Jacob C Mandell1, Jeremy R Wortman2, Tatiana C Rocha2, Les R Folio3, Katherine P Andriole4, Bharti Khurana2. 1. Department of Radiology, Division of Musculoskeletal Imaging and Intervention, Brigham and Women's Hospital, 75 Francis St. Boston, Massachusetts 02115; Harvard Medical School, Boston, Massachusetts. Electronic address: jmandell@bwh.harvard.edu. 2. Harvard Medical School, Boston, Massachusetts; Division of Emergency Radiology, Brigham and Women's Hospital, Boston, Massachusetts. 3. Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland. 4. Harvard Medical School, Boston, Massachusetts; MGH & BWH Center for Clinical Data Science, Boston, Massachusetts.
Abstract
RATIONALE AND OBJECTIVES: This study aims to demonstrate the feasibility of processing computed tomography (CT) images with a custom window blending algorithm that combines soft-tissue, bone, and lung window settings into a single image; to compare the time for interpretation of chest CT for thoracic trauma with window blending and conventional window settings; and to assess diagnostic performance of both techniques. MATERIALS AND METHODS: Adobe Photoshop was scripted to process axial DICOM images from retrospective contrast-enhanced chest CTs performed for trauma with a window-blending algorithm. Two emergency radiologists independently interpreted the axial images from 103 chest CTs with both blended and conventional windows. Interpretation time and diagnostic performance were compared with Wilcoxon signed-rank test and McNemar test, respectively. Agreement with Nexus CT Chest injury severity was assessed with the weighted kappa statistic. RESULTS: A total of 13,295 images were processed without error. Interpretation was faster with window blending, resulting in a 20.3% time saving (P < .001), with no difference in diagnostic performance, within the power of the study to detect a difference in sensitivity of 5% as determined by post hoc power analysis. The sensitivity of the window-blended cases was 82.7%, compared to 81.6% for conventional windows. The specificity of the window-blended cases was 93.1%, compared to 90.5% for conventional windows. All injuries of major clinical significance (per Nexus CT Chest criteria) were correctly identified in all reading sessions, and all negative cases were correctly classified. All readers demonstrated near-perfect agreement with injury severity classification with both window settings. CONCLUSIONS: In this pilot study utilizing retrospective data, window blending allows faster preliminary interpretation of axial chest CT performed for trauma, with no significant difference in diagnostic performance compared to conventional window settings. Future studies would be required to assess the utility of window blending in clinical practice.
RATIONALE AND OBJECTIVES: This study aims to demonstrate the feasibility of processing computed tomography (CT) images with a custom window blending algorithm that combines soft-tissue, bone, and lung window settings into a single image; to compare the time for interpretation of chest CT for thoracic trauma with window blending and conventional window settings; and to assess diagnostic performance of both techniques. MATERIALS AND METHODS: Adobe Photoshop was scripted to process axial DICOM images from retrospective contrast-enhanced chest CTs performed for trauma with a window-blending algorithm. Two emergency radiologists independently interpreted the axial images from 103 chest CTs with both blended and conventional windows. Interpretation time and diagnostic performance were compared with Wilcoxon signed-rank test and McNemar test, respectively. Agreement with Nexus CT Chest injury severity was assessed with the weighted kappa statistic. RESULTS: A total of 13,295 images were processed without error. Interpretation was faster with window blending, resulting in a 20.3% time saving (P < .001), with no difference in diagnostic performance, within the power of the study to detect a difference in sensitivity of 5% as determined by post hoc power analysis. The sensitivity of the window-blended cases was 82.7%, compared to 81.6% for conventional windows. The specificity of the window-blended cases was 93.1%, compared to 90.5% for conventional windows. All injuries of major clinical significance (per Nexus CT Chest criteria) were correctly identified in all reading sessions, and all negative cases were correctly classified. All readers demonstrated near-perfect agreement with injury severity classification with both window settings. CONCLUSIONS: In this pilot study utilizing retrospective data, window blending allows faster preliminary interpretation of axial chest CT performed for trauma, with no significant difference in diagnostic performance compared to conventional window settings. Future studies would be required to assess the utility of window blending in clinical practice.
Authors: Jacob C Mandell; Tatiana C Rocha; Maria Alejandra Duran-Mendicuti; Nityanand P Miskin; Junzi Shi; Bharti Khurana Journal: Emerg Radiol Date: 2018-07-14
Authors: Annemiek Snoeckx; Jeroen Cant; Caro Franck; Elisa Luyckx; Ken Carpentier; Simon Nicolay; Astrid Van Hoyweghen; Maarten J Spinhoven; Pieter Vuylsteke; Paul M Parizel Journal: Cancer Imaging Date: 2019-11-29 Impact factor: 3.909