Dynamic contrast-enhanced MRI (DCE-MRI) has been playing an essential role in the diagnosis, staging, and prognosis of breast cancer, especially highlighted with its high sensitivity to detect suspicious lesions. However, one concern of DCE-MRI in the diagnosis of breast cancer is its moderate specificity (i.e., relatively high false-positive rate) and various in reported clinical studies. Thus, it is of great importance to develop new MRI acquisition and analysis methods that can improve the specificity for cancer diagnosis. 

In this project, we take use of both high-spatial resolution and high-temporal resolution (ultra-fast) DCE-MRI to characterize tumor microenvironment features associated with vascular architecture, blood supply, and interstitial transport, which are known to be hallmarks of cancer.  We developed a novel image analysis framework to extract morphological and functional information of tumor‐associated vessels from the DCE-MRI. Moreover, we established an image-guided computational fluid dynamic model system to estimate blood and interstitial flow fields, as well as drug/nutrition supply dynamics, associated with breast tumors.    

Both techniques provided new metrics improving  the accuracy and specificity of differentiating breast cancer from benign breast lesions.  The results indicate that quantitative imaging characterization of morphological and functional features of breast vasculature and fluid transportation has the potential to improve breast cancer diagnosis.