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Lee CW, Huang CC, Jang YC, Chen KC, Ho SY, Chou CT, Wu WP. Diagnostic Accuracy for Acute Rib Fractures: A Cross-sectional Study Utilizing Automatic Rib Unfolding and 3D Volume-Rendered Reformation. Acad Radiol 2024; 31:1538-1547. [PMID: 37845164 DOI: 10.1016/j.acra.2023.08.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 08/25/2023] [Accepted: 08/26/2023] [Indexed: 10/18/2023]
Abstract
RATIONALE AND OBJECTIVES The aim of this study was to compare the use of computed tomography (CT) with automatic rib unfolding and three-dimensional (3D) volume-rendered imaging in the detection and characterization of rib fractures and flail chest. MATERIALS AND METHODS A total of 130 patients with blunt chest trauma underwent whole-body CT, and five independent readers assessed the presence and characterization of rib fractures using traditional CT images, automatic rib unfolding, and 3D volume-rendered images in separate readout sessions at least 2 weeks apart. A gold standard was established by consensus among the readers based on the combined analysis of conventional and reformatted images. RESULTS Automatic rib unfolding significantly reduced mean reading time by 47.5%-74.9% (P < 0.0001) while maintaining a comparable diagnostic performance for rib fractures (positive predictive value [PPV] of 82.1%-93.5%, negative predictive value [NPV] of 96.8%-98.2%, and 69.4%-94.2% and 96.9%-99.1% for conventional axial images and 70.4%-85.1% and 95.2%-96.6% for 3D images) and better interobserver agreement (kappa of 0.74-0.87). For flail chest, automatic rib unfolding showed a PPV of 85.7%-100%, NPV of 90.4%-99.0%, and 80.0%-100% and 89.7%-100% for conventional axial images and 76.9%-100% and 89.0%-92.1% for 3D images. CONCLUSION Automatic rib unfolding demonstrated equivalent diagnostic performance to conventional images in detecting acute rib fractures and flail chest, with good interobserver agreement and time-saving benefits.
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Affiliation(s)
- Chih-Wei Lee
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan (C.-W.L., Y.-C.J., S.-Y.H., C.T.C., W.-P.W.)
| | - Cheng-Chieh Huang
- Department of Emergency and Critical Care Medicine, Changhua Christian Hospital, Changhua, Taiwan (C.-C.H., K.-C.C.); Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan (C.-C.H.)
| | - Yong-Ching Jang
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan (C.-W.L., Y.-C.J., S.-Y.H., C.T.C., W.-P.W.)
| | - Kuan-Chih Chen
- Department of Emergency and Critical Care Medicine, Changhua Christian Hospital, Changhua, Taiwan (C.-C.H., K.-C.C.)
| | - Shang-Yun Ho
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan (C.-W.L., Y.-C.J., S.-Y.H., C.T.C., W.-P.W.); Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan (S.-Y.H.)
| | - Chen-Te Chou
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan (C.-W.L., Y.-C.J., S.-Y.H., C.T.C., W.-P.W.); Kaohsiung Medical University, Kaohsiung, Taiwan (C.-T.C., W.-P.W)
| | - Wen-Pei Wu
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan (C.-W.L., Y.-C.J., S.-Y.H., C.T.C., W.-P.W.); Kaohsiung Medical University, Kaohsiung, Taiwan (C.-T.C., W.-P.W); Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan (W.-P.W.).
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Hagen F, Grimmer R, Ditt H, Walder L, Wrazidlo R, Karolin B, Hofmann J, Estler A, Horger M. Effects of different virtual monoenergetic CT image data on chest wall post-processing "unfolded ribs" and proposal of an algorithm improvement. Int J Comput Assist Radiol Surg 2023; 18:339-351. [PMID: 35962904 DOI: 10.1007/s11548-022-02721-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 07/12/2022] [Indexed: 02/03/2023]
Abstract
PURPOSE To find out if the use of different virtual monoenergetic data sets enabled by DECT technology might have a negative impact on post-processing applications, specifically in case of the "unfolded ribs" algorithm. Metal or beam hardening artifacts are suspected to generate image artifacts and thus reduce diagnostic accuracy. This paper tries to find out how the generation of "unfolded rib" CT image reformates is influenced by different virtual monoenergetic CT images and looks for possible improvement of the post-processing tool. MATERIAL AND METHODS Between March 2021 and April 2021, thin-slice dual-energy CT image data of the chest were used creating "unfolded rib" reformates. The same data sets were analyzed in three steps: first the gold standard with the original algorithm on mixed image data sets followed by the original algorithm on different keV levels (40-120 keV) and finally using a modified algorithm which in the first step used segmentation based on mixed image data sets, followed by segmentation based on different keV levels. Image quality (presence of artifacts), lesion and fracture detectability were assessed for all series. RESULTS Both, the original and the modified algorithm resulted in more artifact-free image data sets compared to the gold standard. The modified algorithm resulted in significantly more artifact-free image data sets at the keV-edges (40-120 keV) compared the original algorithm. Especially "black artifacts" and pseudo-lesions, potentially inducing false positive findings, could be reduced in all keV level with the modified algorithm. Detection of focal sclerotic, lytic or mixed (k = 0.990-1.000) lesions was very good for all keV levels. The Fleiss-kappa test for detection of fresh and old rib fractures was ≥ 0.997. CONCLUSION The use of different virtual monoenergetic keVs for the "unfolded rib" algorithm is generating different artifacts. Segmentation-based artifacts could be eliminated by the proposed new algorithm, showing the best results at 70-80 keV.
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Affiliation(s)
- Florian Hagen
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076, Tübingen, Germany.
| | | | | | - Lukas Walder
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076, Tübingen, Germany
| | - Robin Wrazidlo
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076, Tübingen, Germany
| | - Baumgartner Karolin
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076, Tübingen, Germany
| | - Johannes Hofmann
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076, Tübingen, Germany
| | - Arne Estler
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076, Tübingen, Germany
| | - Marius Horger
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076, Tübingen, Germany
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Nishii T, Okuyama S, Horinouchi H, Chikuda R, Kamei E, Higuchi S, Ohta Y, Fukuda T. A Real-World Clinical Implementation of Automated Processing Using Intelligent Work Aid for Rapid Reformation at the Orbitomeatal Line in Head Computed Tomography. Invest Radiol 2021; 56:599-604. [PMID: 33813578 DOI: 10.1097/rli.0000000000000779] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
MATERIALS AND METHODS We retrospectively reviewed 781 head CTs (median, 70 years; 441 men) collected by CT systems from 3 vendors. In addition to the orbitomeatal line image reformatted by a CT specialist as a reference, we obtained the fully automated orbitomeatal line image using the intelligent work aid. We calculated the offset angle from the reference of the automatically reformatted image. We defined the large offset angle groups as those with an offset angle greater than 3 degrees. Multivariate logistic regression was used to determine the independent factors for the large offset angle groups. We compared the postprocessing times measured using the intelligent work aid or by a CT specialist. RESULTS With the intelligent work aid, 99.7% of CTs were automatically reformatted to the orbitomeatal line without error. Furthermore, 88.1% of CTs were within the 3 degrees' offset angle when compared with the reference produced by a CT specialist. The median offset angle from the reference was 1.41 degrees. Multivariate analysis showed that the offset angle of the positioning plane was an independent factor (odds ratio, 1.045; P = 0.005) for predicting the large offset angle group. Furthermore, this technique was 4 times faster (6.4 ± 0.7 seconds) than a CT specialist (25.6 ± 6.4 seconds). CONCLUSIONS The intelligent work aid can generate a fast and precise head CT image aligned at the orbitomeatal line, even in real-world clinical CTs. However, precise positioning remains essential.
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Affiliation(s)
- Tatsuya Nishii
- From the Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
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Bessere bildgebende Darstellung der Rippen mit Bearbeitungssoftware. ROFO-FORTSCHR RONTG 2021. [DOI: 10.1055/a-1192-9738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Blum A, Gillet R, Rauch A, Urbaneja A, Biouichi H, Dodin G, Germain E, Lombard C, Jaquet P, Louis M, Simon L, Gondim Teixeira P. 3D reconstructions, 4D imaging and postprocessing with CT in musculoskeletal disorders: Past, present and future. Diagn Interv Imaging 2020; 101:693-705. [PMID: 33036947 DOI: 10.1016/j.diii.2020.09.008] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 09/12/2020] [Accepted: 09/15/2020] [Indexed: 12/30/2022]
Abstract
Three-dimensional (3D) imaging and post processing are common tasks used daily in many disciplines. The purpose of this article is to review the new postprocessing tools available. Although 3D imaging can be applied to all anatomical regions and used with all imaging techniques, its most varied and relevant applications are found with computed tomography (CT) data in musculoskeletal imaging. These new applications include global illumination rendering (GIR), unfolded rib reformations, subtracted CT angiography for bone analysis, dynamic studies, temporal subtraction and image fusion. In all of these tasks, registration and segmentation are two basic processes that affect the quality of the results. GIR simulates the complete interaction of photons with the scanned object, providing photorealistic volume rendering. Reformations to unfold the rib cage allow more accurate and faster diagnosis of rib lesions. Dynamic CT can be applied to cinematic joint evaluations a well as to perfusion and angiographic studies. Finally, more traditional techniques, such as minimum intensity projection, might find new applications for bone evaluation with the advent of ultra-high-resolution CT scanners. These tools can be used synergistically to provide morphologic, topographic and functional information and increase the versatility of CT.
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Affiliation(s)
- A Blum
- Guilloz Imaging Department, CHRU of Nancy, 54000 Nancy, France; Unité INSERM U1254 Imagerie Adaptative Diagnostique et Interventionnelle (IADI), CHRU of Nancy, 54511 Vandœuvre-lès-Nancy, France.
| | - R Gillet
- Guilloz Imaging Department, CHRU of Nancy, 54000 Nancy, France
| | - A Rauch
- Guilloz Imaging Department, CHRU of Nancy, 54000 Nancy, France
| | - A Urbaneja
- Guilloz Imaging Department, CHRU of Nancy, 54000 Nancy, France
| | - H Biouichi
- Guilloz Imaging Department, CHRU of Nancy, 54000 Nancy, France
| | - G Dodin
- Guilloz Imaging Department, CHRU of Nancy, 54000 Nancy, France
| | - E Germain
- Guilloz Imaging Department, CHRU of Nancy, 54000 Nancy, France
| | - C Lombard
- Guilloz Imaging Department, CHRU of Nancy, 54000 Nancy, France
| | - P Jaquet
- Guilloz Imaging Department, CHRU of Nancy, 54000 Nancy, France
| | - M Louis
- Guilloz Imaging Department, CHRU of Nancy, 54000 Nancy, France
| | - L Simon
- Guilloz Imaging Department, CHRU of Nancy, 54000 Nancy, France
| | - P Gondim Teixeira
- Guilloz Imaging Department, CHRU of Nancy, 54000 Nancy, France; Unité INSERM U1254 Imagerie Adaptative Diagnostique et Interventionnelle (IADI), CHRU of Nancy, 54511 Vandœuvre-lès-Nancy, France
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