SBIR Phase I: Clinical assessment of a computerized workstation for characterization of breast lesions using magnetic resonance imaging (MRI)

Award Information
Agency:
National Science Foundation
Branch
n/a
Amount:
$150,000.00
Award Year:
2011
Program:
SBIR
Phase:
Phase I
Contract:
1046859
Award Id:
n/a
Agency Tracking Number:
1046859
Solicitation Year:
2010
Solicitation Topic Code:
IC
Solicitation Number:
n/a
Small Business Information
Polsky Center, Suite 207, 5807 S Woodlawn Ave, Chicago, IL, 60637-1610
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
962079880
Principal Investigator:
James Krocak
(650) 521-4673
jkrocak@chicagobooth.edu
Business Contact:
James Krocak
MBA
(650) 521-4673
jkrocak@chicagobooth.edu
Research Institution:
Stub




Abstract
This Small Business Innovation Research Phase I project for Information and Communication Technology aims to develop a workstation for the characterization of breast lesions on MRI to assist in breast cancer diagnosis. The specific aims of this SBIR proposal are: 1) to acquire and expand a breast lesion MRI database and, 2) to develop and document a workstation to follow FDA submission requirements. In the US alone, 1 out of 8 women will be diagnosed with breast cancer during their lifetime. Proper diagnosis is a critical component of improving patient outcomes. In particular, earlier diagnosis can result in smaller surgical resections and may obviate the need for adjuvant radio- or chemotherapy. Over the past decade, the use of MRI for breast cancer screening and diagnosis has been rapidly expanding due to its notably higher sensitivity over traditional methods such as mammography. As the data from MRI scans consist of over 300 high-resolution images, this migration presents an increasingly difficult and time-consuming challenge for radiologists. The interpretive challenge presented by the data-intense nature of MRI is only compounded when considering that a malignant lesion may present as only a small speck (~0.5 mm) on a single image. The effort aims to extract relevant information from the rich dataset and improve the efficiency, accuracy, and consistency of image interpretation. The proposed analysis and interpretation techniques include automatic lesion segmentation, automatic image information extraction, and intelligent information fusion.

* information listed above is at the time of submission.

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