Electronic Cleansing Research Articles

Artificial intelligence-based diagnostic imaging system with virtual enteroscopy and virtual unfolded views to evaluate small bowel lesions in Crohn's disease.

Since even subtle mucosal changes may be depicted using virtual endoscopy created by the three-dimensional reconstruction of MDCT images, we developed a novel diagnostic imaging system that integrates and displays virtual enteroscopy, curved planar reconstruction, and a virtual unfolded view, the width of which changes with increases/decreases in the inner luminal diameter. The system is also equipped with artificial intelligence that superimposes and displays depressed areas, generates an automatic small bowel centerline that connects fragmented small bowel regions, and performs electronic cleansing. We retrospectively evaluated the diagnostic performance of this system for small bowel lesions in Crohn's disease, which were divided into two groups: endoscopically-observable and endoscopically-unobservable. Lesion detection rates for stenoses, longitudinal ulcers with a cobblestone appearance, and scars were excellent in both groups. This system, when used in combination with endoscopy, shows slight mucosal changes in areas in which an endoscope cannot reach due to strictures, thereby extending the range of observation of the small bowel. This system is a useful diagnostic modality that has the capacity to assess mucosal healing and provide extraluminal information.

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

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. 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. 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. 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.

Fully Automated Electronic Cleansing Using CycleGAN in Computed Tomography Colonography

In computed tomography colonography (CTC), an electric cleansing technique is used to mix barium with residual fluid, and colon residue is removed by image processing. However, a nonhomogenous mixture of barium and residue may not be properly removed. We developed an electronic cleansing method using CycleGAN, a deep learning technique, to assist diagnosis in CTC. In this method, an original computed tomography (CT) image taken during a CTC examination and a manually cleansed image in which the barium area was manually removed from the original CT image were prepared and converted to an image in which the barium was removed from the original CT image using CycleGAN. In the experiment, the electric cleansing images obtained using the conventional method were compared with those obtained using the proposed method. The average barium cleansing rates obtained by the conventional and proposed methods were 72.3% and 96.3%, respectively. A visual evaluation of the images showed that it was possible to remove only barium without removing the intestinal tract. Furthermore, the extraction of colorectal polyps and early stage cancerous lesions in the colon was performed as in the conventional method. These results indicate that the proposed method using CycleGAN may be useful for accurately visualizing the colon without barium.

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

Simple SummaryElectronic cleansing (EC) is used for performing a virtual cleansing of the colon on CT colonography (CTC) images for colorectal cancer screening. However, current EC methods have limited accuracy, and traditional deep learning is of limited use in CTC. We evaluated the feasibility of using self-supervised adversarial learning to perform EC on a limited dataset with subvoxel accuracy. A 3D generative adversarial network was pre-trained to perform EC on the CTC datasets of an anthropomorphic colon phantom, and it was fine-tuned to each input case by use of a self-supervised learning scheme. The visually perceived quality of the virtual cleansing by this method compared favorably to that of commercial EC software on the virtual 3D fly-through examinations of 18 clinical CTC cases. Our results indicate that the proposed self-supervised scheme is a potentially effective approach for addressing the remaining technical problems of EC in CTC for colorectal cancer screening.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.

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

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. 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). Compared with conventional cleansing (66% score ≥ 2), the severity of overall cleansing artifacts was lower in transparent (60% score ≥ 2, p = 0.011) andtranslucent (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. Spectral cleansing provided more robust electronic stool cleansing of iodine-tagged stool at CTC than conventional cleansing. • 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.

Improving the Quality of Electronic Cleansing of Colorectal CT Images Using a Hybrid Method

Introduction: Colorectal cancer, as one of the most important fatal cancers, is caused by the lack of timely diagnosis of colorectal polyps. Presently, because of the advancements in CT imaging of the colorectal device, the CTC-CAD is a promising method for the duly diagnosis of these appendages. In this regard, Electronic Colon Cleansing (ECC) is one of the effective factors that enhance diagnostic accuracy in the methods used in CTC-CAD. Method: In this study, to implement ECC, the thresholding method, statistical functions, and image processing methods were combined. Then, to evaluate the proposed method, 22 images were randomly selected and ranked by seven radiologists. Regarding the extent of the interpretable, the images taken before and after ECC were collected using MD and LM_ECC methods. The concordance of concordance of all three categories of opinions was calculated based on Kendall’s tau-b correlation coefficient test. Findings: The value of t-test between the mean score of radiologists' opinions for the main images and the results of the LM_ECC method (p <0.001) is -9.355, while it is -5.414 between the mean score of radiologists for the MD results and the results obtained from the LM_ECC method (p <0.001). Conclusion: Based on the coefficient of concordance, it is found that there is a high agreement between the ranked opinions of the radiologists, based on which the results of the T-tests show the significant effect of the LM_ECC method on electronic cleansing compared to the main images and the results of MD method.

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.

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.

Dual-energy computed tomography colonography using dual-layer spectral detector computed tomography: Utility of virtual monochromatic imaging for electronic cleansing

ObjectiveTo assess the utility of virtual monochromatic imaging (VMI) using a dual-layer spectral detector CT for electronic cleansing in fecal-tagging CT colonography (CTC). MethodsThis study included 35 patients who underwent fecal-tagging CTC with a dual-layer detector spectral CT scanner. Conventional images at 120 kVp and VMI at 40, 50, and 60 keV were reconstructed. Quantitative image quality parameters, i.e., tagging density and image noise, were compared and the visual image quality was scored on a four-point scale. We recorded the number of the colon segments with appropriate tagging density (≥300 HU) for each patient and used these data to compare the reconstructions. In addition, electronic cleansing performance was semi-quantitatively assessed using a four-point scale. ResultsThe mean tagging density on VMI was significantly higher than that on conventional 120 kVp images. The number of colon segments with appropriate tagging density on VMI were significantly higher than that on conventional 120 kVp images. There was no significant difference among the reconstructed images with respect to image noise. Scores for subjective image quality and electronic cleansing performance on VMI were significantly higher than those on conventional 120 kVp images. ConclusionWith dual-layer spectral detector CT, VMI can yield significantly better fecal-tagged CTC image quality and improve electronic cleansing performance.

CT colonography: size reduction of submerged colorectal polyps due to electronic cleansing and CT-window settings

ObjectivesTo assess whether electronic cleansing (EC) of tagged residue and different computed tomography (CT) windows influence the size of colorectal polyps in CT colonography (CTC).MethodsA database of 894 colonoscopy-validated CTC datasets of a low-prevalence cohort was retrospectively reviewed to identify patients with polyps ≥6 mm that were entirely submerged in tagged residue. Ten radiologists independently measured the largest diameter of each polyp, two-dimensionally, before and after EC in colon, bone, and soft-tissue-windows, in randomised order. Differences in size and polyp count before and after EC were calculated for size categories ≥6 mm and ≥10 mm. Statistical testing involved 95% confidence interval, intraclass correlation and mixed-model ANOVA.ResultsThirty-seven patients with 48 polyps were included. Mean polyp size before EC was 9.8 mm in colon, 9.9 mm in bone and 8.2 mm in soft-tissue windows. After EC, the mean polyp size decreased significantly to 9.4 mm in colon, 9.1 mm in bone and 7.1 mm in soft-tissue windows. Compared to unsubtracted colon windows, EC, performed in colon, bone and soft-tissue windows, led to a shift of 6 (12,5%), 10 (20.8%) and 25 (52.1%) polyps ≥6 mm into the next smaller size category, thus affecting patient risk stratification.ConclusionsEC and narrow CT windows significantly reduce the size of polyps submerged in tagged residue. Polyp measurements should be performed in unsubtracted colon windows.Key Points• EC significantly reduces the size of polyps submerged in tagged residue.• Abdominal CT-window settings significantly underestimate 2D sizes of submerged polyps.• Size reduction in EC is significantly greater in narrow than wide windows.• Underestimation of polyp size due to EC may lead to inadequate treatment.• Polyp measurements should be performed in unsubtracted images using a colon window.

Gradient Directional Second Derivative Pseudo-enhancement Correction and Modified Local Roughness Response Estimation for Electronic Cleansing in CT Colonography

This paper proposes the gradient directional second derivative pseudo-enhancement (PEH) correction method to reduce the computed tomography (CT) attenuations of PEH soft-tissue voxels around fecal-tagging material (FTM) in CT colo-nography and the modified local roughness response estimation, which can efficiently separate air-tagging (AT) layers from air-tissue-tagging (ATT) layers. By integrating these two proposed methods into electronic colon cleansing (ECC), FTM can be effectively removed by a simple thresholding method. For subjective or clinical evaluation, a radiologist grades the quality of cleansing based on five causes of low quality cleansing. The average grade of our proposed method for clinical evaluation is 2.5714 out of 4. For objective evaluations, three cases exist: PEH correction, ECC, and visual assessment. First, PEH correction evaluation measures the success in the removal of PEH voxels with CT attenuations greater than or equal to 500 HU using the proposed PEH correction by comparing the results of this correction with the results from the existing method via CT attenuation profiles. Second, ECC is evaluated by comparing the results from applying our proposed methods with the ground truth. The results indicate that the goodness of cleansing is 0.01% and the global mean relative error is less than 0.016% with 99.99% confidence. Third, visual assessment of our ECC results confirms that soft-tissue components around FTM, especially ATT layers, are preserved.

Electronic cleansing in computed tomography colonography using AT layer identification with integration of gradient directional second derivative and material fraction model

BackgroundIn computed tomography colonography images, electronic cleansing (EC) is applied to remove opacified residual materials, called fecal-tagging materials (FTM), using positive-contrast tagging agents and laxative to facilitate polyp detection.MethodsThe proposed EC, ECprop, integrates the gradient directional second derivative into material fraction model to preserve submerged soft tissue (ST) under FTM. Three-material fraction model is used to remove FTM and artifacts at air-tagging (AT) layers and T-junctions where air, ST, and FTM material meet simultaneously. Moreover, the proposed AT layer identification is used to distinguish AT layers from air-tissue-tagging (ATT) layers in order to preserve ATT layers during cleansing. The clinical evaluation on 467 3-Dimensional band view images was conducted by the abdominal radiologist using four grading levels of cleansing quality with five causes of low quality EC. The amount of the remaining artifacts at T-junctions was approximated from the results of ECprop. The results from ECprop were compared with the results from syngo.via Client 3.0 Software, ECsyngo, and the fast three-material modeling, ECprev, using the preference of the radiologist. Two-tailed paired Wilcoxon signed rank test is used to indicate statistical significance.ResultsThe average grade on cleansing quality is 2.89 out of 4. The artifacts at T-junctions from 86.94% of the test images can be removed, whereas artifacts at T-junctions from only 13.06% of the test images cannot be removed. For 13.06% of the test images, the results from ECprop are more preferable to the results from ECsyngo (p<0.008). For all the test images, the results from ECprop are more preferable to the results from ECprev (p<0.001). Finally, the visual assessment shows that ECprop can preserve ATT layers, submerged polyps and folds while ECprev can preserve only submerged folds but fails to preserve ATT layers.ConclusionFrom our implementation, ECprop can improve the performance of the existing EC, such that it can preserve ST, especially ATT layers and remove the artifacts at T-junctions which have never been proposed by any other methods before.

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.

Noncathartic CT Colonography: Image Quality Assessment and Performance and in a Screening Cohort

Cathartic bowel preparation is a major barrier for colorectal cancer screening. We examined noncathartic CT colonography (CTC) quality and performance using four similar bowel-tagging regimens in an asymptomatic screening cohort. This prospective study included 564 asymptomatic subjects who underwent noncathartic CTC without dietary modification but with 21 g of barium with or without iodinated oral contrast material (four regimens). The quality of tagging with oral agents was evaluated. A gastrointestinal radiologist evaluated examinations using primary 2D search supplemented by electronic cleansing (EC) and 3D problem solving. Results were compared with complete colonoscopy findings after bowel purgation and with retrospective unblinded evaluation in 556 of the 564 (99%) subjects. Of the 556 subjects, 7% (37/556) and 3% (16/556) of patients had 52 and 20 adenomatous polyps ≥ 6 and ≥ 10 mm, respectively. The addition of iodine significantly improved the percentage of labeled stool (p ≤ 0.0002) and specificity (80% vs 89-93%, respectively; p = 0.046). The overall sensitivity of noncathartic CTC for adenomatous polyps ≥ 6 mm was 76% (28/37; 95% CI, 59-88%), which is similar to the sensitivity of the iodinated regimens with most patients (sensitivity: 231 patients, 74% [14/19; 95% CI, 49-91%]; 229 patients, 80% [12/15; 95% CI, 52-96%]). The negative predictive value was 98% (481/490), and the lone cancer was detected (0.2%, 1/556). EC was thought to improve conspicuity of 10 of 21 visible polyps ≥ 10 mm. In this prospective study of asymptomatic subjects, the per-patient sensitivity of noncathartic CTC for detecting adenomas ≥ 6 mm was approximately 76%. Inclusion of oral iodine contrast material improves examination specificity and the percentage of labeled stool. EC may improve polyp conspicuity.

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.