Author: Guillaume Chassagnon; Maria Vakalopoulou; Enzo Battistella; Stergios Christodoulidis; Trieu-Nghi Hoang-Thi; Severine Dangeard; Eric Deutsch; Fabrice Andre; Enora Guillo; Nara Halm; Stefany El Hajj; Florian Bompard; Sophie Neveu; Chahinez Hani; Ines Saab; Alienor Campredon; Hasmik Koulakian; Souhail Bennani; Gael Freche; Aurelien Lombard; Laure Fournier; Hippolyte Monnier; Teodor Grand; Jules Gregory; Antoine Khalil; Elyas Mahdjoub; Pierre-Yves Brillet; Stephane Tran Ba; Valerie Bousson; Marie-Pierre Revel; Nikos Paragios
Title: AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia Document date: 2020_4_22
ID: nxm1jr0x_15
Snippet: Chest CT exams were acquired on 4 different CT models from 3 manufacturers (Aquilion Prime from Canon Medical Systems, Otawara, Japan; Revolution HD from GE Healthcare, Milwaukee, WI; Somatom Edge and Somatom AS+ from Siemens Healthineer, Erlangen, Germany). The different acquisition and reconstruction parameters are summarized in Table 1 . CT exams were mostly acquired at 120 (n=103/180; 57%) and 100 kVp (n=76/180; 42%). Images were reconstructe.....
Document: Chest CT exams were acquired on 4 different CT models from 3 manufacturers (Aquilion Prime from Canon Medical Systems, Otawara, Japan; Revolution HD from GE Healthcare, Milwaukee, WI; Somatom Edge and Somatom AS+ from Siemens Healthineer, Erlangen, Germany). The different acquisition and reconstruction parameters are summarized in Table 1 . CT exams were mostly acquired at 120 (n=103/180; 57%) and 100 kVp (n=76/180; 42%). Images were reconstructed using iterative reconstruction with a 512 × 512 matrix and a slice thickness of 0.625 or 1 mm depending on the CT equipment. Only the lung images reconstructed with high frequency kernels were used for analysis. For each CT examination, dose length product (DLP) and volume Computed Tomography Dose Index (CTDIvol) were collected.
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