Research Project
8478061
DESCRIPTION (provided by applicant): Riverside Research Institute, Beth Israel Deaconess Medical Center, Rutgers University, and the General Electric Corporation propose to undertake an Academic-Industrial Partnership study to improve prostate- cancer (PCA) imaging markedly and thereby improve prostate-biopsy guidance to detect PCa; enhance monitoring, surveillance, and treatment of PCa; and enable planning and execution of focal PCa therapy. The project is highly significant because it addresses a major health problem in the United States and other developed countries. The project will overcome the current inability of established clinical-imaging method to image PCa reliably by combining the capabilities of advanced ultrasound (US) and magnetic-resonance (MR) techniques in a clinically effective manner. The proposed approach will exploit the sensitivity of US to mechanical properties of tissue on a microscopic scale with the sensitivity of MR to chemical constituents of tissue and its ability to sense blood distribution. Each of these modalities senses different and independent properties of tissue and has shown encouraging potential for improved imaging of PCa when used alone; combining parameters derived from each modality can provide far superior sensitivity and specificity for PCa. We will combine US and MR parameters using advanced classifiers such as artificial neural networks and support-vector machines. These classifiers already have produced ROC-curve areas of 0.91 for advanced US methods, and the MR methods have demonstrated equivalent ROC-curve areas in many studies. We will embody the combined capabilities in specifications for a prototype imaging system that can generate prostate tissue-typing images (TTIs) in real-time for targeting biopsies or planning treatment in the operating room or in an off-line setting. The latest Logiq E9 instrument currently being produced by GE already has a capability for fusing previously obtained MR images with US images in real time, which provides an existing framework for combining US and MR parameters and generating real-time TTIs. Successfully generating reliable prostate TTIs based on combined US and MR parameters will represent a quantum advance in PCa management by enabling significant improvements in the diagnosis and treatment of PCa.
