# Automated Quality Evaluation and Harmonization for Multisite ASL MRI Data

> **NIH NIH R21** · UNIVERSITY OF PENNSYLVANIA · 2024 · $207,955

## Abstract

SUMMARY
 Cerebral blood flow (CBF) is a fundamental physiological parameter reflecting cerebrovascular integrity
and brain function. Regional CBF alterations are well documented in Alzheimer's disease and related
dementias (ADRD) and can contribute to ADRD prognosis, diagnosis, and differentiation. Arterial Spin Labeled
(ASL) perfusion magnetic resonance imaging (MRI) is the only non-invasive method for imaging regional CBF
and has been increasingly adopted in multi-site clinical ADRD research. However, ASL signals are prone to
artifact from physiological noise and other nonidealities. Additionally, while multi-site ASL data can provide the
statistical power needed to assess regional CBF changes in the ADRD continuum, they are often acquired with
different ASL protocols from different scanner vendors and software versions leading to bias and variance in
site specific CBF values. Both factors can obscure biological effects of interest, even in large datasets. The
purpose of this R21/R33 study is to develop an ASL research framework for multisite clinical research studies
aimed at I) developing automated indices to rate the quality of CBF maps that can be used reproducibly in
place of more subjective visual inspection for quality control (QC) as well as for identifying the likely source of
artifacts, and II) harmonizing multisite ASL data to remove site effects. The R21 phase will focus on method
prototyping and validations using ASL data from a single vendor platform. Expert ratings for quality of ASL CBF
maps and predetermined features of CBF maps will be used to train a deep learning (DL) algorithm that can
automatically generate a quality index. The DL algorithm will be validated using synthetic data and expert
reading results as well as cross-validations. Another DL algorithm will be used to identify the probable source
of artifacts in input CBF maps. Prototype harmonization will be based on approaches previously used for
structural MRI, but with ASL specific modifications involving mathematical manipulations to preserve more
biological effects and to avoid under-harmonization. The R33 phase will substantially expand the methods
developed in the R21 phase to be generalizable to multiple vendor platforms. A data driven DL network, as
opposed to predetermined features used in the R21 phase, will provide both a composite and a voxel-wise
quality index for the input ASL CBF images; the latter allowing regional CBF data to be retained instead of
discarding the whole volume. We will also expand the capability of harmonization to cover data from unseen
scanners. Finally, we will demonstrate the benefit of these methods in ADRD research by testing hypothesis
that use of these research strategies will increase sensitivity for differentiating patients with mild cognitive
impairment from cognitively normal older subjects. This novel project leverages our decades of expertise in
ASL technologies and ASL-based ADRD research and our access to multi-site ASL...

## Key facts

- **NIH application ID:** 10912038
- **Project number:** 5R21AG080518-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Sudipto Dolui
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $207,955
- **Award type:** 5
- **Project period:** 2023-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10912038

## Citation

> US National Institutes of Health, RePORTER application 10912038, Automated Quality Evaluation and Harmonization for Multisite ASL MRI Data (5R21AG080518-02). Retrieved via AI Analytics 2026-06-23 from https://api.ai-analytics.org/grant/nih/10912038. Licensed CC0.

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