Commercialization of the Shutter-Speed Model for Dynamic MRI in Cancer Diagnosis

Period of Performance: 06/01/2015 - 05/31/2016

$546K

Phase 2 SBIR

Recipient Firm

Imbio, LLC
MINNEAPOLIS, MN 55413
Principal Investigator

Abstract

DESCRIPTION (provided by applicant): Despite remarkable advances in cancer detection and treatment, the disease continues to be a leading cause of mortality in the US accounting for 23% of all deaths in 2011. Cancer of the breast and prostate are by far the most common forms diagnosed in US women and men, respectively, and together are expected to represent more than 450,000 (246,000 prostate, 229,000 breast cancers) new cases and more than 68,000 deaths this year. Since the serious overtreatment of each disease is such a significant issue, improved methods for minimally invasive detection and therapy monitoring are badly needed. Dynamic contrast- enhanced (DCE)-MRI offers substantial promise in this regard. It is a technique acquiring a time-series of T1- weighted MR images before, during, and after intravenous injection of a paramagnetic contrast reagent (CR). The benefits of quantifying the DCE-MRI time-series using a pharmacokinetic model have gained significant interest in recent years and the resulting parametric maps are increasingly important in cancer diagnostics and treatment evaluation. Recent studies demonstrate that quantitative DCE-MRI has the potential to improve accuracy in cancer detection and provide earlier and more accurate evaluation of cancer response to therapy. The overall goal of this SBIR Fast-Track project is to develop and validate a commercial diagnostic software application based on the Shutter-Speed Model (SSM) for quantitative DCE-MRI. The SSM is a novel algorithm that properly accounts for the finite kinetics of water exchange between tissue compartments. This is important because a unique aspect of DCE-MRI is that the CRs are detected indirectly, via their effect on the 1H2O MR signal; CR is the tracer molecule but water is the signal molecule. The SSM approach naturally embraces this feature and has been shown to deliver more reliable discrimination between benign and malignant tissue than the standard tracer DCE pharmacokinetic model.