Feasibility of Dedicated Breast Positron Emission Tomography Image Denoising Using a Residual Neural Network

Objective(s): This study aimed to create a deep learning (DL)-based denoising model using a residual neural network (Res-Net) trained to reduce noise in ring-type dedicated breast positron emission tomography (dbPET) images acquired in about half the emission time, and to evaluate the feasibility and the effectiveness of the model in terms of its noise reduction performance and preservation of quantitative values compared to conventional post-image filtering techniques.Methods: Low-count (LC) and full-count (FC) PET images with acquisition durations of 3 and 7 minutes, respectively, were reconstructed. A Res-Net was trained to create a noise reduction model using fifteen patients’ data. The inputs to the network were LC images and its outputs were denoised PET (LC + DL) images, which should resemble FC images. To evaluate the LC + DL images, Gaussian and non-local mean (NLM) filters were applied to the LC images (LC + Gaussian and LC + NLM, respectively). To create reference images, a Gaussian filter was applied to the FC images (FC + Gaussian). The usefulness of our denoising model was objectively and visually evaluated using test data set of thirteen patients. The coefficient of variation (CV) of background fibroglandular tissue or fat tissue were measured to evaluate the performance of the noise reduction. The SUVmax and SUVpeak of lesions were also measured. The agreement of the SUV measurements was evaluated by Bland–Altman plots.Results: The CV of background fibroglandular tissue in the LC + DL images was significantly lower (9.10 2.76) than the CVs in the LC (13.60  3.66) and LC + Gaussian images (11.51  3.56). No significant difference was observed in both SUVmax and SUVpeak of lesions between LC + DL and reference images. For the visual assessment, the smoothness rating for the LC + DL images was significantly better than that for the other images except for the reference images.Conclusion: Our model reduced the noise in dbPET images acquired in about half the emission time while preserving quantitative values of lesions. This study demonstrates that machine learning is feasible and potentially performs better than conventional post-image filtering in dbPET denoising

Reduced mortality during the COVID-19 outbreak in Japan, 2020: a two-stage interrupted time-series design.

BACKGROUND: Coronavirus disease 2019 (COVID-19) continues to be a major global health burden. This study aims to estimate the all-cause excess mortality occurring in the COVID-19 outbreak in Japan, 2020, by sex and age group. METHODS: Daily time series of mortality for the period January 2015-December 2020 in all 47 prefectures of Japan were obtained from the Ministry of Health, Labour and Welfare, Japan. A two-stage interrupted time-series design was used to calculate excess mortality. In the first stage, we estimated excess mortality by prefecture using quasi-Poisson regression models in combination with distributed lag non-linear models, adjusting for seasonal and long-term variations, weather conditions and influenza activity. In the second stage, we used a random-effects multivariate meta-analysis to synthesize prefecture-specific estimates at the nationwide level. RESULTS: In 2020, we estimated an all-cause excess mortality of -20 982 deaths [95% empirical confidence intervals (eCI): -38 367 to -5472] in Japan, which corresponded to a percentage excess of -1.7% (95% eCI: -3.1 to -0.5) relative to the expected value. Reduced deaths were observed for both sexes and in all age groups except those aged <60 and 70-79 years. CONCLUSIONS: All-cause mortality during the COVID-19 outbreak in Japan in 2020 was decreased compared with a historical baseline. Further evaluation of cause-specific excess mortality is warranted

Global-scale river flood vulnerability in the last 50 years

The impacts of flooding are expected to rise due to population increases, economic growth and climate change. Hence, understanding the physical and spatiotemporal characteristics of risk drivers (hazard, exposure and vulnerability) is required to develop effective flood mitigation measures. Here, the long-term trend in flood vulnerability was analysed globally, calculated from the ratio of the reported flood loss or damage to the modelled flood exposure using a global river and inundation model. A previous study showed decreasing global flood vulnerability over a shorter period using different disaster data. The long-term analysis demonstrated for the first time that flood vulnerability to economic losses in upper-middle, lower-middle and low-income countries shows an inverted U-shape, as a result of the balance between economic growth and various historical socioeconomic efforts to reduce damage, leading to non-significant upward or downward trends. We also show that the flood-exposed population is affected by historical changes in population distribution, with changes in flood vulnerability of up to 48.9%. Both increasing and decreasing trends in flood vulnerability were observed in different countries, implying that population growth scenarios considering spatial distribution changes could affect flood risk projections

Quantifying the effect of autonomous adaptation to global river flood projections: application to future flood risk assessments

This study represents the first attempt to quantify the effects of autonomous adaptation on the projection of global flood hazards and to assess future flood risk by including this effect. A vulnerability scenario, which varies according to the autonomous adaptation effect for conventional disaster mitigation efforts, was developed based on historical vulnerability values derived from flood damage records and a river inundation simulation. Coupled with general circulation model outputs and future socioeconomic scenarios, potential future flood fatalities and economic loss were estimated. By including the effect of autonomous adaptation, our multimodel ensemble estimates projected a 2.0% decrease in potential flood fatalities and an 821% increase in potential economic losses by 2100 under the highest emission scenario together with a large population increase. Vulnerability changes reduced potential flood consequences by 64%–72% in terms of potential fatalities and 28%–42% in terms of potential economic losses by 2100. Although socioeconomic changes made the greatest contribution to the potential increased consequences of future floods, about a half of the increase of potential economic losses was mitigated by autonomous adaptation. There is a clear and positive relationship between the global temperature increase from the pre-industrial level and the estimated mean potential flood economic loss, while there is a negative relationship with potential fatalities due to the autonomous adaptation effect. A bootstrapping analysis suggests a significant increase in potential flood fatalities (+5.7%) without any adaptation if the temperature increases by 1.5 °C–2.0 °C, whereas the increase in potential economic loss (+0.9%) was not significant. Our method enables the effects of autonomous adaptation and additional adaptation efforts on climate-induced hazards to be distinguished, which would be essential for the accurate estimation of the cost of adaptation to climate change

Global-Scale Assessment of Economic Losses Caused by Flood-Related Business Interruption

Estimating river flood risk helps us to develop strategies for reducing the economic losses and making a resilient society. Flood-related economic losses can be categorized as direct asset damage, opportunity losses because of business interruption (BI loss), and high-order propagation effects on global trade networks. Biases in meteorological data obtained from climate models hinder the estimation of BI loss because of inaccurate input data including inundation extent and period. In this study, we estimated BI loss and asset damage using a global river and inundation model driven by a recently developed bias-corrected meteorological forcing scheme. The results from the bias-corrected forcing scheme showed an estimated global BI loss and asset damage of USD 26.9 and 130.9 billion (2005 purchase power party, PPP) (1960–2013 average), respectively. Although some regional differences were detected, the estimated BI loss was similar in magnitude to reported historical flood losses. BI loss tended to be greater in river basins with mild slopes such as the Amazon, which has a long inundation period. Future flood risk projection using the same framework under Representative Concentration Pathway 8.5 (RCP8.5) and Shared Socioeconomic Pathway 3 (SSP3) scenarios showed increases in BI loss and asset damage per GDP by 0.32% and 1.78% (2061–2090 average) compared with a past period (1971–2000 average), respectively

Flood impacts on global crop production: advances and limitations

Considering the anticipated rise in wet extremes due to climate change, effective management of flood risks in global agriculture necessitates an initial assessment of the impact of floods on crop production. Such estimation can inform the development of strategies to enhance the resilience of the global agricultural system against floods, particularly in the face of growing demand for food. To this end, a worldwide calculation of inundation areas’ return periods was conducted using a global river and inundation model output. This information was then linked to a global historical yield map, allowing for the identification of flood-induced crop yield changes. The findings revealed that for return periods over ten years, global average yield losses were estimated to be 4% for soy, 3% for rice, 2% for wheat, and 1% for maize. These losses amounted to a total production loss of 5.5 billion United States dollars during the 1982–2016 period. This first global estimation of flood impacts on crop production contributes to the advancement of flood risk management in agriculture, although the limitations identified in this study need to be addressed in future research