Target and collection optimization for muon colliders

To achieve adequate luminosity in a muon collider it is necessary to produce and collect large numbers of muons. The basic method used in this paper follows closely a proposed scheme which starts with a proton beam impinging on a thick target ({approximately} one interaction length) followed by a long solenoid which collects muons resulting mainly from pion decay. Production and collection of pions and their decay muons must be optimized while keeping in mind limitations of target integrity and of the technology of magnets and cavities. Results of extensive simulations for 8 GeV protons on various targets and with various collection schemes are reported. Besides muon yields results include-energy deposition in target and solenoid to address cooling requirements for these systems. Target composition, diameter, and length are varied in this study as well as the configuration and field strengths of the solenoid channel. A curved solenoid field is introduced to separate positive and negative pions within a few meters of the target. This permits each to be placed in separate RF buckets for acceleration which effectively doubles the number of muons per bunch available for collisions and increases the luminosity fourfold

Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions

Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates. This method was trained and evaluated on a set of 1744 CT scans from the National Lung Screening Trial. To determine whether any reconstruction or only images reconstructed with soft tissue filters can be used for calcification detection, we evaluated the method on soft and medium/sharp filter reconstructions separately. On soft filter reconstructions, the method achieved F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively. On sharp filter reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively. Linearly weighted kappa coefficients for risk category assignment based on per subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter reconstructions, respectively. These results demonstrate that the presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening

Health Systems in Transition: template for authors 2019

HiT health system reviews (HiTs) are based on a template that, revised periodically, provides detailed guidelines and specific questions, definitions, suggestions for data sources, and examples needed to compile HiTs. While the template offers a comprehensive set of questions, it is intended to be used in a flexible way to allow authors and editors to adapt it to their particular national context. The current version of the template is the result of a consultation process with HiT editors, previous HiT authors, Observatory National Lead Institutions (NLIs), WHO Regional Office for Europe, the European Commission, and other Observatory partners. Several sections have been reorganized to improve accessibility and clarity for readers, while the design has been greatly improved to help authors and editors in the writing process. The result is a template that is more user-friendly for authors as it now includes clear sign posting for "essential" versus "discretionary" sections as well as indicators for tables and figures. Other new features include: summary paragraphs for all chapters; a revised and extended chapter on performance assessment; and increased focus on public health and intersectorality

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201