A Tracked MR Hardware/Software System for Motion Robust Pediatric Magnetic Resonance Imaging This research aims to develop improved motion robust magnetic resonance imaging (MRI) technology. Magnetic resonance imaging is a powerful tool that both aids in the diagnosis of individual subjects, guides their clinical care, and provides insights into the mechanisms of normal and atypical development. The characterization of mental health disorders across the lifespan has been facilitated by brain MRI. However, conventional magnetic resonance imaging requires the subject being imaged remain perfectly still for the duration of the image encoding. Young children and adult patients with autism or tuberous sclerosis often have difficulty to hold still long enough for successful imaging. For clinical imaging such patients are frequently sedated or anesthetized, which runs the risk of adverse events, and does not ensure the subjects remain perfectly still. Research studies are typically unable to use sedation or anesthesia due to the possibility of morbidity or mortality. This hampers our ability to identify the neurobiological underpinnings of mental health disorders. The development of motion robust magnetic resonance imaging will enable dramatic improvements in our capacity to chart the trajectory of mental illness over the lifespan. This will have a significant impact on our ability to determine when, where and how to intervene, and enable improved evaluation of potential interventions. This project is a Phase I SBIR in response to the Program Announcement ''Lab to Marketplace: Tools for Brain and Behavioral Research''. The project proposes to utilize a novel combination of hardware for motion tracking and sophisticated post-acquisition processing with scattered data interpolation to enable the construction of high resolution high signal-to-noise ratio images of the brain from subjects who are unable to hold still. In this Phase I study we seek to demonstrate the capacity to compensate for rigid body motion of the head, building on our previous work in the development of MRI compatible tracking hardware, and scattered data interpolation. Successful imaging in the presence of motion will be evaluated through experiments with healthy volunteers and with patients with autism or tuberous sclerosis.

Public Health Relevance

This research aims to develop improved motion robust magnetic resonance imaging technology. The improved imaging technology will be applied and evaluated for its efficacy at enabling high resolution imaging of children and adults at Children's Hospital Boston. Young children and adult patients with autism or tuberous sclerosis often have difficulty to hold still for magnetic resonance imaging. This hampers our ability to identify the neurobiological underpinnings of mental health disorders. The development of motion robust magnetic resonance imaging will enable dramatic improvements in our capacity to chart the trajectory of mental illness over the lifespan. This will have a significant impact on our ability to determine when, where and how to intervene, and enable improved evaluation of potential interventions.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Small Business Technology Transfer (STTR) Grants - Phase I (R41)
Project #
1R41MH086984-01
Application #
7745683
Study Section
Special Emphasis Panel (ZRG1-SBMI-T (10))
Program Officer
Grabb, Margaret C
Project Start
2009-08-26
Project End
2011-07-31
Budget Start
2009-08-26
Budget End
2010-07-31
Support Year
1
Fiscal Year
2009
Total Cost
$375,875
Indirect Cost
Name
Robin Medical, Inc.
Department
Type
DUNS #
146202358
City
Baltimore
State
MD
Country
United States
Zip Code
21203
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Gholipour, Ali; Polak, Martin; van der Kouwe, Andre et al. (2011) Motion-robust MRI through real-time motion tracking and retrospective super-resolution volume reconstruction. Conf Proc IEEE Eng Med Biol Soc 2011:5722-5
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Gholipour, Ali; Estroff, Judy A; Sahin, Mustafa et al. (2010) Maximum a posteriori estimation of isotropic high-resolution volumetric MRI from orthogonal thick-slice scans. Med Image Comput Comput Assist Interv 13:109-16