We are investigating the feasibility of a computer-aided detection (CAD) system to assist radiologists in diagnosing
coronary artery disease in ECG gated cardiac multi-detector CT scans having calcified plaque. Coronary artery stenosis
analysis is challenging if calcified plaque or the iodinated blood pool hides viable lumen. The research described herein
provides an improved presentation to the radiologist by removing obscuring calcified plaque and blood pool. The
algorithm derives a Gaussian estimate of the point spread function (PSF) of the scanner responsible for plaque blooming
by fitting measured CTA image profiles. An initial estimate of the extent of calcified plaque is obtained from the image
evidence using a simple threshold. The Gaussian PSF estimate is then convolved with the initial plaque estimate to
obtain an estimate of the extent of the blooming artifact and this plaque blooming image is subtracted from the CT image
to obtain an image largely free of obscuring plaque. In a separate step, the obscuring blood pool is suppressed using
morphological operations and adaptive region growing. After processing by our algorithm, we are able to project the
segmented plaque-free lumen to form synthetic angiograms free from obstruction. We can also analyze the coronary
arteries with vessel tracking and centerline extraction to produce cross sectional images for measuring lumen stenosis.
As an additional aid to radiologists, we also produce plots of calcified plaque and lumen cross-sectional area along
selected blood vessels. The method was validated using digital phantoms and actual patient data, including in one case, a
validation against the results of a catheter angiogram.
Prostate cancer is diagnosed by histopathology interpretation of hematoxylin and eosin (H and E)-stained tissue sections. Gland and nuclei distributions vary with the disease grade. The morphological features vary with the advance of cancer where the epithelial regions grow into the stroma. An efficient pathology slide image analysis method involved using a tissue microarray with known disease stages. Digital 24-bit RGB images were acquired for each tissue element on the slide with both 10X and 40X objectives. Initial segmentation at low magnification was accomplished using prior spectral characteristics from a training tissue set composed of four tissue clusters; namely, glands, epithelia, stroma and nuclei. The segmentation method was automated by using the training RGB values as an initial guess and iterating the averaging process 10 times to find the four cluster centers. Labels were assigned to the nearest cluster center in red-blue spectral feature space. An automatic threshold algorithm separated the glands from the tissue. A visual pseudo color representation of 60 segmented tissue microarray image was generated where white, pink, red, blue colors represent glands, epithelia, stroma and nuclei, respectively. The higher magnification images provided refined nuclei morphology. The nuclei were detected with a RGB color space principle component analysis that resulted in a grey scale image. The shape metrics such as compactness, elongation, minimum and maximum diameters were calculated based on the eigenvalues of the best-fitting ellipses to the nuclei.
A new bubble wave algorithm provides automatic segmentation of three-dimensional magnetic resonance images of both the peripheral vasculature and the brain. Simple connectivity algorithms are not reliable in these medical applications because there are unwanted connections through background noise. The bubble wave algorithm restricts connectivity using curvature by testing spherical regions on a propagating active contour to eliminate noise bridges. After the user places seeds in both the selected regions and in the regions that are not desired, the method provides the critical threshold for segmentation using binary search. Today, peripheral vascular disease is diagnosed using magnetic resonance imaging with a timed contrast bolus. A new blood pool contrast agent MS-325 (Epix Medical) binds to albumen in the blood and provides high-resolution three-dimensional images of both arteries and veins. The bubble wave algorithm provides a means to automatically suppress the veins that obscure the arteries in magnetic resonance angiography. Monitoring brain atrophy is needed for trials of drugs that retard the progression of dementia. The brain volume is measured by placing seeds in both the brain and scalp to find the critical threshold that prevents connections between the brain volume and the scalp. Examples from both three-dimensional magnetic resonance brain and contrast enhanced vascular images were segmented with minimal user intervention.
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.
Using helical, multi-detector computed tomography (CT) imaging technology operating at sub-second scanning speeds, clinicians are investigating the capabilities of CT for cardiac imaging. In this paper, we describe the application of novel modeling tools to assess CT system capability. These tools allow us to quantify the capabilities of both hardware and software algorithms for cardiac imaging. The model consists of a human thorax, a dynamic model of a human heart, and a complete physics-based, CT system model. The use of the model to predict image quality is demonstrated by varying both the reconstruction algorithm (half-scan, sector-based) and CT system parameters (axial detector resolution). The mathematical tools described provide a means to rapidly evaluate new reconstruction algorithms and CT system designs for cardiac imaging.
With the introduction of helical, multi-detector computed tomography (CT) scanners having sub-second scanning speeds, clinicians are currently investigating the role of CT in cardiac imaging. In this paper, we describe a four-dimensional (4D) x-ray attenuation model of a human heart and the use of this model to assess the capabilities of both hardware and software algorithms for cardiac imaging. We developed a model of the human thorax, composed of several analytical structures, and a model of the human heart, constructed from several elliptical surfaces. A model for each coronary vessel consists of a torus placed at a suitable location on the heart's surface. The motion of the heart during the cardiac cycle was implemented by applying transformational operators to each surface composing the heart. We used the 4D model of the heart to generate forward projection data, which then became input into a model of a CT imaging system. The use of the model to predict image quality is demonstrated by varying both the reconstruction algorithm (sector-based, half-scan) and CT system parameters (gantry speed, spatial resolution). The mathematical model of the human heart, while having limitations, provides a means to rapidly evaluate new reconstruction algorithms and CT system designs for cardiac imaging.
KEYWORDS: Visualization, 3D modeling, Surgery, Tumors, 3D image processing, 3D acquisition, Image processing, Data acquisition, Tissues, Image segmentation
The requirements for 3-D reconstructions to be useful in a clinical environment include availability of the imaging and computing hardware; sophisticated and user-friendly software that can be used by physicians or technicians; robust data that can be efficiently segmented into clinically relevant structures; interactive speed; and the ability to manipulate the visualized structures for the simulation of surgical procedures. We have developed an efficient hardware, software, and application environment that fulfills these requirements and have initiated testing of its performance.
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