# Visual biofeedback to reduce head motion during MRI scans

> **NIH NIH R44** · TURING MEDICAL TECHNOLOGIES INC · 2021 · $300,000

## Abstract

Project Abstract/Summary of Parent Award (no change)
The goal of this application is to deliver a brain MRI technology that feeds back head motion measurements
derived from our Framewise Integrated Real-Time MRI Monitoring (FIRMM) to MRI scan participants in order
to reduce head motion via behavioral training. Because MRI scanning produces high-resolution images and
does not expose patients to radiation, it has become an immensely valuable diagnostic tool, particularly for
imaging the brain. Last year, in the United States alone, there were over 8 million brain MRIs, costing an
estimated $20-30 billion. Unfortunately, brain MRIs are limited by the fact that head motion during the scan can
cause the resulting images to be suboptimal or even unusable. An estimated 20% of all brain MRIs are ruined
by motion, wasting $2-4 billion annually. Currently, there are two predominant strategies to combat head
motion: repeat scanning and anesthesia, both of which are inadequate. Repeat scanning, which consists of
acquiring extra images (to ensure enough usable ones were acquired), increases scanning time and cost, and
can result in too few usable images or unnecessary extra images. Anesthesia, which is given to patients who
are likely to move (such as young children), presents a serious safety risk and is sometimes administered
unnecessarily (i.e. the patient could hold still without anesthesia). Anesthesia is never an option for functional
MRI (fMRI), which requires participants to be awake. The software-based FIRMM-biofeedback solution
proposed in this grant uses MR images (as they are being collected) to compute a patient’s head motion in real
time during an MRI scan. The availability of real time motion information will enable more informed anesthesia
use and reduce excess scanning, making these methods safer and more efficient. Armed with real time motion
information, scan operators will know exactly how many usable images have been acquired, preventing the
acquisition of too many or too few extra images. Additionally, providing physicians with quantitative information
about patient motion will allow them to make an informed decision regarding anesthesia, preventing
unnecessary sedation. The proposed solution focuses on a completely new biobehavioral method for
combating head motion: subject biofeedback. The technology can translate the head motion information into
age-appropriate, visual biofeedback for the scan participant. By providing feedback to patients and research
subjects, the FIRMM-biofeedback technology helps both pediatric and adult patients remain more still,
improving image quality. The proposed research focuses on delivering proof-of-concept for FIRMM-
biofeedback (Phase I) and building and validating a product version of FIRMM-biofeedback (Phase II). The
FIRMM-biofeedback technology provides patients and research subjects with real time head motion
information, with the goal of making MR scans safer, faster, more enjoyable and less expens...

## Key facts

- **NIH application ID:** 10442332
- **Project number:** 3R44MH122066-03S1
- **Recipient organization:** TURING MEDICAL TECHNOLOGIES INC
- **Principal Investigator:** Ken Bruener
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $300,000
- **Award type:** 3
- **Project period:** 2019-09-11 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10442332

## Citation

> US National Institutes of Health, RePORTER application 10442332, Visual biofeedback to reduce head motion during MRI scans (3R44MH122066-03S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10442332. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*