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Area of Science:

  • Radiology and Imaging
  • Cardiovascular Diagnostics
  • Pulmonary Medicine

Background:

  • Pulmonary embolism (PE) diagnosis often relies on complex imaging.
  • Dynamic chest radiography (DCR) offers a potential non-invasive imaging modality.
  • Quantifying lung perfusion defects is crucial for accurate PE assessment.

Purpose of the Study:

  • To develop and validate a processing algorithm for DCR to diagnose PE.
  • To assess the efficacy of lung perfusion (LP) maps and perfusion defect criteria (PDC) in PE detection.
  • To establish the diagnostic performance of fractional perfusion defect area (FPDA) for PE.

Main Methods:

  • Retrospective enrollment of 77 patients who underwent DCR and CT pulmonary angiography.
  • Development of a processing algorithm using LP maps and varying perfusion defect criteria (PDC).
  • Calculation of fractional perfusion defect area (FPDA) and receiver operating characteristic (ROC) analysis for diagnostic performance.

Main Results:

  • The best PDC for PE detection varied between right (-110) and left (-125) lungs.
  • FPDA demonstrated significant diagnostic performance: AUCs of 88% (right lung) and 81% (left lung).
  • High sensitivity and specificity were achieved, with FPDA providing objective PE diagnostic information.

Conclusions:

  • FPDA derived from DCR-based LP maps is a viable tool for automatic PE diagnosis.
  • This non-invasive DCR method provides objective and accurate supplementary data for initial PE diagnosis.
  • The proposed algorithm enhances the diagnostic capabilities of DCR in identifying pulmonary embolism.