Digital subtraction bowel cleansing for CT colonography using morphological and linear filtration methods

Designing a Hybrid Method of Artificial Neural Network and Particle Swarm Optimization to Diagnosis Polyps from Colorectal CT Images

Background

Since colorectal cancer is one of the most important types of cancer in the world that often leads to death, computer-aided diagnostic (CAD) systems are a promising solution for early diagnosis of this disease with fewer side effects than conventional colonoscopy. Therefore, the aim of this research is to design a CAD system for processing colorectal Computerized Tomography (CT) images using a combination of an artificial neural network and a particle swarm optimizer.

Method

First, the data set of the research was created from the colorectal CT images of the patients of Loghman-e Hakim Hospitals in Tehran and Al-Zahra Hospitals in Isfahan who underwent colorectal CT imaging and had conventional colonoscopy done within a maximum period of one month after that. Then the steps of model implementation, including electronic cleansing of images, segmentation, labeling of samples, extraction of features, and training and optimization of the artificial neural network (ANN) with a particle swarm optimizer, were performed. A binomial statistical test and confusion matrix calculation were used to evaluate the model.

Results

The values of accuracy, sensitivity, and specificity of the model with a P value = 0.000 as a result of the McNemar test were 0.9354, 0.9298, and 0.9889, respectively. Also, the result of the P value of the binomial test of the ratio of diagnosis of the model and the radiologist from Loqman Hakim and Al-Zahra Hospitals was 0.044 and 0.021, respectively.

Conclusions

The results of statistical tests and research variables show the efficiency of the CTC-CAD system created based on the hybrid of the ANN and particle swarm optimization compared to the opinion of radiologists in diagnosing colorectal polyps from CTC images.

Comparison of the performance of conventional and spectral-based tagged stool cleansing algorithms at CT colonography

OBJECTIVES

To compare the performance of conventional versus spectral-based electronic stool cleansing for iodine-tagged CT colonography (CTC) using a dual-layer spectral detector scanner.

METHODS

We retrospectively evaluated iodine contrast stool-tagged CTC scans of 30 consecutive patients (mean age: 69 ± 8 years) undergoing colorectal cancer screening obtained on a dual-layer spectral detector CT scanner. One reader identified locations of electronic cleansing artifacts (n = 229) on conventional and spectral cleansed images. Three additional independent readers evaluated these locations using a conventional cleansing algorithm (Intellispace Portal) and two experimental spectral cleansing algorithms (i.e., fully transparent and translucent tagged stool). For each cleansed image set, readers recorded the severity of over- and under-cleansing artifacts on a 5-point Likert scale (0 = none to 4 = severe) and readability compared to uncleansed images. Wilcoxon's signed-rank tests were used to assess artifact severity, type, and readability (worse, unchanged, or better).

RESULTS

Compared with conventional cleansing (66% score ≥ 2), the severity of overall cleansing artifacts was lower in transparent (60% score ≥ 2, p = 0.011) and translucent (50% score ≥ 2, p < 0.001) spectral cleansing. Under-cleansing artifact severity was lower in transparent (49% score ≥ 2, p < 0.001) and translucent (39% score ≥ 2, p < 0.001) spectral cleansing compared with conventional cleansing (60% score ≥ 2). Over-cleansing artifact severity was worse in transparent (17% score ≥ 2, p < 0.001) and translucent (14% score ≥ 2, p = 0.023) spectral cleansing compared with conventional cleansing (9% score ≥ 2). Overall readability was significantly improved in transparent (p < 0.001) and translucent (p < 0.001) spectral cleansing compared with conventional cleansing.

CONCLUSIONS

Spectral cleansing provided more robust electronic stool cleansing of iodine-tagged stool at CTC than conventional cleansing.

KEY POINTS

• Spectral-based electronic cleansing of tagged stool at CT colonography provides higher quality images with less perception of artifacts than does conventional cleansing. • Spectral-based electronic cleansing could potentially advance minimally cathartic approach for CT colonography. Further clinical trials are warranted.

Self-Supervised Adversarial Learning with a Limited Dataset for Electronic Cleansing in Computed Tomographic Colonography: A Preliminary Feasibility Study

Existing electronic cleansing (EC) methods for computed tomographic colonography (CTC) are generally based on image segmentation, which limits their accuracy to that of the underlying voxels. Because of the limitations of the available CTC datasets for training, traditional deep learning is of limited use in EC. The purpose of this study was to evaluate the technical feasibility of using a novel self-supervised adversarial learning scheme to perform EC with a limited training dataset with subvoxel accuracy. A three-dimensional (3D) generative adversarial network (3D GAN) was pre-trained to perform EC on CTC datasets of an anthropomorphic phantom. The 3D GAN was then fine-tuned to each input case by use of the self-supervised scheme. The architecture of the 3D GAN was optimized by use of a phantom study. The visually perceived quality of the virtual cleansing by the resulting 3D GAN compared favorably to that of commercial EC software on the virtual 3D fly-through examinations of 18 clinical CTC cases. Thus, the proposed self-supervised 3D GAN, which can be trained to perform EC on a small dataset without image annotations with subvoxel accuracy, is a potentially effective approach for addressing the remaining technical problems of EC in CTC.

Deep Learning Electronic Cleansing for Single- and Dual-Energy CT Colonography

Electronic cleansing (EC) is used for computational removal of residual feces and fluid tagged with an orally administered contrast agent on CT colonographic images to improve the visibility of polyps during virtual endoscopic "fly-through" reading. A recent trend in CT colonography is to perform a low-dose CT scanning protocol with the patient having undergone reduced- or noncathartic bowel preparation. Although several EC schemes exist, they have been developed for use with cathartic bowel preparation and high-radiation-dose CT, and thus, at a low dose with noncathartic bowel preparation, they tend to generate cleansing artifacts that distract and mislead readers. Deep learning can be used for improvement of the image quality with EC at CT colonography. Deep learning EC can produce substantially fewer cleansing artifacts at dual-energy than at single-energy CT colonography, because the dual-energy information can be used to identify relevant material in the colon more precisely than is possible with the single x-ray attenuation value. Because the number of annotated training images is limited at CT colonography, transfer learning can be used for appropriate training of deep learning algorithms. The purposes of this article are to review the causes of cleansing artifacts that distract and mislead readers in conventional EC schemes, to describe the applications of deep learning and dual-energy CT colonography to EC of the colon, and to demonstrate the improvements in image quality with EC and deep learning at single-energy and dual-energy CT colonography with noncathartic bowel preparation. ^©RSNA, 2018.

Domain-Based Analysis of Colon Polyp in CT Colonography Using Image-Processing Techniques

Background: The purpose of the research was to improve the polyp detection accuracy in CT Colonography (CTC) through effective colon segmentation, removal of tagged fecal matter through Electronic Cleansing (EC), and measuring the smaller polyps. Methods: An improved method of boundary-based semi-automatic colon segmentation with the knowledge of colon distension, an adaptive multistep method for the virtual cleansing of segmented colon based on the knowledge of Hounsfield Units, and an automated method of smaller polyp measurement using skeletonization technique have been implemented. Results: The techniques were evaluated on 40 CTC dataset. The segmentation method was able to delineate the colon wall accurately. The submerged colonic structures were preserved without soft tissue erosion, pseudo enhanced voxels were corrected, and the air-contrast layer was removed without losing the adjacent tissues. The smaller polyp of size less than <10mm was detected correctly. The results were statistically validated qualitatively and quantitatively. Segmented colons were validated through volumetric overlap computation, and accuracy of 95.826±0.6854% was achieved. In polyp measurement, the paired t-test method was applied to compare the difference with ground truth and at α=5%, t=0.9937 and p=0.098 was achieved. The statistical values of TPR=90%, TNR=82.3% and accuracy=88.31% were achieved. Conclusion: An automated system of polyp measurement has been developed starting from colon segmentation to improve the existing CTC solutions. The analysis of domain-based approach of polyp has given good results. A prototype software, which can be used as a low-cost polyp diagnosis tool, has been developed.

Automatic Electronic Cleansing in Computed Tomography Colonography Images using Domain Knowledge

Electronic cleansing is an image post processing technique in which the tagged colonic content is subtracted from colon using CTC images. There are post processing artefacts, like: 1) soft tissue degradation; 2) incomplete cleansing; 3) misclassification of polyp due to pseudo enhanced voxels; and 4) pseudo soft tissue structures. The objective of the study was to subtract the tagged colonic content without losing the soft tissue structures. This paper proposes a novel adaptive method to solve the first three problems using a multi-step algorithm. It uses a new edge model-based method which involves colon segmentation, priori information of Hounsfield units (HU) of different colonic contents at specific tube voltages, subtracting the tagging materials, restoring the soft tissue structures based on selective HU, removing boundary between air-contrast, and applying a filter to clean minute particles due to improperly tagged endoluminal fluids which appear as noise. The main finding of the study was submerged soft tissue structures were absolutely preserved and the pseudo enhanced intensities were corrected without any artifact. The method was implemented with multithreading for parallel processing in a high performance computer. The technique was applied on a fecal tagged dataset (30 patients) where the tagging agent was not completely removed from colon. The results were then qualitatively validated by radiologists for any image processing artifacts.

A Markov Random Field orientation prior for electronic cleansing in CT Colonography

Tagging of the bowel content with an oral contrast facilitates CT Colonography with limited bowel preparation. Electronic colon cleansing (ECC) reconstructs the colon lumen, devoid of feces from a CT scan acquired with fecal fluid tagging. A popular method to estimate the stool composition in an image (with the purpose of removing it) is the well-established Expectation Maximization (EM) method. The tagged fluid residue appears as a contrast enhanced region with a largely horizontal interface with air above it. One of the issues is the partial volume (PV) effect that creates voxels with attenuations similar to that of the colon wall at the boundary of air and tagged fluid. We present here, a novel orientation prior formulated as a Markov Random Field that is included as part of the EM tissue segmentation framework to mitigate this PV effect at the air and tagged fluid layer. We show quantitative results on a simple synthetic dataset and qualitative results on patient data that highlight improvements due to the inclusion of the orientation prior.

Electronic cleansing for dual-energy CT colonography based on material decomposition and virtual monochromatic imaging

CT colonography (CTC) uses orally administered fecal-tagging agents to enhance retained fluid and feces that would otherwise obscure or imitate polyps on CTC images. To visualize the complete region of colon without residual materials, electronic cleansing (EC) can be used to perform virtual subtraction of the tagged materials from CTC images. However, current EC methods produce subtraction artifacts and they can fail to subtract unclearly tagged feces. We developed a novel multi-material EC (MUMA-EC) method that uses dual-energy CTC (DE-CTC) and machine-learning methods to improve the performance of EC. In our method, material decomposition is performed to calculate water-iodine decomposition images and virtual monochromatic (VIM) images. Using the images, a random forest classifier is used to label the regions of lumen air, soft tissue, fecal tagging, and their partial-volume boundaries. The electronically cleansed images are synthesized from the multi-material and VIM image volumes. For pilot evaluation, we acquired the clinical DE-CTC data of 7 patients. Preliminary results suggest that the proposed MUMA-EC method is effective and that it minimizes the three types of image artifacts that were present in previous EC methods.

Electronic cleansing in fecal-tagging dual-energy CT colonography based on material decomposition and virtual colon tagging

Dual-energy CT provides a promising solution to identify tagged fecal materials in electronic cleansing (EC) for fecal-tagging CT colonography (CTC). In this study, we developed a new EC method based on virtual colon tagging (VCT) for minimizing EC artifacts by use of the material decomposition ability in dual-energy CTC images. In our approach, a localized three-material decomposition model decomposes each voxel into a material mixture vector and the first partial derivatives of three base materials: luminal air, soft tissue, and iodine-tagged fecal material. A Poisson-based derivative smoothing algorithm smoothes the derivatives and implicitly smoothes the associated material mixture fields. VCT is a means for marking the entire colonic lumen by virtually elevating the CT value of luminal air as high as that of the tagged fecal materials to differentiate effectively soft-tissue structures from air-tagging mixtures. A dual-energy EC scheme based on VCT method, denoted as VCT-EC, was developed, in which the colonic lumen was first virtually tagged and then segmented by its high values in VCT images. The performance of the VCT-EC scheme was evaluated in a phantom study and a clinical study. Our results demonstrated that our VCT-EC scheme may provide a significant reduction of EC artifacts.

Fast three-material modeling with triple arch projection for electronic cleansing in CTC

In this paper, we propose a fast three-material modeling for electronic cleansing (EC) in computed tomographic colonography. Using a triple arch projection, our three-material modeling provides a very quick estimate of the three-material fractions to remove ridge-shaped artifacts at the T-junctions where air, soft-tissue (ST), and tagged residues (TRs) meet simultaneously. In our approach, colonic components including air, TR, the layer between air and TR, the layer between ST and TR (L(ST/TR)), and the T-junction are first segmented. Subsequently, the material fraction of ST for each voxel in L(ST/TR) and the T-junction is determined. Two-material fractions of the voxels in L(ST/TR) are derived based on a two-material transition model. On the other hand, three-material fractions of the voxels in the T-junction are estimated based on our fast three-material modeling with triple arch projection. Finally, the CT density value of each voxel is updated based on our fold-preserving reconstruction model. Experimental results using ten clinical datasets demonstrate that the proposed three-material modeling successfully removed the T-junction artifacts and clearly reconstructed the whole colon surface while preserving the submerged folds well. Furthermore, compared with the previous three-material transition model, the proposed three-material modeling resulted in about a five-fold increase in speed with the better preservation of submerged folds and the similar level of cleansing quality in T-junction regions.

3D Reconstruction of virtual colon structures from colonoscopy images

This paper presents the first fully automated reconstruction technique of 3D virtual colon segments from individual colonoscopy images. It is the basis of new software applications that may offer great benefits for improving quality of care for colonoscopy patients. For example, a 3D map of the areas inspected and uninspected during colonoscopy can be shown on request of the endoscopist during the procedure. The endoscopist may revisit the suggested uninspected areas to reduce the chance of missing polyps that reside in these areas. The percentage of the colon surface seen by the endoscopist can be used as a coarse objective indicator of the quality of the procedure. The derived virtual colon models can be stored for post-procedure training of new endoscopists to teach navigation techniques that result in a higher level of procedure quality. Our technique does not require a prior CT scan of the colon or any global positioning device. Our experiments on endoscopy images of an Olympus synthetic colon model reveal encouraging results with small average reconstruction errors (4.1 mm for the fold depths and 12.1 mm for the fold circumferences).

Electronic cleansing for 24-h limited bowel preparation CT colonography using principal curvature flow

CT colonography (CTC) is one of the recommended methods for colorectal cancer screening. The subject's preparation is one of the most burdensome aspects of CTC with a cathartic bowel preparation. Tagging of the bowel content with an oral contrast medium facilitates CTC with limited bowel preparation. Unfortunately, such preparations adversely affect the 3-D image quality. Thus far, data acquired after very limited bowel preparation were evaluated with a 2-D reading strategy only. Existing cleansing algorithms do not work sufficiently well to allow a primary 3-D reading strategy. We developed an electronic cleansing algorithm, aimed to realize optimal 3-D image quality for low-dose CTC with 24-h limited bowel preparation. The method employs a principal curvature flow algorithm to remove heterogeneities within poorly tagged fecal residue. In addition, a pattern recognition-based approach is used to prevent polyp-like protrusions on the colon surface from being removed by the method. Two experts independently evaluated 40 CTC cases by means of a primary 2-D approach without involvement of electronic cleansing as well as by a primary 3-D method after electronic cleansing. The data contained four variations of 24-h limited bowel preparation and was based on a low radiation dose scanning protocol. The sensitivity for lesions ≥ 6 mm was significantly higher for the primary 3-D reading strategy (84%) than for the primary 2-D reading strategy (68%) (p = 0.031). The reading time was increased from 5:39 min (2-D) to 7:09 min (3-D) (p = 0.005); the readers' confidence was reduced from 2.3 (2-D) to 2.1 (3-D) ( p = 0.013) on a three-point Likert scale. Polyp conspicuity for cleansed submerged lesions was similar to not submerged lesions (p = 0.06). To our knowledge, this study is the first to describe and clinically validate an electronic cleansing algorithm that facilitates low-dose CTC with 24-h limited bowel preparation.

Informatics in radiology: dual-energy electronic cleansing for fecal-tagging CT colonography

Electronic cleansing (EC) is an emerging technique for the removal of tagged fecal materials at fecal-tagging computed tomographic (CT) colonography. However, existing EC methods may generate various types of artifacts that severely impair the quality of the cleansed CT colonographic images. Dual-energy fecal-tagging CT colonography is regarded as a next-generation imaging modality. EC that makes use of dual-energy fecal-tagging CT colonographic images promises to be effective in reducing cleansing artifacts by means of applying the material decomposition capability of dual-energy CT. The dual-energy index (DEI), which is calculated from the relative change in the attenuation values of a material at two different photon energies, is a reliable and effective indicator for differentiating tagged fecal materials from various types of tissues on fecal-tagging CT colonographic images. A DEI-based dual-energy EC scheme uses the DEI to help differentiate the colonic lumen-including the luminal air, tagged fecal materials, and air-tagging mixture-from the colonic soft-tissue structures, and then segments the entire colonic lumen for cleansing of the tagged fecal materials. As a result, dual-energy EC can help identify partial-volume effects in the air-tagging mixture and inhomogeneous tagging in residual fecal materials, the major causes of EC artifacts. This technique has the potential to significantly improve the quality of EC and promises to provide images of a cleansed colon that are free of the artifacts commonly observed with conventional single-energy EC methods.

Fold-preserving electronic cleansing using a reconstruction model integrating material fractions and structural responses

In this paper, we propose an electronic cleansing method using a novel reconstruction model for removing tagged materials (TMs) in computed tomography (CT) images. To address the partial volume (PV) and pseudoenhancement (PEH) effects concurrently, material fractions and structural responses are integrated into a single reconstruction model. In our approach, colonic components including air, TM, an interface layer between air and TM, and an interface layer between soft-tissue (ST) and TM (IL ST/TM ) are first segmented. For each voxel in IL ST/TM, the material fractions of ST and TM are derived using a two-material transition model, and the structural response to identify the folds submerged in the TM is calculated by the rut-enhancement function based on the eigenvalue signatures of the Hessian matrix. Then, the CT density value of each voxel in IL ST/TM is reconstructed based on both the material fractions and structural responses. The material fractions remove the aliasing artifacts caused by a PV effect in IL ST/TM effectively while the structural responses avoid the erroneous cleansing of the submerged folds caused by the PEH effect. Experimental results using ten clinical datasets demonstrated that the proposed method showed higher cleansing quality and better preservation of submerged folds than the previous method, which was validated by the higher mean density values and fold preservation rates for manually segmented fold regions.

An improved method of automatic colon segmentation for virtual colon unfolding

The technique of virtual colon unfolding (VU) is a non-invasive procedure to detect polyps on the colon inner wall. Compared with conventional virtual colonoscopy, VU is faster and results in fewer uninspected regions. However, the performance of VU is more vulnerable to the quality of colon segmentation. In this paper, an improved colon segmentation method is proposed to enhance the performance of VU. The improved method is with the use of a novel post-processing scheme, which is composed of two parts: attain more accurate centerlines with the help of scalar complementary geodesic distance field and compensate gap-like artifacts based on local morphological information. We validated the improved method on twenty colon cases via two widely used VU techniques, the ray-casting technique and the conformal-mapping technique. Experimental results indicated that with the use of the improved method, the rates of correct response via ray-casting and conformal-mapping techniques were respectively elevated by 14.9% and 13.1%, while the rates of false response were respectively reduced by 8.4% and 10.8%.

Automated image-based colon cleansing for laxative-free CT colonography computer-aided polyp detection

PURPOSE

To evaluate the performance of a computer-aided detection (CAD) system for detecting colonic polyps at noncathartic computed tomography colonography (CTC) in conjunction with an automated image-based colon cleansing algorithm.

METHODS

An automated colon cleansing algorithm was designed to detect and subtract tagged-stool, accounting for heterogeneity and poor tagging, to be used in conjunction with a colon CAD system. The method is locally adaptive and combines intensity, shape, and texture analysis with probabilistic optimization. CTC data from cathartic-free bowel preparation were acquired for testing and training the parameters. Patients underwent various colonic preparations with barium or Gastroview in divided doses over 48 h before scanning. No laxatives were administered and no dietary modifications were required. Cases were selected from a polyp-enriched cohort and included scans in which at least 90% of the solid stool was visually estimated to be tagged and each colonic segment was distended in either the prone or supine view. The CAD system was run comparatively with and without the stool subtraction algorithm.

RESULTS

The dataset comprised 38 CTC scans from prone and/or supine scans of 19 patients containing 44 polyps larger than 10 mm (22 unique polyps, if matched between prone and supine scans). The results are robust on fine details around folds, thin-stool linings on the colonic wall, near polyps and in large fluid/stool pools. The sensitivity of the CAD system is 70.5% per polyp at a rate of 5.75 false positives/scan without using the stool subtraction module. This detection improved significantly (p = 0.009) after automated colon cleansing on cathartic-free data to 86.4% true positive rate at 5.75 false positives/scan.

CONCLUSIONS

An automated image-based colon cleansing algorithm designed to overcome the challenges of the noncathartic colon significantly improves the sensitivity of colon CAD by approximately 15%.

Fully automated colon segmentation for the computation of complete colon centerline in virtual colonoscopy

Virtual colonoscopy detects polyps by navigating along a colon centerline. Complete colon segmentation based on computed tomography (CT) data is a prerequisite to the computation of complete colon centerline. There are two main problems impeding complete segmentation: overdistention/underdistention of colon and the use of oral contrast agents. Overdistention produces loops in the segmented colon, while underdistention may cause the segmented colon collapse into a series of disconnected segments. Use of oral contrast agents, which have high attenuation on CT, may add redundant structures (bones and small bowels) to the segmented colon. A fully automated colon segmentation method is proposed in this paper to address the two problems. We tested the proposed method in 170 cases, including 37 "moderate" and 133 "challenging" cases. Computer-generated centerlines were compared with human-generated centerlines (plotted by three radiologists). The proposed method achieved a 90.56% correct coverage rate with respect to the human-generated centerlines. We also compared the proposed method with two existing colon segmentation methods: Uitert's method and Nappi's method. The results of these two methods were 75.16% and 72.59% correct coverage rates, respectively. Our experimental results indicate that the proposed method could yield more complete colon centerlines than the existing methods.

Colon segmentation for prepless virtual colonoscopy

A novel segmentation framework for a prepless virtual colonoscopy (VC) is presented, which reduces the necessity for colon cleansing before the CT scan. The patient is injected rectally with a water-soluble iodinated contrast medium using manual insufflators and a small rectal catheter. Compared to the air-based contrast medium, this technique can better preserve the color lumen and reduce the partial volume effect. However, the contrast medium, together with the fecal materials and air, makes colon wall segmentation challenging. Our solution makes no assumptions about the shape, size, and location of the fecal material in the colon. This generality allows us to label the fecal material accurately and extract the colon wall reliably. The accuracy of our technique has been verified on 60 human subjects. Compared with current VC technologies, our method is shown to be better in terms of both sensitivity and specificity. Further, in our experiments, the accuracy of the technique was comparable to that of optical colonoscopy results.

Mosaic decomposition: an electronic cleansing method for inhomogeneously tagged regions in noncathartic CT colonography

Electronic cleansing (EC) is a method that segments fecal material tagged by an X-ray-opaque oral contrast agent in computed tomographic 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 (MD), for reduction of the artifacts due to incomplete cleansing of inhomogeneously tagged fecal material in CTC images, especially in noncathartic CTC images. In our approach, the entire colonic region, including the residual fecal regions, was first decomposed into a set of local homogeneous regions, called tiles, after application of a 3-D watershed transform to the CTC images. Each tile was then subjected to a single-class support vector machine (SVM) classifier for soft-tissue discrimination. The feature set of the soft-tissue SVM classifier was selected by a genetic algorithm (GA). A scalar index, called a soft-tissue likelihood, is formulated for differentiation of the soft-tissue tiles from those of other materials. Then, EC based on MD, called MD-cleansing, is performed by first initializing of the level-set front with the classified tagged regions; the front is then 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 residual fecal regions. The performance of the MD-cleansing method was evaluated by use of a phantom and of clinical cases. In the phantom evaluation, our MD-cleansing was trained with the supine (prone) scan and tested on the prone (supine) scan, respectively. In both cases, the sensitivity and specificity of classification were 100%. The average cleansing ratio was 90.6%, and the soft-tissue preservation ratio was 97.6%. In the clinical evaluation, 10 noncathartic CTC cases (20 scans) were collected, and the ground truth of a total of 2095 tiles was established by manual assignment of a material class to each tile. Five cases were randomly selected for training GA/SVM, and the remaining five cases were used for testing. The overall sensitivity and specificity of the proposed classification scheme were 97.1% and 85.3%, respectively, and the accuracy was 94.6%. The area under the ROC curve (Az) was 0.96. Our results indicated that the use of MD-cleansing substantially improved the effectiveness of our EC method in the reduction of incomplete cleansing artifacts.

Virtual eversion and rotation of colon based on outer surface centerline

PURPOSE

Virtual eversion turns the colon's inner surface to its outside while maintaining the original colon path. The virtually everted colon allows both global and local views of the mucosal surface for observation. However, the conventional colon's inner surface centerline commonly used in virtual colonoscopy and virtual flattening is not suitable for virtual eversion. Therefore, the colon's outer surface centerline is introduced for virtual eversion to produce a more accurate representation.

METHODS

An improved level set segmentation method is presented for generating the colon's outer surface. To achieve eversion with fewer errors, the centerline of the colon's outer surface is employed in the proposed virtual eversion method instead of the inner surface centerline. A hybrid sampling method is designed to accelerate the eversion. Virtual rotation is introduced to visualize the lateral and rear views of the colon better. The gathered structures in the high curvature regions can be separated by virtual rotation.

RESULTS

The proposed methods were validated using two three-dimensional phantoms and 87 CT data sets. A study on the observation performance of the everted data showed that the reading times were (63% of time reduction for phantom A, 65% of time reduction for phantom B, and 77% of time reduction for CT data) less than those using virtual colonoscopy, while maintaining the sensibility. The incidence of improperly everted regions in the virtual eversion based on the outer surface centerline was 71% less than that based on the inner surface centerline.

CONCLUSIONS

The virtual eversion based on the outer surface centerline is more accurate than the one based on the inner surface centerline whether the colon's inner surface is smooth or ragged. The time required for polyp detection using the virtual eversion is considerably less than that using the conventional virtual endoscopy. Virtual eversion and virtual rotation are promising methods for the rapid location of colonic polyps. Together with virtual colonoscopy and virtual flattening, virtual eversion and virtual rotation can be integrated to produce a powerful system for diagnosing colonic lesions.

Semi-automated segmentation of the sigmoid and descending colon for radiotherapy planning using the fast marching method

A fast and accurate segmentation of organs at risk, such as the healthy colon, would be of benefit for planning of radiotherapy, in particular in an adaptive scenario. For the treatment of pelvic tumours, a great challenge is the segmentation of the most adjacent and sensitive parts of the gastrointestinal tract, the sigmoid and descending colon. We propose a semi-automated method to segment these bowel parts using the fast marching (FM) method. Standard 3D computed tomography (CT) image data obtained from routine radiotherapy planning were used. Our pre-processing steps distinguish the intestine, muscles and air from connective tissue. The core part of our method separates the sigmoid and descending colon from the muscles and other segments of the intestine. This is done by utilizing the ability of the FM method to compute a specified minimal energy functional integrated along a path, and thereby extracting the colon centre line between user-defined control points in the sigmoid and descending colon. Further, we reconstruct the tube-shaped geometry of the sigmoid and descending colon by fitting ellipsoids to points on the path and by adding adjacent voxels that are likely voxels belonging to these bowel parts. Our results were compared to manually outlined sigmoid and descending colon, and evaluated using the Dice coefficient (DC). Tests on 11 patients gave an average DC of 0.83 (+/-0.07) with little user interaction. We conclude that the proposed method makes it possible to fast and accurately segment the sigmoid and descending colon from routine CT image data.

Informatics in radiology: Electronic cleansing for noncathartic CT colonography: a structure-analysis scheme

Computed tomographic (CT) colonography performed after tagging of fecal matter but without a cathartic agent, or noncathartic CT colonography (also known as laxative-free CT colonography), is regarded as a promising next-generation technique for reducing or eliminating the discomfort associated with cathartic bowel preparation, which is the major barrier to undergoing colon cancer screening. Electronic cleansing is an emerging technique for the removal of tagged fecal materials from CT colonographic images. Three major electronic cleansing artifacts--soft-tissue degradation, pseudo-soft-tissue structures, and incomplete cleansing--severely impair the quality of electronically cleansed noncathartic CT colonographic images and limit the diagnostic utility of this modality. A structure-analysis electronic cleansing scheme was developed that makes use of local morphologic information to identify submerged colonic soft-tissue structures while removing the tagged material. Combined with other cutting-edge image processing techniques, including local roughness analysis, mosaic decomposition, and level set segmentation, structure-analysis cleansing helps eliminate the aforementioned artifacts, providing diagnostic-quality cleansed CT colonographic images for the detection of colon cancer. Noncathartic CT colonography with the application of structure-analysis cleansing is expected to help promote CT colonography as a patient-friendly method of colorectal cancer screening.

A simple image processing approach for electronic cleansing in computed tomographic colonography

The prevalence of colon cancer has seen strong demand in screening for colorectal neoplasia, and this has drawn considerable attention to the technological advances in Computed Tomographic Colonography (CTC). With the assistance of an oral contrast agent, an imaging technique known as Electronic Cleansing (EC), can affect virtual cleaning of the computed tomography (CT) images, to remove fecal material that is tagged by the agent. Technical problems can arise with electronic cleansing however, when the air lumen causes distortions to the tagged regions which result in partial volume effects.

Combining the simple image arithmetic of an electronic cleansing algorithm, with a vertical motion filter at the fluid level of the bowel, artifacts such as those caused by an air lumen are eliminated. Essentially, the filter becomes a vector for that carries the measurement of vertical motion to neutralise the artifact that is causing partial volume effects. Results demonstrate that despite its simplicity, this technique offers accuracy and is able to successfully maintain the normal intra-colonic structure, while supporting digital leaning of tagged residual material appearing on the colon wall.

An EM approach to MAP solution of segmenting tissue mixture percentages with application to CT-based virtual colonoscopy

Electronic colon cleansing (ECC) is an emerging technique developed to segment the colon lumen from a patient's abdominal computed tomography colonography (CTC) images. However, the residue stool and fluid tagged by contrast materials as well as mixed tissue distribution with partial volume (PV) effect impose several challenges for ECC, resulting in incomplete and overcomplete cleansings. To address the PV effect, this work investigated an improved maximum a posteriori expectation-maximization (MAP-EM) image segmentation algorithm which simultaneously estimates tissue mixture percentages within each image voxel and statistical model parameters for the tissue distribution. Given the segmented tissue mixture information beyond the image voxel level, not only the PV effect has been satisfactorily addressed as a particular case of tissue mixture problem, but incomplete and overcomplete ECC causes could also be maximally avoided. For clinical application to CTC that involves several issues transferring from theoretical analysis to practical validation, an innovative initialization procedure and refined estimation strategy were proposed to build an ECC pipeline based on the MAP-EM segmentation. The pipeline was evaluated based on 52 patient CTC studies, downloaded from the website of the Virtual Colonoscopy Screening Resource Center, by two radiologists. A noticeable improvement over the authors' previous ECC pipeline was documented. Several typical cases were also presented to show visually the improved performance of the presented ECC pipeline.

Role of CT colonography in inflammatory bowel disease

CT colonography (CTC), or virtual colonoscopy, is a non-invasive imaging method that uses CT data sets combined with specialized imaging software to examine the colon. CTC is not used routinely in patients with inflammatory bowel disease (IBD). However, investigating contemporarily the colon, other abdominal organs and the peritoneum with CTC is at times useful in patients with IBD, especially when other diagnostic tools fail. Furthermore, since symptoms of colorectal cancer sometimes superimpose to those of inflammatory disease, it may happen to image patients with IBD incidentally. If clinical signs are suggestive for inflammatory disease, exam technique should be modified accordingly and distinguishing radiological findings searched for.

Lesion conspicuity and efficiency of CT colonography with electronic cleansing based on a three-material transition model

OBJECTIVE

The purpose of this article is to report the effect on lesion conspicuity and the practical efficiency of electronic cleansing for CT colonography (CTC).

MATERIALS AND METHODS

Patients were included from the Walter Reed Army Medical Center public database. All patients had undergone extensive bowel preparation with fecal tagging. A primary 3D display method was used. For study I, the data consisted of all patients with polyps > or = 6 mm. Two experienced CTC observers (observer 1 and observer 2) scored the lesion conspicuity considering supine and prone positions separately. For study II, data consisted of 19 randomly chosen patients from the database. The same observers evaluated the data before and after electronic cleansing. Evaluation time, assessment effort, and observer confidence were recorded.

RESULTS

In study I, there were 59 lesions partly or completely covered by tagged material (to be uncovered by electronic cleansing) and 70 lesions surrounded by air (no electronic cleansing required). The conspicuity did not differ significantly between lesions that were uncovered by electronic cleansing and lesions surrounded by air (observer 1, p < 0.5; observer 2, p < 0.6). In study II, the median evaluation time per patient after electronic cleansing was significantly shorter than for original data (observer 1, 20 reduced to 12 minutes; observer 2, 17 reduced to 12 minutes). Assessment effort was significantly smaller for both observers (p < 0.0000001), and observer confidence was significantly larger (observer 1, p < 0.007; observer 2, p < 0.0002) after electronic cleansing.

CONCLUSION

Lesions uncovered by electronic cleansing have comparable conspicuity with lesions surrounded by air. CTC with electronic cleansing sustains a shorter evaluation time, lower assessment effort, and larger observer confidence than without electronic cleansing.

Structure-analysis method for electronic cleansing in cathartic and noncathartic CT colonography

Electronic cleansing (EC) is an emerging method for segmentation of fecal material in CT colonography (CTC) that is used for reducing or eliminating the requirement for cathartic bowel preparation and hence for improving patients' adherence to recommendations for colon cancer screening. In EC, feces tagged by an x-ray-opaque oral contrast agent are removed from the CTC images, effectively cleansing the colon after image acquisition. Existing EC approaches tend to suffer from the following cleansing artifacts: degradation of soft-tissue structures because of pseudo-enhancement caused by the surrounding tagged fecal materials, and pseudo soft-tissue structures and false fistulas caused by partial volume effects at the boundary between the air lumen and the tagged regions, called the air-tagging boundary (AT boundary). In this study, we developed a novel EC method, called structure-analysis cleansing, which effectively avoids these cleansing artifacts. In our method, submerged soft-tissue structures are recognized by their local morphologic signatures that are characterized based on the eigenvalues of a three-dimensional Hessian matrix. A structure-enhancement function is formulated for enhancing of the soft-tissue structures. In addition, thin folds sandwiched between the air lumen and tagged regions are enhanced by analysis of the local roughness based on multi-scale volumetric curvedness. Both values of the structure-enhancement function and the local roughness are integrated into the speed function of a level set method for delineating the tagged fecal materials. Thus, submerged soft-tissue structures as well as soft-tissue structures adhering to the tagged regions are preserved, whereas the tagged regions are removed along with the associated AT boundaries from CTC images. Evaluation of the quality of the cleansing based on polyps and folds in a colon phantom, as well as on polyps in clinical cathartic and noncathartic CTC cases with fluid and stool tagging, showed that our structure-analysis cleansing method is significantly superior to that of our previous thresholding-based EC method. It provides a cleansed colon with substantially reduced subtraction artifacts.

Fundamental elements for successful performance of CT colonography (virtual colonoscopy)

There are many factors affecting the successful performance of CT colonography (CTC). Adequate colonic cleansing and distention, the optimal CT technique and interpretation with using the newest CTC software by a trained reader will help ensure high accuracy for lesion detection. Fecal and fluid tagging may improve the diagnostic accuracy and allow for reduced bowel preparation. Automated carbon dioxide insufflation is more efficient and may be safer for colonic distention as compared to manual room air insufflation. CT scanning should use thin collimation of < or =3 mm with a reconstruction interval of < or =1.5 mm and a low radiation dose. There is not any one correct method for the interpretation of CTC; therefore, readers should be well-versed with both the primary 3D and 2D reviews. Polyps detected at CTC should be measured accurately and reported following the "polyp size-based" patient management system. The time-intensive nature of CTC and the limited resources for training radiologists appear to be the major barriers for implementing CTC in Korea.

Transparent rendering of intraluminal contrast for 3D polyp visualization at CT colonography

We developed a classifier that permits transparent rendering of both tagging material and air to facilitate interpretation of tagged computed tomographic (CT) colonography. With this technique, a reader can simultaneously appreciate polyps on endoluminal views both covered with tagging material and against air, along with unmodified 2-dimensional CT images. Evaluated with 49 polyps from 26 patients (data from public National Library of Medicine, Health Insurance Portability and Accountability Act compliant), 3 readers were able to determine the presence/absence of polyps in tagged locations with equivalent accuracy compared with polyps in air. This method offers an alternative way to visualize tagged CT colonography.

CAD in CT colonography without and with oral contrast agents: progress and challenges

Computed tomographic colonography (CTC), also known as virtual colonoscopy, is an emerging alternative technique for screening of colon cancers. CTC uses CT to provide a series of cross-sectional images of the colon for detection of polyps and masses. Fecal tagging is a means of labeling of residual feces by an oral contrast agent for improving the accuracy in the detection of polyps. Computer-aided diagnosis (CAD) for CTC automatically determines the locations of suspicious polyps and masses in CTC and presents them to radiologists, typically as a second opinion. Despite its relatively short history, CAD has become one of the mainstream techniques that could make CTC prime time for screening of colorectal cancer. Rapid technical developments have advanced CAD substantially during the last several years, and a fundamental scheme for the detection of polyps has been established, in which sophisticated 3D image processing, analysis, and display techniques play a pivotal role. The latest CAD systems indicate a clinically acceptable high sensitivity and a low false-positive rate, and observer studies have demonstrated the benefits of these systems in improving radiologists' detection performance. Some technical and clinical challenges, however, remain unresolved before CAD can become a truly useful tool for clinical practice. Also, new challenges are facing CAD as the methods for bowel preparation and image acquisition, such as tagging of fecal residue with oral contrast agents, and interpretation of CTC images evolve. This article reviews the current status and future challenges in CAD for CTC without and with fecal tagging.

Revisiting oral barium sulfate contrast agents

Oral contrast agents used during CT colonography (CTC) are valuable and may reduce false positive and false negative detections due to stool and residual fluid. Electronic cleansing algorithms are feasible, and oral contrast agents can eliminate the CTC requirement for a clean colon. Recent work shows oral contrast frequently adheres to polyps, with a preference for those with villous histology, a characteristic of advanced polyps. This finding encourages the development of contrast agents that highlight polyps at greatest risk for progression to malignancy. Our review summarizes numerous aspects of oral barium sulfate contrast agents as well as tests to assess adherence and coating ability of the agents, offering arenas to explore and tools for evaluation.

An improved electronic colon cleansing method for detection of colonic polyps by virtual colonoscopy

Electronic colon cleansing (ECC) aims to segment the colon lumen from a patient abdominal image acquired using an oral contrast agent for colonic material tagging, so that a virtual colon model can be constructed. Virtual colonoscopy (VC) provides fly-through navigation within the colon model, looking for polyps on the inner surface in a manner analogous to that of fiber optic colonoscopy. We have built an ECC pipeline for a commercial VC navigation system. In this paper, we present an improved ECC method. It is based on a partial-volume (PV) image-segmentation framework, which is derived using the well-established statistical expectation-maximization algorithm. The presented ECC method was evaluated by both visual inspection and computer-aided detection of polyps (CADpolyp) within the cleansed colon lumens obtained using 20 patient datasets. Compared to our previous ECC pipeline, which does not sufficiently consider the PV effect, the method presented in this paper demonstrates improved polyp detection by both visual judgment and CADpolyp measure.

Tagging-based, electronically cleansed CT colonography: evaluation of patient comfort and image readability

PURPOSE

To prospectively compare the homogeneity, adequacy, and patient acceptance of nonionic iodine-based regimens with those of a barium-based regimen for computed tomographic (CT) colonography with electronic subtraction cleansing.

MATERIALS AND METHODS

After institutional review board approval and informed consent were obtained, 68 subjects (41 men (60%) men, 27 (40%) women; mean age, 60 years +/- 6 [standard deviation]) with average or moderate risk factors for development of colorectal carcinoma were recruited and placed into three study groups. Group 1 (n = 25) ingested 150-mL aliquots of 2% barium sulfate suspension with meals and snacks for 48 hours prior to imaging, without other diet modification or a cathartic. Group 2 (n = 21) ingested 10-mL aliquots of nonionic iodinated contrast material (iopromide) with a concentration of 300 mg per milliliter with meals and snacks for 2 days before imaging, without diet modification or a cathartic. Group 3 (n = 22) ingested nonionic iodinated contrast material (iohexol) with a concentration of 300 mg per milliliter with meals and snacks for 2 days before imaging and ingested 34 g of magnesium citrate the evening prior to imaging. CT colonography was also performed on 10 control subjects who ingested polyethylene glycol electrolyte solution prior to imaging. Subjective and numerical measures of bowel preparation quality, homogeneity, and patient comfort among the noncathartic and cathartic cohorts were compared with nonparametric analysis of variance, the Fisher exact test, and the F test, as appropriate. The study was HIPAA compliant.

RESULTS

Study subjects who received tagging preparations reported significantly improved discomfort scores when compared with those of the control subjects (P < .05, each comparison). There was no significant difference in discomfort scores among groups 1, 2, and 3. For each reader, scores of subtracted image readability were highest for group 3. Dichotomized rates of preparation "success" were also greatest for group 3.

CONCLUSION

In this series, the patient discomfort scores were significantly improved with tagging preparations for CT colonography. Nonionic iodinated contrast material in conjunction with a hyperosmotic laxative (magnesium citrate) was associated with the best subjective and numerical indices of readability.

Hybrid segmentation of colon filled with air and opacified fluid for CT colonography

Reliable segmentation of the colon is a requirement for three-dimensional visualization programs and automatic detection of polyps on computed tomography (CT) colonography. There is an evolving clinical consensus that giving patients positive oral contrast to tag out remnants of stool and residual fluids is mandatory. The presence of positive oral contrast in the colon adds an additional challenge for colonic segmentation but ultimately is beneficial to the patient because the enhanced fluid helps reveal polyps in otherwise hidden areas. Therefore, we developed a new segmentation procedure which can handle both air- and fluid-filled parts of the colon. The procedure organizes individual air- and fluid-filled regions into a graph that enables identification and removal of undesired leakage outside the colon. In addition, the procedure provides a risk assessment of possible leakage to assist the user prior to the tedious task of visual verification. The proposed hybrid algorithm uses modified region growing, fuzzy connectedness and level set segmentation. We tested our algorithm on 160 CT colonography scans containing 183 known polyps. All 183 polyps were in segmented regions. In addition, visual inspection of 24 CT colonography scans demonstrated good performance of our procedure: the reconstructed colonic wall appeared smooth even at the interface between air and fluid and there were no leaked regions.

The role of CT colonography in colorectal cancer screening

Computed tomographic colonography (CTC) is a relatively noninvasive technique for large bowel imaging that has the ability to detect colorectal neoplasia. Already well established as a reliable diagnostic tool in symptomatic patients who are unable to undergo complete colonoscopy, it is now being considered as a viable method for population screening. Advances in technique over the past 10 yr make this an attractive alternative, including reduced bowel preparation and stool tagging, three-dimensional (3D) image reconstruction, computer-aided detection software, and low-radiation dose protocols. CTC may be favored by patients compared to other available screening tests due to the ease of performance and comfort. Although published studies vary in relation to the sensitivity of this test for the detection of polyps, in the best hands a sensitivity of greater than 90% for detection of polyps at least 10 mm in diameter may be obtained. Although not yet endorsed for widespread use by major gastroenterological societies, CTC shows promise as a screening tool.

Polyp size at CT colonography after electronic subtraction cleansing in an anthropomorphic colon phantom

PURPOSE

To evaluate the effect of various bowel contrast material concentrations and subtraction software on size measurements of well-defined polyp lesions in a colon phantom at CT colonography.

MATERIALS AND METHODS

Repeated scanning and a precise reference standard required the use of a colon phantom in which 21 polyps were randomly distributed. Two readers who had each reviewed computed tomographic (CT) colonographic images from more than 100 cases evaluated polyp size on images obtained when the phantom was partially filled with varying concentrations of contrast material, scanned by using CT colonography, and subjected to electronic subtraction cleansing. The single largest dimension was recorded for each reader for a randomized series of polyps. These measurements were compared with a reference standard that was based on a combination of the manufacturer's polyp size specifications and the subsequent verification of these sizes by an independent consensus panel. Six weeks after initial observations, readers evaluated images of the phantom scanned without the presence of contrast material. Polyp size estimations for the two readers for each series were compared with the reference standard to obtain a mean absolute measurement error for each reader for each series. Data for each reader were compared by using a nonparametric Kruskal-Wallis analysis of variance test. A pair-wise comparison of the experimental and control series was then performed by using the Dunn post hoc test.

RESULTS

Contrast material dilutions resulting in an average attenuation of less than 500 HU resulted in complete subtraction and the absence of streak artifacts. There was no statistically significant difference between the average measurement error for contrast attenuations between 300 and 500 HU when compared with that of control. Streak artifact was noticeable for the highest dilution (mean, 840 HU). No statistically significant differences were observed for series in which cleansing software was used in the absence of bowel contrast material.

CONCLUSION

The combination of electronic cleansing and bowel contrast enhancement in the range of 300-500 HU results in no substantial change in readers' estimations of polyp size at CT colonography.