# Multimodality imaging-driven multifidelity modeling of aortic dissection

> **NIH NIH U01** · YALE UNIVERSITY · 2021 · $559,400

## Abstract

PROJECT SUMMARY. Aortic dissections are responsible for significant morbidity and mortality in young and
old individuals alike. Whereas type A (ascending aorta) dissections are treated aggressively via surgery, type B
(descending thoracic aorta) dissections are often monitored for long periods to determine the best treatment.
These lesions can cease to propagate (i.e., stabilize or heal) or they can propagate further and either turn
inward and connect again with the true lumen to form a re-entry tear or turn outward and result in rupture in the
case of an compromised adventitia. Notwithstanding the importance of these later events, there is a pressing
need to understand better the early processes that initiate the dissection and drive its initial propagation as well
as to determine whether the presence of intramural thrombus is protective or not against early or continued
propagation. Over the past 5 years our collaborative team has developed numerous new multimodality imaging
techniques, biomechanical testing methods, and computational modeling approaches across multiple scales
that uniquely positions us to understand better the process of early aortic dissection and the possible
roles played by early intramural thrombus development. In this project, we propose to use nine
complementary mouse models to gain broad understanding of the bio-chemo-mechanical processes that lead
to aortic dissection and to introduce a new machine learning based multifidelity modeling approach to develop
predictive probabilistic multiscale models of dissection. These models will be informed, trained, and validated
via data obtained from a combination of unique in vitro biomechanical phenotyping experiments (wherein we
can, for the first time, quantify the initial delamination process under well-controlled conditions and regional
material properties thereafter) and novel multimodality imaging of delamination / dissection both in vitro and in
vivo. We will consider, for example, the roles of different elastic lamellar geometries; we will assess separate
roles of focal proteolytic activation and pooling of highly negatively charged mucoid material, which can
degrade or swell the wall respectively; and we will model and assess the effects of early thrombus deposition
within a false lumen. We submit that our new probabilistic paradigm, based on statistical autoregressive
schemes and enabled by machine learning tools, could be transformative and lead to a paradigm shift in
disease prediction where historical data, animal experiments, and limited clinical input (e.g., multiomics) can be
used synergistically for robust prognosis and thus interventional planning. Our work is also expected to lead
naturally to an eventual better understanding of the chronic processes associated with dissection via predictive
models that are aided by the expected “revolution of resolution” in diagnostic imaging.

## Key facts

- **NIH application ID:** 10242915
- **Project number:** 5U01HL142518-04
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Jay D. Humphrey
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $559,400
- **Award type:** 5
- **Project period:** 2018-07-05 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10242915

## Citation

> US National Institutes of Health, RePORTER application 10242915, Multimodality imaging-driven multifidelity modeling of aortic dissection (5U01HL142518-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10242915. Licensed CC0.

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