SBIR-STTR Award

This Small Business Innovation Research Phase I project establishes the feasibility of Computer Aided Prognosis of Debilitating Disease. Because disease arises through a complex interaction of multiple molecular signals and pathways often confounding the eventual effect, tools and approaches are needed to identify key pathways that reflect the underlying pathological processes. While both functional imaging modalities have recently emerged, the computational tools that would allow for accurate analysis of these imaging modalities in order to allow for prediction of therapy discovery, development, disease stratification, and personalized medicine are sorely lacking. Previous approaches rely on identifying one or a relatively small number of distinguishing features hypothesized to be precursor to an acute event. CAP seeks to build on this by providing functional characterization that extends the static diagnostic categorization to prognosticate the likely future progression. The research objective is to develop an integrated segmentation, registration, and classification toolkit for prognosis prediction of vascular disease from dynamic time-series imaging data. The goal of Phase I research is a software endpoint. We demonstrate probable clinical utility by the successful extraction of values that meet or exceed the manually produced preliminary studies when assessed on the available animal and human data sets. The broader impact/commercial potential of this project is to develop methods for identifying prognostic imaging signatures of disease aggressiveness and predicting potential patient outcome so as to improve it. The task of distinguishing which subtypes of vascular lesions will have favorable outcome as opposed to unfavorable outcome requires sophisticated image analysis and quantification and feature characterization algorithms to accentuate the subtle imaging differences between these related pathologies. Scientific and technological understanding of how dynamic aspects of disease progression may be discerned from higher-order processing which optimizes information content from imaging assays. This technology represents a cost effective, safe and capable plaque assessment tool, so that patients could be treated more effectively, sooner, and more appropriately. This project creates an end-user capable prototype that may also be extended in preparing first a 510(k) and subsequently a PMA application as a prognostic for individual patient management. vascuVis will support the market initially by selling software licenses and later by developing a pay per use business model. Commercially, over 20,000 MRI units installed worldwide could benefit from this product. At 75K average pricing, the opportunity is as high as $1.5B. The total accessible market for this product could be as high as $1.5B

This Small Business Innovation Research (SBIR) Phase II project proposes to develop robust and effective imaging techniques for assessment of atherosclerotic disease severity for prognostic and longitudinal use. In the United States alone, approximately 5 million patients suffer pre-stroke symptoms of which 795,000 go on to a stroke annually. About 610,000 of these are first or new strokes, while the remainder are recurrent strokes. Despite these statistics, there is no effective test to tell who will or will not suffer acute events or to measure whether medical therapies are effective at reducing the risk. In this project, multivariate quantitative descriptors are developed using data from controlled outcome studies on a specialized model to discover and validate prognostic signatures that, in composite, perform well in both cross-sectional prognostic and longitudinal applications with high predictive value. Phase I results are extended to support histologically verifiable tissue types, expanding the functional and performance attributes of the product with a tie to localized ground truth maps made possible with co-registration of histology with MRI. The plan is to iteratively validate the developed capability under commercially accepted design controls. The broader impact/commercial potential of this project will be the development of effective means for computer-aided prognostics using quantitative imaging phenotypes. Physicians face a complex and heterogeneous series of clinical manifestations of disease. Because disease arises through a complex interaction of multiple molecular signals and pathways often confounding the eventual effect, tools and approaches are needed to identify key pathways that reflect the underlying pathological processes. Functional imaging modalities have recently emerged for characterization of these disease processes and to obtain a better mechanistic understanding of the underlying biologic processes to distinguish more aggressive from less aggressive disease phenotypes. Computer-aided prognosis (CAP) of disease is a new and exciting complement to the field of computer-aided diagnosis (CAD). Since CAP approaches distinguish between different subtypes of a particular disease (as opposed to CAD schemes trying to distinguish diseased from benign processes), there is a need for more sophisticated image analysis, computer vision, and machine learning methods to identify subtle disease signatures that can separate unstable from stable disease. The chosen application in this project is a use case that has the potential to radically increase the power of applications to support clinicians in pursuit of personalized medicine.