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Area of Science:

  • Medical Imaging
  • Radiography
  • Image Processing

Background:

  • Chest X-rays (CXRs) are vital for assessing medical line and tube placement.
  • Limited visualization due to imaging/patient factors risks misplacement, radiation exposure, and delayed care, especially for student radiographers.
  • Post-processing algorithms like grey-scale inversion (GSI) and Advanced Edge Enhancement (AEE) may improve device conspicuity, but their efficacy is unproven.

Purpose of the Study:

  • To evaluate the effectiveness of GSI and AEE algorithms in enhancing the conspicuity of medical lines and tubes on CXRs.
  • To assess the impact of these algorithms on visual image quality and reader confidence among student radiographers.

Main Methods:

  • Twenty standard CXRs with lines/tubes were processed using GSI and AEE.
  • Images were presented in three settings: Standard-GSI, Standard-AEE, and GSI-AEE.
  • Participants (n=25) used a relative visual grading characteristics (VGC) assessment and a 5-point Likert scale for confidence.

Main Results:

  • Reader confidence significantly increased with AEE and GSI-AEE settings (p < 0.001).
  • VGC analysis revealed significantly higher scores for GSI (AUC=0.72, p < 0.001) and AEE (AUC=0.91, p < 0.001) compared to standard CXRs.
  • AEE demonstrated superior performance over GSI in enhancing visual grading scores.

Conclusions:

  • GSI and AEE are valuable tools for optimizing medical line and tube visualization on CXRs.
  • Integration into imaging protocols can enhance diagnostic accuracy and potentially minimize radiation exposure.
  • These algorithms support best practices in radiography by improving resource utilization and patient care.