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Background: Accurate kinetic modelling of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) data requires accurate knowledge of the available tracer concentration in the plasma during the scan time, known as the arterial input function (AIF). The gold standard method to derive the AIF requires collection of serial arterial blood samples but the introduction of long axial field of view (LAFOV) PET systems enables use of non-invasive image derived input functions (IDIF) from large blood pools such as the aorta without any need for bed movement. However, such protocols require a prolonged dynamic PET acquisition which is impractical in a busy clinical setting. Population-based input functions (PBIF) have previously shown potential in accurate Patlak analysis of 18F-FDG datasets and can enable the use of shortened dynamic imaging protocols. We not exploit the high sensitivity and temporal resolution of a LAFOV PET system and explore use of PBIF with abbreviated protocols in 18F-FDG total body kinetic modelling. Methods: Dynamic PET data were acquired in 24 oncological subjects for 65 minutes following the administration of 18F-FDG. IDIFs were extracted from the descending thoracic aorta and a PBIF was generated from 16 datasets. Five different scaled PBIFs (sPBIF) were generated by scaling the PBIF with AUC of IDIF curve tails using various portions of image data (35-65, 40-65, 45-65, 50-65 and 55-65 min post injection). The sPBIFs were compared with the IDIFs using the AUCs and Patlak Ki estimates in tumour lesions and cerebral grey matter. Patlak plot start time (t*) was also varied to evaluate the performance of shorter acquisitions on accuracy of Patlak Ki estimates. Patlak Ki estimates with IDIF and t*=35 min was used as reference and mean bias and precision (standard deviation of bias) were calculated to assess relative performance of different sPBIFs. Comparison of parametric images generated using IDIF and sPBIFs was also performed. Results: There was no statistically significant difference between AUCs of the IDIF and sPBIFs (Wilcoxon test: P>0.05). The sPBIF55-65 showed the best performance with 1.5% bias and %6.8 precision in tumour lesions. Using the sPBIF55-65 with Patlak model, 20 minutes of PET data (i.e. 45 to 65 min post injection) achieved i estimates in tumour lesions compared to the estimates with the IDIF. Parametric images reconstructed using the IDIF and sPBIFs with and without an abbreviated protocol were visually comparable. Using Patlak Ki generated with an IDIF and 30 mins of PET data as reference, Patlak Ki images generated using sPBIF55-65 with 20 minutes of PET data (t*=45 min) provided excellent image quality with structural similarity index measure > 0.99 and peak signal-to-noise ratio > 55 dB. Conclusion: We demonstrate the feasibility of performing accurate 18F-FDG Patlak analysis using sPBIFs with only 20 minutes of PET data from a LAFOV PET scanner.

Purpose: Uncertain bone lesions (UBL) with intensive radiopharmaceutical avidity are frequently observed in patients undergoing [18F]PSMA-1007 PET/CT for the detection of prostate cancer (PC). Such lesions can pose diagnostic conundrums and risk an incorrect staging. The aim of this short communication is to share the results of PET-guided biopsies of such lesions. Methods: A retrospective analysis revealed 10 patients who were referred to our department for PET-guided biopsy of UBL visible in a previous [18F]PSMA-1007 PET/CT. [18F]-PSMA-1007 PET-guided biopsy was conducted for 11 PSMA-avid bone lesions in these 10 patients. The biopsy materials were analysed for tissue typing and immunohistochemistry (IHC) was performed for prostate-specific-membrane-antigen (PSMA) expression. The scans were analysed by two experienced physicians in a consensus read for clinical characteristics and radiopharmaceutical uptake of lesions. Results: 1/11 (9.1%) of the lesions biopsied was confirmed as bone metastasis of PC with intense PSMA-expression while 10/11 (90.9%) lesions were revealed to be unremarkable bone tissue without evidence of PSMA-expression at IHC. Amongst all bone lesions assessed by biopsy, eight were visually classified as being at high risk of malignancy in the PET/CT (SUVmean 12.0 ± 8.1; SUVmax 18.8 ± 13.1), three as equivocal (SUVmean 4.6 ± 2.1; SUVmax 7.2 ± 3.0) and zero as low risk. No UBL had any CT correlate. Conclusions: In this cohort biopsy revealed that a small but relevant number of UBL are true metastases. For those confirmed as benign, no PSMA-expression at IHC was observed, suggesting a non-PSMA mediated cause for intensive [18F]PSMA-1007-uptake of which the reason remains unclear. Readers must interpret such lesions with caution in order to reduce the risk of erroneous staging and subsequent treatment. PET-guided biopsy, particularly in the absence of morphological changes in the CT, can be a useful method to clarify such lesions.

Purpose: Non-invasive quantification of cerebral metabolic rate for glucose (CMRGlc) and characterizing cerebral metabolism of cerebrovascular territories are useful for the understanding of ischemic cerebrovascular disease (ICVD). Here, we proposed a non-invasive quantification approach based on image-derived input function (IDIF) suitable for ICVD patients and monitored the pathophysiological changes after surgical intervention.Methods: Sixteen healthy controls and 26 ischemic cerebrovascular disease patients with baseline and after surgical visits underwent 18F-FDG PET/MR imaging. The voxel-wise CMRGlc maps were derived via our proposed IDIF method. The CMRGlc and standardized uptake value ratio (SUVR) maps were subsequently used to extract quantitative values within 7 volumes of interest (gray matter, white matter, anterior, middle, and posterior cerebral artery, basilar artery, and cerebellar artery territory). Intraclass correlation coefficient (ICC) and absolute percentage error were employed to measure consistency in healthy controls. The quantitative differences of healthy controls and patients at baseline and after surgical visits were statistically analyzed. Results: For healthy controls, there were no significant differences for region CMRGlc values across bilateral and unilateral IDIF measurements (ICC: 0.91-0.98). Significant differences in CMRGlc were observed across the cohorts in all territories (P< 0.001). The CMRGlc values in the ipsilateral side were significantly increased after surgery intervention (P < 0.05) for all territories (percentage changes: 7.4%~22.5%). Only the posterior cerebral artery and basilar artery territories (-2.8% and 1.9%) were significant differences for SUVR (P < 0.05). The diagnostic ability of CMRGlc in various territories (area under curve: 0.923-0.966) was significantly higher than of SUVR. There was a significant association between CMRGlc with the national institutes of health stroke scores (r: -0.54, P = 0.0041).Conclusion: These observations suggested the non-invasive quantification approach based on IDIF allowed the individual metabolism measurement of cerebrovascular territories after surgery and identified the glucose pathology changes underlying territories.

Background: Predicting the risk of disease progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has important clinical significance. This study aims at providing a personalized MCI-to-AD conversion prediction via radiomics-based predictive modeling (RPM) with multicentre 18F-Fluorodeoxyglucose positron emission tomography (FDG PET) data. Method: Three cohorts of 18F-FDG PET data and neuropsychological assessments were gathered from patients examined at Huashan Hospital (n=22), Xuanwu Hospital (n=80), and from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (n=355). Of these, amyloid images were selected for the ADNI and Xuanwu cohorts. First, 430 radiomic features were extracted from the 80 regions of interest (ROIs) for all PET images. These features were then concatenated for feature selection and an RPM model was constructed on the ADNI dataset. In addition, we used clinical scale data to establish a clinical Cox model, and a combined model for comparison. Afterwards, the images from Huashan Hospital were used to validate the stability and reliability of RPM, and the images from Xuanwu Hospital were used to explore the differences of biomarkers at different cognitive stages. Finally, correlation analysis was conducted between the radiomic biomarkers, neuropsychological assessments, and amyloid burden. Results: Experimental results show that the predictive performance of the PET-modal cox model was better than clinical cox model. In the two test data sets, the C index of PET model is 0.75 and 0.73, respectively; The C index of clinical model is 0.68. Moreover, most crucial image biomarkers had significant differences at different cognitive stages, and were significantly correlated with cognitive ability and the amyloid global level standardized uptake value ratio.Conclusion: The preliminary results demonstrated that the developed RPM approach has the potential to monitor the progress in high-risk populations with AD.