CT and MRI in Urinary Tract Infections: A Spectrum of Different Imaging Findings.

There are many acute and chronic infections affecting the urinary tract including bacterial, fungal and viral infections. Urinary tract infections (UTIs) can present in many different patterns with variable degrees of severity varying from asymptomatic and uncomplicated forms to life threatening complicated infections. Cross-sectional imaging techniques-including both computed tomography (CT) and magnetic resonance imaging (MRI)-have become very important tools not only for evaluation of UTIs, but also for detection of associated complications. Selection of either CT or MRI in the UTI evaluation depends on several factors such as the presence of contraindication, experience, cost and availability. CT and MRI help in early detection and management of UTIs that reduce the prevalence and severity of complications. In this article we will present the radiologic findings at CT and MRI in different types of upper and lower UTIs including acute pyelonephritis, intrarenal and perinephric abscesses, pyonephrosis, chronic pyelonephritis, emphysematous UTIs, xanthogranulomatous pyelonephritis, tuberculosis (TB), bilharziasis, fungal infection, corynebacterium infection, ureteritis, cystitis, prostatitis, prostatic abscess and urethritis.

Evaluation of acute post-shock wave lithotripsy renal changes by dynamic magnetic resonance imaging: a prospective clinical study.

PURPOSE: We studied acute renal morphological and hemodynamic changes after shock wave lithotripsy of renal stones. MATERIALS AND METHODS: A total of 60 adult patients with a single renal stone 25 mm or less in a radiologically normal urinary tract were eligible for shock wave lithotripsy and included in analysis. Study exclusion criteria were hypertension, diabetes mellitus, previous recent stone management and other contraindications to shock wave lithotripsy. Renal perfusion and morphological changes were evaluated by dynamic magnetic resonance imaging before, and 2 to 4 hours and 1 week after lithotripsy. RESULTS: In all cases there was a statistically significant decrease in renal perfusion 1 week after shock wave lithotripsy compared to before and 2 to 4 hours after lithotripsy (66% vs 71% and 72% of the aortic blood flow, respectively, p <0.05). At 1-week followup 39 unobstructed renal units (65%) showed no significant difference in renal perfusion at any time while 21 (35%) obstructed renal units showed a significant decrease in renal perfusion compared to before and 2 to 4 hours after lithotripsy (63% vs 76% and 75%, p = 0.003 and 0.005, respectively). Hematomas were observed in 7 cases (12%) 2 to 4 hours after lithotripsy, of which 5 were subcapsular and 2 were intrarenal. Three subcapsular hematomas resolved after 1 week. Localized loss of corticomedullary differentiation was observed in 2 patients (3.3%) with intrarenal hematoma 2 to 4 hours after treatment. Generalized loss of corticomedullary differentiation was observed 1 week after lithotripsy in 5 cases (8.3%). CONCLUSIONS: Shock wave lithotripsy alone induces minimal, reversible acute renal morphological changes and does not induce significant changes in renal perfusion. Posttreatment obstruction has a major effect on renal perfusion on the treated side and must be managed urgently.

Prostate Cancer Diagnosis via Visual Representation of Tabular Data and Deep Transfer Learning.

Prostate cancer (PC) is a prevalent and potentially fatal form of cancer that affects men globally. However, the existing diagnostic methods, such as biopsies or digital rectal examination (DRE), have limitations in terms of invasiveness, cost, and accuracy. This study proposes a novel machine learning approach for the diagnosis of PC by leveraging clinical biomarkers and personalized questionnaires. In our research, we explore various machine learning methods, including traditional, tree-based, and advanced tabular deep learning methods, to analyze tabular data related to PC. Additionally, we introduce the novel utilization of convolutional neural networks (CNNs) and transfer learning, which have been predominantly applied in image-related tasks, for handling tabular data after being transformed to proper graphical representations via our proposed Tab2Visual modeling framework. Furthermore, we investigate leveraging the prediction accuracy further by constructing ensemble models. An experimental evaluation of our proposed approach demonstrates its effectiveness in achieving superior performance attaining an F1-score of 0.907 and an AUC of 0.911. This offers promising potential for the accurate detection of PC without the reliance on invasive and high-cost procedures.

Kidney segmentation from DCE-MRI converging level set methods, fuzzy clustering and Markov random field modeling.

Early diagnosis of transplanted kidney function requires precise Kidney segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging images as a preliminary step. In this regard, this paper aims to propose an automated and accurate DCE-MRI kidney segmentation method integrating fuzzy c-means (FCM) clustering and Markov random field modeling into a level set formulation. The fuzzy memberships, kidney's shape prior model, and spatial interactions modeled using a second-order MRF guide the LS contour evolution towards the target kidney. Several experiments on real medical data of 45 subjects have shown that the proposed method can achieve high and consistent segmentation accuracy regardless of where the LS contour was initialized. It achieves an accuracy of 0.956 ± 0.019 in Dice similarity coefficient (DSC) and 1.15 ± 1.46 in 95% percentile of Hausdorff distance (HD95). Our quantitative comparisons confirm the superiority of the proposed method over several LS methods with an average improvement of more than 0.63 in terms of HD95. It also offers HD95 improvements of 9.62 and 3.94 over two deep neural networks based on the U-Net model. The accuracy improvements are experimentally found to be more profound on low-contrast images as well as DCE-MRI images with high noise levels.

Adrenal tumors with venous thrombosis: a single-institution experience.

OBJECTIVE: To assess the incidence, imaging, surgical approach and prognosis of adrenal tumors associated with venous thrombosis. MATERIAL AND METHODS: Charts of 206 patients who underwent adrenal surgery were reviewed. Data of patients with pathologically confirmed venous thrombosis, utilized diagnostic modalities, operative treatment and prognosis were reviewed and analyzed. RESULTS: Venous thrombosis was confirmed pathologically in 6 patients (2.9%). All were of male gender with age ranging between 2 and 54 years. The mean size of the masses was 11.5 ± 5.2 cm. Venous thrombosis was diagnosed preoperatively in 2 patients, adrenal vein thrombosis in 1 patient, and renal vein thrombosis in the others. Masses were successfully excised via an open approach in association with nephrectomy in 3 cases. There was no operative mortality or gross morbidity. Pathologically, thrombosis was limited to the adrenal vein in 4 patients and extended to the renal vein in 2. Pathology of the masses revealed neuroblastoma in 2, pheochromocytoma in 2, adrenocortical carcinoma in 1, and pleomorphic sarcoma in 1 case. Metastasis developed within 6 months in 3 of these patients. CONCLUSION: Venous thrombosis with adrenal tumors is a rare pathological condition in which open surgery is the standard of care. Primary malignant adrenal masses with venous thrombosis have a poor prognostic outcome.

A multimodal computer-aided diagnostic system for precise identification of renal allograft rejection: Preliminary results.

PURPOSE: Early assessment of renal allograft function post-transplantation is crucial to minimize and control allograft rejection. Biopsy - the gold standard - is used only as a last resort due to its invasiveness, high cost, adverse events (e.g., bleeding, infection, etc.), and the time for reporting. To overcome these limitations, a renal computer-assisted diagnostic (Renal-CAD) system was developed to assess kidney transplant function. METHODS: The developed Renal-CAD system integrates data collected from two image-based sources and two clinical-based sources to assess renal transplant function. The imaging sources were the apparent diffusion coefficients (ADCs) extracted from 47 diffusion-weighted magnetic resonance imaging (DW-MRI) scans at 11 different b-values (b0, b50, b100, ..., b1000 s/mm 2 ), and the transverse relaxation rate (R2*) extracted from 30 blood oxygen level-dependent MRI (BOLD-MRI) scans at 5 different echo times (TEs = 2, 7, 12, 17, and 22 ms). Serum creatinine (SCr) and creatinine clearance (CrCl) were the clinical sources for kidney function evaluation. The Renal-CAD system initially performed kidney segmentation using the level-set method, followed by estimation of the ADCs from DW-MRIs and the R2* from BOLD-MRIs. ADCs and R2* estimates from 30 subjects that have both types of scans were integrated with their associated SCr and CrCl. The integrated biomarkers were then used as our discriminatory features to train and test a deep learning-based classifier, namely stacked autoencoders (SAEs) to differentiate non-rejection (NR) from acute rejection (AR) renal transplants. RESULTS: Using a leave-one-subject-out cross-validation approach along with SAEs, the Renal-CAD system demonstrated 93.3% accuracy, 90.0% sensitivity, and 95.0% specificity in differentiating AR from NR. Robustness of the Renal-CAD system was also confirmed by the area under the curve value of 0.92. Using a stratified tenfold cross-validation approach, the Renal-CAD system demonstrated its reproducibility and robustness by a diagnostic accuracy of 86.7%, sensitivity of 80.0%, specificity of 90.0%, and AUC of 0.88. CONCLUSION: The obtained results demonstrate the feasibility and efficacy of accurate, noninvasive identification of AR at an early stage using the Renal-CAD system.

Computer-Aided Diagnostic System for Early Detection of Acute Renal Transplant Rejection Using Diffusion-Weighted MRI.

OBJECTIVE: Early diagnosis of acute renal transplant rejection (ARTR) is critical for accurate treatment. Although the current gold standard, diagnostic technique is renal biopsy, it is not preferred due to its invasiveness, long recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. METHODS: This paper presents a computer-aided diagnostic (CAD) system for early ARTR detection using (3D + b-value) diffusion-weighted (DW) magnetic resonance imaging (MRI) data. The CAD process starts from kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The evolution is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and on-going kidney-background visual appearances. A B-spline-based three-dimensional data alignment is employed to handle local deviations due to breathing and heart beating. Then, empirical cumulative distribution functions of apparent diffusion coefficients of the segmented DW-MRI at different b-values are collected as discriminatory transplant status features. Finally, a deep-learning-based classifier with stacked nonnegative constrained autoencoders is employed to distinguish between rejected and nonrejected renal transplants. RESULTS: In our initial "leave-one-subject-out" experiment on 100 subjects, [Formula: see text] of the subjects were correctly classified. The subsequent four-fold and ten-fold cross-validations gave the average accuracy of [Formula: see text] and [Formula: see text], respectively. CONCLUSION: These results demonstrate the promise of this new CAD system to reliably diagnose renal transplant rejection. SIGNIFICANCE: The technology presented here can significantly impact the quality of care of renal transplant patients since it has the potential to replace the gold standard in kidney diagnosis, biopsy.

A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction.

This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For noninvasive detection of kidney rejection at an early stage, the proposed CAD system is based on the fusion of both imaging markers and clinical biomarkers. The former are derived from diffusion-weighted magnetic resonance imaging (DW-MRI) by estimating the apparent diffusion coefficients (ADC) representing the perfusion of the blood and the diffusion of the water inside the transplanted kidney. The clinical biomarkers, namely: creatinine clearance (CrCl) and serum plasma creatinine (SPCr), are integrated into the proposed CAD system as kidney functionality indexes to enhance its diagnostic performance. The ADC maps are estimated for a user-defined region of interest (ROI) that encompasses the whole kidney. The estimated ADCs are fused with the clinical biomarkers and the fused data is then used as an input to train and test a convolutional neural network (CNN) based classifier. The CAD system is tested on DW-MRI scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. These results demonstrate the potential of the proposed system for a reliable non-invasive diagnosis of renal transplant status for any DW-MRI scans, regardless of the geographical differences and/or imaging protocol.

Impact of pre-operative MRI-measured membranous urethral length on urinary continence after radical cystectomy and orthotopic substitution: clinical and urodynamic evaluation.

PURPOSE: We aimed to determine the impact of membranous urethral length as measured by preoperative magnetic resonance imaging (MRI) upon continence following radical cystectomy and orthotopic substitution. MATERIALS AND METHODS: A total of 40 male patients (mean age 55.7 +/- 7 years) were subjected to radical cystectomy and orthotopic ileal substitution for bladder cancer. Membranous urethral length was measured by preoperative MRI utilizing coronal oblique high resolution T2 weighted images. In all evaluable patients, day and night continence statuses as well as time to stable continence were recorded. Urodynamic assessment included medium fill pouchometry and urethral pressure profilometry. RESULTS: Of all patients 10 were non-evaluable. Mean follow-up period was 8.1 +/- 1.9 months. All the evaluable patients were continent by daytime. On the other hand, 13 were continent by night (43.3%), 13 showed occasional enuresis (43.3%) and 4 were nightly enuretic (13.4%). Mean membranous urethral lengths were 14 +/- 1.9, 13.8 +/- 1.9 and 12.8 +/- 1.7 mm in the three groups, respectively (P = 0.51). Mean time to reach stable postoperative daytime continence was 5.4 +/- 4.6 whilst it was 12.5 +/- 7.4 weeks for nighttime continence. There was no significant correlation between preoperative membranous urethral length and time to stable day or night continence (R = -0.11, -0.08, respectively). Moreover, such correlation was not observed with postoperative urethral pressure profilometry parameters including maximum urethral pressure, maximum urethral closure pressure or functional urethral length (R = -0.33, -0.38, -0.16, respectively). CONCLUSION: Preoperative MRI-measured membranous urethral length has no value for judgment of postoperative continence status following radical cystectomy and ileal bladder substitution.

A comprehensive non-invasive framework for diagnosing prostate cancer.

Early detection of prostate cancer increases chances of patients' survival. Our automated non-invasive system for computer-aided diagnosis (CAD) of prostate cancer segments the prostate on diffusion-weighted magnetic resonance images (DW-MRI) acquired at different b-values, estimates its apparent diffusion coefficients (ADC), and classifies their descriptors - empirical cumulative distribution functions (CDF) - with a trained deep learning network. To segment the prostate, an evolving geometric (level-set-based) deformable model is guided by a speed function depending on intensity attributes extracted from the DW-MRI with nonnegative matrix factorization (NMF). For a more robust evolution, the attributes are fused with a probabilistic shape prior and estimated spatial dependencies between prostate voxels. To preserve continuity, the ADCs of the segmented prostate volume at different b-values are normalized and refined using a generalized Gauss-Markov random field image model. The CDFs of the refined ADCs at different b-values are considered global water diffusion features and used to distinguish between benign and malignant prostates. A deep learning network of stacked non-negativity-constrained auto-encoders (SNCAE) is trained to classify the benign or malignant prostates on the basis of the constructed CDFs. Our experiments on 53 clinical DW-MRI data sets resulted in 92.3% accuracy, 83.3% sensitivity, and 100% specificity, indicating that the proposed CAD system could be used as a reliable non-invasive diagnostic tool.

Ectopic insertion of the ureter into the seminal vesicle.

We present a case of left ectopic ureter insertion into the left seminal vesicle which is a rare anomaly. The incidence of ectopic insertion of the ureter is more common in females and is usually associated with incontinence, leading to the diagnosis, while in males it is present with infection. Ectopic ureter is defined as abnormal insertion of the ureter, occurring in the posterior urethra in approximately 50% of cases in males. Other sites include the seminal vesicle (approximately one-third), vas deferens, bladder neck, prostate and epididymis, while the urethra and vagina are commonly affected in females. Management is usually addressed to the upper tract only; if there is incontinence it requires removal of the ureteric stump. Our case was initially diagnosed by magnetic resonance imaging and the diagnosis confirmed by computed tomography (CT) guided seminal vesiculography as transrectal guidance for seminal vesiculography was refused by the patient. CT guided seminal vesiculography is less painful and more tolerable than the transrectal route.

Dynamic contrast-enhanced MRI-based early detection of acute renal transplant rejection.

A novel framework for the classification of acute rejection versus nonrejection status of renal transplants from 2-D dynamic contrast-enhanced magnetic resonance imaging is proposed. The framework consists of four steps. First, kidney objects are segmented from adjacent structures with a level set deformable boundary guided by a stochastic speed function that accounts for a fourth-order Markov-Gibbs random field model of the kidney/background shape and appearance. Second, a Laplace-based nonrigid registration approach is used to account for local deformations caused by physiological effects. Namely, the target kidney object is deformed over closed, equispaced contours (iso-contours) to closely match the reference object. Next, the cortex is segmented as it is the functional kidney unit that is most affected by rejection. To characterize rejection, perfusion is estimated from contrast agent kinetics using empirical indexes, namely, the transient phase indexes (peak signal intensity, time-to-peak, and initial up-slope), and a steady-phase index defined as the average signal change during the slowly varying tissue phase of agent transit. We used a kn-nearest neighbor classifier to distinguish between acute rejection and nonrejection. Performance of our method was evaluated using the receiver operating characteristics (ROC). Experimental results in 50 subjects, using a combinatoric kn-classifier, correctly classified 92% of training subjects, 100% of the test subjects, and yielded an area under the ROC curve that approached the ideal value. Our proposed framework thus holds promise as a reliable noninvasive diagnostic tool.

Precise segmentation of 3-D magnetic resonance angiography.

Accurate automatic extraction of a 3-D cerebrovascular system from images obtained by time-of-flight (TOF) or phase contrast (PC) magnetic resonance angiography (MRA) is a challenging segmentation problem due to the small size objects of interest (blood vessels) in each 2-D MRA slice and complex surrounding anatomical structures (e.g., fat, bones, or gray and white brain matter). We show that due to the multimodal nature of MRA data, blood vessels can be accurately separated from the background in each slice using a voxel-wise classification based on precisely identified probability models of voxel intensities. To identify the models, an empirical marginal probability distribution of intensities is closely approximated with a linear combination of discrete Gaussians (LCDG) with alternate signs, using our previous EM-based techniques for precise linear combination of Gaussian-approximation adapted to deal with the LCDGs. The high accuracy of the proposed approach is experimentally validated on 85 real MRA datasets (50 TOF and 35 PC) as well as on synthetic MRA data for special 3-D geometrical phantoms of known shapes.

3D shape analysis for early diagnosis of malignant lung nodules.

An alternative method of diagnosing malignant lung nodules by their shape, rather than conventional growth rate, is proposed. The 3D surfaces of the detected lung nodules are delineated by spherical harmonic analysis that represents a 3D surface of the lung nodule supported by the unit sphere with a linear combination of special basis functions, called Spherical Harmonics (SHs). The proposed 3D shape analysis is carried out in five steps: (i) 3D lung nodule segmentation with a deformable 3D boundary controlled by a new prior visual appearance model; (ii) 3D Delaunay triangulation to construct a 3D mesh model of the segmented lung nodule surface; (iii) mapping this model to the unit sphere; (iv) computing the SHs for the surface; and (v) determining the number of the SHs to delineate the lung nodule. We describe the lung nodule shape complexity with a new shape index, the estimated number of the SHs, and use it for the K-nearest classification into malignant and benign lung nodules. Preliminary experiments on 327 lung nodules (153 malignant and 174 benign) resulted in a classification accuracy of 93.6%, showing that the proposed method is a promising supplement to current technologies for the early diagnosis of lung cancer.

MR urethrogram versus combined retrograde urethrogram and sonourethrography in diagnosis of urethral stricture.

OBJECTIVES: To compare the accuracy of magnetic resonance (MR) urethrogram versus combined RUG and sonourethrography (SUG) in diagnosis urethral stricture with evaluation of their impact in management choice. MATERIAL AND METHODS: From March 2006 through February 2007; 30 male patients (mean age, 45+/-18 years, range 15-75) with clinically suspected urethral stricture. All patients underwent RUG, SUG and MR urethrogram. RESULTS: The final diagnosis of the 30 cases included in our study, after endoscopy and surgical management, was classified into two main groups either isolated stricture (20 cases) or associated with other pathologies (9 cases). There was one case with normal urethral caliber at endoscopy. For the anterior stricture the sensitivity, specificity and overall accuracy of RUG was 91%, 90% and 90%, respectively and for the posterior stricture it was 89%, 91.7% and 90%, respectively. At SUG, all cases of anterior were detected with 100% accuracy while for cases of posterior stricture, the overall accuracy was 60%. MR urethrogram diagnosed all the cases of anterior and posterior stricture with exact delineation of its length except one case of normal caliber was diagnosed falsely at MR as anterior short segment urethral with 100% sensitivity, 91.7% specificity and 95% overall accuracy. CONCLUSION: MR urethrogram has comparable results with the combined RUG and SUG in diagnosing the anterior and posterior urethral strictures as regard the site and extension and degree of spongiofibrosis but MR is superior in diagnosis of associated pathologies with stricture.

Magnetic resonance urethrography in comparison to retrograde urethrography in diagnosis of male urethral strictures: is it clinically relevant?

OBJECTIVE: To compare the clinical relevance of retrograde urethrography (RUG) and magnetic resonance (MR) urethrography in evaluating male urethral strictures. METHODS: Between January and April 2004, 20 men were referred to our institute for management of urethral strictures. The patients were investigated by conventional RUG and multiformat MR urethrography. The patients were examined by urethroscopy under anesthesia to be followed by definitive endoscopic or open operative intervention. The radiologic data were compared by endoscopic as well as operative findings in all the patients. RESULTS: Ten patients were managed by visual internal urethrotomy (VIU) and two by dilatation under anesthesia; two showed normal urethral caliber. Four patients required open urethral reconstructive procedures. Two patients underwent radical cystectomy and cutaneous diversion because of associated bladder or urethral malignancy. Although overall accuracy for diagnosis of urethral strictures was equal between both modalities (85%), MR urethrography provided extra clinical data in seven patients (35%). It was superior to RUG in judging the urethral stricture length in three patients, diagnosing a urethral tumor in one, detecting associated bladder mass in one, characterizing the site of urethra-rectal fistula in one, and accurately delineating the proximal urethra in the last patient. Unlike RUG, MR urethrography provided adequate information about the degree of spongiofibrosis in all patients. CONCLUSION: MR urethrography is a promising tool for defining male urethral strictures and can provide extra guidance for treatment planning that cannot be obtained with RUG.

Quantitative nodule detection in low dose chest CT scans: new template modeling and evaluation for CAD system design.

Automatic diagnosis of lung nodules for early detection of lung cancer is the goal of a number of screening studies worldwide. With the improvements in resolution and scanning time of low dose chest CT scanners, nodule detection and identification is continuously improving. In this paper we describe the latest improvements introduced by our group in automatic detection of lung nodules. We introduce a new template for nodule detection using level sets which describes various physical nodules irrespective of shape, size and distribution of gray levels. The template parameters are estimated automatically from the segmented data (after the first two steps of our CAD system for automatic nodule detection) - no a priori learning of the parameters density function is needed. We show quantitatively that this template modeling approach drastically reduces the number of false positives in the nodule detection (the third step of our CAD system for automatic nodule detection), thus improving the overall accuracy of CAD systems. We compare the performance of this approach with other approaches in the literature and with respect to human experts. The impact of the new template model includes: 1) flexibility with respect to nodule topology - thus various nodules can be detected simultaneously by the same technique; 2) automatic parameter estimation of the nodule models using the gray level information of the segmented data; and 3) the ability to provide exhaustive search for all the possible nodules in the scan without excessive processing time - this provides an enhanced accuracy of the CAD system without increase in the overall diagnosis time.

Diagnosis of ureteral obstruction in patients with compromised renal function: the role of noninvasive imaging modalities.

UNLABELLED: We compared the role of noncontrast computerized tomography (NCCT), magnetic resonance urography (MRU), and combined abdominal radiography (KUB) and ultrasonography (US) in the diagnosis of the cause of ureteral obstruction in patients with compromised renal function. MATERIALS AND METHODS: The study included 149 patients, of whom 110 had bilateral obstruction and 39 had obstruction of a solitary kidney. Therefore, the total number of renal units was 259. All patients had renal impairment with serum creatinine greater than 2.5 mg/dl. Besides conventional KUB and US all patients underwent NCCT and MRU. The gold standard for diagnosis of the cause of obstruction included retrograde or antegrade ureterogram, ureteroscopy and/or open surgery. The sensitivity, specificity and overall accuracy of NCCT, MRU, and combined KUB and US in the diagnosis of ureteral obstruction were calculated in comparison with the gold standard. RESULTS: The definitive cause of ureteral obstruction was calculous in 146 and noncalculous in 113 renal units, including ureteral stricture in 65, bladder or ureter in 43, extraurinary collection in 3 and retroperitoneal fibrosis in 2. The site of stone impaction was identified by NCCT in all 146 renal units (100% sensitivity), by MRU in 101 (69.2% sensitivity), and by combined KUB and US in 115 (78.7% sensitivity) with a difference of significant value in favor of NCCT (p <0.001). Ureteral strictures were identified by NCCT in 18 of the 65 cases (28%) and by MRU in 54 of 65 (83%). Bladder and ureteral tumors causing ureteral obstruction could be diagnosed in approximately half of the patients by NCCT (22 of 43) and in all except 1 by MRU (42 of 43). NCCT and MRU could identify all extraurinary causes of obstruction. Overall of the 113 kidneys with noncalculous obstruction the cause could be identified by MRU in 101 (89% sensitivity), by NCCT in 45 (40% sensitivity), and by combined KUB and US in only 20 (18% sensitivity) with a difference of significant value in favor of MRU (p <0.001). CONCLUSIONS: In patients with renal impairment due to ureteral obstruction NCCT has superior diagnostic accuracy for detecting calculous causes of obstruction but MRU is superior for identifying noncalculous lesions.