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BACKGROUND: Preclinical, postmortem, and positron emission tomography (PET) imaging studies have pointed to neuroinflammation as a key pathophysiological hallmark in primary 4-repeat (4R) tauopathies and its role in accelerating disease progression. OBJECTIVE: We tested whether microglial activation (1) progresses in similar spatial patterns as the primary pathology tau spreads across interconnected brain regions, and (2) whether the degree of microglial activation parallels tau pathology spreading. METHODS: We examined in vivo associations between tau aggregation and microglial activation in 31 patients with clinically diagnosed 4R tauopathies, using 18F-PI-2620 PET and 18F-GE180 (translocator protein [TSPO]) PET. We determined tau epicenters, defined as subcortical brain regions with highest tau PET signal, and assessed the connectivity of tau epicenters to cortical regions of interest using a 3-T resting-state functional magnetic resonance imaging template derived from age-matched healthy elderly controls. RESULTS: In 4R tauopathy patients, we found that higher regional tau PET covaries with elevated TSPO-PET across brain regions that are functionally connected to each other (ß = 0.414, P < 0.001). Microglial activation follows similar distribution patterns as tau and distributes primarily across brain regions strongly connected to patient-specific tau epicenters (ß = -0.594, P < 0.001). In these regions, microglial activation spatially parallels tau distribution detectable with 18F-PI-2620 PET. CONCLUSIONS: Our findings indicate that the spatial expansion of microglial activation parallels tau distribution across brain regions that are functionally connected to each other, suggesting that tau and inflammation are closely interrelated in patients with 4R tauopathies. The combination of in vivo tau and inflammatory biomarkers could therefore support the development of immunomodulatory strategies for disease-modifying treatments in these conditions. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Four-repeat (4R) tauopathies are neurodegenerative diseases characterized by cerebral accumulation of 4R tau pathology. The most prominent 4R tauopathies are progressive supranuclear palsy (PSP) and corticobasal degeneration characterized by subcortical tau accumulation and cortical neuronal dysfunction, as shown by PET-assessed hypoperfusion and glucose hypometabolism. Yet, there is a spatial mismatch between subcortical tau deposition patterns and cortical neuronal dysfunction, and it is unclear how these two pathological brain changes are interrelated. Here, we hypothesized that subcortical tau pathology induces remote neuronal dysfunction in functionally connected cortical regions to test a pathophysiological model that mechanistically links subcortical tau accumulation to cortical neuronal dysfunction in 4R tauopathies. We included 51 Aß-negative patients with clinically diagnosed PSP variants (n = 26) or corticobasal syndrome (n = 25) who underwent structural MRI and 18F-PI-2620 tau-PET. 18F-PI-2620 tau-PET was recorded using a dynamic one-stop-shop acquisition protocol to determine an early 0.5-2.5 min post tracer-injection perfusion window for assessing cortical neuronal dysfunction, as well as a 20-40 min post tracer-injection window to determine 4R-tau load. Perfusion-PET (i.e. early window) was assessed in 200 cortical regions, and tau-PET was assessed in 32 subcortical regions of established functional brain atlases. We determined tau epicentres as subcortical regions with the highest 18F-PI-2620 tau-PET signal and assessed the connectivity of tau epicentres to cortical regions of interest using a resting-state functional MRI-based functional connectivity template derived from 69 healthy elderly controls from the ADNI cohort. Using linear regression, we assessed whether: (i) higher subcortical tau-PET was associated with reduced cortical perfusion; and (ii) cortical perfusion reductions were observed preferentially in regions closely connected to subcortical tau epicentres. As hypothesized, higher subcortical tau-PET was associated with overall lower cortical perfusion, which remained consistent when controlling for cortical tau-PET. Using group-average and subject-level PET data, we found that the seed-based connectivity pattern of subcortical tau epicentres aligned with cortical perfusion patterns, where cortical regions that were more closely connected to the tau epicentre showed lower perfusion. Together, subcortical tau-accumulation is associated with remote perfusion reductions indicative of neuronal dysfunction in functionally connected cortical regions in 4R-tauopathies. This suggests that subcortical tau pathology may induce cortical dysfunction, which may contribute to clinical disease manifestation and clinical heterogeneity.
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Córtex Cerebral , Tomografia por Emissão de Pósitrons , Paralisia Supranuclear Progressiva , Tauopatias , Proteínas tau , Humanos , Masculino , Feminino , Tomografia por Emissão de Pósitrons/métodos , Idoso , Tauopatias/diagnóstico por imagem , Tauopatias/metabolismo , Tauopatias/patologia , Proteínas tau/metabolismo , Pessoa de Meia-Idade , Córtex Cerebral/diagnóstico por imagem , Córtex Cerebral/metabolismo , Córtex Cerebral/patologia , Paralisia Supranuclear Progressiva/diagnóstico por imagem , Paralisia Supranuclear Progressiva/metabolismo , Paralisia Supranuclear Progressiva/patologia , Paralisia Supranuclear Progressiva/fisiopatologia , Imageamento por Ressonância Magnética/métodosRESUMO
The coal-dominated electricity system, alongside increasing industrial electricity demand, places China into a dilemma between industrialization and environmental impacts. A practical solution is to exploit air quality and health cobenefits of industrial energy efficiency measures, which has not yet been integrated into China's energy transition strategy. This research examines the pivotal role of industrial electricity savings in accelerating coal plant retirements and assesses the nexus of energy-pollution-health by modeling nationwide coal-fired plants at individual unit level. It shows that minimizing electricity needs by implementing more efficient technologies leads to the phaseout of 1279 hyper-polluting units (subcritical, <300 MW) by 2040, advancing the retirement of these units by an average of 7 years (3-16 years). The retirements at different locations yield varying levels of air quality improvements (9-17%), across six power grids. Reduced exposure to PM2.5 could avoid 123,100 pollution-related cumulative deaths over the next 20 years from 2020, of which â¼75% occur in the Central, East, and North grids, particularly coal-intensive and populous provinces (e.g., Shandong and Jiangsu). These findings provide key indicators to support geographically specific policymaking and lay out a rationale for decision-makers to incorporate multiple benefits into early coal phaseout strategies to avoid lock-in risk.
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Poluição do Ar , Carvão Mineral , Eletricidade , Centrais Elétricas , China , Humanos , Poluentes AtmosféricosRESUMO
In Alzheimer's disease, amyloid-beta (Aß) triggers the trans-synaptic spread of tau pathology, and aberrant synaptic activity has been shown to promote tau spreading. Aß induces aberrant synaptic activity, manifesting in increases in the presynaptic growth-associated protein 43 (GAP-43), which is closely involved in synaptic activity and plasticity. We therefore tested whether Aß-related GAP-43 increases, as a marker of synaptic changes, drive tau spreading in 93 patients across the aging and Alzheimer's spectrum with available CSF GAP-43, amyloid-PET and longitudinal tau-PET assessments. We found that (1) higher GAP-43 was associated with faster Aß-related tau accumulation, specifically in brain regions connected closest to subject-specific tau epicenters and (2) that higher GAP-43 strengthened the association between Aß and connectivity-associated tau spread. This suggests that GAP-43-related synaptic changes are linked to faster Aß-related tau spread across connected regions and that synapses could be key targets for preventing tau spreading in Alzheimer's disease.
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Doença de Alzheimer , Disfunção Cognitiva , Humanos , Doença de Alzheimer/metabolismo , Proteína GAP-43/genética , Proteína GAP-43/metabolismo , Proteínas tau/metabolismo , Peptídeos beta-Amiloides/metabolismo , Encéfalo/metabolismo , Tomografia por Emissão de Pósitrons , Disfunção Cognitiva/metabolismo , Biomarcadores/metabolismoRESUMO
BACKGROUND: Due to the high attenuation of metals, severe artifacts occur in cone beam computed tomography (CBCT). The metal segmentation in CBCT projections usually serves as a prerequisite for metal artifact reduction (MAR) algorithms. PURPOSE: The occurrence of truncation caused by the limited detector size leads to the incomplete acquisition of metal masks from the threshold-based method in CBCT volume. Therefore, segmenting metal directly in CBCT projections is pursued in this work. METHODS: Since the generation of high quality clinical training data is a constant challenge, this study proposes to generate simulated digital radiographs (data I) based on real CT data combined with self-designed computer aided design (CAD) implants. In addition to the simulated projections generated from 3D volumes, 2D x-ray images combined with projections of implants serve as the complementary data set (data II) to improve the network performance. In this work, SwinConvUNet consisting of shift window (Swin) vision transformers (ViTs) with patch merging as encoder is proposed for metal segmentation. RESULTS: The model's performance is evaluated on accurately labeled test datasets obtained from cadaver scans as well as the unlabeled clinical projections. When trained on the data I only, the convolutional neural network (CNN) encoder-based networks UNet and TransUNet achieve only limited performance on the cadaver test data, with an average dice score of 0.821 and 0.850. After using both data II and data I during training, the average dice scores for the two models increase to 0.906 and 0.919, respectively. By replacing the CNN encoder with Swin transformer, the proposed SwinConvUNet reaches an average dice score of 0.933 for cadaver projections when only trained on the data I. Furthermore, SwinConvUNet has the largest average dice score of 0.953 for cadaver projections when trained on the combined data set. CONCLUSIONS: Our experiments quantitatively demonstrate the effectiveness of the combination of the projections simulated under two pathways for network training. Besides, the proposed SwinConvUNet trained on the simulated projections performs state-of-the-art, robust metal segmentation as demonstrated on experiments on cadaver and clinical data sets. With the accurate segmentations from the proposed model, MAR can be conducted even for highly truncated CBCT scans.
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Artefatos , Tomografia Computadorizada de Feixe Cônico , Processamento de Imagem Assistida por Computador , Metais , Tomografia Computadorizada de Feixe Cônico/métodos , Metais/química , Processamento de Imagem Assistida por Computador/métodos , Humanos , Simulação por Computador , AlgoritmosRESUMO
Thermal noise caused by the imaged object is an intrinsic limitation in magnetic resonance imaging (MRI), resulting in an impaired clinical value of the acquisitions. Recently, deep learning (DL)-based denoising methods achieved promising results by extracting complex feature representations from large data sets. Most approaches are trained in a supervised manner by directly mapping noisy to noise-free ground-truth data and, therefore, require extensive paired data sets, which can be expensive or infeasible to obtain for medical imaging applications. In this work, a DL-based denoising approach is investigated which operates on complex-valued reconstructed magnetic resonance (MR) images without noise-free target data. An extension of Stein's unbiased risk estimator (SURE) and spatially resolved noise maps quantifying the noise level with pixel accuracy were employed during the training process. Competitive denoising performance was achieved compared to supervised training with mean squared error (MSE) despite optimizing the model without noise-free target images. The proposed DL-based method can be applied for MR image enhancement without requiring noise-free target data for training. Integrating the noise maps as an additional input channel further enables the regulation of the desired level of denoising to adjust to the preference of the radiologist.
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Importance: For the Alzheimer disease (AD) therapies to effectively attenuate clinical progression, it may be critical to intervene before the onset of amyloid-associated tau spreading, which drives neurodegeneration and cognitive decline. Time points at which amyloid-associated tau spreading accelerates may depend on individual risk factors, such as apolipoprotein E ε4 (ApoE4) carriership, which is linked to faster disease progression; however, the association of ApoE4 with amyloid-related tau spreading is unclear. Objective: To assess if ApoE4 carriers show accelerated amyloid-related tau spreading and propose amyloid positron emission tomography (PET) thresholds at which tau spreading accelerates in ApoE4 carriers vs noncarriers. Design, Setting, and Participants: This cohort study including combined ApoE genotyping, amyloid PET, and longitudinal tau PET from 2 independent samples: the Alzheimer's Disease Neuroimaging Initiative (ADNI; n = 237; collected from April 2015 to August 2022) and Avid-A05 (n = 130; collected from December 2013 to July 2017) with a mean (SD) tau PET follow-up time of 1.9 (0.96) years in ADNI and 1.4 (0.23) years in Avid-A05. ADNI is an observational multicenter Alzheimer disease neuroimaging initiative and Avid-A05 an observational clinical trial. Participants classified as cognitively normal (152 in ADNI and 77 in Avid-A05) or mildly cognitively impaired (107 in ADNI and 53 in Avid-A05) were selected based on ApoE genotyping, amyloid-PET, and longitudinal tau PET data availability. Participants with ApoE ε2/ε4 genotype or classified as having dementia were excluded. Resting-state functional magnetic resonance imaging connectivity templates were based on 42 healthy participants in ADNI. Main Outcomes and Measures: Mediation of amyloid PET on the association between ApoE4 status and subsequent tau PET increase through Braak stage regions and interaction between ApoE4 status and amyloid PET with annual tau PET increase through Braak stage regions and connectivity-based spreading stages (tau epicenter connectivity ranked regions). Results: The mean (SD) age was 73.9 (7.35) years among the 237 ADNI participants and 70.2 (9.7) years among the 130 Avid-A05 participants. A total of 107 individuals in ADNI (45.1%) and 45 in Avid-A05 (34.6%) were ApoE4 carriers. Across both samples, we found that higher amyloid PET-mediated ApoE4-related tau PET increased globally (ADNI b, 0.15; 95% CI, 0.05-0.28; P = .001 and Avid-A05 b, 0.33; 95% CI, 0.14-0.54; P < .001) and in earlier Braak regions. Further, we found a significant association between ApoE4 status by amyloid PET interaction and annual tau PET increases consistently through early Braak- and connectivity-based stages where amyloid-related tau accumulation was accelerated in ApoE4carriers vs noncarriers at lower centiloid thresholds, corrected for age and sex. Conclusions and Relevance: The findings in this study indicate that amyloid-related tau accumulation was accelerated in ApoE4 carriers at lower amyloid levels, suggesting that ApoE4 may facilitate earlier amyloid-driven tau spreading across connected brain regions. Possible therapeutic implications might be further investigated to determine when best to prevent tau spreading and thus cognitive decline depending on ApoE4 status.
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Doença de Alzheimer , Disfunção Cognitiva , Humanos , Idoso , Doença de Alzheimer/diagnóstico por imagem , Doença de Alzheimer/genética , Doença de Alzheimer/complicações , Apolipoproteína E4/genética , Proteínas tau/metabolismo , Anticorpos Monoclonais , Estudos de Coortes , Peptídeos beta-Amiloides/metabolismo , Amiloide , Apolipoproteínas E/genética , Encéfalo/patologia , Tomografia por Emissão de Pósitrons , Disfunção Cognitiva/patologia , GenótipoRESUMO
Objective.Incorporating computed tomography (CT) reconstruction operators into differentiable pipelines has proven beneficial in many applications. Such approaches usually focus on the projection data and keep the acquisition geometry fixed. However, precise knowledge of the acquisition geometry is essential for high quality reconstruction results. In this paper, the differentiable formulation of fan-beam CT reconstruction is extended to the acquisition geometry.Approach.The CT fan-beam reconstruction is analytically derived with respect to the acquisition geometry. This allows to propagate gradient information from a loss function on the reconstructed image into the geometry parameters. As a proof-of-concept experiment, this idea is applied to rigid motion compensation. The cost function is parameterized by a trained neural network which regresses an image quality metric from the motion-affected reconstruction alone.Main results.The algorithm improves the structural similarity index measure (SSIM) from 0.848 for the initial motion-affected reconstruction to 0.946 after compensation. It also generalizes to real fan-beam sinograms which are rebinned from a helical trajectory where the SSIM increases from 0.639 to 0.742.Significance.Using the proposed method, we are the first to optimize an autofocus-inspired algorithm based on analytical gradients. Next to motion compensation, we see further use cases of our differentiable method for scanner calibration or hybrid techniques employing deep models.
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Redes Neurais de Computação , Tomografia Computadorizada por Raios X , Imagens de Fantasmas , Tomografia Computadorizada por Raios X/métodos , Algoritmos , Calibragem , Processamento de Imagem Assistida por Computador/métodos , Tomografia Computadorizada de Feixe Cônico , ArtefatosRESUMO
BACKGROUND: Morbidity burdens from ambient air pollution are associated with market and non-market costs and are therefore important for policymaking. The estimation of morbidity burdens is based on concentration-response functions (CRFs). Most existing CRFs for short-term exposures to PM2.5 assume a fixed risk estimate as a log-linear function over an extrapolated exposure range, based on evidence primarily from Europe and North America. OBJECTIVES: We revisit these CRFs by performing a systematic review for seven morbidity endpoints previously assessed by the World Health Organization, including data from all available regions. These endpoints include all cardiovascular hospital admission, all respiratory hospital admission, asthma hospital admission and emergency room visit, along with the outcomes that stem from morbidity, such as lost work days, respiratory restricted activity days, and child bronchitis symptom days. METHODS: We estimate CRFs for each endpoint, using both a log-linear model and a nonlinear model that includes additional parameters to better fit evidence from high-exposure regions. We quantify uncertainties associated with these CRFs through randomization and Monte Carlo simulations. RESULTS: The CRFs in this study show reduced model uncertainty compared with previous CRFs in all endpoints. The nonlinear CRFs produce more than doubled global estimates on average, depending on the endpoint. Overall, we assess that our CRFs can be used to provide policy analysis of air pollution impacts at the global scale. It is however important to note that improvement of CRFs requires observations over a wide range of conditions, and current available literature is still limited. DISCUSSION: The higher estimates produced by the nonlinear CRFs indicates the possibility of a large underestimation in current assessments of the morbidity impacts attributable to air pollution. Further studies should be pursued to better constrain the CRFs studied here, and to better characterize the causal relationship between exposures to PM2.5 and morbidity outcomes.
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Poluição do Ar , Asma , Criança , Humanos , Avaliação do Impacto na Saúde , Poluição do Ar/efeitos adversos , Asma/epidemiologia , Morbidade , Material Particulado/efeitos adversosRESUMO
The healthcare sector is growing in importance as people continue to age and pandemics complicate the boundary conditions of such systems. The number of innovative approaches to solve singular tasks and problems in this area is only slowly increasing. This is particularly evident when looking at medical technology planning, medical training and process simulation. In this paper a concept for versatile digital improvements to these problems by using state of the art development methods of Virtual Reality (VR) and Augmented Reality (AR) are presented. The programming and design of the software is done with the help of Unity Engine, which provides an open interface for docking with the developed framework for future work. The solutions were tested under domain specific environments and have shown good results and positive feedback.
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Realidade Aumentada , Humanos , Escolaridade , Simulação por Computador , Setor de Assistência à Saúde , PandemiasRESUMO
Equipping rooms used for medical purposes, like e.g., intensive care units, is an expensive and time-consuming task. In order to avoid extensive subsequent adjustments due to inappropriate layout visualization or geometric conditions difficult to identify in 2D plans, it is of utmost importance to provide an optimal planning environment to future users such as physicians and nurses. In this paper we present the concept of a fully automatized pipeline, which is designed to visualize computer aided design (CAD) data using virtual reality (VR). The immersive VR experience results in improvement of efficiency in the decision-making process during the planning phase due to better spatial imagination. The pipeline was successfully tested with CAD data from existing Intensive Care Units. The results indicate that the pipeline can be a valuable tool in the field of spatial planning in healthcare, due to simple usage and fast conversion of CAD data. The next step will be the development of a plugin for CAD tools to allow for interactions with the CAD models in Virtual Reality, which is not yet possible without manual intervention.
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Realidade Virtual , Humanos , Computadores , Desenho Assistido por Computador , Atenção à SaúdeRESUMO
Historical documents contain essential information about the past, including places, people, or events. Many of these valuable cultural artifacts cannot be further examined due to aging or external influences, as they are too fragile to be opened or turned over, so their rich contents remain hidden. Terahertz (THz) imaging is a nondestructive 3D imaging technique that can be used to reveal the hidden contents without damaging the documents. As noise or imaging artifacts are predominantly present in reconstructed images processed by standard THz reconstruction algorithms, this work intends to improve THz image quality with deep learning. To overcome the data scarcity problem in training a supervised deep learning model, an unsupervised deep learning network (CycleGAN) is first applied to generate paired noisy THz images from clean images (clean images are generated by a handwriting generator). With such synthetic noisy-to-clean paired images, a supervised deep learning model using Pix2pixGAN is trained, which is effective to enhance real noisy THz images. After Pix2pixGAN denoising, 99% characters written on one-side of the Xuan paper can be clearly recognized, while 61% characters written on one-side of the standard paper are sufficiently recognized. The average perceptual indices of Pix2pixGAN processed images are 16.83, which is very close to the average perceptual index 16.19 of clean handwriting images. Our work has important value for THz-imaging-based nondestructive historical document analysis.
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Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality. Recently, deep learning (DL)-based methods were introduced, outperforming conventional denoising algorithms on this task due to their high model capacity. However, for the transition of DL-based denoising to clinical practice, these data-driven approaches must generalize robustly beyond the seen training data. We, therefore, propose a hybrid denoising approach consisting of a set of trainable joint bilateral filters (JBFs) combined with a convolutional DL-based denoising network to predict the guidance image. Our proposed denoising pipeline combines the high model capacity enabled by DL-based feature extraction with the reliability of the conventional JBF. The pipeline's ability to generalize is demonstrated by training on abdomen CT scans without metal implants and testing on abdomen scans with metal implants as well as on head CT data. When embedding RED-CNN/QAE, two well-established DL-based denoisers in our pipeline, the denoising performance is improved by 10%/82% (RMSE) and 3%/81% (PSNR) in regions containing metal and by 6%/78% (RMSE) and 2%/4% (PSNR) on head CT data, compared to the respective vanilla model. Concluding, the proposed trainable JBFs limit the error bound of deep neural networks to facilitate the applicability of DL-based denoisers in low-dose CT pipelines.
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Processamento de Imagem Assistida por Computador , Tomografia Computadorizada por Raios X , Humanos , Processamento de Imagem Assistida por Computador/métodos , Reprodutibilidade dos Testes , Tomografia Computadorizada por Raios X/métodos , Redes Neurais de Computação , Algoritmos , Razão Sinal-RuídoRESUMO
BACKGROUND: Computed tomography (CT) is widely used as an imaging tool to visualize three-dimensional structures with expressive bone-soft tissue contrast. However, CT resolution can be severely degraded through low-dose acquisitions, highlighting the importance of effective denoising algorithms. PURPOSE: Most data-driven denoising techniques are based on deep neural networks, and therefore, contain hundreds of thousands of trainable parameters, making them incomprehensible and prone to prediction failures. Developing understandable and robust denoising algorithms achieving state-of-the-art performance helps to minimize radiation dose while maintaining data integrity. METHODS: This work presents an open-source CT denoising framework based on the idea of bilateral filtering. We propose a bilateral filter that can be incorporated into any deep learning pipeline and optimized in a purely data-driven way by calculating the gradient flow toward its hyperparameters and its input. Denoising in pure image-to-image pipelines and across different domains such as raw detector data and reconstructed volume, using a differentiable backprojection layer, is demonstrated. In contrast to other models, our bilateral filter layer consists of only four trainable parameters and constrains the applied operation to follow the traditional bilateral filter algorithm by design. RESULTS: Although only using three spatial parameters and one intensity range parameter per filter layer, the proposed denoising pipelines can compete with deep state-of-the-art denoising architectures with several hundred thousand parameters. Competitive denoising performance is achieved on x-ray microscope bone data and the 2016 Low Dose CT Grand Challenge data set. We report structural similarity index measures of 0.7094 and 0.9674 and peak signal-to-noise ratio values of 33.17 and 43.07 on the respective data sets. CONCLUSIONS: Due to the extremely low number of trainable parameters with well-defined effect, prediction reliance and data integrity is guaranteed at any time in the proposed pipelines, in contrast to most other deep learning-based denoising architectures.
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Algoritmos , Tomografia Computadorizada por Raios X , Processamento de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , Razão Sinal-Ruído , Tomografia Computadorizada por Raios X/métodosRESUMO
High-resolution X-ray microscopy (XRM) is gaining interest for biological investigations of extremely small-scale structures. XRM imaging of bones in living mice could provide new insights into the emergence and treatment of osteoporosis by observing osteocyte lacunae, which are holes in the bone of few micrometres in size. Imaging living animals at that resolution, however, is extremely challenging and requires very sophisticated data processing converting the raw XRM detector output into reconstructed images. This paper presents an open-source, differentiable reconstruction pipeline for XRM data which analytically computes the final image from the raw measurements. In contrast to most proprietary reconstruction software, it offers the user full control over each processing step and, additionally, makes the entire pipeline deep learning compatible by ensuring differentiability. This allows fitting trainable modules both before and after the actual reconstruction step in a purely data-driven way using the gradient-based optimizers of common deep learning frameworks. The value of such differentiability is demonstrated by calibrating the parameters of a simple cupping correction module operating on the raw projection images using only a self-supervisory quality metric based on the reconstructed volume and no further calibration measurements. The retrospective calibration directly improves image quality as it avoids cupping artefacts and decreases the difference in grey values between outer and inner bone by 68-94%. Furthermore, it makes the reconstruction process entirely independent of the XRM manufacturer and paves the way to explore modern deep learning reconstruction methods for arbitrary XRM and, potentially, other flat-panel computed tomography systems. This exemplifies how differentiable reconstruction can be leveraged in the context of XRM and, hence, is an important step towards the goal of reducing the resolution limit of in vivo bone imaging to the single micrometre domain.
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Processamento de Imagem Assistida por Computador , Microscopia , Animais , Calibragem , Processamento de Imagem Assistida por Computador/métodos , Camundongos , Microscopia/métodos , Estudos Retrospectivos , Raios XRESUMO
The remaining carbon budget for limiting global warming to 1.5 degrees Celsius will probably be exhausted within this decade1,2. Carbon debt3 generated thereafter will need to be compensated by net-negative emissions4. However, economic policy instruments to guarantee potentially very costly net carbon dioxide removal (CDR) have not yet been devised. Here we propose intertemporal instruments to provide the basis for widely applied carbon taxes and emission trading systems to finance a net-negative carbon economy5. We investigate an idealized market approach to incentivize the repayment of previously accrued carbon debt by establishing the responsibility of emitters for the net removal of carbon dioxide through 'carbon removal obligations' (CROs). Inherent risks, such as the risk of default by carbon debtors, are addressed by pricing atmospheric CO2 storage through interest on carbon debt. In contrast to the prevailing literature on emission pathways, we find that interest payments for CROs induce substantially more-ambitious near-term decarbonization that is complemented by earlier and less-aggressive deployment of CDR. We conclude that CROs will need to become an integral part of the global climate policy mix if we are to ensure the viability of ambitious climate targets and an equitable distribution of mitigation efforts across generations.
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The development of single-photon counting detectors and arrays has made tremendous steps in recent years, not the least because of various new applications, e.g., LIDAR devices. In this work, a 3D imaging device based on real thermal light intensity interferometry is presented. By using gated SPAD technology, a basic 3D scene is imaged in reasonable measurement time. Compared to conventional approaches, the proposed synchronized photon counting allows the use of more light modes to enhance 3D ranging performance. Advantages like robustness to atmospheric scattering or autonomy by exploiting external light sources can make this ranging approach interesting for future applications.
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Existing estimates of optimal climate policy ignore the possibility that carbon tax revenues could be used in a progressive way; model results therefore typically imply that near-term climate action comes at some cost to the poor. Using the Nested Inequalities Climate Economy (NICE) model, we show that an equal per capita refund of carbon tax revenues implies that achieving a 2°C target can pay large and immediate dividends for improving well-being, reducing inequality and alleviating poverty. In an optimal policy calculation that weighs the benefits against the costs of mitigation, the recommended policy is characterized by aggressive near-term climate action followed by a slower climb towards full decarbonization; this pattern-which is driven by a carbon revenue Laffer curve-prevents runaway warming while also preserving tax revenues for redistribution. Accounting for these dynamics corrects a long-standing bias against strong immediate climate action in the optimal policy literature.
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Over the last decades, energy and pollution control policies combined with structural changes in the economy decoupled emission trends from economic growth, increasingly also in the developing world. It is found that effective implementation of the presently decided national pollution control regulations should allow further economic growth without major deterioration of ambient air quality, but will not be enough to reduce pollution levels in many world regions. A combination of ambitious policies focusing on pollution controls, energy and climate, agricultural production systems and addressing human consumption habits could drastically improve air quality throughout the world. By 2040, mean population exposure to PM2.5 from anthropogenic sources could be reduced by about 75% relative to 2015 and brought well below the WHO guideline in large areas of the world. While the implementation of the proposed technical measures is likely to be technically feasible in the future, the transformative changes of current practices will require strong political will, supported by a full appreciation of the multiple benefits. Improved air quality would avoid a large share of the current 3-9 million cases of premature deaths annually. At the same time, the measures that deliver clean air would also significantly reduce emissions of greenhouse gases and contribute to multiple UN sustainable development goals. This article is part of a discussion meeting issue 'Air quality, past present and future'.
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The coal-dominated electricity system poses major challenges for India to tackle air pollution and climate change. Although the government has issued a series of clean air policies and low-carbon energy targets, a key barrier remains enforcement. Here, we quantify the importance of policy implementation in India's electricity sector using an integrated assessment method based on emissions scenarios, air quality simulations, and health impact assessments. We find that limited enforcement of air pollution control policies leads to worse future air quality and health damages (e.g., 14â¯200 to 59â¯000 more PM2.5-related deaths in 2040) than when energy policies are not fully enforced (5900 to 8700 more PM2.5-related deaths in 2040), since coal power plants with end-of-pipe controls already emit little air pollution. However, substantially more carbon dioxide will be emitted if low-carbon and clean coal policies are not successfully implemented (e.g., 400 to 800 million tons more CO2 in 2040). Thus, our results underscore the important role of effectively implementing existing air pollution and energy policy to simultaneously achieve air pollution, health, and carbon mitigation goals in India.