Computer aided quantification of emphysema in high resolution CT data is based on identifying low attenuation areas below clinically determined Hounsfield thresholds. However, the emphysema quantification is prone to error since a gravity effect can influence the mean attenuation of healthy lung parenchyma up to ± 50 HU between ventral and dorsal lung areas. Comparing ultra-low-dose (7 mAs) and standard-dose (70 mAs) CT scans of each patient we show that measurement of the ventrodorsal gravity effect is patient specific but reproducible. It can be measured and corrected in an unsupervised way using robust fitting of a linear function.
Deformable models have already been successfully applied to the semi-automatic segmentation of organs from
medical images. We present an approach which enables the fully automatic segmentation of the heart from multi-slice
computed tomography images. Compared to other approaches, we address the complete segmentation chain
comprising both model initialization and adaptation.
A multi-compartment mesh describing both atria, both ventricles, the myocardium around the left ventricle
and the trunks of the great vessels is adapted to an image volume. The adaptation is performed in a coarse-to-
fine manner by progressively relaxing constraints on the degrees of freedom of the allowed deformations. First,
the mesh is translated to a rough estimate of the heart's center of mass. Then, the mesh is deformed under the
action of image forces. We first constrain the space of deformations to parametric transformations, compensating
for global misalignment of the model chambers. Finally, a deformable adaptation is performed to account for
more local and subtle variations of the patient's anatomy.
The whole heart segmentation was quantitatively evaluated on 25 volume images and qualitatively validated
on 42 clinical cases. Our approach was found to work fully automatically in 90% of cases with a mean surface-
to-surface error clearly below 1.0 mm. Qualitatively, expert reviewers rated the overall segmentation quality as
4.2±0.7 on a 5-point scale.
Low-dose three-dimensional Cone Beam Computed Tomography (CBCT) is becoming increasingly popular in the clinical practice of dental medicine. Two-dimensional Bolton Standards of dentofacial development are routinely used to identify deviations from normal craniofacial anatomy. With the advent of CBCT three dimensional imaging, we propose a set of methods to extend these 2D Bolton Standards to anatomically correct surface based 3D standards to allow analysis of morphometric changes seen in craniofacial complex. To create 3D surface standards, we have implemented series of steps. 1) Converting bi-plane 2D tracings into set of splines 2) Converting the 2D splines curves from bi-plane projection into 3D space curves 3) Creating labeled template of facial and skeletal shapes and 4) Creating 3D average surface Bolton standards. We have used datasets from patients scanned with Hitachi MercuRay CBCT scanner providing high resolution and isotropic CT volume images, digitized Bolton Standards from age 3 to 18 years of lateral and frontal male, female and average tracings and converted them into facial and skeletal 3D space curves. This new 3D standard will help in assessing shape variations due to aging in young population and provide reference to correct facial anomalies in dental medicine.
The noninvasive assessment of coronary atherosclerosis holds great promise for the future of cardiovascular medicine, and multidetector computed tomography (MDCT) has recently taken the lead in this area. Earlier studies have shown the ability of MDCT to visualize the coronary lumen and various types of atherosclerotic plaque. The aims of this project are to design, implement, and validate a complete system for the automated, quantitative analysis of coronary MDCT images. The developed system uses graph algorithms and knowledge-based cost functions to automatically segment the lumen and wall, and then uses pattern classification techniques to identify and quantify the tissue types found within the detected vascular wall. The system has been validated in comparison with expert tracings and labels, as well as in comparison with intravascular ultrasound (IVUS). In the former, the radial position of the lumen and adventitia were compared at 360 corresponding angular locations in 299 vascular cross sections (from 13 vessels in 5 patients: 5 RCA, 4 LAD, 4 LCX). Results show a border positioning error of 0.150 ± 0.090 mm unsigned / 0.007 ± 0.001 mm signed for the lumen, and 0.210 ± 0.120 mm unsigned / 0.020 ± 0.030 mm signed for the vessel wall. In the comparison with IVUS, the luminal and vascular cross sectional areas were compared in 7 vessels; good correlation was shown for both the lumen (R=0.83) and the vessel wall (R=0.76). The plaque characterization algorithm correctly classified 92% of calcified plaques and 87% of non-calcified plaques.
High-density objects, such as metal prostheses or surgical clips, generate streak-like artifacts in CT images. We designed a radial adaptive filter, which directly operates on the corrupted reconstructed image, to effectively and efficiently reduce such artifacts. The filter adapts to the severity of local artifacts to preserve spatial resolution as much as possible. The widths and direction of the filter are derived from the local structure tensor. Visual inspection shows that this novel radial adaptive filter is superior with respect to existing methods in the case of mildly distorted images. In the presence of strong artifacts we propose a hybrid approach. An image corrected with a standard method, which performs well on images with regions of severe artifacts, is fused with an adaptively filtered clone to combine the strengths of both methods.
CT angiography (CTA) is increasingly used for vascular disease assessment because of its non-invasive characteristics. In order to get a comprehensive overview of the vascular anatomy, the bone has to be removed since it occludes the cranial vessels. One of the commonly used algorithms is bone subtraction, which obtains vessel images by subtracting pre-contrast images from post-contrast images. The current problems are that it removes too much vessel and sometimes pieces of bone still exist near vessel. The purpose of this study is to provide radiologist with a fuzzy technique tool to add back parts of missing vessel. A seed point is put on part of the vessel that was not well preserved and an area is selected to restrict the vessel growing. Vessel extraction is based on fuzzy-connectedness technique proposed by Udupa in 1996. Incorporating intensity information from both pre-contrast and post-contrast images creates the membership images. The value of each voxel in the membership images represents strength of fuzzy connectedness. The bigger the strength value, the more likely the voxel belongs to the classified vessel. After choosing a threshold for the strength, the vessel is extracted and added back. This method may also apply to the whole images to segment out the bone and the vessel. The study will improve the current vessel extraction and bone removal algorithms and provide a good tool for aiding radiologist to diagnose vascular diseases.
With the recent, rapid development of multidetector computed tomography (MDCT), excitement has built around the possibility of noninvasively imaging the coronary arteries. While the development of hardware and reconstruction technologies have advanced significantly, current image analysis techniques are dominated by manual interpretation using maximum intensity projections and volume rendering. If MDCT is to become the tool that it aims to be, objective, quantitative methods of image analysis will be necessary - not only to facilitate the study of atherosclerosis and coronary heart disease, but also for the accurate and timely interpretation of clinical data. This study focuses on the interobserver variability associated with the analysis of coronary MDCT images and a method for automatic segmentation of the same images. In the study of interobserver variability, six independent experts manually traced the luminal border in 60 randomly selected vascular cross sections (5 cross section each from: 4 LAD, 4 LCX, and 4 RCA). The images were acquired with an Mx8000 IDT 16-slice MDCT scanner. The mean unsigned difference for all observers was 0.38 ± 0.26 mm, with an average maximum difference of 1.32 mm. Using the expertly identified luminal borders, an independent standard was created by averaging the six sets of contours. This standard was then used to validate a prototypical automated segmentation system that uses dynamic programming and a knowledge-based cost function to optimally segment the luminal border. The resulting border positioning error was 0.17 ± 0.12 mm.
The objective of this study was to evaluate a new Cardiac Enhancement Filter (CEF) for noise reduction and edge enhancement of Computed Tomography Cardiac Angiography examinations. The filter is an adaptive noise reduction filter designed to achieve near real time functioning. Using a CT performance phantom, standard measurements of image quality, including spatial resolution, low contrast resolution, and image noise were assessed with and without the CEF. Quantitative assessment of the CEF showed slightly improved spatial resolution at 50% and 10% modulation, similar low contrast resolution and significantly lower image noise (up to 38%) characteristics. Two patient datasets were used for the clinical evaluation of the filter. The filter effectively reduced image noise by 13 to 22% in clinical datasets. These datasets exhibited a significant decrease in image noise without loss of vessel sharpness or introduction of new image artifacts. The results of the initial testing are encouraging, yet additional investigations are required to further assess the filter's clinical utility.
The Renal Artery Stenosis (RAS) is the major cause of renovascular hypertension and CT angiography has shown tremendous promise as a noninvasive method for reliably detecting renal artery stenosis. The purpose of this study was to validate the semi-automated methods to assist in extraction of renal branches and characterizing the associated renal artery stenosis. Automatically computed diagnostic images such as straight MIP, curved MPR, cross-sections, and diameters from multi-slice CT are presented and evaluated for its acceptance. We used vessel-tracking image processing methods to extract the aortic-renal vessel tree in a CT data in axial slice images. Next, from the topology and anatomy of the aortic vessel tree, the stenosis, and thrombus section and branching of the renal arteries are extracted. The results are presented in curved MPR and continuously variable MIP images. In this study, 15 patients were scanned with contrast on Mx8000 CT scanner (Philips Medical Systems), with 1.0 mm thickness, 0.5mm slice spacing, and 120kVp and a stack of 512x512x150 volume sets were reconstructed. The automated image processing took less than 50 seconds to compute the centerline and borders of the aortic/renal vessel tree. The overall assessment of manual and automatically generated stenosis yielded a weighted kappa statistic of 0.97 at right renal arteries, 0.94 at the left renal branches. The thrombus region contoured manually and semi-automatically agreed upon at 0.93. The manual time to process each case is approximately 25 to 30 minutes.
Computed tomography angiography (CTA) is a procedure gaining usage in the diagnosis of aneurysms located in the aorta, carotid arteries, and in other locations and has also shown promise in the planning of stent placement procedures. Recently, automatic vessel segmentation programs have been developed that can extract the entire aortic vessel tree and provide information to the user regarding the size, length, and tortuosity of the blood vessels. This study was designed to determine if using the full width at half maximum (FWHM) value is an accurate method of determining the diameter of contrast-enhanced blood vessels. A phantom used to simulate vessels of various diameters was filled with a nonionic iodine solution and scanned using a 16-detector CT scanner (Mx8000IDT, Philips Medical Systems, Inc.). The phantom was scanned with varying concentrations of contrast solution to emulate the variation of enhancement that may be seen clinically. The data was analyzed using an application on a workstation (MxView, Philips Medical Systems, Inc.), which allowed for the calculation of FWHM of a user-defined region of interest. The results indicate that the full width at half maximum is an accurate method of calculating the diameter of a blood vessel, regardless of contrast concentration. The full width at half maximum is an easily calculated value, which could potentially be used in an automatic segmentation algorithm to determine the diameters of extracted vessels.
Liver resection and transplantation surgeries require careful planning and accurate knowledge of the vascular and gross anatomy of the liver. This study aims to create a semi-automatic method for segmenting the liver, along with its entire venous vessel tree from multi-detector computed tomograms. Using fast marching and region-growth techniques along with morphological operations, we have developed a software package which can isolate the liver and the hepatic venous network from a user-selected seed point. The user is then presented with volumetric analysis of the liver and a 3-Dimensional surface rendering. Software tools allow the user to then analyze the lobes of the liver based upon venous anatomy, as defined by Couinaud. The software package also has utilities for data management, key image specification, commenting, and reporting. Seven patients were scanned with contrast on the Mx8000 CT scanner (Philips Medical Systems), the data was analyzed using our method and compared with results found using a manual method. The results show that the semi-automated method utilizes less time than manual methods, with results that are consistent and similar. Also, display of the venous network along with the entire liver in three dimensions is a unique feature of this software.
The abdominal aorta is the most common site for an aneurysm, which may lead to hemorrhage and death, to develop. The aim of this study was to develop a semi-automated method to de-lineate the blood flow and thrombus region, subsequently detect the centerline of these vessels to make measurements necessary for stent design from computed tomograms. We developed a robust method of tracking the aortic vessel tree from a user selected seed point using series of image processing such as fast marching method to delineate the blood flow, morphological and distance transforms methods to extract centerlines, and finally by reinitializing the fast marching in a blood filled region subtracted CT volume to obtain the thrombus borders. Fifteen patients were scanned with contrast on Mx8000 CT scanner (Philips Medical Systems), with a 1.3 mm thickness, 1.0 mm slice spacing, and a stack of 512x512x380 volume data sets were reconstructed. The automated image processing took approximately 30 to 90 seconds to compute the centerline and borders of the aortic vessel tree. We compared our results with manual and 3D volume rendering methods and found automatic method is superior in accuracy of spatial localization (0.94-0.97 ANOVA K) and accuracy of diameter determination (0.88-0.98).
KEYWORDS: Computed tomography, Image processing, Scanners, Prototyping, 3D image processing, Image enhancement, 3D displays, 3D acquisition, Angiography, Medical imaging
The abdominal aorta is the most common site for an aneurysm, which may lead to hemorrhage and death, to develop. The aim of this study was to develop a semi-automated method to de-lineate the vessels and detect the center-line of these vessels to make measurements necessary for stent design from multi-detector computed tomograms. We developed a robust method of tracking the aortic vessel tree with branches from a user selected seed point along the vessel path using scale space approaches, central transformation measures, vessel direction findings, iterative corrections and a priori information in determining the vessel branches. Fifteen patients were scanned with contrast on Mx8000 CT scanner (Philips Medical Systems), with a 3.2 mm thickness, 1.5 mm slice spacing, and a stack of 512x512x320 volume data sets were reconstructed. The algorithm required an initial user input to locate the vessel seen in axial CT slice. Next, the automated image processing took approximately two minutes to compute the centerline and borders of the aortic vessel tree. The results between the manually and automatically generated vessel diameters were compared and statistics were computed. We observed our algorithm was consistent (less than 0.01 S.D) and similar (less than 0.1 S.D) to manual results.
Three dimensional (3D) plain film radiographic cephalometric analysis of boney skull landmarks may be used for patient diagnosis, treatment planning, prosthetic design, intra- operatively, and outcome assessment. To test the accuracy and reliability of 50 cephalometric landmarks, three dry human skulls, with and without metallic markers affixed to the landmarks, were digitized in our 3dCEPH software by 4 operators. The average inter-operator variability about mean landmark position, across all operators, for all 3 skull image pairs, was 3.33 mm. Ten landmarks exhibiting least variability were 1.15 mm average distance from the mean, including: B point 0.69 mm, Lower Incisal Edge 0.85 mm, and Anterior Nasal Spine 0.90 mm. The average rms error from the metallic fiducials for these 4 operators across all 50 landmarks, and 3 skulls was 5.03 mm. The 10 landmarks with the least variability exhibited 2.01 mm average distance from the fiducial, including: B point 1.69 mm, upper incisal edge 1.71 mm, lower incisal edge 1.78 mm. Additional studies are needed to test the robusticity of the hypothesis of homologous anatomy. Homology of landmarks is important to cephalometric comparisons between image pairs representing patient and 'normative,' pre- and post-surgical alteration, and different ages of the same patient.
The Bolton Standards 'normative' cohort (16 males, 16 females) have been invited back to the Bolton-Brush Growth Study Center for new biorthogonal plain film head x-rays and 3D (three dimensional) head CT-scans. A set of 29 3D landmarks were identified on both their biplane head film and 3D CT images. The current 3D CT image is then superimposed onto the landmarks collected from the current biplane head films. Three post-doctoral fellows have collected 37 3D landmarks from the Bolton Standards' 40 - 70 year old biplane head films. These films were captured annually during their growing period (ages 3 - 18). Using 29 of these landmarks the current 3D CT image is next warped (via thin plate spline) to landmarks taken from each participant's 18th year biplane head films, a process that is successively reiterated back to age 3. This process is demonstrated here for one of the Bolton Standards. The outer skull surfaces will be extracted from each warped 3D CT image and an average will be generated for each age/sex group. The resulting longitudinal series of average 'normative' boney skull surface images may be useful for craniofacial patient: diagnosis, treatment planning, stereotactic procedures, and outcomes assessment.
We have developed a visualization environment, CEPH, for the analysis of plain film, biothogonally registered, head x-rays, i.e., cephalograms. Most importantly this program facilitates the collection of 3D landmark data from biorthogonal cephalogram pairs, i.e., correlated frontal and lateral images. To this end we have implemented tools for: contrast enhancement, image compression, morphological feature extraction, and grayscale shape recognition for automatic landmark detection. These data are useful for clinical craniofacial: (1) diagnosis, (2) treatment planning, (3) computed-assisted surgery, and (4) post-procedure follow-up. We are currently using the CEPH visualization environment to produce average landmark sets from the nearly 2000 'Bolton Standards' images. These images were collected for the Bolton-Brush Growth Study at Case Western Reserve University. Two dimensional landmark data captured from these images are universally recognized and used as diagnostic and treatment 'norms.' We are planning to make new three dimensional Bolton Standards data available this year. To this end we have scanned all of the Bolton Standards cephalograms at 2400 by 2400 resolution with 12 bits of grayscale information on a TDI scanner.
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