# A computational approach for intramural length estimation in anomalous aortic origin of a coronary artery

## TL;DR

This paper introduces a new computational method to estimate intramural length in a heart condition called anomalous aortic origin of a coronary artery, which could help improve risk assessment and surgical planning.

## Contribution

A semi-automatic computational method for estimating intramural length in AAOCA patients, validated against surgical measurements.

## Key findings

- The computational method showed an overall RMSE of 3.4 mm, comparable to radiologic estimates.
- For L-AAOCA subjects, the method had lower RMSE than radiologic estimates.
- The method is accurate relative to surgical measurements and could aid in risk stratification and surgical planning.

## Abstract

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.

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.

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).

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.