# Automated Intracranial Vessel Wall Analysis Pipeline for Multi-contrast Multi-platform Applications

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2024 · $554,071

## Abstract

Intracranial atherosclerotic disease (ICAD) can lead to ischemic stroke and there is increasing evidence that
ICAD, even in the absence of stenosis, is associated with embolic stroke of undetermined source (ESUS). Vessel
wall magnetic resonance imaging (MRI) of the intracranial vasculature is increasingly in demand to diagnose
such ESUS patients so that appropriate treatment can be administered. Multi-contrast intracranial vessel wall
(IVW) MRI with efficient analysis is currently recommended by the American Society of Neuroradiology to
diagnose various vessel wall pathologies including ICAD. While this urgent need for IVW has stimulated
availability of 3D spin echo sequences on major scanner platforms (VISTA on Philips, SPACE on Siemens and
CUBE on GE), there are no efficient and effective methods for clinicians to analyze the multi-contrast IVW MRI
sequences that can be obtained on modern clinical MRI scanners. Quantitative measurements of the vessel wall
across scanner platforms are also required to enable multi-center studies for ICAD assessment in ESUS.
Variability in sequence implementation affects multi-center studies on multiple scanner platforms and must be
overcome to enable robust IVW measurements. Therefore, we propose to develop an automated IVW analysis
pipeline for multi-contrast multi-platform application using a domain adaptive and deep learning approach. We
have pioneered multiple semiautomatic approaches (3D-registration, artery tracing, artery labeling, multi-planar
reformatting, vessel wall segmentation, multi-contrast feature identification) towards vessel wall quantification.
Leveraging this expertise, we will develop a novel artificial intelligence (AI) empowered multiplanar viewing for
artery characterization (AI-MOCHA) pipeline as follows: In Aim 1 we will construct the MOCHA pipeline and train
AI-MOCHA using transfer learning from labeled IVW images and test AI-MOCHA against radiologist labeled IVW
from ICAD patients. We will also test whether AI-MOCHA improves the inter-scan and inter-reader reproducibility
of IVW image analysis. In Aim 2 we will develop domain adaptation to overcome scanner-platform differences in
IVW images and develop a Domain Adaptive AI-MOCHA. We will then show that domain adaptive AI-MOCHA
improves the inter-scan and inter-reader reproducibility of IVW image analysis over AI-MOCHA. In Aim 3, we will
test the hypothesis that non-stenotic ICAD is more frequently detected by AI-MOCHA in the vascular territory of
ESUS than in other territories using Domain Adaptive AI-MOCHA. To achieve this, we will scan 65 ESUS
subjects each on the Philips, Siemens and GE 3T scanner platforms in a multi-center setting (three different
hospitals) and demonstrate the utility of domain adaptive AI-MOCHA for robust and efficient IVW analysis. In
doing so, we will not only establish the importance of non-stenotic ICAD in ESUS but also develop a clinically
applicable IVW analysis pipeline for monitoring ICAD progression t...

## Key facts

- **NIH application ID:** 10910103
- **Project number:** 5R01NS127317-03
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Mahmud Mossa-Basha
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $554,071
- **Award type:** 5
- **Project period:** 2022-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10910103

## Citation

> US National Institutes of Health, RePORTER application 10910103, Automated Intracranial Vessel Wall Analysis Pipeline for Multi-contrast Multi-platform Applications (5R01NS127317-03). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10910103. Licensed CC0.

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