F31HL176137
Project Grant
Overview
Grant Description
Computational models of mechanical thrombectomy in acute ischemic stroke treatment - project summary
Stroke is the second leading cause of death worldwide, with acute ischemic strokes representing 87% of all stroke cases.
Given the number of individuals experiencing acute ischemic strokes, there is a pressing need to improve therapies for this disease.
A recent treatment for large vessel occlusion, which accounts for up to 46% of all acute ischemic strokes and 98.8% of all poststroke mortality, is mechanical thrombectomy.
The main forms of thrombectomy include using a stent retriever to trap and extract the occlusive clot or an aspiration catheter to remove the clot via suction.
While this is an effective treatment for large vessel occlusion, studies show that only 50% of treated patients achieve good functional outcomes.
Additionally, up to 40% of patients experience distal embolization, where the clot breaks into fragments during removal; these fragments travel distally and cause further damage to the cerebral vasculature.
Individual studies have shown that adjusting treatment parameters such as I) stent retriever length, size, and withdrawal speed, or II) aspiration catheter size, distance from the clot, and suction pressure improve patient outcomes.
These studies are limited though, and no study has thoroughly investigated the impact of all these treatment parameters on procedural outcomes such as I) degree of recanalization, II) number of passes needed to recanalize, and III) occurrence of distal embolization for clots of varied geometry, composition, and location.
It has been noted that computational models could overcome this gap; they can be used to systematically test all treatment parameters for clots of varied geometry, composition, and location.
The advantage of this approach is in the reduction of time and resources needed to study thrombectomy treatments, as experiments can be difficult to perform, costly, and time-consuming.
Despite this, few computational models exist that capture the complex physics of thrombectomy, let alone investigate the sensitivity of procedural outcomes to treatment parameters.
To fill this gap, the proposed project aims to develop and validate an open-source computational model of thrombectomy (Aim 1) and optimize thrombectomy for clots of varied geometry, composition, and location using computational models (Aim 2).
The aims will use state-of-the-art techniques in experimental and computational biomechanics.
Our current lack of knowledge is potentially withholding better treatment strategies for large vessel occlusion in acute ischemic stroke.
Thus, my ultimate goal is to identify optimal thrombectomy treatment options and inspire the development of new thrombectomy devices and strategies.
Along with the research strategy, the fellowship training plan is organized to offer the applicant professional development toward an independent research career in a top-tier institutional environment at the University of Texas at Austin under the sponsorship of a leading expert in blood clot biomechanics.
Upon completion of the training program, the applicant will be ideally prepared for further postdoctoral training and eventually a faculty position in cardiovascular disease research.
Stroke is the second leading cause of death worldwide, with acute ischemic strokes representing 87% of all stroke cases.
Given the number of individuals experiencing acute ischemic strokes, there is a pressing need to improve therapies for this disease.
A recent treatment for large vessel occlusion, which accounts for up to 46% of all acute ischemic strokes and 98.8% of all poststroke mortality, is mechanical thrombectomy.
The main forms of thrombectomy include using a stent retriever to trap and extract the occlusive clot or an aspiration catheter to remove the clot via suction.
While this is an effective treatment for large vessel occlusion, studies show that only 50% of treated patients achieve good functional outcomes.
Additionally, up to 40% of patients experience distal embolization, where the clot breaks into fragments during removal; these fragments travel distally and cause further damage to the cerebral vasculature.
Individual studies have shown that adjusting treatment parameters such as I) stent retriever length, size, and withdrawal speed, or II) aspiration catheter size, distance from the clot, and suction pressure improve patient outcomes.
These studies are limited though, and no study has thoroughly investigated the impact of all these treatment parameters on procedural outcomes such as I) degree of recanalization, II) number of passes needed to recanalize, and III) occurrence of distal embolization for clots of varied geometry, composition, and location.
It has been noted that computational models could overcome this gap; they can be used to systematically test all treatment parameters for clots of varied geometry, composition, and location.
The advantage of this approach is in the reduction of time and resources needed to study thrombectomy treatments, as experiments can be difficult to perform, costly, and time-consuming.
Despite this, few computational models exist that capture the complex physics of thrombectomy, let alone investigate the sensitivity of procedural outcomes to treatment parameters.
To fill this gap, the proposed project aims to develop and validate an open-source computational model of thrombectomy (Aim 1) and optimize thrombectomy for clots of varied geometry, composition, and location using computational models (Aim 2).
The aims will use state-of-the-art techniques in experimental and computational biomechanics.
Our current lack of knowledge is potentially withholding better treatment strategies for large vessel occlusion in acute ischemic stroke.
Thus, my ultimate goal is to identify optimal thrombectomy treatment options and inspire the development of new thrombectomy devices and strategies.
Along with the research strategy, the fellowship training plan is organized to offer the applicant professional development toward an independent research career in a top-tier institutional environment at the University of Texas at Austin under the sponsorship of a leading expert in blood clot biomechanics.
Upon completion of the training program, the applicant will be ideally prepared for further postdoctoral training and eventually a faculty position in cardiovascular disease research.
Awardee
Funding Goals
TO FOSTER HEART AND VASCULAR RESEARCH IN THE BASIC, TRANSLATIONAL, CLINICAL AND POPULATION SCIENCES, AND TO FOSTER TRAINING TO BUILD TALENTED YOUNG INVESTIGATORS IN THESE AREAS, FUNDED THROUGH COMPETITIVE RESEARCH TRAINING GRANTS. SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM: TO STIMULATE TECHNOLOGICAL INNOVATION, USE SMALL BUSINESS TO MEET FEDERAL RESEARCH AND DEVELOPMENT NEEDS, FOSTER AND ENCOURAGE PARTICIPATION IN INNOVATION AND ENTREPRENEURSHIP BY SOCIALLY AND ECONOMICALLY DISADVANTAGED PERSONS, AND INCREASE PRIVATE-SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT FUNDING. SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM: TO STIMULATE TECHNOLOGICAL INNOVATION, FOSTER TECHNOLOGY TRANSFER THROUGH COOPERATIVE R&D BETWEEN SMALL BUSINESSES AND RESEARCH INSTITUTIONS, AND INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL R&D.
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Texas
United States
Geographic Scope
State-Wide
University Of Texas At Austin was awarded
Project Grant F31HL176137
worth $41,266
from National Heart Lung and Blood Institute in August 2025 with work to be completed primarily in Texas United States.
The grant
has a duration of 3 years and
was awarded through assistance program 93.837 Cardiovascular Diseases Research.
The Project Grant was awarded through grant opportunity Ruth L. Kirschstein National Research Service Award (NRSA) Individual Predoctoral Fellowship (Parent F31).
Status
(Ongoing)
Last Modified 9/24/25
Period of Performance
8/25/25
Start Date
8/24/28
End Date
Funding Split
$41.3K
Federal Obligation
$0.0
Non-Federal Obligation
$41.3K
Total Obligated
Activity Timeline
Transaction History
Modifications to F31HL176137
Additional Detail
Award ID FAIN
F31HL176137
SAI Number
F31HL176137-1450576208
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Public/State Controlled Institution Of Higher Education
Awarding Office
75NH00 NIH National Heart, Lung, and Blood Institute
Funding Office
75NH00 NIH National Heart, Lung, and Blood Institute
Awardee UEI
V6AFQPN18437
Awardee CAGE
9B981
Performance District
TX-90
Senators
John Cornyn
Ted Cruz
Ted Cruz
Modified: 9/24/25