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Rapid Morphological Measurement Method of Aortic Dissection Stent Based on Spatial Observation Point Set
Mateng Bai1,2, Da Li1,2, Kaiyao Xu1
1Department of Applied Mechanics, Sichuan University, No. 24 South Section 1, Chengdu 610065, China.
Bioengineering (Basel, Switzerland)
|February 25, 2023
Summary
This study introduces an automated method for measuring aortic dissection stent morphology, improving accuracy and efficiency over manual techniques. The new approach reduces measurement time and costs, enabling faster clinical diagnosis and prognostic assessment.
Area of Science:
- Medical Imaging
- Biomedical Engineering
- Cardiovascular Surgery
Background:
- Accurate measurement of post-operative stent morphology in aortic dissection patients is crucial for diagnosis and prognosis.
- Current manual measurement methods are time-consuming, error-prone, and hinder data association analysis.
- A need exists for an automated, accurate, and efficient method for stent morphology assessment.
Purpose of the Study:
- To develop and validate an automated method for measuring stent morphology in aortic dissection patients.
- To improve the accuracy and efficiency of morphological parameter extraction compared to traditional manual methods.
- To enable rapid assessment of complex stent features, such as circumferential deflection angle.
Main Methods:
- 3D reconstruction of 109 CT scans from 26 patients using mimics software.
- Development of a fully automatic stent segmentation and observation point extraction algorithm.
- Validation on a test set of 8 cases (408 points), comparing automated measurements with manual ones.
Main Results:
- High agreement between automated and manual measurements for low-complexity parameters (e.g., stent end position, slip volume; r > 0.988).
- Automated method corrected medium-complexity parameter errors (e.g., support ring diameter) with an average of 1.38 mm (max 4 mm).
- Low error rate (2.2%) and high spatial accuracy (0.73 mm average distance) for automated observation point extraction.
- Successful measurement of complex parameters like circumferential deflection angle, not feasible with traditional methods.
Conclusions:
- The proposed automated method significantly reduces observation time and data processing costs.
- It offers rapid and accurate acquisition of morphological parameters, including complex ones.
- The approach facilitates quick adaptation for new parameter requirements via 'combinatorial functions' without data set modification.
Keywords:
aortic dissectioncompleteness datasetmorphological parametersstatistical time complexitythoracic aortic stent
