# Optimizing MRI for Neurologic Screening using Radiologist Crowdsourcing

> **NIH NIH R21** · UNIVERSITY OF WISCONSIN-MADISON · 2022 · $414,305

## Abstract

ABSTRACT
Over the past several decades, the diagnosis and treatment of patients presenting with acute neurologic
symptoms has advanced tremendously due to new therapies and the rise of imaging techniques as means to
triage patients for treatment. Currently, imaging information is predominately provided by CT which provides
depiction of large vessel occlusions and tissue perfusion. MRI additionally provides contrasts that are simply not
available from CT and a far superior depiction of tissue damage and stroke mimics. To this point, recent studies
using MRI as a frontline diagnostic tool in the emergency setting have demonstrated improved outcomes over
the use of CT. Unfortunately, the use of MRI in emergency and screening applications is highly limited by its
extended imaging time. This time increases costs, delays timely treatment, and sensitizes the scan to bulk and
physiologic motion. While a variety of techniques have been previously proposed to accelerate MRI acquisitions,
disruptive and paradigm shifting deep learning image reconstruction technology is currently being developed
offering unprecedented reductions in scan times. This technology thus holds potential to transform MRI into a
modality capable of rapid screening for neurologic disorders. However, deep learning image reconstructions
require a performance metric to set what information can be lost across the reconstruction (e.g. noise, distortions)
and what information must be retained (e.g. contrast, resolution, imaging features). Common performance
metrics are the mean squared error (MSE) or structural similarity (SSIM) of reconstructed images with a ground
truth; though, it is well known that these engineering-based metrics are often poor reflections of radiologic image
quality. The overall goal of this project is to develop a radiologically optimal, five-minute, multi-contrast deep
learning accelerated MRI screening protocol. We specifically aim to develop methods for probing and
incorporating radiologic preference into deep learning based MRI reconstructions. To achieve this, we will
develop methodology to probe image preference from human observer ranking of differentially corrupted MRI
images of the same subject. Through crowdsourced ranking studies, we aim to investigate differences in
perceived image quality between expert and non-expert observers, among multiple tasks, and in reference to
engineering based metrics. Subsequently, data from the expert radiologist ranking will be used to train an image
perception model that approximates the radiologist’s preferences. This model will be used to optimize the
sampling patterns and reconstruction for a multi-contrast neurologic screen protocol, which will be evaluated in
a pilot human subject study comparing the deep learning protocol to an abbreviated protocol using traditional
methods. The successful completion of this project will provide a rapid MRI method capable of providing timely
and relevant information for neurologic scr...

## Key facts

- **NIH application ID:** 10527680
- **Project number:** 1R21NS125094-01A1
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Kevin Michael Johnson
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $414,305
- **Award type:** 1
- **Project period:** 2022-07-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10527680

## Citation

> US National Institutes of Health, RePORTER application 10527680, Optimizing MRI for Neurologic Screening using Radiologist Crowdsourcing (1R21NS125094-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10527680. Licensed CC0.

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