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STTR Phase I: A Clinical Decision Support Tool for Brain Magnetic Resonance Imaging (MRI) in Children

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 1722445
Agency Tracking Number: 1722445
Amount: $225,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: BM
Solicitation Number: N/A
Solicitation Year: 2016
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-07-01
Award End Date (Contract End Date): 2018-06-30
Small Business Information
529 S BROADWAY # 4041
LOS ANGELES, CA 90013-2365
United States
DUNS: 079439264
HUBZone Owned: Yes
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Sinchai Tsao
 (213) 453-1833
Business Contact
 Sinchai Tsao
Phone: (213) 453-1833
Research Institution
 Children's Hospital Los Angeles
 Natasha Lepore
The Saban Research Institute 4650 Sunset Boulevard, MS # 97
Los Angeles, CA 90027-6062
United States

 Domestic Nonprofit Research Organization

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to enable doctors to maximize the diagnostic information extracted from costly Pediatric Magnetic Resonance Imaging (MRI) scans of the brain. Annually, approximately 3.5 million brain MRI scans are performed with average prices with interpretation from $500 - $2,000. Even though the scans are expensive to acquire and interpret, doctors generally only use visual inspection of the images to diagnose abnormalities. The proposed software will be relatively cheap compared to the overall cost of an MRI and is expected to let doctors make accurate measurements of key structures in the brain. This is important because developmental and other diseases can cause small volume and surface area changes that cannot be easily seen by the human eye. By detecting these changes early, doctors should be able to treat the disease at early stages, potentially leading to better quality of life and financial savings. The proposed tool is also expected to enable doctors to better track treatment outcomes, by measuring the effect a drug or treatment has on particular structures in the brain. Ultimately, it is expected that the proposed technology will improve quality of care as well as reduce healthcare costs. The proposed project leverages current advances in computational technology such as machine learning and computer vision to automatically measure volume, surface area and shapes in critical parts of the brain in children. These measurements are then compared to a large database of children over the ages of 0-12 years to determine if they have deviated from normal. Although MRIs have been increasing adopted as the diagnostic tool of choice for childhood brain disorders due to the lack of radiation, there has yet to be a tool that allows doctors to accurately measure changes in a child's brain from MRI scans. Because, in children, the brain is rapidly changing as she/he grows, it is difficult to determine visually whether the changes are due to normal development or disease. By measuring the brain accurately and comparing it to a large database already collected by the proposing team, it is expected that a pediatric doctor will be able to determine whether a child's brain has deviated from normal. It could also allow physicians to better select treatments and monitor the patient response to a specific therapy.

* Information listed above is at the time of submission. *

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