SBIR/STTR Research Award

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

Award identifier cb9133ce33cea7b72bacc540124849c693f2e2b81547c0442ed5224ecad28330Awarding agency National Science Foundation

Award amount

$150,000

Program and phase

SBIR · Phase I

Award year

2011

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Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
Quantitative Insights, Inc.
Proposal award date
2011-01-01
Contract end date
2011-06-30
UEI
Not reported
Solicitation number
Not reported
Topic code
IC

Research 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.

Technology keywords

Not reported

Agency intelligence

National Science Foundation

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