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A computational approach for intramural length estimation in anomalous aortic origin of a coronary artery
Vikram Shah1, Lauren Ferrino2, Dana Reaves-O'Neal2
1Department of Computational Applied Mathematics and Operations Research, Rice University, Houston, TX, United States.
Insights
A new computational method accurately estimates intramural length in anomalous aortic origin of a coronary artery (AAOCA) patients. This tool aids in surgical planning and risk stratification for AAOCA, a condition linked to sudden cardiac death.
Area of Science:
- Cardiovascular Imaging
- Computational Anatomy
- Medical Device Technology
Background:
- Anomalous aortic origin of a coronary artery (AAOCA) is a congenital heart defect associated with sudden cardiac death.
- Intramural (IM) length is a critical high-risk feature in AAOCA.
- Current radiologic measurements of IM length show variable agreement with surgical findings.
Purpose of the Study:
- To develop and validate a semi-automatic computational method for estimating IM length in AAOCA.
- To compare the accuracy of the computational method against radiologic and surgical measurements.
- To assess the potential of computational tools for risk stratification in AAOCA.
Main Methods:
- A retrospective cohort of 58 surgical AAOCA patients was analyzed.
- Computed tomography angiography (CTA) images were used to create 3D aorta and coronary artery models.
- A novel algorithm calculated IM length based on the distance from the coronary centerline to the aortic wall.
Main Results:
- The computational method achieved an overall root-mean-square error (RMSE) of 3.4 mm, comparable to radiologic estimates (3.2 mm).
- For left-sided AAOCA (L-AAOCA), the method showed lower RMSE (3.6 mm) than radiologic estimates (4.7 mm).
- For right-sided AAOCA (R-AAOCA), the method's RMSE (3.4 mm) was slightly higher than radiologic estimates (2.8 mm).
Conclusions:
- The developed computational approach provides accurate intramural length measurements, comparable to surgical outcomes.
- This method holds promise for improving risk stratification and surgical planning for AAOCA patients.
- Quantifying AAOCA morphology computationally may enhance patient management and outcomes.
Introduction:
Anomalous aortic origin of a coronary artery (AAOCA) is associated with sudden cardiac death. The intramural (IM) length is considered high-risk, yet radiologic measurements by computed tomography angiography (CTA) show variable agreement with measurements at surgery. We aimed to develop a semi-automatic computational method to estimate IM length in a retrospective cohort of surgical AAOCA patients.
Methods:
In 58 patients [49 right(R), 9 left(L)], CTA images were used to generate 3D segmentations of the aorta and a centerline of the anomalous coronary. The distance from the centerline to the aortic segmentation was calculated. The IM length was estimated from a transition point in the derivative of the distance curve and compared to radiologic and surgical measurements.
Results:
Our method demonstrated an overall root-mean-square error (RMSE) of 3.4 mm, comparable to radiologic estimates (3.2 mm). For L-AAOCA subjects, our method showed lower root-mean-square error compared to radiologic estimates (our method: 3.6 mm, radiologic: 4.7 mm). For R-AAOCA subjects, the RMSE was higher in our method compared to radiologic estimates (our method: 3.4 mm, radiologic: 2.8 mm).
Conclusion:
This is a pilot study of a computational approach to measure intramural length that is shown to be accurate relative to surgical measurements. Computational methods that represent and quantify morphology, including acute take-off angle, ostial characteristics, minimal luminal area, and intramural length, may be helpful for risk stratification and surgical planning in AAOCA.
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