As fossil fuels are phased out in favour of renewable energy, electric cars and other low-carbon technologies, the future clean energy system is likely to require less overall mining than the... Show moreAs fossil fuels are phased out in favour of renewable energy, electric cars and other low-carbon technologies, the future clean energy system is likely to require less overall mining than the current fossil-fuelled system. However, material extraction and waste flows, new infrastructure development, land-use change, and the provision of new types of goods and services associated with decarbonization will produce social and environmental pressures at localized to regional scales. Demand-side solutions can achieve the important outcome of reducing both the scale of the climate challenge and material resource requirements. Interdisciplinary systems modelling and analysis are needed to identify opportunities and trade-offs for demand-led mitigation strategies that explicitly consider planetary boundaries associated with Earth’s material resources. Show less
The objective is to assess the performance of seven semiautomatic and two fully automatic segmentation methods on [F-18]FDG PET/CT lymphoma images and evaluate their influence on tumor... Show moreThe objective is to assess the performance of seven semiautomatic and two fully automatic segmentation methods on [F-18]FDG PET/CT lymphoma images and evaluate their influence on tumor quantification. All lymphoma lesions identified in 65 whole-body [F-18]FDG PET/CT staging images were segmented by two experienced observers using manual and semiautomatic methods. Semiautomatic segmentation using absolute and relative thresholds, k-means and Bayesian clustering, and a self-adaptive configuration (SAC) of k-means and Bayesian was applied. Three state-of-the-art deep learning-based segmentations methods using a 3D U-Net architecture were also applied. One was semiautomatic and two were fully automatic, of which one is publicly available. Dice coefficient (DC) measured segmentation overlap, considering manual segmentation the ground truth. Lymphoma lesions were characterized by 31 features. Intraclass correlation coefficient (ICC) assessed features agreement between different segmentation methods. Nine hundred twenty [F-18]FDG-avid lesions were identified. The SAC Bayesian method achieved the highest median intra-observer DC (0.87). Inter-observers' DC was higher for SAC Bayesian than manual segmentation (0.94 vs 0.84, p < 0.001). Semiautomatic deep learning-based median DC was promising (0.83 (Obs1), 0.79 (Obs2)). Threshold-based methods and publicly available 3D U-Net gave poorer results (0.56 <= DC <= 0.68). Maximum, mean, and peak standardized uptake values, metabolic tumor volume, and total lesion glycolysis showed excellent agreement (ICC >= 0.92) between manual and SAC Bayesian segmentation methods. The SAC Bayesian classifier is more reproducible and produces similar lesion features compared to manual segmentation, giving the best concordant results of all other methods. Deep learning-based segmentation can achieve overall good segmentation results but failed in few patients impacting patients' clinical evaluation. Show less
Purpose The aim of this study was to re-evaluate the differentiation of patients with dementia with Lewy bodies (DLB) from Alzheimer's disease (AD) and Parkinson's disease (PD) using a quantitative... Show morePurpose The aim of this study was to re-evaluate the differentiation of patients with dementia with Lewy bodies (DLB) from Alzheimer's disease (AD) and Parkinson's disease (PD) using a quantitative analysis of I-123-FP-CIT SPECT scans.Methods Thirty-six patients with in vivo I-123-FP-CIT SPECT and neuropathological diagnoses were included. Based on neuropathological criteria, patients were further subclassified into nine AD, eight DLB, ten PD and nine with other diagnoses. An additional 16 healthy controls (HC) scanned with I-123-FP-CIT SPECT were also included. All images were visually assessed as normal versus abnormal uptake by consensus of five nuclear medicine physicians. Bihemispheric mean was calculated for caudate binding potential (CBP), putamen binding potential (PBP) and putamen-to-caudate ratio (PCR).Results Patients with DLB had significantly lower CBP and PBP than patients with AD and significantly higher PCR than patients with PD. Qualitative visual analysis of the images gave an accuracy of 88% in the evaluation of the status of the nigrostriatal pathway considering all individuals, and 96% considering only the patients with PD, AD and DLB. Quantitative analyses provided a balanced accuracy of 94%, 94% and 100% in binary classifications DLB versus AD, DLB versus PD and PD versus AD, respectively, and an accuracy of 93% in the differentiation among patients with DLB, AD and PD simultaneously. No statistically significant differences were observed between the AD and HC.Conclusions This study demonstrates a very high diagnostic accuracy of the quantitative analysis of(I-123-FP-CIT SPECT data to differentiate among patients with DLB, PD and AD. Show less
Objectives To compare lesion features extracted from F-18-FDG PET/CT images acquired on analog and digital scanners, on consecutive imaging data from the same subjects. Methods Whole-body F-18-FDG... Show moreObjectives To compare lesion features extracted from F-18-FDG PET/CT images acquired on analog and digital scanners, on consecutive imaging data from the same subjects. Methods Whole-body F-18-FDG PET/CT images from 55 oncological patients were acquired twice after a single F-18-FDG injection, with a digital and an analog PET/CT scanner, alternately. Twenty-nine subjects were examined first on the digital, and 26 first on the analog equipment. Image reconstruction was performed using manufacturer standard clinical protocols and protocols that fulfilled EARL1 specifications. Twenty-five features based on lesion standardized uptake value (SUV) and geometry were assessed. To compare these features, intraclass correlation coefficient (ICC), relative difference (RD), absolute value of RD (|RD|), and repeatability coefficient (RC) were used. Results In total, 323 F-18-FDG avid lesions were identified. High agreement (ICC > 0.75) was obtained for most of the lesion features pulled out from both scanners' imaging data, especially when reconstruction protocols fulfilled EARL1 specifications. For EARL1 reconstruction images, the features frequently used in clinics, SUVmax, SUVpeak, SUVmean, metabolic tumor volume, and total lesion glycolysis, reached an ICC of 0.92, 0.95, 0.87, 0.98, and 0.98, and a median RD (digital-analog) of 3%, 5%, 4%, - 3% and 1%, respectively. Using standard reconstruction protocols, the ICC were 0.84, 0.93, 0.80, 0.98, and 0.98, and the RD were 20%, 11%, 13%, - 7%, and 7%, respectively. Conclusion Under controlled acquisition and reconstruction parameters, most of the features studied can be used for research and clinical work. This is especially important for multicenter studies and patient follow-ups. Show less
Roth, D.; Nelson, D.R.; Bruchfeld, A.; Liapakis, A.; Silva, M.; Monsour, H.; ... ; Greaves, W. 2015
