Publications by authors named "F Pfeiffer"

Background: Dark-field radiography has been proven to be a promising tool for the assessment of various lung diseases.

Purpose: To evaluate the potential of dose reduction in dark-field chest radiography for the detection of the Coronavirus SARS-CoV-2 (COVID-19) pneumonia.

Materials And Methods: Patients aged at least 18 years with a medically indicated chest computed tomography scan (CT scan) were screened for participation in a prospective study between October 2018 and December 2020.

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We aim to optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO) on computed tomography (CT) images. 7194 contrast-enhanced CT images (3597 with COPD; 3597 healthy controls) from 78 subjects were selected retrospectively (01.2018-12.

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Speckle-based X-ray imaging (SBI) is a phase-contrast method developed at and for highly coherent X-ray sources, such as synchrotrons, to increase the contrast of weakly absorbing objects. Consequently, it complements the conventional attenuation-based X-ray imaging. Meanwhile, attempts to establish SBI at less coherent laboratory sources have been performed, ranging from liquid metal-jet X-ray sources to microfocus X-ray tubes.

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Background: Dark-field radiography imaging exploits the wave character of x-rays to measure small-angle scattering on material interfaces, providing structural information with low radiation exposure. We explored the potential of dark-field imaging of bone microstructure to improve the assessment of bone strength in osteoporosis.

Methods: We prospectively examined 14 osteoporotic/osteopenic and 21 non-osteoporotic/osteopenic human cadaveric vertebrae (L2-L4) with a clinical dark-field radiography system, micro-computed tomography (CT), and spectral CT.

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Metal oxide sensor-based electronic nose (E-Nose) technology provides an easy to use method for breath analysis by detection of volatile organic compound (VOC)-induced changes of electrical conductivity. Resulting signal patterns are then analyzed by machine learning (ML) algorithms. This study aimed to establish breath analysis by E-Nose technology as a diagnostic tool for severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pneumonia within a multi-analyst experiment.

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