Biomedical Image Processing

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This project uses image processing techniques to analyze many types of biological, clinical and biomedical images. Current research focuses on four general areas: (1) the structural biology of macromolecules using image processing of electron micrographs; (2) medical imaging, which includes (a) PET, (b) ultrasound in speech pathology, (c) ultrasound in cardiac imaging, (d) CT imaging, (e) MRI, (f) EPR imaging, (g) imaging in cancer research, and (h) imaging related to neural disfunction; (3) general laboratory imaging; and (4) biomedical 3D reconstruction algorithm development. The Image Processing Research Section, Computational Bioscience and Engineering Laboratory has a long term collaborative research effort with the institutes involving the use of image processing techniques and advanced computational techniques to analyze electron micrographs with the goal of determining macromolecular structures. Recent efforts have concentrated on the 3D reconstruction, analysis and interpretation of the structures of icosahedral virus capsids. The Image Processing Research Section, Computational Bioscience and Engineering Laboratory has a long-term commitment to providing computational and engineering expertise to a variety of clinical and biomedical activities at NIH. Specifically, PET, ultrasound, CT, MRI, EPR, imaging in cancer research, and imaging related to neural disfunction have been supported in a number of ways. For example, our participation in the development of new animal PET scanner technology has extended image resolution far beyond what was available from previous state-of-the-art scanners. To support scientific research in the NIH intramural program, CIT has made major progress in the development of a platform-independent, n-dimensional, general-purpose, extensible image processing and visualization program written in the JAVA programming language. The MIPAV (Medical Image Processing Analysis and Visualization) application enables quantitative analysis and visualization of medical images of numerous modalities (i.e. PET, MRI, CT, microscopy, etc.). It has been used to analyze tumors for Diagnostic Radiology, assisted in longitudinal studies in collaboration with NIDCR, analysis of MRI images for NIMH, and has been used by NCI for the analysis of 2D microscopic samples. We have also been instrumental in the development of unique imaging software called NIH magic for the Cardiology Branch of the National Heart Lung and Blood Institute (NHLBI) which will contribute to the developing field of cardiac tissue viability studies. This software is also being used by Speech pathology in the Department of Rehabilitation Medicine to perform 3D visualization and is currently being extended for use in other imaging projects. In addition, we have adapted the our prior 3-D PET reconstruction algorithms for a new generation of inexpensive small animal PET scanners, consisting of opposed arrays of pixelated scintillation crystals. Since these pixelated scanners are suitable for high-volume small animal studies, we are developing client-server software to facilitate production mode reconstruction processing on CIT's high performance computing cluster. We have developed and implemented iterative algorithms for EPR reconstructions, including the multiplicative arithmetic reconstruction technique (MART) and a least-squares/entropy maximization algorithm. Our current work in EPR is focusing on adapting these algorithms to very noisy imaging environments, e.g., high-gradient pulsed EPR.