# Harmonizing data acquisition, reconstruction, and analysis for reproducible, cross-vendor, open source MRI

> **NIH NIH R01** · BRIGHAM AND WOMEN'S HOSPITAL · 2024 · $634,164

## Abstract

Abstract
In this 5-year R01 project entitled “Harmonizing data acquisition, reconstruction, and analysis for reproducible,
cross-vendor, open-source MRI,” we address the significant barriers to scientific progress due to the large inter-
scanner variability (often more than 10-20%) present in multi-site MRI data which substantially diminishes the
power of neuroimaging studies to detect subtle pathologies in neuropsychiatric disorders. Inter-scanner biases
are a result of differences in implementation of closed-source product sequences (e.g., gradient and
radiofrequency pulse shapes and timing), the choice of reconstruction algorithms, as well as variations inherent
to the scanner hardware (e.g., gradient strength). Another major challenge is the significant barrier to develop
new sequences for each vendor separately. This inhibits the translation of new MRI technologies to research
laboratories, as vendor-specific sequence development environments are closed-source, proprietary, and suffer
from a steep learning curve.
 In this project, we address these challenges by proposing an “end-to-end” harmonization framework. We
propose to develop and disseminate a single open-source vendor-neutral MRI pulse sequence development
environment containing both standard MRI protocols (e.g., T1-weighted, T2-weighted, and diffusion MRI) and
cutting-edge quantitative acquisitions (T1, T2, T2*, and quantitative susceptibility maps (QSM)), a unified image
reconstruction framework, and novel algorithms for post-acquisition data harmonization to enable multi-site
reproducible research and mitigate inter-scanner variability and bias. Our quantitative MRI acquisitions will be
efficient (5 min as opposed to more than 15 min) and also comprise of fast, distortion-free diffusion MRI
sequences. The performance of standard contrast-weighted protocols and the accuracy of novel quantitative
imaging sequences will be rigorously validated on phantoms and in-vivo data acquired from all major vendors
(Siemens, Philips, GE). Further, we will develop and validate novel data harmonization algorithms that will
remove any remaining scanner-induced discrepancies in the data due to hardware differences. One of the goals
of this project is to reduce inter-scanner variability to the level of those observed within-scanner. The technical
developments proposed in this grant will dramatically increase reproducibility across sites and allow for seamless
execution of multi-site neuroimaging studies. Thus, the increased statistical power of multi-site studies will
facilitate detection of subtle changes in neuropsychiatric disorders. Our open-source first-of-its-kind platform will
also accelerate cross-vendor sequence development and enable immediate translation of new sequences into
research studies (which currently takes several years).

## Key facts

- **NIH application ID:** 10881748
- **Project number:** 5R01EB032378-03
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Berkin Bilgic
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $634,164
- **Award type:** 5
- **Project period:** 2022-09-15 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10881748

## Citation

> US National Institutes of Health, RePORTER application 10881748, Harmonizing data acquisition, reconstruction, and analysis for reproducible, cross-vendor, open source MRI (5R01EB032378-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10881748. Licensed CC0.

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