European Association of Nuclear Medicine (EANM) Research Limited-accredited Siemens Biograph mCT PET/CT system (Siemens Healthineers, Knoxville, USA). PET data were analysed using manual, gradient-based segmentation and five different semi-automatic methods: three direct PET image-derived delineations (41MAX, A50P and SUV40) and two based on a majority-vote approach (MV2 and MV3), without and with (suffix '+') manual lesion addition. Correlation between the different segmentation methods and their respective associations with overall survival was assessed. RESULTS: Correlation between the MATVs derived by the manual segmentation and semi-automated tumour segmentations ranged from R2 = 0.41 for A50P to R2 = 0.85 for SUV40+ and MV2+, respectively. Manual MATV segmentation did not differ significantly from the semi-automatic methods SUV40 (∆MATV mean ± SD 0.08 ± 0.60 mL, P = 0.303), SUV40+ (∆MATV - 0.10 ± 0.51 mL, P = 0.126), MV2+ (∆MATV - 0.09 ± 0.62 mL, P = 0.252) and MV3+ (∆MATV - 0.03 ± 0.55 mL, P = 0.615). Log-rank tests showed statistically significant overall survival differences between above and below median MATV patients for all segmentation methods with areas under the ROC curves of 0.806 for manual segmentation and between 0.756 [41MAX] and 0.807 [MV3+] for semi-automatic segmentations. CONCLUSIONS: Simple and fast semi-automated FDG PET segmentation workflows yield accurate and reproducible MATV measurements that correlate well with manual segmentation in metastatic melanoma. The most readily applicable and user-friendly SUV40 method allows feasible MATV measurement in prospective multicentre studies required for validation of this potential PET imaging biomarker for clinical use

Use of population input functions for reduced scan duration whole-body Patlak F-18-FDG PET imaging

Abstract: Whole-body Patlak images can be obtained from an acquisition of first 6 min of dynamic imaging over the heart to obtain the arterial input function (IF), followed by multiple whole-body sweeps up to 60 min pi. The use of a population-averaged IF (PIF) could exclude the first dynamic scan and minimize whole-body sweeps to 30–60 min pi. Here, the effects of (incorrect) PIFs on the accuracy of the proposed Patlak method were assessed. In addition, the extent of mitigating these biases through rescaling of the PIF to image-derived IF values at 30–60 min pi was evaluated. Methods: Using a representative IF and rate constants from the literature, various tumour time-activity curves (TACs) were simulated. Variations included multiplication of the IF with a positive and negative gradual linear bias over 60 min of 5, 10, 15, 20, and 25% (generating TACs using an IF different from the PIF); use of rate constants (K 1, k 3, and both K 1 and k 2) multiplied by 2, 1.5, and 0.75; and addition of noise (μ = 0 and σ = 5, 10 and 15%). Subsequent Patlak analysis using the original IF (representing the PIF) was used to obtain the influx constant (K i) for the differently simulated TACs. Next, the PIF was scaled towards the (simulated) IF value using the 30–60-min pi time interval, simulating scaling of the PIF to image-derived values. Influence of variabilities in IF and rate constants, and rescaling the PIF on bias in K i was evaluated. Results: Percentage bias in K i observed using simulated modified IFs varied from − 16 to 16% depending on the simulated amplitude and direction of the IF modifications. Subsequent scaling of the PIF reduced these K i biases in most cases (287 out of 290) to < 5%. Conclusions: Simulations suggest that scaling of a (possibly incorrect) PIF to IF values seen in whole-body dynamic imaging from 30 to 60 min pi can provide accurate Ki estimates. Consequently, dynamic Patlak imaging protocols may be performed for 30–60 min pi making whole-body Patlak imaging clinically feasible

Image Quality and Activity Optimization in Oncologic F-18-FDG PET Using the Digital Biograph Vision PET/CT System

The first Biograph Vision PET/CT system (Siemens Healthineers) was installed at the University Medical Center Groningen. Improved performance of this system could allow for a reduction in activity administration or scan duration. This study evaluated the effects of reduced scan duration in oncologic 18F-FDG PET imaging on quantitative and subjective imaging parameters and its influence on clinical image interpretation. Methods: Patients referred for a clinical PET/CT scan were enrolled in this study, received a weight-based 18F-FDG injected activity, and underwent list-mode PET acquisition at 180 s per bed position (s/bp). Acquired PET data were reconstructed using the vendor-recommended clinical reconstruction protocol (hereafter referred to as "clinical"), using the clinical protocol with additional 2-mm gaussian filtering (hereafter referred to as "clinical+G2"), and-in conformance with European Association of Nuclear Medicine Research Ltd. (EARL) specifications-using different scan durations per bed position (180, 120, 60, 30, and 10 s). Reconstructed images were quantitatively assessed for comparison of SUVs and noise. In addition, clinically reconstructed images were qualitatively evaluated by 3 nuclear medicine physicians. Results: In total, 30 oncologic patients (22 men, 8 women; age: 48-88 y [range], 67 ± 9.6 y [mean ± SD]) received a single weight-based (3 MBq/kg) 18F-FDG injected activity (weight: 45-123 kg [range], 81 ± 15 kg [mean ± SD]; activity: 135-380 MBq [range], 241 ± 47.3 MBq [mean ± SD]). Significant differences in lesion SUVmax were found between the 180-s/bp images and the 30- and 10-s/bp images reconstructed using the clinical protocols, whereas no differences were found in lesion SUVpeak EARL-compliant images did not show differences in lesion SUVmax or SUVpeak between scan durations. Quantitative parameters showed minimal deviation (∼5%) in the 60-s/bp images. Therefore, further subjective image quality assessment was conducted using the 60-s/bp images. Qualitative assessment revealed the influence of personal preference on physicians' willingness to adopt the 60-s/bp images in clinical practice. Although quantitative PET parameters differed minimally, an increase in noise was observed. Conclusion: With the Biograph Vision PET/CT system for oncologic 18F-FDG imaging, scan duration or activity administration could be reduced by a factor of 3 or more with the use of the clinical+G2 or the EARL-compliant reconstruction protocol

Image Quality and Semiquantitative Measurements on the Biograph Vision PET/CT System:Initial Experiences and Comparison with the Biograph mCT

In May 2018, the Biograph Vision PET/CT system was installed at the University Medical Center Groningen. This study evaluated the initial experiences with this new PET/CT system in terms of perceived image quality and semiquantitative analysis in comparison to the Biograph mCT as a reference. Methods: In total, 20 oncologic patients were enrolled and received a single 3 MBq/kg injected dose of 18F-FDG followed by a dual-imaging PET scan. Ten patients were scanned on the Biograph mCT first, whereas the other 10 patients were scanned on the Biograph Vision first. The locally preferred clinically reconstructed images were blindly reviewed by 3 nuclear medicine physicians and scored (using a Likert scale of 1–5) on tumor lesion demarcation, overall image quality, and image noise. In addition, these clinically reconstructed images were used for semiquantitative analysis by measurement of SUVs in tumor lesions. Images acquired using reconstructions conform with the European Association of Nuclear Medicine Research Ltd. (EARL) specifications were also used for measurements of SUV in tumor lesions and healthy tissues for comparison between systems. Results: The 18F-FDG dose received by the 14 men and 6 women (age range, 36–84; mean ± SD, 61 ± 16 y) ranged from 145 to 405 MBq (mean ± SD, 268 ± 59.3). Images acquired on the Biograph Vision were scored significantly higher on tumor lesion demarcation, overall image quality, and image noise than images acquired on the Biograph mCT (P < 0.001). The overall interreader agreement showed a Fleiss κ of 0.61 (95% confidence interval, 0.53–0.70). Furthermore, the SUVs in tumor lesions and healthy tissues agreed well (within 95%) between PET/CT systems, particularly when EARL-compliant reconstructions were used on both systems. Conclusion: In this initial study, the Biograph Vision showed improved image quality compared with the Biograph mCT in terms of lesion demarcation, overall image quality, and visually assessed signal-to-noise ratio. The 2 systems are comparable in semiquantitatively assessed image biomarkers in both healthy tissues and tumor lesions. Improved quantitative performance may, however, be feasible using the clinically optimized reconstruction settings

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

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

Association of homozygous variants of STING1 with outcome in human cervical cancer

DNA-sensing receptor Cyclic GMP-AMP Synthase (cGAS) and its downstream signaling effector STimulator of INterferon Genes (STING) have gained significant interest in the field of tumor immunology, as a dysfunctional cGAS-STING pathway is associated with poor prognosis and worse response to immunotherapy. However, studies so far have not taken into account the polymorphic nature of the STING-encoding STING1 gene. We hypothesized that the presence of allelic variance in STING1 would cause variation between individuals as to their susceptibility to cancer development, cancer progression, and potential response to (immuno)therapy. To start to address this, we defined the genetic landscapes of STING1 in cervical scrapings and investigated their corresponding clinical characteristics across a unique cohort of cervical cancer patients and compared them with independent control cohorts. Although we did not observe an enrichment of particular STING1 allelic variants in cervical cancer patients, we did find that the occurrence of homozygous variants HAQ/HAQ and R232H/R232H of STING1 were associated with both younger age of diagnosis and higher recurrence rate. These findings were accompanied by worse survival, despite comparable mRNA and protein levels of STING and numbers of infiltrated CD8(+) T cells. Our findings suggest that patients with HAQ/HAQ and R232H/R232H genotypes may have a dysfunctional cGAS-STING pathway that fails to promote efficient anticancer immunity. Interestingly, the occurrence of these genotypes coincided with homozygous presence of the V48V variant, which was found to be individually associated with worse outcome. Therefore, we propose V48V to be further evaluated as a novel prognostic marker for cervical cancer