Ovarian cancer has the highest mortality rate of all gynecologic cancers, claiming about 14,000 lives each year in the United States alone. The five-year survival rate for patients with ovarian cancer is only about 50% in spite of recent advances in treatment. This dismal survival rate is primarily due to the fact that there are no effective methods to detect the disease in its early stages when it can be readily treated. Treatment is also very challenging as oncologists must select between a large number of possible medications with very little guidance on whether or not a particular patient will respond to a specific drug. Creare, working under funding from the Army, has developed new signal processing methods that have the potential to improve detection and treatment of this disease.

Creare developed tools that enable processing of data from time-lapse magnetic resonance scanning (Dynamic Contrast-Enhanced Magnetic Resonance Imaging or DCE-MRI) to provide clinicians with a more detailed picture of the disease. DCE-MRI is a noninvasive method of measuring perfusion in different parts of the body that has emerged as a powerful method for characterizing tumors and determining treatment efficacy in other forms of cancer (most notably brain and prostate). However, prior to the start of our work, DCE-MRI techniques could not be readily applied to abdominal tumors caused by ovarian cancer due to respiratory motion. To address this, we developed automated processing methods that greatly attenuate respiratory motion during MR imaging to provide high-resolution imaging of blood flow in tumors and surrounding tissues even when they are near the lungs or diaphragm. We also developed signal processing methods to parameterize blood flow in an effort to provide early detection of aberrant blood flow that could be associated with the early stages of ovarian cancer.