Within the EXPLORER Consortium, the construction of the world's first total-body PET/CT scanner has recently been completed. The 194-cm axial field of view of the EXPLORER PET/CT scanner is sufficient to cover, for the first time, the entire human adult body in a single acquisition in more than 99% of the population and allows total-body pharmacokinetic studies with frame durations as short as 1 s. The large increase in sensitivity arising from total-body coverage as well as increased solid angle for detection at any point within the body allows whole-body 18 F-FDG PET studies to be acquired with unprecedented count density, improving the signal-tonoise ratio of the resulting images. Alternatively, the sensitivity gain can be used to acquire diagnostic PET images with very small amounts of activity in the field of view (25 MBq, 0.7 mCi or less), with very short acquisition times (∼1 min or less) or at later time points after the tracer's administration. We report here on the first human imaging studies on the EXPLORER scanner using a range of different protocols that provide initial evidence in support of these claims. These case studies provide the foundation for future carefully controlled trials to quantitatively evaluate the improvements possible through total-body PET imaging.

No abstract

To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (18 F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to that of radiologic readers. Materials and Methods: Prospective 18 F-FDG PET brain images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (2109 imaging studies from 2005 to 2017, 1002 patients) and retrospective independent test set (40 imaging studies from 2006 to 2016, 40 patients) were collected. Final clinical diagnosis at follow-up was recorded. Convolutional neural network of InceptionV3 architecture was trained on 90% of ADNI data set and tested on the remaining 10%, as well as the independent test set, with performance compared to radiologic readers. Model was analyzed with sensitivity, specificity, receiver operating characteristic (ROC), saliency map, and t-distributed stochastic neighbor embedding. Results: The algorithm achieved area under the ROC curve of 0.98 (95% confidence interval: 0.94, 1.00) when evaluated on predicting the final clinical diagnosis of AD in the independent test set (82% specificity at 100% sensitivity), an average of 75.8 months prior to the final diagnosis, which in ROC space outperformed reader performance (57% [four of seven] sensitivity, 91% [30 of 33] specificity; P , .05). Saliency map demonstrated attention to known areas of interest but with focus on the entire brain. Conclusion: By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis.

Purpose To compare vertebral bone marrow fat content quantified with proton MR spectroscopy (1H-MRS) with the volume of abdominal adipose tissue, lumbar spine volumetric bone mineral density (vBMD), and blood biomarkers in postmenopausal women with and without type 2 diabetes mellitus (T2DM). Materials and Methods Thirteen postmenopausal women with T2DM and 13 age- and BMI-matched healthy controls were included in this study. All subjects underwent 1H-MRS of L1–L3 to quantify vertebral bone marrow fat content (FC) and unsaturated lipid fraction (ULF). QCT was performed to assess vBMD of L1–L3. The volumes of abdominal subcutaneous/visceral/total adipose tissue were determined from the QCT images and adjusted for abdominal body volume (SATadj/VATadj/TATadj). Fasting blood tests included plasma glucose and HbA1c. Results Mean FC showed an inverse correlation with vBMD (r=−0.452; p<0.05) in the whole study population. While mean FC was similar in the diabetic women and healthy controls (69.3 ± 7.5% vs. 67.5 ± 6.1%; p>0.05), mean ULF was significantly lower in the diabetic group (6.7 ± 1.0% vs. 7.9 ± 1.6%; p<0.05). SATadj and TATadj correlated significantly with mean FC in the whole study population (r=0.538 and r=0.466; p<0.05). In contrast to the control group, significant correlations of mean FC with VATadj and HbA1c were observed in the diabetic group (r=0.642 and r=0.825; p<0.05). Conclusion This study demonstrated that vertebral bone marrow fat content correlates significantly with SATadj, TATadj, and lumbar spine vBMD in postmenopausal women with and without T2DM, but with VATadj and HbA1c only in women with T2DM.

IntroductionThe goals of this study were (i) to compare the prevalence of focal knee abnormalities, the mean cartilage T2 relaxation time, and the spatial distribution of cartilage magnetic resonance (MR) T2 relaxation times between subjects with and without risk factors for Osteoarthritis (OA), (ii) to determine the relationship between MR cartilage T2 parameters, age and cartilage morphology as determined with whole-organ magnetic resonance imaging scores (WORMS) and (iii) to assess the reproducibility of WORMS scoring and T2 relaxation time measurements including the mean and grey level co-occurrence matrix (GLCM) texture parameters.MethodsSubjects with risk factors for OA (n = 92) and healthy controls (n = 53) were randomly selected from the Osteoarthritis Initiative (OAI) incidence and control cohorts, respectively. The specific inclusion criteria for this study were (1) age range 45-55 years, (2) body mass index (BMI) of 19-27 kg/m2, (3) Western Ontario and McMaster University (WOMAC) pain score of zero and (4) Kellgren Lawrence (KL) score of zero at baseline. 3.0 Tesla MR images of the right knee were analyzed using morphological gradings of cartilage, bone marrow and menisci (WORMS) as well as compartment specific cartilage T2 mean and heterogeneity. Regression models adjusted for age, gender, and BMI were used to determine the difference in cartilage parameters between groups.ResultsWhile there was no significant difference in the prevalence of knee abnormalities (cartilage lesions, bone marrow lesions, meniscus lesions) between controls and subjects at risk for OA, T2 parameters (mean T2, GLCM contrast, and GLCM variance) were significantly elevated in those at risk for OA. Additionally, a positive significant association between cartilage WORMS score and cartilage T2 parameters was evident.ConclusionsOverall, this study demonstrated that subjects at risk for OA have both higher and more heterogeneous cartilage T2 values than controls, and that T2 parameters are associated with morphologic degeneration.