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Arwed Elias Michael

As seen in: Nature, Academic Radiology
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Articles

Photon Counting CT of the Head: Retrospective Analysis of Image Quality and Dose in Comparison to Energy Integrating Detector CT

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By Denise Schoenbeck, Jan Robert Kroeger, Christoph Moenninghoff, Julius Henning Niehoff, Jan Borggrefe, Haidara Almansour, Ahmed E. Othman, Marc Alexander Brockmann, Arwed Elias Michael
| Academic Radiology Verified
Original InvestigationArticles in PressOpen access Affiliations & Notes aDepartment of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Bochum, Germany (D.S., J.R.K., C.M., J.H.N., J.B.) bDepartment of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Mainz, Germany (H.A., A.O., M.A.B., A.E.M.) Article Info Publication History: Received March 2, 2026; Revised April 15, 2026; Accepted April 21, 2026;...
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Photon Counting Computed Tomography in Rectal Cancer: Associations Between Iodine Concentration, Histopathology and Treatment Response: A Pilot Study.

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By Jan Robert Kröger, Julius Henning Niehoff, Arwed Elias Michael, Andreas Wienke, Alexey Surov, Jan Borggrefe, Raihanatou Diallo-Danebrock, Amin Radi, Berthold Gerdes, Saleem Elhabash
| Academic Radiology Verified
Common computed tomography (CT) investigation plays a limited role in characterizing and assessing the response of rectal cancer (RC) to neoadjuvant radiochemotherapy (NARC). Photon counting computed tomography (PCCT) improves the imaging quality and can provide multiparametric spectral image information including iodine concentration (IC). Our purpose was to analyze associations between IC and histopathology in RC and to evaluate the role of IC in response prediction to NARC.
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Evaluation of the clinical performance of an AI-based application for the automated analysis of chest X-rays - Scientific Reports

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By Arwed Elias Michael
| Nature Verified
Abstract The AI-Rad Companion Chest X-ray (AI-Rad, Siemens Healthineers) is an artificial-intelligence based application for the analysis of chest X-rays. The purpose of the present study is to evaluate the performance of the AI-Rad. In total, 499 radiographs were retrospectively included. Radiographs were independently evaluated by radiologists and the AI-Rad.
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