3D Digital mammography (3DDM) is a new technology that provides high resolution X-ray breast tomographic data. Like any other tomographic medical imaging modalities, viewing a stack of tomographic images may require time especially if the images are of large matrix size. In addition, it may cause difficulty to conceptually construct 3D breast structures. Therefore, there is a need to readily visualize the data in 3D. However, one of the issues that hinder the usage of volume rendering (VR) is finding an automatic way to generate transfer functions that efficiently map the important diagnostic information in the data. We have developed a method that randomly samples the volume. Based on the mean and the standard deviation of these samples, the technique determines the lower limit and upper limit of a piecewise linear ramp transfer function. We have volume rendered several 3DDM data using this technique and compared visually the outcome with the result from a conventional automatic technique. The transfer function generated through the proposed technique provided superior VR images over the conventional technique. Furthermore, the improvement in the reproducibility of the transfer function correlated with the number of samples taken from the volume at the expense of the processing time.
Measurement of brain structures could lead to important diagnostic information and could indicate the success or failure of a certain pharmaceutical drug. We have developed a totally unsupervised technique that segments and quantifies brain structures from T2 dual echo MR images. The technique classified four different tissue clusters in a scatter plot (air, CSF, brain, and face). Several novel image-processing techniques were implemented to reduce the spread of these clusters and subsequently generate tissue based T2 windows. These T2 windows encompassed all the information needed to segment and subsequently quantify the corresponding tissues in an automatic fashion. We have applied the technique on nineteen MR data sets (16 normal and 3 Alzheimer diseased [AD] patients). The measurements from the T2 window technique differentiated AD patients from normal subjects. The mean value of the %CSF from total the brain was %29.2 higher for AD patients from the %CSF for normal subjects. Furthermore, the technique ran under 30 seconds per data set on a PC with 550 MHz dual processors.
The objective of this work was to acquire co-registered digital tomosynthesis mammograms and 3-D breast ultrasound images of breast phantoms. A prototype mammography compression paddle was built for this application and installed on an x-ray tomosynthesis prototype system (GE). Following x-ray exposure, an automated two-dimensional ultrasound probe mover assembly is precisely positioned above the compression plate, and an attached high-frequency ultrasound transducer is scanned over the acoustically coupled phantom or localized region of interest within the phantom through computerized control. The co-ordinate system of one of the two data sets is then transformed into that of the other, and matching regions of interest on either image set can be simultaneously viewed on the x-ray and ultrasound images thus enhancing qualitative visualization, localization and characterization of regions of interest. The potentials of structured noise reduction, cyst versus solid mass differentiation and full 3-D visualization of multi-modality registered data sets in a single automated combined examination are realized for the first time. Elements of system design and required image correction algorithms will be described and phantom studies with this prototype, automated system on an anthropomorphic breast phantom will be presented.
Cortical bone is the major barrier in visualizing the 3-D blood vessel tree from CT Angiography [CTA] data. Thus, we have developed a novel semi-automatic technique that removes the cortical bone and retains the clinical diagnostic information such as blood vessels, aneurysms, and calcifications. The technique is based on a methodical composite set of filters that use region-growing, adaptive, and morphological filtering algorithms. While using only voxel intensity value and region size information, this technique retains most of the CTA data untouched. We have implemented this method on 10 CTA abdomen and head data sets. The accuracy of the method was tested and proved successful by visual inspection of all segmented slices. The segmented CTA data were also visualized in 3-D with different Ray Casting Volume Rendering techniques (e.g. Maximum Intensity Projection). The blood vessels along with other diagnostic information were clearly visualized in 3-D without the obstruction of bone. The segmentation technique ran under one second per slice (image size is 512x512x2 bytes) on a PC with 550 MHz processor.
Currently, many applications for virtual endoscopy (VE) are available but fly-through is still troublesome. We are using Virtual Endoscopy Software Application (VESA) in our laboratory. VESA generates a 3D model with surface rendering method and a fly-through trajectory automatically. In this study, our goal is to evaluate the usefulness of VESA for generating virtual endoscopy (VE) images and automated fly- through trajectory. We applied VESA to clinical cases including colon, biliary ducts, aortic dissection and larynx. Original cross-sectional images were either spiral CT or MRI. VESA's advantages are following features. First, VESA can generate VE images with simple operation. Second, a point to point correspondence is established between 2D images/3D models and VE images. Third, automated trajectory runs more closely to the center of the hollow organ. VESA is a user- friendly tool for generating the VE images and its automated trajectory reduces the operating time. VESA provides a unique visualization component and makes VE more practical.
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