# Arterial input function Independent Measures of Perfusion with Physics Driven Models

> **NIH NIH R21** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2021 · $187,512

## Abstract

ABSTRACT
Acute Ischemic Stroke (AIS) affects approximately 700,000 patients each year in the United States. [Benjamin
EJ 2019, Circulation] Though the introduction of intravenous thrombolytics improved patient outcomes, the
development of effective treatment regimens with mechanical thrombectomy has significantly altered the
clinical management of AIS patients, especially when appropriate patients are selected for intervention. The
current treatment selection approaches utilize patient specific data heavily relies on quantitative neuroimaging
approaches, derived from either Computer Tomography (CT), or to a lesser extent magnetic resonance
imaging (MRI). CT, with its relative availability within the US, has been the primary modality used for stroke
patient triage.
Brain perfusion imaging has been central to the evaluation of the ischemic penumbra and infarct core enabling
precision in patient selection for intra-arterial thrombolysis. Typically dynamic CT perfusion scans with
repeated scans 40 to 60 time points with the administration of iodinated contrast are obtained upon the arrival
in the emergency room. These images are automatically or semi-automatically post-processed into perfusion
metrics, using a number of FDA approved software packages. These packages all essentially rely on a similar
post-processing pathway for the dynamically acquired images, consisting of motion correction, arterial input
function selection and some form of deconvolution post-processing. A set of perfusion maps are generated,
typically including cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT) and time
to the maximum contrast concentration (Tmax). The software packages then apply thresholds to the CBF and
Tmax maps to generate a presumed ischemic “core” from the CBF and “penumbra” from the Tmax. However,
the dependence of these values on the arterial input function (AIF) selected has resulted in extensive efforts to
automate AIF selection, or explore systematic methods to produce local AIFs to improve perfusion
measurements. Defining a perfusion metric that is independent of AIF selection could substantially improve
stroke perfusion analysis, and reduce patient radiation exposure. The goal of this study is to evaluate a physics
based model of cerebral perfusion for evaluating perfusion parameters from CT perfusion modalities. The
critical requirements of the new technique include independence from AIF selection, quantitative and stable
measurements of perfusion that are clinically relevant and predictive of stroke outcomes.

## Key facts

- **NIH application ID:** 10353761
- **Project number:** 1R21NS125369-01
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Yueh Z Lee
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $187,512
- **Award type:** 1
- **Project period:** 2021-09-30 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10353761

## Citation

> US National Institutes of Health, RePORTER application 10353761, Arterial input function Independent Measures of Perfusion with Physics Driven Models (1R21NS125369-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10353761. Licensed CC0.

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