KEYWORDS: Colon, Virtual colonoscopy, Image segmentation, Magnetic resonance imaging, 3D vision, 3D image processing, Volume rendering, Tissues, 3D modeling, Visualization
The aim of this study is to develop a virtual colonoscopy (VC) workstation that supports both CT (computed tomography) and MR (magnetic resonance) imaging procedures. The workflow should be optimized and be able to take advantage of both image modalities. The technological break through is at the real-time volume rendering of spatial-intensity-inhomogeneous MR images to achieve high quality 3D endoluminal view. VC aims at visualizing CT or MR tomography images for detection of colonic polyp and lesion. It is also called as CT/MR colonography based on the imaging modality that is employed. The published results of large scale clinical trial demonstrated more than 90% of sensitivity on polyp detection for certain CT colonography (CTC) workstation. A drawback of the CT colonoscopy is the radiation exposure. MR colonography (MRC) is free from the X-ray radiation. It achieved almost 100% specificity for polyp detection in published trials. The better tissue contrast in MR image allows the accurate diagnosis of inflammatory bowel disease also, which is usually difficult in CTC. At present, most of the VC workstations are designed for CT examination. They are not able to display multi-sequence MR series concurrently in a single application. The automatic correlation between 2D and 3D view is not available due to the difficulty of 3D model building for MR images. This study aims at enhancing a commercial VC product that was successfully used for CTC to equally support dark-lumen protocol MR procedure also.
Objective: To investigate the feasibility of laxative-free bowel preparation to relieve the patient stress in colon cleansing for virtual colonoscopy. Materials and Methods: Three different bowel-preparation protocols were investigated by 60 study cases from 35 healthy male volunteers. All the protocols utilize low-residue diet for two days and differ in diet for the third day - the day just prior to image acquisition in the fourth day morning. Protocol Diet-1 utilizes fluid or liquid diet in the third day, Diet-2 utilizes a food kit, and Diet-3 remains the low-residue diet. Oral contrast of barium sulfate (2.1%, 250 ml) was added respectively to the dinner in the second day and the three meals in the third day. Two doses of MD-Gastroview (60 ml) were ingested each in the evening of the third day and in the morning before image acquisition. Images were acquired by a single-slice detector spiral CT (computed tomography) scanner with 5 mm collimation, 1 mm reconstruction, 1.5-2.0:1.0 pitch, 100-150 mA, and 120 kVp after the colons were inflated by CO2. The contrasted colonic residue materials were electronically removed from the CT images by specialized computer-segmentation algorithms. Results: By assumptions that the healthy young volunteers have no polyp and the image resolution is approximately 4 mm, a successful electronic cleansing is defined as “no more than five false positives and no removal of a colon fold part greater than 4 mm” for each study case. The successful rate is 100% for protocol Diet-1, 77% for Diet-2 and 57% for Diet-3. Conclusion: A laxative-free bowel preparation is feasible for virtual colonoscopy.
Objective: To investigate a less stressful bowel preparation for polyp screening by virtual colonoscopy (VC) with follow-up biopsy on the positive findings by optical colonoscopy (OC). Materials and Methods: Fifty-eight volunteers of age older than 40 -- receiving low-residue diet and laxatives of magnesium citrate, bisacodyl tablets and suppository -- were divided into three groups. In Group I, 16 volunteers took three 40cc oral doses of MD-Gastroview with the three meals respectively, the day prior to VC procedure. In Group II, 18 volunteers ingested barium sulfate suspension (2% w/v, 250 cc/dose) at bedtime and in the next day morning of VC. In Group III, 24 volunteers received 60 cc of MD-Gastroview at bedtime and in the next day morning of VC. Following colon inflation with CO2, computer tomography (CT) abdominal images were acquired by a standard single-slice detector-band VC protocol, i.e., 5 mm collimation, 1 mm reconstruction, 1.5-2.0:1.0 pitch, 120 kVp and 100-150 mA. The CT density of the tagged residual fluid was measured. An image segmentation algorithm was applied to remove electronically the residue fluid. Results: The average fluid density was 97 HU for Group I, 221 HU for Group II2, and 599 HU for Group III. These three groups’ density means are significantly different (p < 0.001 one-way ANOVA). After the electronic cleansing, the % of cleansed fluid regions was 5.5%, 16.5% and 93.1% (p<0.0001 Chi square) for these groups respectively. Conclusion: A less-stressful bowel preparation with low residue diet and MD-Gastroview oral contrast is feasible for VC screening with follow-up biopsy on the positive findings by OC.
In this paper, we proposed a new efficient implementation for simulation of surgery planning for congenital aural
atresia. We first applied a 2-level image segmentation schema to classify the inner ear structures. Based on it, several
3D texture volumes were generated and sent to graphical pipeline on a PC platform. By exploiting the texturingmapping
capability on the PC graphics/video board, a 3D image was created with high quality showing the accurate
spatial relationships of the complex surgical anatomy of congenitally atretic ears. Furthermore, we exploited the
graphics hardware-supported per-fragment function to perform the geometric clipping on 3D volume data to
interactively simulate the procedure of surgical operation. The result was very encouraging.
An efficient noise treatment scheme has been developed to achieve low-dose CT diagnosis based on currently available CT hardware and image reconstruction technologies. The scheme proposed includes two main parts: filtering in sinogram domain and smoothing in image domain. The acquired projection sinograms were first treated by our previously proposed Karhunen-Loeve (K-L) domain penalized weighted least-square (PWLS) filtering, which fully utilizes the prior statistical noise property and three-dimensional (3D) spatial information for an accurate restoration of the low-dose projections. To treat the streak artifacts due to photon starvation, we also incorporated an adaptive filtering into our PWLS framework, which selectively smoothes those channels contributing most to the streak artifacts. After the sinogram filtering, the image was reconstructed by the conventional filtered backprojection (FBP) method. The image is assumed as piecewise regions each has a unique texture. Therefore, an edge-preserving smoothing (EPS) with locally-adaptive parameters to the noise variation was applied for further noise reduction in image domain. Experimental phantom projections acquired by a GE spiral computed tomography (CT) scanner under 10 mAs tube current were used to evaluate the proposed smoothing scheme. The reconstructed imaged demonstrated that the smoothing scheme with appropriate control parameters provides a significant improvement on noise suppression without sacrificing the spatial resolution.
Segmentation of magnetic resonance (MR) images plays an important role in quantitative analysis of brain tissue morphology and pathology. However, the inherent effect of image-intensity inhomogeneity renders a challenging problem and must be considered in any segmentation method. For example, the adaptive fuzzy c-mean (AFCM) image segmentation algorithm proposed by Pham and Prince can provide very good results in the presence of the inhomogeneity effect under the condition of low noise levels. Their results deteriorate quickly as the noise level goes up. In this paper, we present a new fuzzy segmentation algorithm to improve the noise performance of the AFCM algorithm. It achieves accurate segmentation in the presence of inhomogeneity effect and high noise levels by incorporating the spatial neighborhood information into the objective function. This new algorithm was tested by both simulated experimental and real clinical MR images. The results demonstrated the improved performance of this new algorithm over the AFCM in the clinical environment where the inhomogeneity and noise levels are commonly encountered.
We present an automatic and robust tagged-residue detection technique using vector quantization based classification. This technique enables electronic cleansing even on poorly tagged datasets, leading to more effective virtual colonoscopy.
In order to reduce the sensitivity towards intensity variation among the tagged residual material, we use a multi-step technique. First, we apply classification using an unsupervised and self-adapting vector quantization algorithm. Then, we sort the resultant classes by their average intensities. We apply thresholding on these classes based on a conservative threshold. This helps us in differentiating soft tissue inside tagged material from poorly tagged region or noise.
We present an electronic colon cleansing algorithm using a new segmentation technique based on segmentation rays. These rays are specially designed to analyze the intensity profile as they traverse through the dataset. When this intensity profile matches any of the pre-defined profiles, the rays perform certain task of reconstruction. We use these rays to detect the intersection between air and residual fluid, and between residual fluid and soft-tissue. One of the most important advantages of segmentation rays over other segmentation techniques is the detection of partial volume regions. Segmentation rays can accurately detect partial volume regions and remove them if needed. Once partial volume is eliminated, removal of other unwanted regions (e.g., tagged fluid) is relatively easy. This approach to electronic cleansing is extremely fast as it requires minimal computation.
Virtual colonoscopy provides a safe, minimal-invasive approach to detect colonic polyps using medical imaging and computer graphics technologies. Residual stool and fluid are problematic for optimal viewing of the colonic mucosa. Electronic cleansing techniques combining bowel preparation, oral contrast agents, and image segmentation were developed to extract the colon lumen from computed tomography (CT) images of the colon. In this paper, we present a new electronic colon cleansing technology, which employs a hidden Markov random filed (MRF) model to integrate the neighborhood information for overcoming the non-uniformity problems within the tagged stool/fluid region. Prior to obtaining CT images, the patient undergoes a bowel preparation. A statistical method for maximum a posterior probability (MAP) was developed to identify the enhanced regions of residual stool/fluid. The method utilizes a hidden MRF Gibbs model to integrate the spatial information into the Expectation Maximization (EM) model-fitting MAP algorithm. The algorithm estimates the model parameters and segments the voxels iteratively in an interleaved manner, converging to a solution where the model parameters and voxel labels are stabilized within a specified criterion. Experimental results are promising.
KEYWORDS: Signal to noise ratio, Interference (communication), Single photon emission computed tomography, Electronic filtering, Digital filtering, Image filtering, Filtering (signal processing), Denoising, Data modeling, Smoothing
A theoretically based transformation, which reorders SPECT sinograms degraded by the Poisson noise according to their signal-to-noise ratio (SNR), has been proposed. The transformation is equivalent to the maximum noise fraction (MNF) approach developed for Gaussian noise treatment. It is a two-stage transformation. The first stage is the Anscombe transformation, which converts Poisson distributed variable into Gaussian distributed one with constant variance. The second one is the Karhunen-Loeve (K-L) transformation along the direction of the slices, which simplifies the complex task of three-dimensional (3D) filtering into 2D spatial process slice-by-slice. In the K-L domain, the noise property of constant variance remains for all components, while the SNR of each component decreases proportional to its eigenvalue, providing a measure for the significance of each components. The availability of the noise covariance matrix in this method eliminates the difficulty of separating noise from signal. Thus we can construct an accurate 2D Wiener filter for each sinogram component in the K-L domain, and design a weighting window to make the filter adaptive to the SNR of each component, leading to an improved restoration of SPECT sinograms. Experimental results demonstrate that the proposed method provides a better noise reduction without sacrifice of resolution.
We present a fully automatic algorithm for brain magnetic resonance (MR) image segmentation. The three-dimensional (3D) volumetric MR dataset is first interpolated for an adequate local intensity vector on each voxel. Then a morphology dilation filter and region growing technique are applied to extract the region of brain volume, strapping away the skull, scalp and other tissues. The principal component analysis (PCA) is utilized to generate a series of feature vectors from the local vectors via the Karhunen-Loeve (K-L) transformation for those voxels within the extracted region. We choose those first few principal components that sum up to, at least, 90% percent of the total variance for optimizing the dimensions of the feature vectors. Then a modified self-adaptive online vector quantization algorithm is applied to these feature vectors for classification. The presented algorithm requires no prior knowledge of the data distribution except a maximum number of distinct groups for classification, which can be set based on anatomical knowledge. Numerical analysis of the algorithm and experimental tests on brain MR images are presented. Results demonstrate efficient, robust, and self-adaptive properties of the presented algorithm.
A correction method for inhomogeneity of magnetic resonance (MR) images was developed based on renormalization transformation. It is a post-processing algorithm on images. Unlike previous post-processing methods, which need to determine either a filter size or a free adjustable parameter for different applications, this presented method is fully automated. Tests on physical phantom data, patients' brain and neck MR images were presented.
A method for quantitative analysis of multiple sclerosis (MS) was presented. An automatic self-adaptive image segmentation algorithm was first employed to classify voxels in multi- spectral magnetic resonance (MR) images. The segmentation results from multi-spectral MR images were then combined to obtain reliable results. The volumes of brain tissues and cerebral spinal fluid (CSF) were finally extracted. Since it is fully automated, the results of the segmentation algorithm are completely reproducible. The repeatability of the presented method was evaluated on volunteer data sets. The variation is less than 0.2% for the intra-cranial volume, the whole brain volume, the central CSF, the white matter (WM) and the gray matter (GM). The variation of 3% for the entire CSF is mainly due to the peripheral CSF part, which has more partial volume effect and is less important than the central one. Methods for minimizing this variation are under investigation. These measurements demonstrate the potential for study on whole brain atrophy and cerebral atrophy. Feasibility studies on 14 MS patients were performed. The results are promising.
One of the most important tasks for virtual endoscopy is path planning for viewing the lumen of hollow organs. For geometry complex objects, for example the lungs, it remains an unsolved problem. While alternative visualization modes have been proposed, for example, cutting and flattening the hollow wall, a skeleton of the lumen is still necessary as a reference for the cutting. A general-purpose skeletonization algorithm often generates redundant skeletons because of the local shape variation. In this study, a multistage skeletonization method for tree-like volumes, such as airway system, blood vessels, and colon, was presented. By appropriately defining the distance between voxels, the distance to the root from each voxel in the volume can be effectively determined with means of region growing techniques. The end points of all branches and the shortest path from each end point to the root can be extracted based on this distance map. A post-processing algorithm is applied to the shortest paths to remove redundant ones and to centralize the remained ones. The skeleton generated is one-voxel wide, along which every branch of the 'tree' can be viewed. For effectively processing volume of large size, a modified multiresolution analysis was also developed to scale down the binary segmented volume. Tests on airway, vessel, and colon dataset were promising.
In our previous work, we developed a virtual colonoscopy system on a high-end 16-processor SGI Challenge with an expensive hardware graphics accelerator. The goal of this work is to port the system to a low cost PC in order to increase its availability for mass screening. Recently, Mitsubishi Electric has developed a volume-rendering PC board, called VolumePro, which includes 128 MB of RAM and vg500 rendering chip. The vg500 chip, based on Cube-4 technology, can render a 2563 volume at 30 frames per second. High image quality of volume rendering inside the colon is guaranteed by the full lighting model and 3D interpolation supported by the vg500 chip. However, the VolumePro board is lacking some features required by our interactive colon navigation. First, VolumePro currently does not support perspective projection which is paramount for interior colon navigation. Second, the patient colon data is usually much larger than 2563 and cannot be rendered in real-time. In this paper, we present our solutions to these problems, including simulated perspective projection and axis aligned boxing techniques, and demonstrate the high performance of our virtual colonoscopy system on low cost PCs.
Computed tomography (CT) based virtual cystoscopy (VC) has been studied as a potential tool for screening bladder cancer. It is accurate in localizing tumor of size larger than 1 cm and less expensive, as compared to fiberoptic cystoscopy. However, it is invasive and difficult to perform due to using Foley catheter for bladder insufflating with air. In a previous work, we investigated a magnetic resonance imaging (MRI) based VC scheme with urine as a natural contrast solution, in which a MRI acquisition protocol and an adaptive segmentation method were utilized. Both bladder lumen and wall were successfully delineated. To suppress motion artifact and insight pathological change on the bladder wall images, a multi-scan MRI scheme was presented in this study. One transverse and another coronal acquisitions of T1-weighted that cover the whole bladder were obtained twice, at one time the bladder is full of urine and at another time it is near the empty. Four bladder volumes extracted from those 4 datasets were registered first using a flexible three- dimensional (3D) registration algorithm. Then, associated 4 lumen surfaces were viewed simultaneously with the help of an interactive 3D visualization system. This MRI-based VC was tested on volunteers and demonstrated the feasibility to mass screening for bladder cancer.
We have designed and implemented a prototype system to aid in the surgical repair of congenital aural atresia. A two- level segmentation algorithm was first developed to separate tissues of similar intensity or low tissue contrast. Then an interactive visualization modular was built to display the labeled tissues. The system allows a 3-stage interactive planning in which positioning, marking and drilling simulates the surgical operation of congenital atresia repair. A voxel-based volume CSG operation was implemented to ensure the efficiency of interactive planning. Six patients with congenital aural atresia underwent virtual planning in preparation for surgery. This technique has proved to be a valuable planning tool, with the potential for virtual representation of the surgical reconstruction.
Residual stool and fluid and wall collapses are problematic for virtual colonoscopy. Electronic colon cleansing techniques combining both bowel preparation and image processing were developed to segment the colon lumen from the abdominal computed tomographic (CT) images. This paper describes our bowel preparation and image segmentation techniques and presents some preliminary results. A feasibility study using magnetic resonance imaging (MRI) is also reported.
We focus on color mapping between gray tons of computed tomographic images and color texture of visible human or optical images. Particularly, we propose probabilistic segmentation based on gradient entropy and Bayesian estimation to solve the material mixture problems. The approach can fill in the gap between segmentation and rendering to eliminate artifacts (jagged edges) produced by incorrect classification of material mixture and to estimate accurate surface normal for volume shading.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.