Assessing tumor response to therapy is a crucial step in personalized treatments. Pharmacokinetic (PK) modeling provides quantitative information about tumor perfusion and vascular permeability that are associated with prognostic factors. A fundamental step in most PK analyses is calculating the signal that is generated in the tumor vasculature. This signal is usually inseparable from the extravascular extracellular signal. It was shown previously using in vivo and phantom experiments that independent component analysis (ICA) is capable of calculating the intravascular time-intensity curve in dynamic contrast enhanced (DCE)-MRI. A novel adaptive complex independent component analysis (AC-ICA) technique is developed in this study to calculate the intravascular time-intensity curve and separate this signal from the DCE-MR images of tumors. The use of the complex-valued DCE-MRI images rather than the commonly used magnitude images satisfied the fundamental assumption of ICA, i.e., linear mixing of the sources. Using an adaptive cost function in ICA through estimating the probability distribution of the tumor vasculature at each iteration resulted in a more robust and accurate separation algorithm. The AC-ICA algorithm provided a better estimate for the intravascular time-intensity curve than the previous ICA-based method. A simulation study was also developed in this study to realistically simulate DCE-MRI data of a leaky tissue mimicking phantom. The passage of the MR contrast agent through the leaky phantom was modeled with finite element analysis using a diffusion model. Once the distribution of the contrast agent in the imaging field of view was calculated, DCE-MRI data was generated by solving the Bloch equation for each voxel at each time point. The intravascular time-intensity curve calculation results were compared to the previously proposed ICA-based intravascular time-intensity curve calculation method that applied ICA to the magnitude of the DCE-MRI data (Mag-ICA) using both simulated and experimental tissue mimicking phantoms. The AC-ICA demonstrated superior performance compared to the Mag-ICA method. AC-ICA provided more accurate estimate of intravascular time-intensity curve, having smaller error between the calculated and actual intravascular time-intensity curves compared to the Mag-ICA. Furthermore, it showed higher robustness in dealing with datasets with different resolution by providing smaller variation between the results of each datasets and having smaller difference between the intravascular time-intensity curves of various resolutions. Thus, AC-ICA has the potential to be used as the intravascular time-intensity curve calculation method in PK analysis and could lead to more accurate PK analysis for tumors.

Multimodality medical imaging takes advantage of the strengths of different imaging modalities to provide a more complete picture of the anatomy under investigation. Many complementary modalities have been combined to form such systems and some are gaining use clinically. One combination that has not been developed, in large part due to technical difficulties, is a combined magnetic resonance (MR) and ultrasound (US) imaging system. Such a system offers the potential to combine the strengths of these modalities in a wide range of diagnostic and therapeutic applications. The goal of this study was to evaluate the feasibility of performing simultaneous multimodality US and MR imaging. An US imaging system capable of operation in a clinical MR imager was developed, and methods to perform simultaneous imaging were investigated. Simultaneous imaging was feasible without any mutual interference by either filtering the transmitted and received US signal, or by synchronizing data acquisition between the two imaging systems. Spatial registration between the two modalities was achieved by using a reference phantom with implanted glass beads in orthogonal planes. Excellent agreement was observed between spatial measurements of an object made with both modalities, and the feasibility of using this system in vivo was demonstrated in a rabbit model. Simultaneous US and MR imaging is achievable, and can provide complementary information about an object under investigation. This demonstration of technical feasibility and the development of a prototype system open up the potential to investigate the promising clinical applications of this combined technology.

Content-based retrieval of images is the ability to retrieve images that are similar to a query image. Oracle8i Visual Information Retrieval provides this facility based on technology licensed from Virage, Inc. This product is built on top of Oracle8i interMedia which enables storage, retrieval and management of images, audios and videos. Images are matched using attributes such as color, texture and structure and efficient content-based retrieval is provided using indexes of an image index type. The design of the index type is based on a multi-level filtering algorithm. The filters reduce the search space so that the expensive comparison algorithm operates on a small subset of the data. Bitmap indexes are used to evaluate the first filter resulting in a design which performs well and is scalable. The image index type is built using Oracle8i extensible indexing technology, allowing users to create, use, and drop instances of this index type as they would any other standard index. In this paper we present an overview of the product, the design of the image index type, and some performance results of our product.