Tomographic Colonography Research Articles

Texture analysis of CT colonography to develop a novel imaging biomarker for the management of colorectal cancer

AbstractBackgroundRecent studies have focused on evaluating the biomarker value of textural features in radiological images. Our study investigated whether or not a texture analysis of computed tomographic colonography (CTC) images could be a novel biomarker for colorectal cancer (CRC).MethodsThis retrospective study investigated 263 patients with CRC who underwent contrast‐enhanced CTC (CE‐CTC) before curative surgery between January 2014 and December 2017. Multiple texture analyses (fractal, histogram, and gray‐level co‐occurrence matrix [GLCM] texture analyses) were applied to CE‐CTC (portal‐venous phase), and fractal dimension (FD), skewness, kurtosis, entropy, and GLCM texture parameters, including GLCM‐correlation, GLCM‐autocorrelation, GLCM‐entropy, and GLCM‐homogeneity, of the tumor were calculated. These texture parameters were compared with pathological factors (tumor depth, lymph node metastasis, vascular invasion, and lymphatic invasion) and overall survival (OS).ResultsTumor depth was significantly associated with FD, kurtosis, entropy, GLCM‐correlation, GLCM‐autocorrelation, GLCM‐entropy, and GLCM‐homogeneity (p = 0.001, 0.001, 0.001, 0.001, 0.018, 0.008, and 0.001, respectively); lymph node metastasis was associated with GLCM‐homogeneity (p = 0.004); lymphatic invasion was associated with GLCM‐correlation and GLCM‐homogeneity (p = 0.001 and 0.012, respectively); and venous invasion was associated with FD, entropy, GLCM‐correlation, GLCM‐autocorrelation, and GLCM‐entropy of the tumor (p = 0.001, 0.033, 0.021, 0.046, respectively). In the Kaplan–Meier analysis, patients with high GLCM‐correlation tumors or high GLCM‐homogeneity tumors showed a significantly worse OS than others (p = 0.001 and 0.04, respectively). Multivariate analyses showed that the GLCM correlation was an independent prognostic factor for the OS (p = 0.021).ConclusionCE‐CTC‐derived texture parameters may be clinically useful biomarkers for managing CRC patients.

Drainage pattern of the splenic flexure vein and its accompanying arteries using three-dimensional computed tomography angiography: a single-centre study of 600 patients.

The splenic flexure has variable vascular anatomy, and the details of the venous forms are not known. In this study, we report the flow pattern of the splenic flexure vein (SFV) and the positional relationship between the SFV and arteries such as the accessory middle colic artery (AMCA). This was a single-centre study using preoperative enhanced CT colonography images of 600 colorectal surgery patients. CT images were reconstructed into 3D angiography. SFV was defined as a vein flowing centrally from the marginal vein of the splenic flexure visible on CT. AMCA was defined as the artery feeding the left side of the transverse colon, separate from the left branch of the middle colic artery. The SFV returned to the inferior mesenteric vein (IMV) in 494 cases (82.3%), the superior mesenteric vein in 51 cases (8.5%) and the splenic vein in seven cases (1.2%). The AMCA was present in 244 cases (40.7%). The AMCA branched from the superior mesenteric artery or its branches in 227 cases (93.0% of cases with existing AMCA). In the 552 cases in which the SFV returned to the IMV, superior mesenteric vein or splenic vein, the left colic artery was the most frequent artery accompanying the SFV (42.2%), followed by the AMCA (38.1%) and the left branch of the middle colic artery (14.3%). The most common flow pattern of the vein in the splenic flexure is from the SFV to IMV. The SFV is frequently accompanied by the left colic artery or AMCA.

Determination of lower radiation dose limit for automatic measurement of adipose tissue.

The purpose of this study was to determine the lower limit of radiation dose required to measure visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) volumes when a fat quantification and noise reduction techniques (NRTs) are combined. For this purpose, we utilized CT colonography (CTC) images taken at low doses and manually segmented VAT and SAT fat volumes as ground truth. In order to derive the acceptable precision of the measurements needed to estimate the lower limit of radiation dose, we estimated the effect of different positioning during CT scanning on fat measurements using manually segmented VAT and SAT against normal dose. As a result, the acceptable accuracy of SAT and VAT was found to be 94.5% and 85.2%, respectively. Using these thresholds, the lower radiation dose limit required to accurately measure SAT using 5.25-mm slice-thick images was 1.5mGy of size-specific dose estimates (SSDE), while the lower radiation dose limit required to accurately measure VAT was 0.4mGy of SSDE. The lower dose limit for SAT and VAT combined was 1.5mGy, which was equivalent to an estimated effective dose of 0.38mSv. Alternatively, without noise reduction, SAT could not achieve acceptable accuracy even for images with a slice thickness of 5.25mm, while VAT required noise reduction for images with a slice thickness of 1.25mm, but could achieve acceptable accuracy without noise reduction for images with a slice thickness of 5.25mm.

Physical Simulator for Colonoscopy: A Modular Design Approach and Validation

Simulators for gastrointestinal (GI) endoscopy offer the opportunity to train and assess clinician skills in a low-risk environment. Physical simulators can enable direct instrument-to-organ interactions not provided by virtual platforms. However, they present scarce visual realism and limited variability in their anatomical conditions. Herein, we present an innovative and low-cost methodology for the design and fabrication of modular silicone colon simulators. The fabrication pipeline envisages parametric customization and development of 3D-printed molds for silicon pouring to obtain colon segments. The size of each colon segment is based on clinical data extracted from CT colonography images. Straight and curved segments are connected through silicone conjuncts to realize a customized and modular monolithic physical simulator. A 130 cm-long colon simulator prototype with assorted magnetically-connected polyps was fabricated and laid on a custom-made sensorized abdominal phantom. Content, face and construct validity of the designed simulator were assessed by 17 GI endoscopists. In summary, this work showed promising results for improving accessibility and flexibility of current colonoscopy physical simulators, paving the way for modular and personalized training programs.

Improving early detection of colorectal cancer in Aotearoa New Zealand; how do the direct access criteria perform?

Colorectal cancer (CRC) is a common malignancy in New Zealand, and there is increasing pressure on investigative resources for diagnosis. The national direct access referral guidelines from the Ministry of Health (MoH) guide who should be referred for investigation, but their performance in detecting CRC and other significant diseases has not been reported previously. This paper describes the yield, by direct access criterion, of all referrals through the direct access pathway to the Canterbury District Health Board (CDHB) during 2018. First referrals received through the direct access colonoscopy/computed tomography colonography (CTC) pathway for 2018 were audited. Patients were assigned to symptom groups corresponding to the MoH direct access criteria, and demographic data were captured. Diagnostic outcomes were collected through analysis of all endoscopy, CT colonography and histology reports in the 18 months following referral for primary analysis, with further follow-up through to May 2021 to detect missed pathology. Three thousand two hundred referrals were analysed, and 88.5% underwent colorectal investigation. 128 CRC were diagnosed, 176 advanced polyps, 49 cases of inflammatory bowel disease (IBD) and there were 56 other significant findings. The yield by category for the direct access criteria varied between 0-15.0%, and one urgent criterion had a CRC yield lower than two semi-urgent categories. For patients whose symptoms met at least one of the criteria, excluding those referred with suspected IBD, the combined CRC yield was 4.9%, compared with 1.8% in those who did not meet criteria. The sensitivity and specificity of the criteria for CRC (excluding IBD) was 90% and 23% respectively. There were no CRC detected during the extended follow-up period. In this referred population, the MoH direct access colonoscopy/CTC criteria varied significantly in their CRC yield, with an arbitrary distinction between urgent and semi-urgent categories. The low specificity of the criteria means the number needed to investigate to detect one CRC was one in 22. Improved diagnostic algorithms are urgently required to improve both the sensitivity and specificity, thereby more appropriately allocating finite resources to those patients who are most in need of investigation.

Deep learning in CT colonography: differentiating premalignant from benign colorectal polyps

ObjectivesTo investigate the differentiation of premalignant from benign colorectal polyps detected by CT colonography using deep learning.MethodsIn this retrospective analysis of an average risk colorectal cancer screening sample, polyps of all size categories and morphologies were manually segmented on supine and prone CT colonography images and classified as premalignant (adenoma) or benign (hyperplastic polyp or regular mucosa) according to histopathology. Two deep learning models SEG and noSEG were trained on 3D CT colonography image subvolumes to predict polyp class, and model SEG was additionally trained with polyp segmentation masks. Diagnostic performance was validated in an independent external multicentre test sample. Predictions were analysed with the visualisation technique Grad-CAM++.ResultsThe training set consisted of 107 colorectal polyps in 63 patients (mean age: 63 ± 8 years, 40 men) comprising 169 polyp segmentations. The external test set included 77 polyps in 59 patients comprising 118 polyp segmentations. Model SEG achieved a ROC-AUC of 0.83 and 80% sensitivity at 69% specificity for differentiating premalignant from benign polyps. Model noSEG yielded a ROC-AUC of 0.75, 80% sensitivity at 44% specificity, and an average Grad-CAM++ heatmap score of ≥ 0.25 in 90% of polyp tissue.ConclusionsIn this proof-of-concept study, deep learning enabled the differentiation of premalignant from benign colorectal polyps detected with CT colonography and the visualisation of image regions important for predictions. The approach did not require polyp segmentation and thus has the potential to facilitate the identification of high-risk polyps as an automated second reader.Key Points• Non-invasive deep learning image analysis may differentiate premalignant from benign colorectal polyps found in CT colonography scans.• Deep learning autonomously learned to focus on polyp tissue for predictions without the need for prior polyp segmentation by experts.• Deep learning potentially improves the diagnostic accuracy of CT colonography in colorectal cancer screening by allowing for a more precise selection of patients who would benefit from endoscopic polypectomy, especially for patients with polyps of 6–9 mm size.

Preoperative evaluation of vascular anatomy of right colic vessels using enhanced computed tomographic colonography.

The purpose of this study is to define the blood vessels from a surgical perspective and show the frequency of vascular anatomical anomalies as well as the positional relationship with the surrounding organs, including the number of jejunal veins that cross the dissection area in our series. From January 2016 to December 2018, 126 patients who received ileocecal resection or right hemicolectomy for colonic cancer in our institution were retrospectively analyzed by preoperative enhanced computed tomographic colonography images that were obtained using an 80-detector row CT scanner and workstation. The ileocolic artery/vein, right colic artery/vein and middle colic artery/vein were defined as the vessels that flow directly from or into the superior mesenteric artery/vein. All colic veins that flowed into the gastro-colic trunk were defined as accessory right colic veins. The accessory right colonic vein existed more than two in 62.6% of cases. In 11 cases (8.9%), the inflow point of the ileocecal vein was on the ventral side of the pancreas. There was one jejunal vein that straddled the dissection area in 31% and two in 6.3%. This study elucidated the vascular anatomy and positional relationship with surrounding organs that is required in central vascular ligation during complete mesocolic excision for right sided colon cancer.

Small bowel to closest human body surface distance calculation through a custom-made software using CT-based datasets.

Screening of the gastrointestinal tract is imperative for the detection and treatment of physiological and pathological disorders in humans. Ingestible devices (e.g., magnetic capsule endoscopes) represent an alternative to conventional flexible endoscopy for reducing the invasiveness of the procedure and the related patient's discomforts. However, to properly design localization and navigation strategies for capsule endoscopes, the knowledge of anatomical features is paramount. Therefore, authors developed a semi-automatic software for measuring the distance between the small bowel and the closest human external body surface, using CT colonography images. In this study, volumetric datasets of 30 patients were processed by gastrointestinal endoscopists with the dedicated custom-made software and results showed an average distance of 79.29 ± 23.85 mm.

Machine Learning–based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-of-Concept Study

Background CT colonography does not enable definite differentiation between benign and premalignant colorectal polyps. Purpose To perform machine learning-based differentiation of benign and premalignant colorectal polyps detected with CT colonography in an average-risk asymptomatic colorectal cancer screening sample with external validation using radiomics. Materials and Methods In this secondary analysis of a prospective trial, colorectal polyps of all size categories and morphologies were manually segmented on CT colonographic images and were classified as benign (hyperplastic polyp or regular mucosa) or premalignant (adenoma) according to the histopathologic reference standard. Quantitative image features characterizing shape (n = 14), gray level histogram statistics (n = 18), and image texture (n = 68) were extracted from segmentations after applying 22 image filters, resulting in 1906 feature-filter combinations. Based on these features, a random forest classification algorithm was trained to predict the individual polyp character. Diagnostic performance was validated in an external test set. Results The random forest model was fitted using a training set consisting of 107 colorectal polyps in 63 patients (mean age, 63 years ± 8 [standard deviation]; 40 men) comprising 169 segmentations on CT colonographic images. The external test set included 77 polyps in 59 patients comprising 118 segmentations. Random forest analysis yielded an area under the receiver operating characteristic curve of 0.91 (95% CI: 0.85, 0.96), a sensitivity of 82% (65 of 79) (95% CI: 74%, 91%), and a specificity of 85% (33 of 39) (95% CI: 72%, 95%) in the external test set. In two subgroup analyses of the external test set, the area under the receiver operating characteristic curve was 0.87 in the size category of 6-9 mm and 0.90 in the size category of 10 mm or larger. The most important image feature for decision making (relative importance of 3.7%) was quantifying first-order gray level histogram statistics. Conclusion In this proof-of-concept study, machine learning-based image analysis enabled noninvasive differentiation of benign and premalignant colorectal polyps with CT colonography. © RSNA, 2021 Online supplemental material is available for this article.

Technical notes on high-frequency ultrasound duodenography and colonography imaging of giardial lesions

Background: Worldwide G. lamblia is the third most common agent of diarrheal disease with over 300 million cases annually. Simple technical notes for clinicians are presented on use of high-frequency ultrasound imaging for duodenography and colonography in patients with Giardia lamblia infection. Methods: Ultrasound images were obtained from 100 consecutive patients with symptomatic giardiasis and 40 healthy controls. High-frequency annular array transducer of 7.5 MHz was used to obtain B-mode ultrasound grayscale and color images of the duodenum and colon with and without water contrast. The diagnosis of G. lamblia was based on clinical presentation, serial stool microscopy, and finding of flagellates in duodenal aspirates. Results: We demonstrated normal duodenum and colon echoanatomy in control subjects. In patients with giardiasis, the lesions of the duodenum and colon were associated with increased dimensions and wall thickness compared to healthy controls (P < 0.05). The ultrasound features of giardial lesions were characterized by increased wall echogenicity, flattening or loss of duodenal folds and/or colonic haustration, hyperechoic floating foci demonstrating chaotic motility, increased perilesional tissue echogenicity, and altered colonic peristalsis. Conclusion: In conclusion, high-frequency B-mode ultrasound imaging with and without water contrast demonstrated the details of duodenal and colonic echoanatomy in normal subjects and patients with giardiasis. The technique could be applied in the clinical setting of rural practitioners.

3D visualization cloud based model to detect and classify the polyps according to their sizes for CT colonography

In the medical field, Medical diagnosis using Computed tomography (CT) has become increasingly popular due to their non-invasive approach and quick overall turnaround time. 3D visualization for CT Colonography involves the assessment and diagnosis of a patient to find the presence of cancerous polyps in the colon, by taking a Computed Tomography scan of the patient, and evaluating the reports. This technique reduces evaluation time by allowing the doctors themselves to analyze the CT scans without the need for a radiologist to generate an initial report. This technique also avoids an invasive procedure on the patient. This paper gives the insightsof developing computer aided system for the detection of Polyps in CT Colonography images using the principles of Image Processing and the Deep Learning, specifically an ensemble of Convolutional Neural Networks using GoogleNet architecture and 3D reconstruction of the same. The accuracy achieved by the proposed system for region classification, region 1 polyp detection and region 2 polyp detection are 98.75%, 93.75% and 94.03% respectively, and their F1 Scores are 0.88, 0.82 and 0.84 respectively.

A Survey on Machine Learning and Deep Learning-based Computer-Aided Methods for Detection of Polyps in CT Colonography.

Colon cancer generally begins as a neoplastic growth of tissue, called polyps, originating from the inner lining of the colon wall. Most colon polyps are considered harmless but over the time, they can evolve into colon cancer, which, when diagnosed in later stages, is often fatal. Hence, time is of the essence in the early detection of polyps and the prevention of colon cancer. To aid this endeavor, many computer-aided methods have been developed, which use a wide array of techniques to detect, localize and segment polyps from CT Colonography images. In this paper, a comprehensive state-of-the-art method is proposed and categorize this work broadly using the available classification techniques using Machine Learning and Deep Learning. The performance of each of the proposed approach is analyzed with existing methods and also how they can be used to tackle the timely and accurate detection of colon polyps.

Computed tomography in patients with colon cancer (case studies and literature review)

The radicalism of planned surgical interventions in patients with colon cancer can be increased due to precision computed tomographic diagnosis. Establishing the x‑ray stage of the disease involves determining the localization of the tumor and the extent of lesion of the intestinal wall, visualization of regional lymph nodes, tumor damage of adjacent organs, distant metastases, peritoneal carcinoma, etc. Computed tomography is considered to be a «golden» standard in the diagnosis of colon cancers, but general surgeons in everyday practice use the diagnostic capabilities of сomputed tomography not in it full capacity. An analysis of the diagnostic methods in 78 patients with colon cancer treated in oncological and general hospitals is presented. The clinical examples show the practical possibilities of computed tomographic diagnosis in the preoperative and postoperative periods in cases of radical and non‑radical surgery for left colon cancer. Based on a review of the scientific literature and the results of our own clinical observations, it was established that computed tomographic scan of the abdominal and chest cavities and colonoscopy is a necessary diagnostic component of the examination of the primary patient to establish the clinical and X‑ray stage of colon cancer according to the Tumor — Nodes — Metastases algorithm. For the surgeon and radiologist, the TNM category helps to unite their efforts in the preoperative diagnosis of colon cancer. Thanks to the precision computed tomographic diagnosis, intraoperative errors can be avoided and the surgical procedures can be made more radical. Dispensary сomputed tomography monitoring of patients with colon cancer (in particular, сomputed tomography colonography) allows timely detection of tumor recurrence, its metastases, and monitoring the quality of the primary surgical procedure.

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.

Abstract C67: Addressing racial disparity in colorectal cancer screening with CT colonography: Experience in an African-American cohort

Abstract Purpose: Incidence and mortality from colorectal cancer (CRC) are higher among minorities, including African Americans, compared with non-Hispanic whites, and CRC screening rates for African Americans lag behind non-Hispanic whites. The purpose of this investigation was to evaluate computed tomographic (CT) colonography performance and outcome measures in an African-American cohort. Materials and Methods: Institutional review board-approved and HIPAA-compliant study. Informed consent was waived. Retrospective evaluation of the African-American cohort from the Atlanta Veterans Affairs Medical Center CT colonography screening program. Colonoscopy referral rates and histologic diagnoses were evaluated. Results: CT colonography studies (n = 2,490; mean age 59.5 years [range 50-80]; 85.5% male), were performed between 2007 and 2016. The per-patient positive rate of CT colonography was 9.8% for polyps 6-9 mm, 5.4% for polyps 10-29 mm, and 1.3% for masses &amp;gt; 30 mm. Based on CT colonography findings and patient preference, 13.9% of patients were referred to optical colonoscopy and 2.9% were referred to CT colonography follow-up. Of patients recommended to undergo colonoscopy, 80.6% completed the procedure and colonic pathology was confirmed in 83.2% thereof. Histologic diagnoses were 221 tubular adenomas (161 6-9 mm; 60 &amp;gt; 10 mm), 60 tubulovillous adenomas/villous adenomas (14 6-9 mm; 46 &amp;gt; 10 mm), 31 hyperplastic polyps (26 6-9 mm; 5 &amp;gt; 10 mm), 17 adenocarcinomas (all &amp;gt; 10 mm), 10 adenomas with high-grade dysplasia (all &amp;gt; 10 mm), and 3 serrated polyps (1 6-9 mm; 2 &amp;gt; 10 mm). The per-patient positivity rate for patients who had optical colonoscopy (n = 279) was 71.3% for adenoma, 9.7% for hyperplastic polyp, and 6.1% for adenocarcinoma. Conclusions: CT colonography is an effective screening modality for African-American adults and could potentially improve rates of colorectal screening in African-Americans. Citation Format: Todd Fibus, Courtney C. Moreno, Elizabeth A. Krupinski, David H. Kim, Perry J. Pickhardt. Addressing racial disparity in colorectal cancer screening with CT colonography: Experience in an African-American cohort [abstract]. In: Proceedings of the Tenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2017 Sep 25-28; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2018;27(7 Suppl):Abstract nr C67.

JOURNAL CLUB: Extracolonic Findings at CT Colonography: Systematic Review and Meta-Analysis.

The purpose of this study was to perform a systematic review and meta-analysis of published studies on CT colonography (CTC) in which extracolonic findings were assessed. A systematic review of studies of screening CTC and of CTC to evaluate symptoms (1994-June 2017) was conducted to estimate the rate of extra-colonic findings and associated additional workup recommendations. The primary outcome was potentially important extracolonic findings, defined as CT Colonography Imaging Reporting and Data System (C-RADS) category E4 or as having high clinical importance (if C-RADS was not used). Secondary outcomes included likely unimportant extracolonic findings (i.e., C-RADS category E3 or similar). Random-effects and meta-regression analyses were used to generate pooled estimates and to explore risk factors for extracolonic findings related to various cohort characteristics. Primary data were acquired from 44 studies (49,676 patients). The pooled rate of potentially important extracolonic findings was 4.9% (95% CI, 3.7-6.4%) with a high degree of heterogeneity (I2 = 95%). This estimate progressively declined over time (9% per year since 2006) and was significantly related to the reporting system (lower for C-RADS than for low, moderate, high clinical significance reporting). C-RADS-specific meta-analysis (32,746 patients) showed rates of potentially significant extracolonic findings in 2.8% (95% CI, 1.9-3.5%) of the cohort without symptoms and 5.2% (95% CI, 3.5-7.6%) of the cohort with symptoms and in 5.7% (95% CI, 3.3-9.8%) of seniors (≥ 65 years) versus 2.3% (95% CI, 1.2-4.5%) of those younger than 65 years. The overall pooled rates of recommended workup were 8.2% (95% CI, 6.6-10.1%) for all extracolonic findings and 4.0% (95% CI, 2.7-5.9%) for potentially important extracolonic findings. With use of the more robust C-RADS classification, potentially important extracolonic findings at CTC occur in less than 3% of cohorts without symptoms. For all extracolonic findings, the rate of suggested or recommended additional workup is approximately 8% but decreases to 4% for potentially important extracolonic findings.

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.

Potential pitfalls in the anorectal region during CT colonography: A discussion and pictorial overview of common pitfalls

Common potential anorectal pitfalls are presented with accompanying CT colonography images. Although most lesions are benign, care must be taken in not missing an underlying cancer.

An Improved Method of Colon Segmentation in Computed Tomography Colonography Images Using Domain Knowledge

Computed Tomography Colonography is a medical imaging technique used for finding the polyps of different shapes and sizes in colon. First step in this diagnosis procedure is the effective segmentation of colon in presence of contrast used for tagging fecal and endoluminal fluids. In this paper, an improved colon segmentation method is proposed with minimal user intervention. The method is hybrid approach, which is composed of three parts: adaptive smoothing for de-noising the colon lumen by preserving the edges followed by edge based colon boundary recognition, connected component labeling for colon segments delineation and finally colon assessment on axial slices to aid radiologists in decision making. The main finding of the study was, colon was properly segmented at mucous membrane including the base of all the soft tissue structures which is required for the polyp height measurement and the finer details of the colonic content was absolutely preserved. The method was evaluated using retrospectively collected CT scans of 40 patients. The results were qualitatively and quantitatively validated by radiologist through 2D MPR and 3D visualizations. The method was implemented with multithreading for parallel processing in a high performance computer. And it took approximately 2 minutes for processing and segmenting 500 CTC images. The accuracy of result achieved through overlap computation for subset of images was 94.614±.7754%

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.