1
|
Steuwe A, Kamp B, Afat S, Akinina A, Aludin S, Bas EG, Berger J, Bohrer E, Brose A, Büttner SM, Ehrengut C, Gerwing M, Grosu S, Gussew A, Güttler F, Heinrich A, Jiraskova P, Kloth C, Kottlors J, Kuennemann MD, Liska C, Lubina N, Manzke M, Meinel FG, Meyer HJ, Mittermeier A, Persigehl T, Schmill LP, Steinhardt M, The Racoon Study Group, Antoch G, Valentin B. Standardization of a CT Protocol for Imaging Patients with Suspected COVID-19-A RACOON Project. Bioengineering (Basel) 2024; 11:207. [PMID: 38534481 DOI: 10.3390/bioengineering11030207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/09/2024] [Accepted: 02/15/2024] [Indexed: 03/28/2024] Open
Abstract
CT protocols that diagnose COVID-19 vary in regard to the associated radiation exposure and the desired image quality (IQ). This study aims to evaluate CT protocols of hospitals participating in the RACOON (Radiological Cooperative Network) project, consolidating CT protocols to provide recommendations and strategies for future pandemics. In this retrospective study, CT acquisitions of COVID-19 patients scanned between March 2020 and October 2020 (RACOON phase 1) were included, and all non-contrast protocols were evaluated. For this purpose, CT protocol parameters, IQ ratings, radiation exposure (CTDIvol), and central patient diameters were sampled. Eventually, the data from 14 sites and 534 CT acquisitions were analyzed. IQ was rated good for 81% of the evaluated examinations. Motion, beam-hardening artefacts, or image noise were reasons for a suboptimal IQ. The tube potential ranged between 80 and 140 kVp, with the majority between 100 and 120 kVp. CTDIvol was 3.7 ± 3.4 mGy. Most healthcare facilities included did not have a specific non-contrast CT protocol. Furthermore, CT protocols for chest imaging varied in their settings and radiation exposure. In future, it will be necessary to make recommendations regarding the required IQ and protocol parameters for the majority of CT scanners to enable comparable IQ as well as radiation exposure for different sites but identical diagnostic questions.
Collapse
Affiliation(s)
- Andrea Steuwe
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Benedikt Kamp
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Alena Akinina
- Clinic and Outpatient Clinic for Radiology, University Hospital Halle (Saale), 06120 Halle, Germany
| | - Schekeb Aludin
- Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein Campus Kiel, 24105 Kiel, Germany
| | - Elif Gülsah Bas
- Department of Diagnostic and Interventional Radiology, University Hospital of Marburg, 35043 Marburg, Germany
| | - Josephine Berger
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Evelyn Bohrer
- Department of Diagnostic and Interventional Radiology, University Hospital Giessen, Justus Liebig University, Klinikstr. 33, 35392 Giessen, Germany
| | - Alexander Brose
- Department of Diagnostic and Interventional Radiology, University Hospital Giessen, Justus Liebig University, Klinikstr. 33, 35392 Giessen, Germany
| | - Susanne Martina Büttner
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Constantin Ehrengut
- Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Liebigstraße 20, 04103 Leipzig, Germany
| | - Mirjam Gerwing
- Clinic of Radiology, University of Münster, 48149 Münster, Germany
| | - Sergio Grosu
- Department of Radiology, LMU University Hospital, LMU Munich, 81377 Munich, Germany
| | - Alexander Gussew
- Clinic and Outpatient Clinic for Radiology, University Hospital Halle (Saale), 06120 Halle, Germany
| | - Felix Güttler
- Department of Radiology, Jena University Hospital, Friedrich Schiller University, 07747 Jena, Germany
| | - Andreas Heinrich
- Department of Radiology, Jena University Hospital, Friedrich Schiller University, 07747 Jena, Germany
| | - Petra Jiraskova
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, 81675 Munich, Germany
| | - Christopher Kloth
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Jonathan Kottlors
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | | | - Christian Liska
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
| | - Nora Lubina
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
| | - Mathias Manzke
- Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Schillingallee 36, 18057 Rostock, Germany
| | - Felix G Meinel
- Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Schillingallee 36, 18057 Rostock, Germany
| | - Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Liebigstraße 20, 04103 Leipzig, Germany
| | - Andreas Mittermeier
- Department of Radiology, LMU University Hospital, LMU Munich, 81377 Munich, Germany
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Lars-Patrick Schmill
- Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein Campus Kiel, 24105 Kiel, Germany
| | - Manuel Steinhardt
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, 81675 Munich, Germany
| | | | - Gerald Antoch
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Birte Valentin
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| |
Collapse
|
2
|
Cavaliere C, Baldi D, Brancato V, Aiello M, Salvatore M. A customized anthropomorphic 3D-printed phantom to reproducibility assessment in computed tomography: an oncological case study. Front Oncol 2023; 13:1123796. [PMID: 37700836 PMCID: PMC10493384 DOI: 10.3389/fonc.2023.1123796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 08/07/2023] [Indexed: 09/14/2023] Open
Abstract
Introduction Studies on computed tomography (CT) reproducibility at different acquisition parameters have to take into account radiation dose administered and related ethical issues. 3D-printed phantoms provide the possibility to investigate these features deeply and to foster CT research, also taking advantage by outperforming new generation scanners. The aim of this study is to propose a new anthropomorphic 3D-printed phantom for chest lesions, tailored on a real patient CT scan, to investigate the variability of volume and Hounsfield Unit (HU) measurements at different CT acquisition parameters. Methods The chest CT of a 75-year-old patient with a paramediastinal lung lesion was segmented based on an eight-compartment approach related to HU ranges (air lung, lung interstitium, fat, muscle, vascular, skin, bone, and lesion). From each mask produced, the 3D.stl model was exported and linked to a different printing infill value, based on a preliminary test and HU ratios derived from the patient scan. Fused deposition modeling (FDM) technology printing was chosen with filament materials in polylactic acid (PLA). Phantom was acquired at 50 mAs and three different tube voltages of 80, 100, and 120 kVp on two different scanners, namely, Siemens Somatom Force (Siemens Healthineers, Erlangen, Germany; same setting of real patient for 80 kVp acquisition) and GE 750 HD CT (GE Healthcare, Chicago, IL). The same segmentation workflow was then applied on each phantom acquisition after coregistration pipeline, and Dice Similarity Coefficient (DSC) and HU averages were extracted and compared for each compartment. Results DSC comparison among real patient versus phantom scans at different kVp, and on both CT scanners, demonstrated a good overlap of different compartments and lesion vascularization with a higher similarity for lung and lesion masks for each setting (about 0.9 and 0.8, respectively). Although mean HU was not comparable with real data, due to the PLA material, the proportion of intensity values for each compartment remains respected. Discussion The proposed approach demonstrated the reliability of 3D-printed technology for personalized approaches in CT research, opening to the application of the same workflow to other oncological fields.
Collapse
|