  Thoracic aortic aneurysm (TAA) is a localized dilatation of the aorta
resulting from compromised wall composition, structure, and function, which can
lead to life-threatening dissection or rupture. Several genetic mutations and
predisposing factors that contribute to TAA have been studied in mouse models
to characterize specific changes in aortic microstructure and material
properties that result from a wide range of mechanobiological insults.
Assessments of TAA progression in vivo is largely limited to measurements of
aneurysm size and growth rate. It has been shown that aortic geometry alone is
not sufficient to predict the patient-specific progression of TAA but
computational modeling of the evolving biomechanics of the aorta could predict
future geometry and properties from initiating insults. In this work, we
present an integrated framework to train a deep operator network
(DeepONet)-based surrogate model to identify contributing factors for TAA by
using FE-based datasets of aortic growth and remodeling resulting from
prescribed insults. For training data, we investigate multiple types of TAA
risk factors and spatial distributions within a constrained mixture model to
generate axial--azimuthal maps of aortic dilatation and distensibility. The
trained network is then capable of predicting the initial distribution and
extent of the insult from a given set of dilatation and distensibility
information. Two DeepONet frameworks are proposed, one trained on sparse
information and one on full-field grayscale images, to gain insight into a
preferred neural operator-based approach. Performance of the surrogate models
is evaluated through multiple simulations carried out on insult distributions
varying from fusiform to complex. We show that the proposed approach can
predict patient-specific mechanobiological insult profile with a high accuracy,
particularly when based on full-field images.
