Multi-Model Detection and Quantification of Multiple Sclerosis in MR Imaging

Award Information
Agency:
Department of Health and Human Services
Branch
n/a
Amount:
$329,377.00
Award Year:
2009
Program:
STTR
Phase:
Phase I
Contract:
1R41NS060473-01A2
Award Id:
94042
Agency Tracking Number:
NS060473
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
INFOTECH SOFT, INC., 1201 Brickell Ave, MIAMI, FL, 33131
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
035354070
Principal Investigator:
AKMALYOUNIS
(305) 670-5111
AKMAL.YOUNIS@INFOTECHSOFT.COM
Business Contact:
PATRICSHIRONOSHITA
() -
kabuka@infotechsoft.com
Research Institute:
UNIVERSITY OF MIAMI CORAL GABLES

UNIVERSITY OF MIAMI
1204 DICKINSON DR
CORAL GABLES, FL, 33134 2701

Nonprofit college or university
Abstract
DESCRIPTION (provided by applicant): Multiple sclerosis (MS), a neurodegenerative disease that afflicts the central nervous system, is characterized by lesion formation and atrophy of the brain and spinal cord. Atrophy was reported to occur early in the di sease and to increase with the disease progression in various cortical and sub-cortical regions, reflecting widespread loss of myelin, axons and neural cell bodies. Published studies in the past decade have demonstrated that recent advances in Magnetic Res onance Imaging (MRI) have exhibited great progress in the detection, visualization and quantification of the onset and progression of MS disease. In order for these advances in the diagnosis and assessment of the progression of the disease to continue and systematically change the clinical evaluation of MS, techniques for accurate, automated, and robust detection of MS lesions and quantification of brain atrophy must be developed to enable increased utilization in clinical settings. The main objective of th is proposal is to develop an artificial immune classification (AIC) technique for accurate, automated and robust MRI data analysis for the purpose of MS lesion detection and quantification of regional brain atrophy. The proposed AIC technique for quantitat ive measurement of the effect and progression of MS disease aims to tackle current challenges in assessing MS through a generic and unified approach that relies on artificial immune functions to enable accurate identification of different tissue classes in the brain. During phase I of the grant, a prototype of the proposed AIC technique will be developed and evaluated in a pilot study involving real MRI data of MS patients and controls. In addition, the evaluation will involve simulated MRI data at varying levels of MS disease burden, noise, and intensity in-homogeneity. Phase I will provide a proof-of-concept of the proposed AIC technique as well as demonstrate its practical feasibility for assessment of MS lesion burden and regional accuracy for quantifyin g white matter and gray matter. PUBLIC HEALTH RELEVANCE: The enhancements resulting from the proposed project in detection accuracy and quantification of brain abnormalities due to multiple sclerosis (MS) would enable better understanding of the disease pr ocesses, progression and varying effects on different regions of the brain, which would allow improved planning of clinical trials focusing on MS.

* information listed above is at the time of submission.

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