Automating aortic cross-sectional measurement of 3D aorta models.

Abstract

Purpose: Aortic dissection carries a mortality as high as 50%, but surgical palliation is also fraught with morbidity risks of stroke or paralysis. As such, a significant focus of medical decision making is on longitudinal aortic diameters. We hypothesize that three-dimensional (3D) modeling affords a more efficient methodology toward automated longitudinal aortic measurement. The first step is to automate the measurement of manually segmented 3D models of the aorta. We developed and validated an algorithm to analyze a 3D segmented aorta and output the maximum dimension of minimum cross-sectional areas in a stepwise progression from the diaphragm to the aortic root. Accordingly, the goal is to assess the diagnostic validity of the 3D modeling measurement as a substitute for existing 2D measurements.

Approach: From January 2021 to June 2022, 66 3D non-contrast steady-state free precession magnetic resonance images of aortic pathology with clinical aortic measurements were identified; 3D aorta models were manually segmented. A novel mathematical algorithm was applied to each model to generate maximal aortic diameters from the diaphragm to the root, which were then correlated to clinical measurements.

Results: With a 76% success rate, we analyzed the resulting 50 3D aortic models utilizing the automated measurement tool. There was an excellent correlation between the automated measurement and the clinical measurement. The intra-class correlation coefficient and $p$-value for each of the nine measured locations of the aorta were as follows: sinus of valsalva, 0.99, $<0.001$; sino-tubular junction, 0.89, $<0.001$; ascending aorta, 0.97, $<0.001$; brachiocephalic artery, 0.96, $<0.001$; transverse segment 1, 0.89, $<0.001$; transverse segment 2, 0.93, $<0.001$; isthmus region, 0.92, $<0.001$; descending aorta, 0.96, $<0.001$; and aorta at diaphragm, 0.3, $<0.001$.

Conclusions: Automating diagnostic measurements that appease clinical confidence is a critical first step in a fully automated process. This tool demonstrates excellent correlation between measurements derived from manually segmented 3D models and the clinical measurements, laying the foundation for transitioning analytic methodologies from 2D to 3D.