SBIR-STTR Award

This Small Business Innovation Research (SBIR) Phase I Project proposes to develop and deliver to health care enterprises a next generation imaging, analytics, and search solution that meets not only the current needs of multi-enterprise medical image viewing, but satisfies emerging demands related to clinical decision support and mobile health. The project addresses one of the ?Big Data? problems of medical imaging, i.e. providing access anywhere within the healthcare enterprise to large studies, advanced imaging tools, and image-based analytics across a spectrum of devices from powerful personal computers to mobile devices. The broader impact/commercial potential of this project is to translate novel research using quantitative imaging biomarkers into actual clinical practice. Medical imaging is commonly used for cancer screening, treatment planning, and monitoring but the results that come from purely qualitative interpretations of these images are not always definitive. Recent progress has shown that high-throughput extraction and analysis of advanced quantitative imaging features from medical images (?radiomics?) can be used to increase the accuracy and confidence of cancer screening in certain cases. The goals of this project are to incorporate these analytics into a commercially available system for medical image display and distribution which is necessary for widespread clinical adoption. These advances will enable health care providers to lower costs associated with unnecessary follow up exams as well as to improve patient outcomes through identification of tumors that are more likely to be resistant to treatment and to more efficiently and accurately monitor the response to treatment using medical imaging.

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to significantly improve the care of cancer patients by providing an integrated platform for clinical data and image analytics to their care providers for better clinical decision making. Tight integration of clinical data with radiology images will enable evidence-based approaches to be used by care providers in oncology. The tools being developed in this project will enable accelerated transition of comprehensive data-driven cancer research into clinical practice for decision making related to diagnosis and treatment of cancer. Recent trends, such as wide adoption of electronic medical records and radiological images and availability of powerful computing at reasonable prices have made it possible to improve the prognostic and diagnostic power of data, in particular imaging data analysis in healthcare. The integrated analytics platform being developed will streamline treatment monitoring by imaging and reduce diagnostic errors; hence increasing the quality to cancer care.The proposed project aims to develop an integrated, minable, clinical and imaging data analytics platform for oncology. The platform combines recent advances in data mining, context search, image segmentation and deformable registration into an imaging system deployed at the point of care. The diagnostic potential of clinical and image data will be enhanced by the ability to compare lesion characteristics of the current case to a large repository of lesions from other studies with known diagnosis. The proposed search tool will generate patient cohorts with a given set of diagnosis and treatment conditions and present the related images to the diagnostician. Additional technology will locate and track tumors and provide detailed characterization such as size, shape, location and texture with detailed analytics. A large, minable database of segmented tumors and detailed metrics will advance research into identifying ?imaging biomarkers?. The embedded imaging platform will allow clinicians to access these decision support tools across a wide spectrum of devices from powerful personal computers to tablets and other mobile devices.