Computed Tomography Postprocessing for Abdominal Aortic Aneurysm Lumen Recognition in Unenhanced Examinations

Area of Science:

• Medical Imaging
• Computational Anatomy
• Diagnostic Tools

Background:

• Contrast medium (CM) in computed tomography (CT) poses risks like nephrotoxicity and allergic reactions.
• Abdominal aortic aneurysms (AAAs) diagnosis often relies on CM-enhanced CT scans.
• There is a need for safer diagnostic methods for AAAs.

Purpose of the Study:

• To introduce a novel mathematical algorithm for diagnosing AAAs.
• To enable visualization of the inner aortic lumen without CM.
• To reduce the reliance on CM in CT scans for AAA diagnosis.

Main Methods:

• Evaluation of CT scans with and without CM for patients with AAA.
• Implementation of a mathematical algorithm involving region of interest selection, Kantorovich algorithm for enhancement, wavelet decomposition for frequency analysis, and histogram-based thresholding for contour extraction.
• Validation by comparing algorithm-derived images with CM-enhanced scans and calculating pixel misclassification (En) and area difference (ΔA) errors.

Main Results:

• The algorithm was applied to 233 CT images.
• Mean errors ranged from 0.12 to 0.17 for En and 0.11 to 0.35 for ΔA.
• Overall mean errors for all images were 0.14 (En) and 0.28 (ΔA).

Conclusions:

• The developed mathematical algorithm accurately detects the vessel lumen and extracts its contours.
• The algorithm shows promising results for clinical application.
• This method offers a potential way to reduce CM usage in CT scans for AAA diagnosis.