Jointly Optimized Deep Neural Networks to Synthesize Monoenergetic Images from Single-Energy CT Angiography for Improving Classification of Pulmonary Embolism

Detector-based spectral CT offers the possibility of obtaining spectral information from which discrete acquisitions at different energy levels can be derived, yielding so-called virtual monoenergetic images (VMI). In this study, we aimed to develop a jointly optimized deep-learning framework based on dual-energy CT pulmonary angiography (DE-CTPA) data to generate synthetic monoenergetic images (SMI) for improving automatic pulmonary embolism (PE) detection in single-energy CTPA scans. For this purpose, we used two datasets: our institutional DE-CTPA dataset D1, comprising polyenergetic arterial series and the corresponding VMI at low-energy levels (40 keV) with 7892 image pairs, and a 10% subset of the 2020 RSNA Pulmonary Embolism CT Dataset D2, which consisted of 161,253 polyenergetic images with dichotomous slice-wise annotations (PE/no PE). We trained a fully convolutional encoder-decoder on D1 to generate SMI from single-energy CTPA scans of D2, which were then fed into a ResNet50 network for training of the downstream PE classification task. The quantitative results on the reconstruction ability of our framework revealed high-quality visual SMI predictions with reconstruction results of 0.984 ± 0.002 (structural similarity) and 41.706 ± 0.547 dB (peak signal-to-noise ratio). PE classification resulted in an AUC of 0.84 for our model, which achieved improved performance compared to other naïve approaches with AUCs up to 0.81. Our study stresses the role of using joint optimization strategies for deep-learning algorithms to improve automatic PE detection. The proposed pipeline may prove to be beneficial for

Prediction of low-keV monochromatic images from polyenergetic CT scans for improved automatic detection of pulmonary embolism

Detector-based spectral computed tomography is a recent dual-energy CT (DECT) technology that offers the possibility of obtaining spectral information. From this spectral data, different types of images can be derived, amongst others virtual monoenergetic (monoE) images. MonoE images potentially exhibit decreased artifacts, improve contrast, and overall contain lower noise values, making them ideal candidates for better delineation and thus improved diagnostic accuracy of vascular abnormalities. In this paper, we are training convolutional neural networks~(CNN) that can emulate the generation of monoE images from conventional single energy CT acquisitions. For this task, we investigate several commonly used image-translation methods. We demonstrate that these methods while creating visually similar outputs, lead to a poorer performance when used for automatic classification of pulmonary embolism (PE). We expand on these methods through the use of a multi-task optimization approach, under which the networks achieve improved classification as well as generation results, as reflected by PSNR and SSIM scores. Further, evaluating our proposed framework on a subset of the RSNA-PE challenge data set shows that we are able to improve the Area under the Receiver Operating Characteristic curve (AuROC) in comparison to a na\"ive classification approach from 0.8142 to 0.8420.Comment: 4 pages, ISBI 202

Anaphylactoid reactions during hemodialysis in sheep are ACE inhibitor dose-dependent and mediated by bradykinin

Anaphylactoid reactions during hemodialysis in sheep are ACE inhibitor dose-dependent and mediated by bradykinin. Anaphylactoid reactions (AR) have been attributed to the generation of bradykinin (BK) when AN69 membranes are used together with angiotensin converting enzyme (ACE) inhibitors during hemodialysis. However, conclusive evidence for the involvement of the BK as the mediator of these AR is still lacking. This study examined the degree of contact activation in an animal model caused by three PAN membranes—AN69, PAN DX, and SPAN—and the effects of different doses of the ACE inhibitor enalapril (ENA) and the BK B2-receptor antagonist icatibant on AR during hemodialysis. Six sheep were dialyzed for one hour with or without ENA pre-treatment using the different membranes in random order. Severe AR were observed only during hemodialysis with AN69 dialyzers together with ENA pre-treatment; the severity of AR increased with the ENA dose. Mild hypotension was noted during hemodialysis with AN69 without ACE inhibition and with PAN DX and 20 mg ENA. Compared to pre-dialysis values, maximum generation of BK after blood passage through the dialyzer was found at five minutes: 73-fold (AN69 without ENA), 161-fold (AN69 with 10 mg ENA), 97-fold (AN69 with 20 mg ENA), 108-fold (AN69 with 30 mg ENA), 154-fold (AN69 with 30 mg ENA and 0.1 mg/kg icatibant), 18-fold (PAN DX without ENA), and 42-fold (PAN DX with 20 mg ENA). Elevated BK levels in arterial blood were detected during hemodialysis with AN69 membranes even without ACE inhibition (2.5-fold); pre-treatment with 20 mg ENA further increased arterial BK concentrations (4-fold). Furthermore, a marked decline of prekallikrein and high molecular weight kininogen concentrations was noted for both AN69 and PAN DX membranes. Anaphylactoid reactions during hemodialysis were completely prevented by icatibant even after pre-treatment with ENA and in the presence of high BK concentrations. Concentrations of prekallikrein, high molecular weight kininogen, and BK remained unchanged and no AR were observed during hemodialysis with SPAN and pre-treatment with 20 mg ENA. Our findings confirm that AR during hemodialysis with the negatively charged AN69 membrane are mediated by BK, since they can be prevented by the BK B2-receptor antagonist icatibant

Anomalous enhancement of the coupling to the magnetic resonance mode in underdoped Pb-Bi2212

High-resolution angle-resolved photoemission with variable excitation energies is used to disentangle bilayer splitting effects and intrinsic (self-energy) effects in the electronic spectral function near the (π,0) point of differently doped (Pb,Bi)2Sr2CaCu2O8+δ. In contrast to overdoped samples, where intrinsic effects at the (pi,0) point are virtually absent, we find in underdoped samples intrinsic effects in the superconducting-state (π,0) spectra of the antibonding band. This intrinsic effect is present only below the critical temperature and weakens considerably with doping. Our results give strong support for models which involve a strong coupling of electronic excitations with the resonance mode seen in inelastic neutron scattering experiments

Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding

In clinical radiology reports, doctors capture important information about the patient's health status. They convey their observations from raw medical imaging data about the inner structures of a patient. As such, formulating reports requires medical experts to possess wide-ranging knowledge about anatomical regions with their normal, healthy appearance as well as the ability to recognize abnormalities. This explicit grasp on both the patient's anatomy and their appearance is missing in current medical image-processing systems as annotations are especially difficult to gather. This renders the models to be narrow experts e.g. for identifying specific diseases. In this work, we recover this missing link by adding human anatomy into the mix and enable the association of content in medical reports to their occurrence in associated imagery (medical phrase grounding). To exploit anatomical structures in this scenario, we present a sophisticated automatic pipeline to gather and integrate human bodily structures from computed tomography datasets, which we incorporate in our PAXRay: A Projected dataset for the segmentation of Anatomical structures in X-Ray data. Our evaluation shows that methods that take advantage of anatomical information benefit heavily in visually grounding radiologists' findings, as our anatomical segmentations allow for up to absolute 50% better grounding results on the OpenI dataset as compared to commonly used region proposals. The PAXRay dataset is available at https://constantinseibold.github.io/paxray/.Comment: 33rd British Machine Vision Conference (BMVC 2022

Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting

Objective: To develop an automatic COVID-19 Reporting and Data System (CO-RADS)–based classification in a multi-demographic setting. Methods: This multi-institutional review boards–approved retrospective study included 2720 chest CT scans (mean age, 58 years [range 18–100 years]) from Italian and Russian patients. Three board-certified radiologists from three countries assessed randomly selected subcohorts from each population and provided CO-RADS–based annotations. CT radiomic features were extracted from the selected subcohorts after preprocessing steps like lung lobe segmentation and automatic noise reduction. We compared three machine learning models, logistic regression (LR), multilayer perceptron (MLP), and random forest (RF) for the automated CO-RADS classification. Model evaluation was carried out in two scenarios, first, training on a mixed multi-demographic subcohort and testing on an independent hold-out dataset. In the second scenario, training was done on a single demography and externally validated on the other demography. Results: The overall inter-observer agreement for the CO-RADS scoring between the radiologists was substantial (k = 0.80). Irrespective of the type of validation test scenario, suspected COVID-19 CT scans were identified with an accuracy of 84%. SHapley Additive exPlanations (SHAP) interpretation showed that the “wavelet_(LH)_GLCM_Imc1” feature had a positive impact on COVID prediction both with and without noise reduction. The application of noise reduction improved the overall performance between the classifiers for all types. Conclusion: Using an automated model based on the COVID-19 Reporting and Data System (CO-RADS), we achieved clinically acceptable performance in a multi-demographic setting. This approach can serve as a standardized tool for automated COVID-19 assessment. Keypoints: • Automatic CO-RADS scoring of large-scale multi-demographic chest CTs with mean AUC of 0.93 ± 0.04. • Validation procedure resembles TRIPOD 2b and 3 categories, enhancing the quality of experimental design to test the cross-dataset domain shift between institutions aiding clinical integration. • Identification of COVID-19 pneumonia in the presence of community-acquired pneumonia and other comorbidities with an AUC of 0.92

Regional social legitimacy of entrepreneurship: Implications for entrepreneurial intention and start-up behaviour

Regional social legitimacy of entrepreneurship: implications for entrepreneurial intention and start-up behaviour, Regional Studies. A new understanding of the role of regional culture in the emergence of business start-up behaviour is developed. The focal construct is regional social legitimacy: the perception of the desirability and appropriateness of entrepreneurship in a region. The econometric analysis utilizes a combination of bespoke longitudinal survey data from 65 regions in Austria and Finland, and variables capturing regional socio-economic characteristics derived from official statistics. The study demonstrates that, and explains how, regional social legitimacy influences the relationships between individual entrepreneurial beliefs, intentions and start-up behaviour and how these interaction effects are conditioned by the socio-economic characteristics of the region

Lattice symmetry breaking in cuprate superconductors: Stripes, nematics, and superconductivity

This article will give an overview on both theoretical and experimental developments concerning states with lattice symmetry breaking in the cuprate high-temperature superconductors. Recent experiments have provided evidence for states with broken rotation as well as translation symmetry, and will be discussed in terms of nematic and stripe physics. Of particular importance here are results obtained using the techniques of neutron and x-ray scattering and scanning tunneling spectroscopy. Ideas on the origin of lattice-symmetry-broken states will be reviewed, and effective models accounting for various experimentally observed phenomena will be summarized. These include both weak-coupling and strong-coupling approaches, with a discussion on their distinctions and connections. The collected experimental data indicate that the tendency toward uni-directional stripe-like ordering is common to underdoped cuprates, but becomes weaker with increasing number of adjacent CuO_2 layers.Comment: Review article prepared for Adv. Phys., 66 pg, 22 figs. Comments welcome, (v2) extensions and clarifications, added references, final version to be publishe