Project title CCD x-ray fluoroscopy using binary optical technique

Markov random-field priors will be investigated to model the local structure of the residual-stool configuration. An optimal Bayesian inference will be used to compute the solution recognizing the residual- stool patterns. (3) To investigate visualization techniques for achieving interactive navigation and differentiating residual stools from polyps. Camera control and interactive surface and volume rendering will be studied to enable the physician to inspect the colonic surface and sub-surface tissue intuitively and interactively. With success of the specific aims, an accurate and cost-effective procedure for massive colon screening should be possible. Thesaurus Terms: colon polyp, computer assisted diagnosis, computer simulation, diagnosis design/evaluation, endoscopy, gastrointestinal visualization, neoplasm/cancer radiodiagnosis barium, computed axial tomography, computer program/software, contrast media, noninvasive diagnosis bioimaging/biomedical imaging, clinical research, human subject

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State University New York Stony Brook Stony Brook, NY 11794 1999 Radiology 12-Aug-98 31-Jul-00 National Cancer Institute ZRR1

)ROJECT TITL! CCD X-RAY FLUOROSCOPY USING BINARY OPTICAL TECHNIQUE Grant Number: PI Name:

7R01CA70209-03 Liu, Hong

Abstract: We propose to develop an optically-coupled CCD x-ray system using a novel, optical multiplexing imaging technique. A group of binary optical lenses is used instead of an image intensifier to couple the latent image from a scintillating screen to the CCD. Low readout-rate CCD imagers can be effectively used for fluoroscopic imaging without increasing detector noise. This technology overcomes disadvantages of conventional image intensifier fluoroscopy, including: aberrations, noise, contrast degradation, and restricted dynamic range and spatial resolution of radiographic images. Our preliminary studies indicate that the proposed system, operating at 16 frames per second, is x-ray quantum noise limited. Compared with an image intensifier and conventional fluoroscopy, it offers improved spatial resolution (up to 14 lp/mm), better contrast sensitivity, and a wider dynamic range (12-14 bit) for the same radiation dose. We plan to build a laboratory prototype and measure modulation transfer function, signalto-noise ratio, detective quantum efficiency and lesion detectability, as a function of frame rate. We will conduct contrast-

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detail analysis, needle biopsy, and observer studies, comparing the proposed system to image intensifier fluoroscopic systems and stereotactic CCD imaging systems. The proposed technology is applicable to radiologic interventional procedures requiring real-time imaging. It is ideally suited for low Kvp image-guided breast interventional procedures such as: (1) preoperative lesion localization; (2) image-guided needle biopsy; galactography; (4) real-time imaging to evaluate implant envelopes; and potentially, (5) real-time image-guided local interstitial therapy, such as lesion ablation. At higher Kvp it is applicable to certain interventional procedures such as;(1)fluoroscopic evaluation and guide biopsy of pulmonary lesions; (2) gastrointestinal fluoroscopic procedures such as endoscopic retrograde cholangiopancreatography (ERCP), percutaneous transhepatic cholangiography (PTHC), and biliary drainage procedures; (3) musculoskeletal interventional procedures such as facet joint anesthetic blocks, bone biopsy, and vertebroplasty; and (4) neurologic interventional procedures requiring high resolution, real-time fluoroscopic imaging such as cerebral aneurysm occlusion. Thesaurus Terms: biomedical equipment development, charge coupled device camera, diagnosis design/evaluation, fluoroscopy, image enhancement, optics phantom model bioimaging/biomedical imaging

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Fiscal Year: Department: Project Start: Project End: ICD: IRG:

Johns Hopkins University 3400 N Charles St Baltimore, MD 21218 1999 Radiology 30-Sep-97 31-Jul-01 National Cancer Institute ZRG7

~ROJECT TITLE COMPUTER AIDED DIAGNOSIS OF BREAST CANCER INVASION Grant Number: PI Name:

5R29CA75547-02 Lo, Joseph Y.

Abstract: DESCRIPTION: The purpose of this study is to develop a computer-aided diagnosis (CADx) system to predict breast lesion malignancy and invasion based on medical findings. Artificial neural network (ANN) techniques will be used to predict whether mammographically suspect lesions are benign, in situ cancer, or invasive cancer. The ANN inputs will be derived from existing, available information such as patient history and radiologists descriptions of lesion morphology following the ACR Breast Imaging Reporting and Data System (BI-RADS). ANNs are well suited for this diagnostic task because, like humans, ANNs can be taught to perform diagnostic tasks accurately and robustly when given appropriate training