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Purpose: We are developing a computerized segmentation tool for the inner and outer bladder wall as a part of an image analysis pipeline for CT urography (CTU)., Materials and Methods: A data set of 172 CTU cases was collected retrospectively with Institutional Review Board (IRB) approval. The data set was randomly split into two independent sets of training (81 cases) and testing (92 cases) which were manually outlined for both the inner and outer wall. We trained a deep-learning convolutional neural network (DL-CNN) to distinguish the bladder wall from the inside and outside of the bladder using neighborhood information. Approximately, 240 000 regions of interest (ROIs) of 16 × 16 pixels in size were extracted from regions in the training cases identified by the manually outlined inner and outer bladder walls to form a training set for the DL-CNN; half of the ROIs were selected to include the bladder wall and the other half were selected to exclude the bladder wall with some of these ROIs being inside the bladder and the rest outside the bladder entirely. The DL-CNN trained on these ROIs was applied to the cases in the test set slice-by-slice to generate a bladder wall likelihood map where the gray level of a given pixel represents the likelihood that a given pixel would belong to the bladder wall. We then used the DL-CNN likelihood map as an energy term in the energy equation of a cascaded level sets method to segment the inner and outer bladder wall. The DL-CNN segmentation with level sets was compared to the three-dimensional (3D) hand-segmented contours as a reference standard., Results: For the inner wall contour, the training set achieved the average volume intersection, average volume error, average absolute volume error, and average distance of 90.0 ± 8.7%, -4.2 ± 18.4%, 12.9 ± 13.9%, and 3.0 ± 1.6 mm, respectively. The corresponding values for the test set were 86.9 ± 9.6%, -8.3 ± 37.7%, 18.4 ± 33.8%, and 3.4 ± 1.8 mm, respectively. For the outer wall contour, the training set achieved the values of 93.7 ± 3.9%, -7.8 ± 11.4%, 10.3 ± 9.3%, and 3.0 ± 1.2 mm, respectively. The corresponding values for the test set were 87.5 ± 9.9%, -1.2 ± 20.8%, 11.9 ± 17.0%, and 3.5 ± 2.3 mm, respectively., Conclusions: Our study demonstrates that DL-CNN-assisted level sets can effectively segment bladder walls from the inner bladder and outer structures despite a lack of consistent distinctions along the inner wall. However, even with the addition of level sets, the inner and outer walls may still be over-segmented and the DL-CNN-assisted level sets may incorrectly segment parts of the prostate that overlap with the outer bladder wall. The outer wall segmentation was improved compared to our previous method and the DL-CNN-assisted level sets were also able to segment the inner bladder wall with similar performance. This study shows the DL-CNN-assisted level set segmentation tool can effectively segment the inner and outer wall of the bladder., (© 2018 American Association of Physicists in Medicine.)

Purpose: To evaluate the feasibility of using an objective computer-aided system to assess bladder cancer stage in CT Urography (CTU)., Materials and Methods: A dataset consisting of 84 bladder cancer lesions from 76 CTU cases was used to develop the computerized system for bladder cancer staging based on machine learning approaches. The cases were grouped into two classes based on pathological stage ≥ T2 or below T2, which is the decision threshold for neoadjuvant chemotherapy treatment clinically. There were 43 cancers below stage T2 and 41 cancers at stage T2 or above. All 84 lesions were automatically segmented using our previously developed auto-initialized cascaded level sets (AI-CALS) method. Morphological and texture features were extracted. The features were divided into subspaces of morphological features only, texture features only, and a combined set of both morphological and texture features. The dataset was split into Set 1 and Set 2 for two-fold cross-validation. Stepwise feature selection was used to select the most effective features. A linear discriminant analysis (LDA), a neural network (NN), a support vector machine (SVM), and a random forest (RAF) classifier were used to combine the features into a single score. The classification accuracy of the four classifiers was compared using the area under the receiver operating characteristic (ROC) curve (A z )., Results: Based on the texture features only, the LDA classifier achieved a test A z of 0.91 on Set 1 and a test A z of 0.88 on Set 2. The test A z of the NN classifier for Set 1 and Set 2 were 0.89 and 0.92, respectively. The SVM classifier achieved test A z of 0.91 on Set 1 and test A z of 0.89 on Set 2. The test A z of the RAF classifier for Set 1 and Set 2 was 0.89 and 0.97, respectively. The morphological features alone, the texture features alone, and the combined feature set achieved comparable classification performance., Conclusion: The predictive model developed in this study shows promise as a classification tool for stratifying bladder cancer into two staging categories: greater than or equal to stage T2 and below stage T2., (© 2017 American Association of Physicists in Medicine.)

Purpose: The authors are developing a computerized system for bladder segmentation in CT urography (CTU) as a critical component for computer-aided detection of bladder cancer., Methods: A deep-learning convolutional neural network (DL-CNN) was trained to distinguish between the inside and the outside of the bladder using 160 000 regions of interest (ROI) from CTU images. The trained DL-CNN was used to estimate the likelihood of an ROI being inside the bladder for ROIs centered at each voxel in a CTU case, resulting in a likelihood map. Thresholding and hole-filling were applied to the map to generate the initial contour for the bladder, which was then refined by 3D and 2D level sets. The segmentation performance was evaluated using 173 cases: 81 cases in the training set (42 lesions, 21 wall thickenings, and 18 normal bladders) and 92 cases in the test set (43 lesions, 36 wall thickenings, and 13 normal bladders). The computerized segmentation accuracy using the DL likelihood map was compared to that using a likelihood map generated by Haar features and a random forest classifier, and that using our previous conjoint level set analysis and segmentation system (CLASS) without using a likelihood map. All methods were evaluated relative to the 3D hand-segmented reference contours., Results: With DL-CNN-based likelihood map and level sets, the average volume intersection ratio, average percent volume error, average absolute volume error, average minimum distance, and the Jaccard index for the test set were 81.9% ± 12.1%, 10.2% ± 16.2%, 14.0% ± 13.0%, 3.6 ± 2.0 mm, and 76.2% ± 11.8%, respectively. With the Haar-feature-based likelihood map and level sets, the corresponding values were 74.3% ± 12.7%, 13.0% ± 22.3%, 20.5% ± 15.7%, 5.7 ± 2.6 mm, and 66.7% ± 12.6%, respectively. With our previous CLASS with local contour refinement (LCR) method, the corresponding values were 78.0% ± 14.7%, 16.5% ± 16.8%, 18.2% ± 15.0%, 3.8 ± 2.3 mm, and 73.9% ± 13.5%, respectively., Conclusions: The authors demonstrated that the DL-CNN can overcome the strong boundary between two regions that have large difference in gray levels and provides a seamless mask to guide level set segmentation, which has been a problem for many gradient-based segmentation methods. Compared to our previous CLASS with LCR method, which required two user inputs to initialize the segmentation, DL-CNN with level sets achieved better segmentation performance while using a single user input. Compared to the Haar-feature-based likelihood map, the DL-CNN-based likelihood map could guide the level sets to achieve better segmentation. The results demonstrate the feasibility of our new approach of using DL-CNN in combination with level sets for segmentation of the bladder.

Objective: The purpose of this study was to evaluate the accuracy of our autoinitialized cascaded level set 3D segmentation system as compared with the World Health Organization (WHO) criteria and the Response Evaluation Criteria In Solid Tumors (RECIST) for estimation of treatment response of bladder cancer in CT urography., Materials and Methods: CT urograms before and after neoadjuvant chemo-therapy treatment were collected from 18 patients with muscle-invasive localized or locally advanced bladder cancers. The disease stage as determined on pathologic samples at cystectomy after chemotherapy was considered as reference standard of treatment response. Two radiologists measured the longest diameter and its perpendicular on the pre- and posttreatment scans. Full 3D contours for all tumors were manually outlined by one radiologist. The autoinitialized cascaded level set method was used to automatically extract 3D tumor boundary. The prediction accuracy of pT0 disease (complete response) at cystectomy was estimated by the manual, autoinitialized cascaded level set, WHO, and RECIST methods on the basis of the AUC., Results: The AUC for prediction of pT0 disease at cystectomy was 0.78 ± 0.11 for autoinitialized cascaded level set compared with 0.82 ± 0.10 for manual segmentation. The difference did not reach statistical significance (p = 0.67). The AUCs using RECIST criteria were 0.62 ± 0.16 and 0.71 ± 0.12 for the two radiologists, both lower than those of the two 3D methods. The AUCs using WHO criteria were 0.56 ± 0.15 and 0.60 ± 0.13 and thus were lower than all other methods., Conclusion: The pre- and posttreatment 3D volume change estimates obtained by the radiologist's manual outlines and the autoinitialized cascaded level set segmentation were more accurate for irregularly shaped tumors than were those based on RECIST and WHO criteria.

Purpose: The authors are developing a computer-aided detection system for bladder cancer on CT urography (CTU). In this study, the authors focused on developing a system for detecting masses fully or partially within the contrast-enhanced (C) region of the bladder., Methods: With IRB approval, a data set of 70 patients with biopsy-proven bladder lesions fully or partially immersed within the contrast-enhanced region (C region) of the bladder was collected for this study: 35 patients for the training set (39 malignant, 7 benign lesions) and 35 patients for the test set (49 malignant, 4 benign lesions). The bladder in the CTU images was automatically segmented using the authors' conjoint level set analysis and segmentation system, which they developed specifically to segment the bladder. A closed contour of the C region of the bladder was generated by maximum intensity projection using the property that the dependently layering contrast material in the bladder will be filled consistently to the same level along all CTU slices due to gravity. Potential lesion candidates within the C region contour were found using the authors' Straightened Periphery ANalysis (SPAN) method. SPAN transforms a bladder wall to a straightened thickness profile, marks suspicious pixels on the profile, and clusters them into regions of interest to identify potential lesion candidates. The candidate regions were automatically segmented using the authors' autoinitialized cascaded level set segmentation method. Twenty-three morphological features were automatically extracted from the segmented lesions. The training set was used to determine the best subset of these features using simplex optimization with the leave-one-out case method. A linear discriminant classifier was designed for the classification of bladder lesions and false positives. The detection performance was evaluated on the independent test set by free-response receiver operating characteristic analysis., Results: At the prescreening step, the authors' system achieved 84.4% sensitivity with an average of 4.3 false positives per case (FPs/case) for the training set, and 84.9% sensitivity with 5.4 FPs/case for the test set. After linear discriminant analysis (LDA) classification with the selected features, the FP rate improved to 2.5 FPs/case for the training set, and 4.3 FPs/case for the test set without missing additional true lesions. By varying the threshold for the LDA scores, at 2.5 FPs/case, the sensitivities were 84.4% and 81.1% for the training and test sets, respectively. At 1.7 FPs/case, the sensitivities decreased to 77.8% and 75.5%, respectively., Conclusions: The results demonstrate the feasibility of the authors' method for detection of bladder lesions fully or partially immersed in the contrast-enhanced region of CTU.

We are developing a computerized system for bladder segmentation on CT urography (CTU), as a critical component for computer-aided detection of bladder cancer. The presence of regions filled with intravenous contrast and without contrast presents a challenge for bladder segmentation. Previously, we proposed a conjoint level set analysis and segmentation system (CLASS). In case the bladder is partially filled with contrast, CLASS segments the non-contrast (NC) region and the contrast-filled (C) region separately and automatically conjoins the NC and C region contours; however, inaccuracies in the NC and C region contours may cause the conjoint contour to exclude portions of the bladder. To alleviate this problem, we implemented a local contour refinement (LCR) method that exploits model-guided refinement (MGR) and energy-driven wavefront propagation (EDWP). MGR propagates the C region contours if the level set propagation in the C region stops prematurely due to substantial non-uniformity of the contrast. EDWP with regularized energies further propagates the conjoint contours to the correct bladder boundary. EDWP uses changes in energies, smoothness criteria of the contour, and previous slice contour to determine when to stop the propagation, following decision rules derived from training. A data set of 173 cases was collected for this study: 81 cases in the training set (42 lesions, 21 wall thickenings, 18 normal bladders) and 92 cases in the test set (43 lesions, 36 wall thickenings, 13 normal bladders). For all cases, 3D hand segmented contours were obtained as reference standard and used for the evaluation of the computerized segmentation accuracy. For CLASS with LCR, the average volume intersection ratio, average volume error, absolute average volume error, average minimum distance and Jaccard index were 84.2 ± 11.4%, 8.2 ± 17.4%, 13.0 ± 14.1%, 3.5 ± 1.9 mm, 78.8 ± 11.6%, respectively, for the training set and 78.0 ± 14.7%, 16.4 ± 16.9%, 18.2 ± 15.0%, 3.8 ± 2.3 mm, 73.8 ± 13.4% respectively, for the test set. With CLASS only, the corresponding values were 75.1 ± 13.2%, 18.7 ± 19.5%, 22.5 ± 14.9%, 4.3 ± 2.2 mm, 71.0 ± 12.6%, respectively, for the training set and 67.3 ± 14.3%, 29.3 ± 15.9%, 29.4 ± 15.6%, 4.9 ± 2.6 mm, 65.0 ± 13.3%, respectively, for the test set. The differences between the two methods for all five measures were statistically significant (p < 0.001) for both the training and test sets. The results demonstrate the potential of CLASS with LCR for segmentation of the bladder.

Purpose: The authors are developing a computerized system for bladder segmentation on CTU, as a critical component for computer aided diagnosis of bladder cancer., Methods: A challenge for bladder segmentation is the presence of regions without contrast (NC) and filled with intravenous contrast (C). The authors have designed a Conjoint Level set Analysis and Segmentation System (CLASS) specifically for this application. CLASS performs a series of image processing tasks: preprocessing, initial segmentation, 3D and 2D level set segmentation, and postprocessing, designed according to the characteristics of the bladder in CTU. The NC and the C regions of the bladder were segmented separately in CLASS. The final contour is obtained in the postprocessing stage by the union of the NC and C contours. With Institutional Review Board (IRB) approval, the authors retrospectively collected 81 CTU scans, in which 40 bladders contained lesions, 26 contained diffuse wall thickening, and 15 were considered to be normal. The bladders were segmented by CLASS and the performance was assessed by rating the quality of the contours on a 10-point scale (1 = "very poor," 5 = "fair," 10 = "perfect"). For 30 bladders, 3D hand-segmented contours were obtained and the segmentation accuracy of CLASS was evaluated and compared to that of a single level set method in terms of the average minimum distance, average volume intersection ratio, average volume error and Jaccard index., Results: Of the 81 bladders, the average quality rating for CLASS was 6.5 ± 1.3. Thirty nine bladders were given quality ratings of 7 or above. Only five bladders had ratings under 5. The average minimum distance, average volume intersection ratio, average volume error, and average Jaccard index for CLASS were 3.5 ± 1.3 mm, (79.0 ± 8.2)%, (16.1 ± 16.3)%, and (75.7 ± 8.4)%, respectively, and for the single level set method were 5.2 ± 2.6 mm, (78.8 ± 16.3)%, (8.3 ± 33.1)%, (71.0 ± 15.4)%, respectively., Conclusions: The results demonstrate the potential of CLASS for segmentation of the bladder.

Rationale and Objectives: To develop a computerized system for segmentation of bladder lesions on computed tomography urography (CTU) scans for detection and characterization of bladder cancer., Materials and Methods: We have developed an auto-initialized cascaded level set method to perform bladder lesion segmentation. The segmentation performance was evaluated on a preliminary dataset including 28 CTU scans from 28 patients collected retrospectively with institutional review board approval. The bladders were partially filled with intravenous contrast material. The lesions were located fully or partially within the contrast-enhanced area or in the non-contrast-enhanced area of the bladder. An experienced abdominal radiologist marked 28 lesions (14 malignant and 14 benign) with bounding boxes that served as input to the automated segmentation system and assigned a difficulty rating on a scale of 1 to 5 (5 = most subtle) to each lesion. The contours from automated segmentation were compared to three-dimensional contours manually drawn by the radiologist. Three performance metric measures were used for comparison. In addition, the automated segmentation quality was assessed by an expert panel of two experienced radiologists, who provided quality ratings of the contours on a scale from 1 to 10 (10 = excellent)., Results: The average volume intersection ratio, the average absolute volume error, and the average distance measure were 67.2 ± 16.9%, 27.3 ± 26.9%, and 2.89 ± 1.69 mm, respectively. Of the 28 segmentations, 18 were given quality ratings of 8 or above. The average rating was 7.9 ± 1.5. The average quality ratings for lesions with difficulty ratings of 1, 2, 3, and 4 were 8.8 ± 0.9, 7.9 ± 1.8, 7.4 ± 0.9, and 6.6 ± 1.5, respectively., Conclusion: Our preliminary study demonstrates the feasibility of using the three-dimensional level set method for segmenting bladder lesions in CTU scans., (Copyright © 2013 AUR. Published by Elsevier Inc. All rights reserved.)

Several promising clinical applications for dual-energy computed tomography (CT) in genitourinary imaging have been reported. Dual-energy CT not only provides excellent morphologic detail but also can supply material-specific and quantitative information that may be particularly useful in genitourinary imaging. Dual-energy CT has unique capabilities for characterizing renal lesions by quantifying iodine content and helping identify the mineral contents of renal stones, information that is important for patient care. Virtual unenhanced images reconstructed from dual-energy CT datasets can be useful for detecting calculi within the iodine-filled urinary collecting system, potentially reducing the need for an unenhanced scanning phase at CT urography. Although the underlying principles of dual-energy CT are the same regardless of scanner type, single-source dual-energy scanners with fast kilovoltage switching differ from dual-source dual-energy scanners both in image data acquisition and in processing methods; an understanding of these differences may help optimize dual-energy CT genitourinary protocols. Dual-energy CT performed with a dual-source scanner or with a single-source scanner with fast kilovoltage switching also has some important limitations. Further advances in scanning protocols and refinement of processing techniques to reduce image noise may lead to more widespread use of dual-energy CT., (© RSNA, 2012.)

We are developing a Conjoint Level set Analysis and Segmentation System (CLASS) for bladder segmentation on CTU, which is a critical component for computer aided diagnosis of bladder cancer. A challenge for bladder segmentation is the presence of regions without contrast (NC) and filled with IV contrast (C). According to the characteristics of the bladder in CTU, CLASS is designed to perform number tasks such as preprocessing, initial segmentation, 3D and 2D level set segmentation and post-processing. CLASS segments separately the NC and the C regions of the bladder. In the post-processing stage the final contour is obtained based on the union of the NC and C contours. 70 bladders were segmented. Of the 70 bladders 31 contained lesions, 24 contained wall thickening, and 15 were normal. The performance of CLASS was assessed by rating the quality of the contours on a 5-point scale (1="very poor", 3="fair", 5="excellent"). The average quality ratings for the 12 completely no contrast (NC) and 5 completely contrast-filled (C) bladder contours were 3.3±1.0 and 3.4±0.5, respectively. The average quality ratings for the 53 NC and 53 C regions of the 53 partially contrast-filled bladders were 4.0±0.7 and 4.0±1.0, respectively. Quality ratings of 4 or above were given for 87% (46/53) NC and 77% (41/53) C regions. Only 4% (2/53) NC and 9% (5/53) C regions had ratings under 3. After combining the NC and C contours for each of the 70 bladders, 66% (46/70) had quality ratings of 4 or above. Only 6% (4/70) had ratings under 3. The average quality rating was 3.8±0.7. The results demonstrate the potential of CLASS for automated segmentation of the bladder.

Multi-detector computed tomography urography (CTU) is now well established as the imaging modality of choice for comprehensive evaluation of the kidneys and urinary tract, having largely replaced excretory urography. Over the past decade, CTU techniques have continued to evolve with the goal of improving urothelial surface visualization. Numerous benign and malignant conditions of the kidneys, ureters, and urinary bladder can be accurately depicted by CTU. This article provides a contemporary review of CTU imaging protocols, image postprocessing techniques, appearances of various urinary tract pathologic conditions, and pitfalls in image interpretation.

Objective: The purpose of this article is to review the epidemiology, staging, and treatment of bladder cancer; to discuss the role of MDCT urography for the evaluation of patients with known or suspected bladder cancer; and to address the role of MDCT urography in patients who require follow-up imaging after a diagnosis of bladder cancer has been made., Conclusion: MDCT urography now has a large role in the evaluation of patients with known and suspected bladder cancer. However, its precise role has not been established. Because many bladder neoplasms will not be detected by MDCT urography and more research is needed to determine the optimal technique for diagnosing bladder cancer, we think that MDCT urography cannot replace cystoscopy at present.

Objective: To determine uroradiologists' opinions and practices regarding computed tomographic (CT) urography., Methods: A Web-based survey was sent via e-mail to all 259 members of the Society of Uroradiology. Of the 229 successfully delivered e-mails, 90 (39%) members responded., Results: Of 90 uroradiologists, 87% perform CT urography. Compared with intravenous (IV) urography, 69% of uroradiologists use CT urography more than 75% of the time urinary tract imaging is requested; 27% stated that CT urography has completely replaced IV urography. Most uroradiologists perform CT urography using multidetector-row CT alone (79%) and use a 3-phase technique (52%) using a single injection (76%) of contrast material at 3 mL/s (52%) without a compression device (81%) and with the patient in supine position (80%)., Conclusions: Most uroradiologists use CT urography in their practice today; some no longer perform IV urography. Variability in multidetector-row CT technique suggests that more research is needed to determine the optimal protocol.

The aim was to develop clinical guidelines for multidetector computed tomography urography (CTU) by a group of experts from the European Society of Urogenital Radiology (ESUR). Peer-reviewed papers and reviews were systematically scrutinized. A summary document was produced and discussed at the ESUR 2006 and ECR 2007 meetings with the goal to reach consensus. True evidence-based guidelines could not be formulated, but expert guidelines on indications and CTU examination technique were produced. CTU is justified as a first-line test for patients with macroscopic haematuria, at high-risk for urothelial cancer. Otherwise, CTU may be used as a problem-solving examination. A differential approach using a one-, two- or three-phase protocol is proposed, whereby the clinical indication and the patient population will determine which CTU protocol is employed. Either a combined nephrographic-excretory phase following a split-bolus intravenous injection of contrast medium, or separate nephrographic and excretory phases following a single-bolus injection can be used. Lower dose (CTDIvol 5-6 mGy) is used for benign conditions and normal dose (CTDIvol 9-12 mGy) for potential malignant disease. A low-dose (CTDIvol 2-3 mGy) unenhanced series can be added on indication. The expert-based CTU guidelines provide recommendations to optimize techniques and to unify the radiologist's approach to CTU.

Purpose: To investigate changes in usage of computed tomography urography (CTU), indications for CTU, and rates of positive findings over time., Methods: Retrospective review of data from April 2000 to December 2005 assessed rates of overall positive findings, rates of suspected transitional cell carcinomas (TCCs), benign genitourinary (GU), and significant non-GU findings. Data were analyzed based on specialty of ordering physicians and on requisition indications., Results: One thousand two hundred seventy-one patients had 1746 CTUs, including 952 men (1259 studies) and 319 women (487 studies) with mean age of 61 years. Computed tomography urographies increased from 265 in 2001 to 443 in 2004. Eighty-nine percent were ordered by urologists, 4% by oncologists, 1% by emergency physicians, and 6% by other specialties. Sixty-two percent of first-time studies were ordered for possible GU malignancy, 24% for hematuria, and 14% for other reasons. Eight hundred sixty-one examinations (49%) showed significant findings. The rate of all positive examinations, analyzed in 6-month periods, varied from 37% to 54%, but no time trend was identified. First-time patient examinations had positive examinations in 46% to 62% of cases. Similarly, no trends were found for examinations interpreted as possible TCC (17%-32%), renal stones (9%-18%), renal masses (1%-6%), causes of hematuria (15%-26%), and acute non-GU findings (2%-9%). The rate of positive findings by ordering specialty varied minimally from 49% to 53%. No change occurred in the proportions of indications for CTU over time., Conclusions: In 5 years, the number of CTU examinations per year increased 1.5-fold. The rate of CTU findings positive for suspected TCC, stones, and other causes of hematuria showed no decline or increase. If precautions are taken regarding proper indications for CTU, the overall rates of positive findings may not substantially change over time, thereby only submitting high-risk patients to this examination.

Objective: To compare urinary tract distension and opacification obtained with single-bolus 3-phase and split-bolus 2-phase multidetector row computed tomographic urography (CTU) techniques., Methods: Twenty-six single-bolus 3-phase CTU examinations were retrospectively compared with 26 split-bolus 2-phase examinations. Two readers reviewed excretory-phase imaging to record quantitative measurements of urinary tract distension and qualitative measurements of urinary tract opacification., Results: Single-bolus 3-phase CTU technique provided better overall urinary tract distension than did split-bolus 2-phase technique (P = 0.05). Single-bolus CTU images demonstrated superior lower pole and interpolar region intrarenal collecting system distension when compared with split-bolus CTU images (P < or = 0.04). Qualitative opacification scores for the lower urinary tract were significantly higher, using single-bolus technique for only 1 of 2 reviewers. No significant differences between techniques in intrarenal collecting system opacification, ureteral distension, or ureteral opacification were identified., Conclusions: Improved urinary tract distension is obtained with single-bolus 3-phase CTU technique.

Multi-detector computed tomography urography (CTU) is a robust imaging modality for evaluation of the kidneys and urinary tract. When compared to excretory urography (EU), CTU's superior contrast resolution appears to more effectively detect and characterize numerous benign and malignant conditions involving the kidneys, upper urinary tracts, and urinary bladder. While the concept of CTU has been utilized for nearly a decade, there is still no universally accepted technique. In fact, numerous articles evaluating various CTU techniques and associated ancillary maneuvers have been recently described in the literature. This review will discuss various indications, specific techniques, image reconstruction/reformatting, detection of pathology, and pitfalls related to CTU.

Purpose: To retrospectively compare the effects of abdominal compression, intravenous saline hydration, and two imaging delays on both distention and opacification of the intrarenal collecting system and ureter during multi-detector row computed tomographic (CT) urography., Materials and Methods: Institutional review board approval for reviewing images and medical records of the patients was obtained; informed patient consent was not required. Excretory phase images obtained from multi-detector row CT urography in 85 patients (57 men, 28 women) were reviewed. Examinations were performed by using one of four techniques: abdominal compression and intravenous hydration with 250 mL of normal saline, compression only, intravenous hydration with saline only, and neither compression nor saline hydration. Excretory phase imaging was performed at 300 and 450 seconds for each patient. Two reviewers measured urinary tract distention on transverse images and graded opacification and image quality on volume-rendered images. Effects were compared by using statistical mixed models with repeated-measures analysis of variance., Results: Saline hydration significantly improved opacification (P = .02) and overall image quality (P < .001) of the intrarenal collecting system and proximal ureter. Delayed excretory phase image acquisition of 450 seconds significantly increased distention of the intrarenal collecting system and proximal ureter (P < .001). No significant effects involving the lower segment of the ureter were seen with any technique; however, there were fewer nonvisualized distal ureteral segments with the longer imaging delay., Conclusion: Compression does not significantly improve distention or opacification of the urinary tract. Saline hydration is effective in improving opacification of the proximal urinary tract. Longer imaging delays improve distention of the proximal urinary tract and may aid in visualization of the lower segment of the ureter., ((c) RSNA, 2005.)

Recent improvements in helical CT hardware and software have provided imagers with the tools to obtain an increasingly large number of very thin axial images. As a result, a number of new applications for multislice CT have recently been developed, one of which is CT urography. The motivation for performing CT urography is the desire to create a single imaging test that can completely assess the kidneys and urinary tract for urolithiasis, renal masses and mucosal abnormalities of the renal collecting system, ureters and bladder. Although the preferred technique for performing multislice CT urography has not yet been determined and results are preliminary, early indications suggest that this examination can detect even subtle benign and malignant urothelial abnormalities and that it has the potential to completely replace excretory urography within the next several years. An important limitation of multislice CT urography is increased patient radiation exposure encountered when some of the more thorough recommended techniques are utilized.

Many conditions seen by urologists require imaging examinations with iodinated radiographic contrast material as a key part of the primary evaluation of the patient. A basic understanding of contrast media, risks of administration, choice of agents, and premedication regimens for high-risk patients, is beneficial in helping patients prepare for their examinations. Urologists may be the primary physicians administering contrast material or may be working with radiologists in the care of patients receiving contrast agents. Because contrast reactions may occur unexpectedly, even during examinations in which the agents are not given intravenously, urologists should be able to recognize and treat the various types of adverse reactions.

Purpose: To assess the decisions made by uroradiologists regarding choice of type of intravenous contrast material (low-osmolar contrast media [LOCM] vs conventional ionic agents) and frequency of use of corticosteroid prophylaxis., Materials and Methods: A questionnaire was mailed to 158 members of the Society of Uroradiology. There were 108 responses received, yielding a response rate of 68%. Results from 76 represented institutions were tabulated., Results: Most respondents practice at institutions in which LOCM are used selectively rather than universally. Corticosteroid prophylaxis in patients at risk is used with similar frequency at both types of institutions. There is considerable diversity in pretreatment regimens (ie, type and dose of corticosteroid used). Although antihistamines are used by many uroradiologists (almost always in conjunction with corticosteroids), H2 receptor antagonists are used at only a few institutions., Conclusion: At institutions in which LOCM are used selectively, the majority of respondents use LOCM quite liberally, with most choosing these agents in patients at risk. Corticosteroid prophylaxis is widely used by respondents. There is much variation in the type of pretreatment regimen and its use in specific clinical settings.

Two thousand thirty-four consecutive patients presented for excretory urography within a 22-month period. Of 57 observed adverse reactions (incidence 2.8%), 54 occurred in 1219 low-risk patients injected with conventional ionic contrast media (HOCM) (incidence 4.4%) while three reactions were noted in the 815 high-risk patients receiving low-osmolar contrast media (LOCM) (incidence 0.4%). Despite strict enforcement of an unchanging list of high-risk criteria by the same pool of radiologists, LOCM use was not constant, increasing in use over time from 26.5% to 55.3% of urograms. In addition, frequency of LOCM selection increased transiently (from 33% to 52%) following a single life-threatening reaction to HOCM. Our results suggest that strict guidelines for use of LOCM are subject to loose individual physician interpretation. Physicians' perceptions of safety made it increasingly difficult to withhold LOCM and progressively more patients were included in high-risk groups.