The long term objective of the project is to implement new motion robust magnetic resonance imaging (MRI) technology into wide clinical and research use. Magnetic resonance imaging is a powerful tool that 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 non-cooperative adult patients 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, is costly, and does not ensure the subjects remain perfectly still. Research studies are typically unable to use sedation or anesthesia due to its significant risk. This hampers our ability to identify the neurobiological underpinnings of mental health disorders. The ability to use routinely 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. The project proposes to utilize a combination of hardware for prospective motion tracking during scan and post-acquisition processing to enable the construction of high resolution and high signal-to-noise ratio images of the brain from subjects who are unable to hold still.
The specific aims of this Phase IIB SBIR application are to optimize the EndoScout configuration and workflow for routine clinical and research use; to implement of the hybrid prospective/re-acquisition/retrospective framework into a wide scale clinical and research use; and to develop new, sophisticate motion correction approaches. This is a joint project by Robin Medical, Inc., which will optimize the motion tracking system for routine clinical and human research use; the Research Section at the Department of Radiology, Children?s Hospital Boston, which will implement the prospective motion correction algorithm into wide range of imaging sequences and will conduct the clinical studies of the project; and the Martinos Center at the Massachusetts General Hospital that will develop advanced sequences for prospective motion correction, using the EndoScout tracking data along with image navigators, and will use the motion immune MRI sequences in ongoing human research studies.

Public Health Relevance

The proposed project aims to enable MRI in children and non-cooperative adults without sedation or anesthesia. If successful, it will reduce healthcare costs as sedation or anesthesia for MRI doubles or triples the cost of the scan; it will expand the use of MRI in children and non-cooperative adults to smaller hospitals that do not have the required resources to conduct MRI under sedation or anesthesia; and it will eliminate the risks associated with sedation or anesthesia and thus will enable to expand the use of MRI to clinical and research application where it is now not being used due to associated risks.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
Project #
2R44MH086984-06A1
Application #
9200269
Study Section
Special Emphasis Panel (ZRG1-SBIB-T (10)B)
Program Officer
Grabb, Margaret C
Project Start
2016-09-23
Project End
2019-08-31
Budget Start
2016-09-23
Budget End
2017-08-31
Support Year
6
Fiscal Year
2016
Total Cost
$798,308
Indirect Cost
Name
Robin Medical, Inc.
Department
Type
DUNS #
146202358
City
Baltimore
State
MD
Country
United States
Zip Code
21203
Coll-Font, Jaume; Erem, Burak; Brooks, Dana H (2018) A Potential-Based Inverse Spectral Method to Noninvasively Localize Discordant Distributions of Alternans on the Heart From the ECG. IEEE Trans Biomed Eng 65:1554-1563
Sourati, Jamshid; Gholipour, Ali; Dy, Jennifer G et al. (2018) Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support ( 11045:83-91
Afacan, Onur; Erem, Burak; Roby, Diona P et al. (2016) Evaluation of motion and its effect on brain magnetic resonance image quality in children. Pediatr Radiol 46:1728-1735
Afacan, Onur; Gholipour, Ali; Mulkern, Robert V et al. (2016) Fetal lung apparent diffusion coefficient measurement using diffusion-weighted MRI at 3 Tesla: Correlation with gestational age. J Magn Reson Imaging 44:1650-1655