The goal of this STTR 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 expensive.
Magnetic resonance imaging (MRI) has unrivaled clinical and research utility, is non-invasive, and provides extremely high spatial resolution; however, MRIs have one severe limitation: subject motion during an MRI scan greatly diminishes the quality of the resulting images. Despite this fact, motion is not routinely monitored during MRI scans. We have developed software (FIRMM) that allows for non-invasive monitoring of subject motion during brain MRIs. The goal of this proposal is to build and validate biofeedback technology (FIRMM- biofeedback) that uses FIRMM head motion information and visually displays it in the scanner bore, in order to actively reduce head motion via biobehavioral training, thus increasing image quality, scanning quality and patient satisfaction.
