Academic Journal
Chest X-Ray Imaging Algorithms for Optimising the Visualisation of Medical Lines and Tubes: A Study With Student Radiographers.
| Τίτλος: | Chest X-Ray Imaging Algorithms for Optimising the Visualisation of Medical Lines and Tubes: A Study With Student Radiographers. |
|---|---|
| Συγγραφείς: | Pui V; Discipline of Medical Imaging Sciences, The University of Sydney, Sydney, Australia., Robinson J; Discipline of Medical Imaging Sciences, The University of Sydney, Sydney, Australia., Punch A; Discipline of Medical Imaging Sciences, The University of Sydney, Sydney, Australia., Nocum D; Discipline of Medical Imaging Sciences, The University of Sydney, Sydney, Australia., Awwad DA; Discipline of Medical Imaging Sciences, The University of Sydney, Sydney, Australia.; Concord Repatriation General Hospital, Sydney Local Health District, Sydney, Australia. |
| Πηγή: | Journal of medical radiation sciences [J Med Radiat Sci] 2026 Sep; Vol. 73 (3), pp. 291-299. Date of Electronic Publication: 2026 Mar 29. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Wiley Publishing Asia Pty Ltd Country of Publication: United States NLM ID: 101620352 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2051-3909 (Electronic) Linking ISSN: 20513895 NLM ISO Abbreviation: J Med Radiat Sci Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: [Malden, MA] : Wiley Publishing Asia Pty Ltd, [2013]- |
| Ιατρικοί όροι (MeSH): | Radiography, Thoracic*/methods , Algorithms* , Image Processing, Computer-Assisted*, Humans |
| Περίληψη: | Introduction: Chest X-rays (CXRs) play a crucial role in determining the placement of medical lines and tubes. However, visualisation of these devices can be limited due to imaging and patient factors, increasing the risk of undetected misplacements, repeated radiation exposure and delayed patient management. These challenges may be more pronounced for student radiographers who are still developing their radiography skills. Although X-ray post-processing algorithms, such as grey-scale inversion (GSI) and Advanced Edge Enhancement (AEE), have the potential to optimise line and tube conspicuity, their effectiveness is not well established. Hence, this study aimed to evaluate the usefulness of GSI and AEE algorithms in enhancing visual image quality and reader confidence among student radiographers. Methods: Twenty standard CXRs, demonstrating lines or tubes, were copied and processed using GSI and AEE algorithms. Images were paired as follows: Setting 1 (Standard-GSI), Setting 2 (Standard-AEE), Setting 3 (GSI-AEE). In each setting, participants used a relative visual grading characteristics (VGC) assessment to compare algorithms and indicated reader confidence using a 5-point Likert scale. Results: Twenty-five students completed the VGC assessment. The Friedman test revealed a significant increase in reader confidence (χ2 (3) = 48.02, p < 0.001) in Settings 2 and 3. VGC analysis showed significantly higher visual grading scores with both GSI (AUC Conclusion: GSI and AEE appear to be valuable tools that merit integration into imaging protocols to promote best practices by optimising the use of resources, supporting diagnostic accuracy and potentially helping to minimise radiation exposure. (© 2026 The Author(s). Journal of Medical Radiation Sciences published by John Wiley & Sons Australia, Ltd on behalf of Australian Society of Medical Imaging and Radiation Therapy and New Zealand Institute of Medical Radiation Technology.) |
| References: | E. Eisenhuber, C. M. Schaefer‐Prokop, H. Prosch, and W. Schima, “Bedside Chest Radiography,” Respiratory Care 57, no. 3 (2012): 427–443. E. M. P. Fan, S. B. Tan, and S. Y. Ang, “Nasogastric Tube Placement Confirmation: Where We Are and Where We Should Be Heading,” Proceedings of Singapore Healthcare 26, no. 3 (2017): 189–195. E. Keyte, G. Roe, A. Jeanes, and J. K. Kraft, “Immediate Chest Radiograph Interpretation by Radiographers Improves Patient Safety Related to Nasogastric Feeding Tube Placement in Children,” Pediatric Radiology 51, no. 9 (2021): 1621–1625. L. de Man, M. Wentzel, C. van Rooyen, and E. Turton, “Comparison Between Ultrasound and Chest X‐Ray to Confirm Central Venous Catheter Tip Position,” South African Journal of Radiology 27, no. 1 (2023): 2587‐ 2587‐. P. K. Gupta, K. Gupta, M. Jain, and T. Garg, “Postprocedural Chest Radiograph: Impact on the Management in Critical Care Unit,” Anesthesia, Essays and Researches 8, no. 2 (2014): 139–144. J. Kolikof, K. Peterson, and A. M. Baker, Central Venous Catheter (StatPearls [Internet]., 2023). L. Zhang, X. Zhu, Y. He, and G. Zhang, “Study on the Value of Nasogastric Tube Nutrition Nursing in Critical Patients With Indwelling Nasogastric Tube,” American Journal of Translational Research 13, no. 8 (2021): 9694–9700. S. Taylor and A. R. Manara, “X‐Ray Checks of NG Tube Position: A Case for Guided Tube Placement,” British Journal of Radiology 94, no. 1124 (2021): 20210432‐. H. Alsleem and R. Davidson, “Quality Parameters and Assessment Methods of Digital Radiography Images,” Radiographer 59, no. 2 (2012): 46–55. J. Broder, “Imaging the Chest: The Chest Radiograph,” in Diagnostic Imaging for the Emergency Physician (Elsevier—Health Sciences Division, 2011), 185–296. F. A. Mettler and M. FA, Jr., Essentials of Radiology, 4th ed. (Elsevier—Health Sciences Division, 2019). Canon Europa, “Canon Advanced Edge Enhancement Brochure,” (2022), https://canon.a.bigcontent.io/v1/static/Brochure_Advanced_Edge_Enhancement_Canon_AEEY20VB_Screen. M. Liu, S. Mei, P. Liu, Y. Gasimov, and C. Cattani, “A New X‐Ray Medical‐Image‐Enhancement Method Based on Multiscale Shannon–Cosine Wavelet,” Entropy 24, no. 12 (2022): 1754. R. Kushol, R. Md Nishat, M. S. Salekin, and A. B. M. A. Rahman, “Contrast Enhancement of Medical X‐Ray Image Using Morphological Operators With Optimal Structuring Element” (2019), Ithaca: Cornell University Library, arXiv.org, http://arxiv.org. A. Shah, K. P. Iyengar, and R. Botchu, “Gray Scale Inversion Imaging (GSI) in Trauma and Orthopaedics,” Journal of Orthopaedics 30 (2022): 62–65. M. Eftekhari, M. Jalili, A. Karim, A. Doosti‐Irani, and H. Mirfazaelian, “Diagnostic Accuracy of Inverted Grayscale Mode in Radiographs: A Systematic Review and Meta‐Analysis,” Frontiers in Emergency Medicine 7, no. 3 (2023): e29. J. Kirchner, D. Gadek, J.‐P. Goltz, et al., “Standard Versus Inverted Digital Luminescence Radiography in Detecting Pulmonary Nodules: A ROC Analysis,” European Journal of Radiology 82, no. 10 (2013): 1799–1803. J. W. Robinson, J. T. Ryan, M. F. McEntee, et al., “Grey‐Scale Inversion Improves Detection of Lung Nodules,” British Journal of Radiology 86, no. 1021 (2013): 27961545. J. B. M. D. Park, Y. S. M. D. Cho, and H. J. M. D. Choi, “Diagnostic Accuracy of the Inverted Grayscale Rib Series for Detection of Rib Fracture in Minor Chest Trauma,” American Journal of Emergency Medicine 33, no. 4 (2015): 548–552. R. E. Ledda, M. Silva, N. McMichael, et al., “The Diagnostic Value of Grey‐Scale Inversion Technique in Chest Radiography,” Radiologia Medica 127, no. 3 (2022): 294–304. P. Lee, A. Tahmasebi, J. K. Dave, et al., “Comparison of Gray‐Scale Inversion to Improve Detection of Pulmonary Nodules on Chest X‐Rays Between Radiologists and a Deep Convolutional Neural Network,” Current Problems in Diagnostic Radiology 52, no. 3 (2023): 180–186. E. Musalar, S. Ekinci, O. Ünek, et al., “Conventional vs Invert‐Grayscale X‐Ray for Diagnosis of Pneumothorax in the Emergency Setting,” American Journal of Emergency Medicine 35, no. 9 (2017): 1217–1221. J. D. Thompson, N. B. Thomas, D. J. Manning, and P. Hogg, “The Impact of Greyscale Inversion for Nodule Detection in an Anthropomorphic Chest Phantom: A Free‐Response Observer Study,” British Journal of Radiology 89, no. 1064 (2016): 20160249. M. P. Lungren, E. Samei, H. Barnhart, et al., “Gray‐Scale Inversion Radiographic Display for the Detection of Pulmonary Nodules on Chest Radiographs,” Clinical Imaging 36, no. 5 (2012): 515–521. E. Kjelle and C. Chilanga, “The Assessment of Image Quality and Diagnostic Value in X‐Ray Images: A Survey on Radiographers' Reasons for Rejecting Images,” Insights Into Imaging 13, no. 1 (2022): 36. J. Mount, “Reject Analysis: A Comparison of Radiographer and Radiologist Perceptions of Image Quality,” Radiography 22, no. 2 (2016): e112–e117. Australian Health Practitioner Regulation Agency, “Professional Capabilities for Medical Radiation Practitioners” (2020), https://www.medicalradiationpracticeboard.gov.au/Registration‐Standards/Professional‐Capabilities.aspx. M. D. Şahin, “Effect of Item Order on Certain Psychometric Properties: A Demonstration on a Cyberloafing Scale,” Frontiers in Psychology 12 (2021): 590545. R. A. Hammon, H. Seuss, M. Hammon, et al., “Improved Visualization of Peripherally Inserted Central Catheters on Chest Radiographs of Neonates Using Fractional Multiscale Image Processing,” BMC Medical Imaging 19, no. 1 (2019): 3. S. V. Kristensen, C. Outzen, L. M. Grau, et al., “Can Advanced Edge Enhancement Software Improve Image Quality to Visualise Tubes, Catheters and Wires in Digital Chest Radiographs?,” Radiography 29, no. 1 (2023): 165–170. M. Båth and L. G. Mansson, “Visual Grading Characteristics (VGC) Analysis: A Non‐Parametric Rank‐Invariant Statistical Method for Image Quality Evaluation,” British Journal of Radiology 80, no. 951 (2007): 169–176. M. Båth, S. Zachrisson, and L. G. Månsson, “VGC Analysis: Application of the ROC Methodology to Visual Grading Tasks” (2008), SPIE. M. Båth and J. Hansson, “VGC Analyzer: A Software for Statistical Analysis of Fully Crossed Multiple‐Reader Multiple‐Case Visual Grading Characteristics Studies,” Radiation Protection Dosimetry 169, no. 1–4 (2016): 46–53. S. Nakagawa, “A Farewell to Bonferroni: The Problems of Low Statistical Power and Publication Bias,” Behavioral Ecology 15, no. 6 (2004): 1044–1045. C. B. Outzen, D. Maron, J. Nissen, et al., “The Influence of a Novel Edge Enhancement Software on Image Quality of DR Hand Images of Patients With Rheumatoid Arthritis,” Radiography 27, no. 3 (2021): 877–882. E. Kjelle, A. K. Schanche, and L. Hafskjold, “To Keep or Reject, That Is the Question ‐ A Survey on Radiologists and Radiographers' Assessments of Plain Radiography Images,” Radiography 27, no. 1 (2021): 115–119. M. A. Steward, M. S. Semsem, and M. K. Currie, “The Cost of Perfection: An Investigation Into the Unnecessary Rejection of Clinically Acceptable Lateral Wrist Imaging,” Journal of Medical Imaging and Radiation Sciences 55, no. 3 (2024): 101489. J. Ley‐Zaporozhan, H. Shoushtari, R. Menezes, et al., “Enhanced Pneumothorax Visualization in ICU Patients Using Portable Chest Radiography,” PLoS One 13, no. 12 (2018): e0209770. M. Lawson, L. Qian, K. K. Lau, T. Lau, D. Massey, and M. Badawy, “Efficacy of the Scatter Correction Algorithm in Portable Chest Radiography,” Emergency Radiology 29, no. 5 (2022): 809–817. H. Targett, D. Hutchinson, R. Hartley, et al., “Enhanced Visualization of Mobile Chest X‐Ray Images in the Intensive Care Setting Using Software Scatter Correction,” Acta Radiologica 64, no. 2 (2023): 563–571. D. H. Foos, D. F. Yankelevitz, X. Wang, et al., “Improved Visualization of Tubes and Lines in Portable Intensive Care Unit Radiographs: A Study Comparing a New Approach to the Standard Approach,” Clinical Imaging 35, no. 5 (2011): 346–352. B. Svenson, L. Larsson, and M. Båth, “Optimization of Exposure in Panoramic Radiography While Maintaining Image Quality Using Adaptive Filtering,” Acta Odontologica Scandinavica 74, no. 3 (2016): 229–235. |
| Contributed Indexing: | Keywords: advanced edge enhancement; chest X‐ray; grey‐scale inversion; image processing; lines; quality assurance; tubes |
| Entry Date(s): | Date Created: 20260330 Date Completed: 20260902 Latest Revision: 20260902 |
| Update Code: | 20260902 |
| PubMed Central ID: | PMC13398624 |
| DOI: | 10.1002/jmrs.70086 |
| PMID: | 41906415 |
| Βάση Δεδομένων: | MEDLINE |
καταχωρήστε σχόλιο πρώτοι!