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Dual-energy electronic cleansing (DE-EC) provides a promising means for cleansing the tagged fecal materials in fecaltagging CT colonography (CTC). However, the increased radiation dose due to the double exposures in dual-energy CTC (DE-CTC) scanning is a major limitation for the use of DE-EC in clinical practice. The purpose of this study was to develop and evaluate a low-dose DE-EC scheme in fecal-tagging DE-CTC. In this study, a custom-made anthropomorphic colon phantom, which was filled with simulated tagged materials by non-ionic iodinated contrast agent (Omnipaque iohexol, GE Healthcare), was scanned by a dual-source CT scanner (SOMATON Definition Flash, Siemens Healthcare) at two photon energies: 80 kVp and 140 kVp with nine different tube current settings ranging from 12 to 74 mAs for 140 kVp, and then reconstructed by soft-tissue reconstruction kernel (B30f). The DE-CTC images were subjected to a low-dose DE-EC scheme. First, our image-space DE-CTC denoising filter was applied for reduction of image noise. Then, the noise-reduced images were processed by a virtual lumen tagging method for reduction of partial volume effect and tagging inhomogeneity. The results were compared with the registered CTC images of native phantom without fillings. Preliminary results showed that our low-dose DE-EC scheme achieved the cleansing ratios, defined by the proportion of the cleansed voxels in the tagging mask, between 93.18% (12 mAs) and 96.62% (74 mAs). Also, the soft-tissue preservation ratios, defined by the proportion of the persevered voxels in the soft-tissue mask, were maintained in the range between 94.67% and 96.41%.

Partial volume effect and inhomogeneity are two major causes of artifacts in electronic cleansing (EC) for non-cathartic CT colonography (CTC). Our purpose was to develop a novel method of EC for non-cathartic dual-energy CTC (DECTC) using a subvoxel multi-spectral material classifier and a regional material decomposition method for differentiation of residual fecal materials from colonic soft-tissue structures. In this study, an anthropomorphic colon phantom, which was filled with a mixture of aqueous fiber (psyllium), ground foodstuff (cereal), and non-ionic iodinated agent (Omnipaque iohexol, GE Healthcare, Milwaukee, WI), was scanned by a dual-energy CT scanner (SOMATON, Siemens) with two photon energies: 80 kVp and 140 kVp. The DE-CTC images were subjected to a dual-energy EC (DE-EC) scheme, in which a multi-spectral material classifier was used to compute the fraction of each material within one voxel by an expectation-maximization (EM) algorithm. This was followed by a regional material segmentation method for identifying of homogeneous sub-regions (tiles) as fecal materials from other tissue types. The results were compared with the structural-analysis cleansing (SA-EC) method based upon the CTC images of native phantom without fillings. The mean cleansing ratio of the DE-EC scheme was 96.57±1.21% compared to 76.3±5.56% of the SA-EC scheme. The soft-tissue preservation ratio of the DE-EC scheme was 97.05%±0.64% compared to 99.25±0.77% of the SA-EC scheme.

Electronic cleansing (EC) is a method that segments fecal material tagged by an X-ray-opaque oral contrast agent in CT colonography (CTC) images, and effectively removes the material for digitally cleansing the colon. In this study, we developed a novel EC method, called mosaic decomposition, for reduction of the artifacts due to incomplete cleansing of heterogeneously tagged fecal material in reduced- or non-cathartic fecal-tagging CTC examinations. In our approach, a segmented colonic lumen, including the tagged regions, was first partitioned into a set of local homogeneous regions by application of a watershed transform to the gradient of the CTC images. Then, each of the local homogeneous regions was classified into five different material classes, including air, soft tissue, tagged feces, air bubbles, and foodstuff, based on texture features of the tile. A single index, called a soft-tissue index, is formulated for differentiation of these materials from the submerged solid soft-tissue structures such as polyps and folds. Here, a larger value of the index indicates a higher likelihood of soft tissue. Then, EC is performed by first initializing the level-set front with the classified tagged regions, and the front is evolved by use of a speed function that was designed, based on the soft-tissue index, to reserve the submerged soft-tissue structures while suppressing the air bubbles and foodstuff. Visual assessment and application of our computer-aided detection (CAD) of polyps showed that the use of our new EC method substantially improved the detection performance of CAD, indicating the effectiveness of our EC method in reducing incomplete cleansing artifacts.

Fecal-tagging CT colonography (CTC) presents an opportunity to minimize colon cleansing while maintaining high diagnostic accuracy for the detection of colorectal lesions. However, the pseudo-enhancement introduced by tagging agents presents several problems for the application of computer-aided detection (CAD). We developed a correction method that minimizes pseudo-enhancement in CTC data by modeling of the pseudo-enhancement as a cumulative Gaussian energy distribution. The method was optimized by use of an anthropomorphic colon phantom, and its effect on our fully automated CAD scheme was tested by use of leave-one-patient-out evaluation on 23 clinical CTC cases with reduced colon cleansing based upon dietary fecal tagging. There were 28 colonoscopy-confirmed polyps ≥6 mm. Visual evaluation indicated that the method reduced CT attenuation of pseudo-enhanced polyps to standard soft-tissue Hounsfield unit (HU) range without affecting untagged regions. At a 90% detection sensitivity for polyps ≥6 mm, CAD yielded 8.5 false-positive (FP) detections and 3.9 FP detections per volumetric scan without and with the application of the pseudo-enhancement correction method. These results indicate that the pseudo-enhancement correction method is a potentially useful pre-processing step for automated detection of polyps in fecal-tagging CTC, and that CAD can yield a high detection sensitivity with a relatively low FP rate in CTC with patient-friendly reduced colon preparation.

Electronic cleansing (EC) is an emerging method for segmentation of fecal materials, which are tagged by an X-ray-opaque oral contrast agent in CT colonography (CTC) images, effectively removing them for digital cleansing of the colon. Due to the partial volume effect, voxels at the boundary between the lumen and tagged materials, called the L-T boundary, not only have CT values that are close to those of soft-tissue structures, but also have gradient values that are similar to a thin soft-tissue structure that is sandwiched between the tagged region and the lumen, which we call a tagging-tissue-air layer. Degradation of thin colonic wall and folds, as well as creation of pseudo soft-tissue structures at the periphery of tagged regions, are main artifacts in existing EC approaches, which tend to use a gradient-based method to delineate tagged regions. In this study, we developed a novel delineation method of tagged regions by applying local iso-surface roughness. The local iso-surface roughness is defined by the sum of differences between the local curvedness at adjacent scales over all scales. In our approach, the roughness values around the periphery of the tagged regions are integrated into the speed function of a level-set segmentation method for delineation of the tagged regions. As a result, L-T boundaries are subtracted along with tagged regions, whereas the thin soft-tissue structures within the tagging-tissue-air layers are preserved. Application of our computer-aided detection (CAD) scheme showed that the use of the new EC method substantially reduced the number of false-positive detections compared with that of our previous gradient-based method.

Measurement of the volume of focal liver tumors, called liver tumor volumetry, is indispensable for assessing the growth of tumors and for monitoring the response of tumors to oncology treatments. Traditional edge models, such as the maximum gradient and zero-crossing methods, often fail to detect the accurate boundary of a fuzzy object such as a liver tumor. As a result, the computerized volumetry based on these edge models tends to differ from manual segmentation results performed by physicians. In this study, we developed a novel computerized volumetry method for fuzzy objects, called dynamic-thresholding level set (DT level set). An optimal threshold value computed from a histogram tends to shift, relative to the theoretical threshold value obtained from a normal distribution model, toward a smaller region in the histogram. We thus designed a mobile shell structure, called a propagating shell, which is a thick region encompassing the level set front. The optimal threshold calculated from the histogram of the shell drives the level set front toward the boundary of a liver tumor. When the volume ratio between the object and the background in the shell approaches one, the optimal threshold value best fits the theoretical threshold value and the shell stops propagating. Application of the DT level set to 26 hepatic CT cases with 63 biopsy-confirmed hepatocellular carcinomas (HCCs) and metastases showed that the computer measured volumes were highly correlated with those of tumors measured manually by physicians. Our preliminary results showed that DT level set was effective and accurate in estimating the volumes of liver tumors detected in hepatic CT images.

Digital bowel cleansing (DBC) is an emerging method for segmentation of fecal materials, which are tagged by an X-ray-opaque oral contrast agent in CT colonography (CTC) images, effectively removing them for digital cleansing of the colon. Existing DBC approaches tend to use simple thresholding-based methods for the removal of tagged fecal materials; however, because of the pseudo-enhancement of polyps caused by the surrounding tagged fecal materials, such methods tend erroneously to remove a part of or the entire polyps submerged in these materials. In this study, we developed a novel DBC method that preserves the soft-tissue structures submerged in or partially covered by tagged fecal materials. In our approach, submerged soft-tissue structures are characterized by their local shape signatures that are calculated based on the eigenvalues of a Hessian matrix. A structure-enhancement function is formulated for enhancing of the soft-tissue structures, and the values of the function are integrated into the speed function of a level set method to delineate the submerged soft-tissue structures while removing the tagged fecal materials. In an analysis of 10 submerged polyps, our new DBC method was shown to delineate polyps better than was possible with our previously reported cleansing method based on thresholding. Application of our computer-aided detection (CAD) scheme showed that the use of the new DBS method substantially reduced the number of false-positive detections compared with those of our previous, thresholding-based method.

With the rapid development of multi-detector computed tomography (MDCT), resulting in increasing temporal and spatial resolution of data sets, clinical use of computed tomographic angiography (CTA) is rapidly increasing. Analysis of vascular structures is much needed in CTA images; however, the basis of the analysis, vessel segmentation, can still be a challenging problem. In this paper, we present a fast interactive method for CTA vessel segmentation, called vesselness propagation. This method is a two-step procedure, with a pre-processing step and an interactive step. During the pre-processing step, a vesselness volume is computed by application of a CTA transfer function followed by a multi-scale Hessian filtering. At the interactive stage, the propagation is controlled interactively in terms of the priority of the vesselness. This method was used successfully in many CTA applications such as the carotid artery, coronary artery, and peripheral arteries. It takes less than one minute for a user to segment the entire vascular structure. Thus, the proposed method provides an effective way of obtaining an overview of vascular structures.

An accurate and reproducible centerline is needed in many vascular applications, such as virtual angioscopy, vessel quantification, and surgery planning. This paper presents a progressive optimization algorithm to refine a centerline after it is extracted. A new centerline model definition is proposed that allows quantifiable minimum cross-sectional area. A centerline is divided into a number of segments. Each segment corresponds to a local generalized cylinder. A reference frame (cross-section) is set up at the center point of each cylinder. The position and the orientation of the cross-section are optimized within each cylinder by finding the minimum cross-sectional area. All local-optimized center points are approximated by a NURBS curve globally, and the curve is re-sampled to the refined set of center points. This refinement iteration, local optimization plus global approximation, converges to the optimal centerline, yielding a smooth and accurate central axis curve. The application discussed in this paper is vessel quantification and virtual angioscopy. However, the algorithm is a general centerline refinement method that can be applied to other applications that need accurate and reproducible centerlines.

The developmental dysplasia of the hip (DDH) is a congenital malformation affecting the proximal femurs and acetabulum that are subluxatable, dislocatable, and dislocated. Early diagnosis and treatment is important because failure to diagnose and improper treatment can result in significant morbidity. In this paper, we designed and implemented a computer aided system for the diagnosis and treatment planning of this disease. With the design, the patient received CT (computed tomography) or MRI (magnetic resonance imaging) scan first. A mixture-based PV partial-volume algorithm was applied to perform bone segmentation on CT image, followed by three-dimensional (3D) reconstruction and display of the segmented image, demonstrating the special relationship between the acetabulum and femurs for visual judgment. Several standard procedures, such as Salter procedure, Pemberton procedure and Femoral Shortening osteotomy, were simulated on the screen to rehearse a virtual treatment plan. Quantitative measurement of Acetabular Index (AI) and Femoral Neck Anteversion (FNA) were performed on the 3D image for evaluation of DDH and treatment plans. PC graphics-card GPU architecture was exploited to accelerate the 3D rendering and geometric manipulation. The prototype system was implemented on PC/Windows environment and is currently under clinical trial on patient datasets.

An efficient technique is described for rendering Finite Element Modeling (FEM) volume data. The data are not a regular 3D grid. This algorithm can deal with most kinds of FEM data, such as hexahedron 8 nodes, hexahedron 20 nodes etc. Two methods to visualize the FEM data have been presented in the rendering stage. The comparison of these two methods have also been discussed later in this paper.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Aiming at detail enhancement in volume rendering, a new volume illumination model, called Composed Scattering Model (CSM), is presented. In order to enhance different details in data, scattering intensity is decomposed into volume scattering intensity and surface scattering intensity, and composed with boundary detection operators. CSM can generate images containing more details than current volume rendering models. This model has been applied to the direct volume rendering of 3D data sets obtained by CT and MRI. The resultant images show not only rich details but also clear boundary surfaces. CSM is demonstrated as an accurate volume rendering model suited for detail enhancement in volume data set.

This paper presents a method of automatic segmentation in sequence medical tissue slice images with high noise. A seed growing method which is constrained by the parameter table is used to detect edge. Priority and step parameter are used to adaptive adjust the detection. The paper described the mechanism to change the priority and detect parameter and their step in detail. The authors designed an evaluation function to search the state space.