Deep-learning-based method for the segmentation of ureter and renal pelvis on non-enhanced CT scans.

This study aimed to develop a deep-learning (DL) based method for three-dimensional (3D) segmentation of the upper urinary tract (UUT), including ureter and renal pelvis, on non-enhanced computed tomography (NECT) scans. A total of 150 NECT scans with normal appearance of the left UUT were chosen for this study. The dataset was divided into training (n = 130) and validation sets (n = 20). The test set contained 29 randomly chosen cases with computed tomography urography (CTU) and NECT scans, all with normal appearance of the left UUT. An experienced radiologist marked out the left renal pelvis and ureter on each scan. Two types of frameworks (entire and sectional) with three types of DL models (basic UNet, UNet3 + and ViT-UNet) were developed, and evaluated. The sectional framework with basic UNet model achieved the highest mean precision (85.5%) and mean recall (71.9%) on the test set compared to all other tested methods. Compared with CTU scans, this method had higher axial UUT recall than CTU (82.5% vs 69.1%, P < 0.01). This method achieved similar or better visualization of UUT than CTU in many cases, however, in some cases, it exhibited a non-ignorable false-positive rate. The proposed DL method demonstrates promising potential in automated 3D UUT segmentation on NECT scans. The proposed DL models could remarkably improve the efficiency of UUT reconstruction, and have the potential to save many patients from invasive examinations such as CTU. DL models could also serve as a valuable complement to CTU.

The Impact of Pelvicalyceal System Anatomy on Stone Formation in Patients with Single Lower Pole Calyceal Stone on Computed Tomography Urography.

BACKGROUND: To compare various lower pole pelvicalyceal anatomical factors of stone bearing kidney with contralateral normal kidneys and determine whether these factors predispose to stone formation in one kidney. METHODS: A descriptive study was done with Computed Tomography of 54 patients with solitary lower pole calculus in one kidney and normal contralateral kidney were included. Various lower pole pelvicalyceal anatomical factors like infundibulopelvic angle, infundibular width, infundibular length and calyceopelvic height of both stone bearing and contralateral kidneys were measured and compared for any differences Results: The mean infundibular width was 5.4±1.9mm on stone bearing kidneys and 5.2±2.05mm on contralateral normal kidneys. The mean infundibular length was 18.9±4.4mm on stone bearing kidneys and 18.8±3.9mm on contralateral normal kidneys. The mean infundibulopelvic angle was 47.9±10.8° on stone bearing kidneys and 47.6±11.2° on contralateral kidneys. The mean calyceopelvic height was 15.7±4.6mm on stone bearing kidneys and 15.5±3.9mm (range 7.5to 23.1mm) on contralateral kidneys. There were no statistically significant differences between stone bearing and contralateral normal kidneys in respect to these pelvicalyceal anatomical factors. CONCLUSIONS: In this study, we found no significant difference in lower pole pelvicalyceal anatomical factors between stone bearing kidneys and contralateral normal kidneys and therefore these factors do not seem to have significant role in stone formation in one kidney compared with the other.

Radiation exposure in pediatric videourodynamics: An evaluation of safety in comparison to voiding cystourethrogram.

INTRODUCTION: Children with spina bifida (SB) undergo a videourodynamic study (VUDS) or urodynamic study and voiding cystourethrogram (VCUG). A standardized protocol for imaging during a pediatric VUDS has not been established. Our aim is to quantify radiation exposure and establish a baseline for children with spina bifida (SB) undergoing VUDS in current practice at our institution. METHODS: This is a retrospective study from 2013 to 2020 of consecutive pediatric SB patients undergoing VUDS by a single provider. Patients were categorized into three groups based on age; group 1 (0-2 YR), group 2 (2-10 YR), group 3 (>10 YR). Radiation data was reported as mean air kerma (AK), dose area product (DAP) and exposure time (seconds). Effective dose (ED) was calculated based on radiation quantity (Air Kerma, AK) and organ sensitivity. The lifetime attributable risk (LAR) was calculated based on AK and a risk coefficient. Data points calculated for patients undergoing VUDS were then compared to age matched institutional VCUG data in the same age groups. RESULTS: 398 patients undergoing VUDS met inclusion criteria and 262 independent patients underwent VCUG. ED increased with age in both VUDS and VCUG. All VCUG groups were found to have a higher ED than VUDS. The LAR for VUDS groups 1-3 was 0.001, 0.002, and 0.006, respectively. Reported in percentages, there is a 0.1%, 0.2%, and 0.6% chance, respectively, of age groups 1, 2 and 3 developing cancer as a result of the radiation exposure from a VUDS. DISCUSSION: Our study found that ED was low across all age groups for VUDS, comparing favorably to the VCUG groups. VCUG was selected as a benchmark comparison for its diagnostic similarities and, at times, overlapping indications. Few studies have described ED with respect to VUDS or extrapolate the ED of VUDS into LAR in the pediatric population. We recognize that we have not determined the true ED of the gonads and bladder, rather we have overestimated, as the data is based on an international reference point proximal to the exposed individual. However, LAR was calculated for each age group and revealed that patients are at a negligible increased risk of developing malignancy secondary to exposure compared to the general population. CONCLUSION: Our current practice for pediatric VUDS has exhibited consistently low radiation exposure amongst all age groups. Moving forward, we have the foundation and flexibility to create an imaging protocol for pediatric VUDS, while taking more calculated steps toward incorporating ALARA, as low as reasonably achievable, principles. A protocol adhering to the ALARA principle could provide consistency across institutions and aid in multi-institutional studies.

Radiomics-based machine learning approach for the prediction of grade and stage in upper urinary tract urothelial carcinoma: a step towards virtual biopsy.

OBJECTIVES: Upper tract urothelial carcinoma (UTUC) is a rare, aggressive lesion, with early detection a key to its management. This study aimed to utilise computed tomographic urogram data to develop machine learning models for predicting tumour grading and staging in upper urothelial tract carcinoma patients and to compare these predictions with histopathological diagnosis used as reference standards. METHODS: Protocol-based computed tomographic urogram data from 106 patients were obtained and visualised in 3D. Digital segmentation of the tumours was conducted by extracting textural radiomics features. They were further classified using 11 predictive models. The predicted grades and stages were compared to the histopathology of radical nephroureterectomy specimens. RESULTS: Classifier models worked well in mining the radiomics data and delivered satisfactory predictive machine learning models. The multilayer panel showed 84% sensitivity and 93% specificity while predicting UTUC grades. The Logistic Regression model showed a sensitivity of 83% and a specificity of 76% while staging. Similarly, other classifier algorithms [e.g. Support Vector classifier (SVC)] provided a highly accurate prediction while grading UTUC compared to clinical features alone or ureteroscopic biopsy histopathology. CONCLUSION: Data mining tools could handle medical imaging datasets from small (<2 cm) tumours for UTUC. The radiomics-based machine learning algorithms provide a potential tool to model tumour grading and staging with implications for clinical practice and the upgradation of current paradigms in cancer diagnostics. CLINICAL RELEVANCE: Machine learning based on radiomics features can predict upper tract urothelial cancer grading and staging with significant improvement over ureteroscopic histopathology. The study showcased the prowess of such emerging tools in the set objectives with implications towards virtual biopsy.

Is the excretory phase necessary to identify upper tract urothelial neoplasms at CT urography? A 10-year population-based study.

PURPOSE: To assess the proportion of upper tract urothelial carcinomas (UTUC) that are evident without the excretory phase at CT urography (CTU), and the proportion of potentially avoidable radiation. METHODS: UTUCs diagnosed between January 2008-December 2017 were retrospectively identified from a population-based cancer registry. For each patient, US, non-urographic CT, and MRI exams were assessed for a primary mass and secondary imaging findings (hydronephrosis, urinary tract thickening, luminal distention, fat stranding, and lymphadenopathy/metastatic disease). CTUs were assessed for primary and secondary findings, and whether the tumor was evident as a filling defect on excretory phase. The dose-length product (DLP) of potentially avoidable excretory phases was calculated as a fraction of total DLP. RESULTS: 288 patients (mean age, 72±11 years, 165 males) and 545 imaging examinations were included. Of 192 patients imaged with 370 non-urographic CTs, a primary mass was evident in 154 (80.2%), secondary findings were evident in 172 (89.6%), and primary or secondary findings were evident in 179 (93.2%). Of 175 CTUs, primary and secondary findings were evident in 157 (89.7%) and 166 (94.9%) examinations, respectively, and primary or secondary findings were evident in 170/175 (97.1%). 131/175 (74.9%) UTUCs were evident as a filling defect, including the 5/175 (2.9%) UTUCs without primary or secondary findings. Of 144 CTUs with available DLP data, the proportion of potentially avoidable radiation was 103.7/235.8 (44.0%) Gy⋅cm. CONCLUSION: In our population, almost all UTUCs were evident via primary or secondary imaging findings without requiring the excretory phase. These results support streamlining protocols and pathways.

A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria.

OBJECTIVE: To evaluate whether artificial intelligence (AI) based automatic image analysis utilising convolutional neural networks (CNNs) can be used to evaluate computed tomography urography (CTU) for the presence of urinary bladder cancer (UBC) in patients with macroscopic hematuria. METHODS: Our study included patients who had undergone evaluation for macroscopic hematuria. A CNN-based AI model was trained and validated on the CTUs included in the study on a dedicated research platform (Recomia.org). Sensitivity and specificity were calculated to assess the performance of the AI model. Cystoscopy findings were used as the reference method. RESULTS: The training cohort comprised a total of 530 patients. Following the optimisation process, we developed the last version of our AI model. Subsequently, we utilised the model in the validation cohort which included an additional 400 patients (including 239 patients with UBC). The AI model had a sensitivity of 0.83 (95% confidence intervals [CI], 0.76-0.89), specificity of 0.76 (95% CI 0.67-0.84), and a negative predictive value (NPV) of 0.97 (95% CI 0.95-0.98). The majority of tumours in the false negative group (n = 24) were solitary (67%) and smaller than 1 cm (50%), with the majority of patients having cTaG1-2 (71%). CONCLUSIONS: We developed and tested an AI model for automatic image analysis of CTUs to detect UBC in patients with macroscopic hematuria. This model showed promising results with a high detection rate and excessive NPV. Further developments could lead to a decreased need for invasive investigations and prioritising patients with serious tumours.

Quantitative measurement of the ureter on three-dimensional magnetic resonance urography images using deep learning.

BACKGROUND: Accurate measurement of ureteral diameters plays a pivotal role in diagnosing and monitoring urinary tract obstruction (UTO). While three-dimensional magnetic resonance urography (3D MRU) represents a significant advancement in imaging, the traditional manual methods for assessing ureteral diameters are characterized by labor-intensive procedures and inherent variability. In the realm of medical image analysis, deep learning has led to a paradigm shift, yet the development of a comprehensive automated tool for the precise segmentation and measurement of ureters in MR images is an unaddressed challenge. PURPOSE: The ureter was quantitatively measured on 3D MRU images using a deep learning model. METHODS: A retrospective cohort of 445 3D MRU scans (443 patients, 52 ± 18 years; 217 female patients) was collected and split into training, validation, and internal testing cohorts. A 3D V-Net model was trained for urinary tract segmentation, and a post-processing algorithm was developed for ureteral measurements. The accuracy of the segmentation was evaluated using the Dice similarity coefficient (DSC) and volume intraclass correlation coefficient (ICC), with ground truth segmentations provided by experienced radiologists. The external cohort comprised 50 scans (50 patients, 55 ± 21 years; 30 female patients), and the model-predicted ureteral diameter measurements were compared with manual measurements to assess system performance. The various diameter parameters of ureter among the different measurement methods (ground truth, auto-segmentation with automatic diameter extraction, and manual segmentation with automatic diameter extraction) were assessed with Friedman tests and post hoc Dunn test. The effectiveness of the UTO diagnosis was assessed by receiver operating characteristic (ROC) curves and their respective areas under the curve (AUC) between different methods. RESULTS: In both the internal test and external cohorts, the mean DSC values for bilateral ureters exceeded 0.70. The ICCs for the bilateral ureter volume obtained by comparing the model and manual segmentation were all greater than 0.96 (p  < â€¯0.05), except for the right ureter in the internal test cohort, for which the ICC was 0.773 (p  < â€¯0.05). The mean DSCs for interobserver and intraobserver reliability were all above 0.97. The maximum diameter of the ureter exhibited no statistically significant differences either in the dilated (p = 0.08) or in the non-dilated (p = 0.32) ureters across the three measurement methods. The AUCs of ground truth, auto-segmentation with automatic diameter extraction, and manual segmentation with automatic diameter extraction in diagnosing UTO were 0.988 (95% CI: 0.934, 1.000), 0.961 (95% CI: 0.893, 0.991), and 0.979 (95% CI: 0.919, 0.998), respectively. There was no statistical difference between AUCs of the different methods (p > 0.05). CONCLUSION: The proposed deep learning model and post-processing algorithm provide an effective means for the quantitative evaluation of urinary diseases using 3D MRU images.

Quantitative differentiation of non-invasive bladder urothelial carcinoma and inverted papilloma based on CT urography.

PURPOSE: To investigate the value of CT urography (CTU) indicators in the quantitative differential diagnosis of bladder urothelial carcinoma (BUC) and inverted papilloma of the bladder (IPB). MATERIAL AND METHODS: The clinical and preoperative CTU imaging data of continuous 103 patients with histologically confirmed BUC or IPB were retrospectively analyzed. The imaging data included 6 qualitative indicators and 7 quantitative measures. The recorded clinical information and imaging features were subjected to univariate and multivariate logistic regression analysis to find independent risk factors for BUC, and a combined multi-indicator prediction model was constructed, and the prediction model was visualized using nomogram. ROC curve analysis was used to calculate and compare the predictive efficacy of independent risk factors and nomogram. RESULTS: Junction smoothness, maximum longitudinal diameter, tumor-wall interface and arterial reinforcement rate were independent risk factors for distinguishing BUC from IPB. The AUC of the combined model was 0.934 (sensitivity = 0.808, specificity = 0.920, accuracy = 0.835), and its diagnostic efficiency was higher than that of junction smoothness (AUC=0.667, sensitivity = 0.654, specificity = 0.680, accuracy = 0.660), maximum longitudinal diameter (AUC=0.757, sensitivity = 0.833, specificity = 0.604, accuracy = 0.786), tumor-wall interface (AUC=0.888, sensitivity = 0.755, specificity = 0.808, accuracy = 0.816) and Arterial reinforcement rate (AUC=0.786, sensitivity = 0.936, specificity = 0.640, accuracy = 0.864). CONCLUSION: Above qualitative and quantitative indicators based on CTU and the combination of them may be helpful to the differential diagnosis of BUC and IPB, thus better assisting in clinical decision-making. KEY POINTS: 1. Bladder urothelial carcinoma (BUC) and inverted papilloma of the bladder (IPB) exhibit similar clinical symptoms and imaging presentations. 2. The diagnostic value of CT urography (CTU) in distinguishing between BUC and IPB has not been documented. 3. BUC and IPB differ in lesion size, growth pattern and blood supply. 4. The diagnostic efficiency is optimized by integrating multiple independent risk factors into the prediction model.

Evaluation of differential renal function in children - a comparative study between magnetic resonance urography and dynamic renal scintigraphy.

BACKGROUND: Urinary system anomalies, both congenital and acquired, constitute a relatively common clinical problem in children. The main role of diagnostic imaging is to determine early diagnosis and support therapeutic decisions to prevent the development of chronic renal disease. The aim of this study was to evaluate the utility of magnetic resonance urography (MRU) in assessment of urinary system in children, by comparing differential renal function calculated using MRU with dynamic renal scintigraphy (DRS). MATERIALS AND METHODS: The study group consisted of 46 patients aged 1 week to 17 years (median 7 (0.5; 13) years, 17 (37%) girls, 29 (63%) boys), who underwent dynamic renal scintigraphy due to various clinical reasons. All participants underwent MRU, which was used to measure differential renal function. Functional analysis was performed using dedicated external software (CHOP-fMRU and pMRI without prior knowledge of DRS results. MRU results acquired using pMRI were assessed for inter and intraobserver agreement. RESULTS: Statistical analysis of the results showed excellent agreement between MRU and DRS in measuring differential renal function with Pearson correlation coefficient 0.987 for CHOP-fMRU and 0.971 for pMRI, p < 0.001. Interclass correlation coefficient (ICC) for these programs was 0.987 (95% CI 0.976-0.993) and 0.969 (95% CI 0.945-0.983) respectively, p < 0.001. The Bland-Altman 95% limits of agreement for CHOP-fMRU results vs. DRS was - 6.29-5.50 p.p. and for pMRI results vs. DRS - 9.15-9.63 p.p. The differential renal function measurements calculated in pMRI showed excellent intraobserver and interobserver agreement with ICC 0.996 (95% CI 0.994-0.998) and 0.992 (95% CI 0.986-0.996) respectively, p < 0.001. CONCLUSIONS: The study showed no significant differences between magnetic resonance urography and dynamic renal scintigraphy in calculating differential renal function. It indicates high utility of MRU in the evaluation of urinary system in children.

Low-dose versus conventional CT urography using dual-source CT with different time-current product values and the same tube voltage: image quality and diagnostic performance in various diagnoses.

OBJECTIVES: To compare the image quality and diagnostic performance of low-dose CT urography to that of concurrently acquired conventional CT using dual-source CT. METHODS: This retrospective study included 357 consecutive CT urograms performed by third-generation dual-source CT in a single institution between April 2020 and August 2021. Two-phase CT images (unenhanced phase, excretory phase with split bolus) were obtained with two different tube current-time products (280 mAs for the conventional-dose protocol and 70 mAs for the low-dose protocol) and the same tube voltage (90 kVp) for the two X-ray tubes. Iterative reconstruction was applied for both protocols. Two radiologists independently performed quantitative and qualitative image quality analysis and made diagnoses. The correlation between the noise level or the effective radiation dose and the patients' body weight was evaluated. RESULTS: Significantly higher noise levels resulting in a significantly lower liver signal-to-noise ratio and contrast-to-noise ratio were noted in low-dose images compared to conventional images (P < .001). Qualitative analysis by both radiologists showed significantly lower image quality in low-dose CT than in conventional CT images (P < .001). Patient's body weight was positively correlated with noise and effective radiation dose (P < .001). Diagnostic performance for various diseases, including urolithiasis, inflammation, and mass, was not different between the two protocols. CONCLUSIONS: Despite inferior image quality, low-dose CT urography with 70 mAs and 90 kVp and iterative reconstruction demonstrated diagnostic performance equivalent to that of conventional CT for identifying various diseases of the urinary tract. ADVANCES IN KNOWLEDGE: Low-dose CT (25% radiation dose) with low tube current demonstrated diagnostic performance comparable to that of conventional CT for a variety of urinary tract diseases.

Estimation of renal function using iodine maps in dual-energy spectral computed tomography urography: a feasibility and accuracy study.

PURPOSE: To explore the feasibility of measuring glomerular filtration rate (GFR) using iodine maps in dual-energy spectral computed tomography urography (DEsCTU) and correlate them with the estimated GFR (eGFR) based on the equation of creatinine-cystatin C. MATERIALS AND METHODS: One hundred and twenty-eight patients referred for DEsCTU were retrospectively enrolled. The DEsCTU protocol included non-contrast, nephrographic, and excretory phase imaging. The CT-derived GFR was calculated using the above 3-phase iodine maps (CT-GFRiodine) and 120 kVp-like images (CT-GFR120kvp) separately. CT-GFRiodine and CT-GFR120kvp were compared with eGFR using paired t-test, correlation analysis, and Bland-Altman plots. The receiver operating characteristic curves were used to test the renal function diagnostic performance with CT-GFR120kvp and CT-GFRiodine. RESULTS: The difference between eGFR (89.91 ± 18.45 ml·min-1·1.73 m-2) as reference standard and CT-GFRiodine (90.06 ± 20.89 ml·min-1·1.73 m-2) was not statistically significant, showing excellent correlation (r = 0.88, P < 0.001) and agreement (± 19.75 ml·min-1·1.73 m-2, P = 0.866). The correlation between eGFR and CT-GFR120kvp (66.13 ± 19.18 ml·min-1·1.73 m-2) was poor (r = 0.36, P < 0.001), and the agreement was poor (± 40.65 ml·min-1·1.73 m-2, P < 0.001). There were 62 patients with normal renal function and 66 patients with decreased renal function based on eGFR. The CT-GFRiodine had the largest area under the curve (AUC) for distinguishing between normal and decreased renal function (AUC = 0.951). CONCLUSION: The GFR can be calculated accurately using iodine maps in DEsCTU. DEsCTU could be a non-invasive and reliable one-stop-shop imaging technique for evaluating both the urinary tract morphology and renal function.

A computed tomography urography-based machine learning model for predicting preoperative pathological grade of upper urinary tract urothelial carcinoma.

OBJECTIVES: Development and validation of a computed tomography urography (CTU)-based machine learning (ML) model for prediction of preoperative pathology grade of upper urinary tract urothelial carcinoma (UTUC). METHODS: A total of 140 patients with UTUC who underwent CTU examination from January 2017 to August 2023 were retrospectively enrolled. Tumor lesions on the unenhanced, medullary, and excretory periods of CTU were used to extract Features, respectively. Feature selection was screened by the Pearson and Spearman correlation analysis, least absolute shrinkage and selection operator algorithm, random forest (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost). The logistic regression (LR) was used to screen for independent influencing factors of clinical baseline characteristics. Machine learning models based on different feature datasets were constructed and validated using algorithms such as LR, RF, SVM, and XGBoost. By computing the selected features, a radiomics score was generated, and a diverse feature dataset was constructed. Based on the training set, 16 ML models were created, and their performance was evaluated using the validation set for metrics including sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC), and others. RESULTS: The training set consisted of 98 patients (mean age: 64.5 ± 10.5 years; 30 males), whereas the validation set consisted of 42 patients (mean age: 65.3 ± 9.78 years; 17 males). Hydronephrosis was the best independent influence factor (p < 0.05). The RF model had the best performance in predicting high-grade UTUC, with AUC of 0.914 (95% Confidence Interval [95%CI] 0.852-0.977) and 0.903 (95%CI 0.809-0.997) in the training set and validation set, and accuracy of 0.878 and 0.857, respectively. CONCLUSIONS: An ML model based on the RF algorithm exhibits excellent predictive performance, offering a non-invasive approach for predicting preoperative high-grade UTUC.

Feasibility and Implementation of a 4D Free-Breathing Variable Density Stack-of-Stars Functional Magnetic Resonance Urography in Young Children Without Sedation.

BACKGROUND: Functional magnetic resonance urography (MRU) is well established in the diagnostic workup of urinary tract anomalies in children, providing comprehensive morphological and functional information. However, dynamic contrast-enhanced images acquired in the standard Cartesian k-space manner are prone to motion artifacts. A newly introduced 4D high spatiotemporal resolution dynamic contrast-enhanced magnetic resonance imaging based on variable density elliptical centric radial stack-of-stars sharing technique has shown improved image quality regarding motions under free breathing. OBJECTIVE: The aims of this study were to implement this 4D free-breathing sequence for functional MRU and to compare its image quality and analyzability with standard breath-hold Cartesian MRU. MATERIALS AND METHODS: We retrospectively evaluated all functional 4D MRU performed without general anesthesia between September 2021 and December 2022 and compared them with matched pairs (age, affected kidney, diagnosis) of standard Cartesian MRU between 2016 and 2022. Image analysis was performed by 2 radiologists independently regarding the following criteria using a 4-point Likert scale, with 4 being the best: overall image quality, diagnostic confidence, respiratory motion artifacts, as well as sharpness and contrast of aorta, kidneys, and ureters. We also measured vertical kidney motion due to respiratory motion and compared the variance for each kidney using F test. Finally, both radiologists calculated the volume, split renal volume (vDRF), split renal Patlak function (pDRF), and split renal function considering the volume and Patlak function (vpDRF) for each kidney. Values were compared using Bland-Altman plots and F test. RESULTS: Forty children (20 for 4D free-breathing and standard breath-hold, respectively) were enrolled. Ten children of each group were examined using feed-and-sleep technique (median age: 4D, 3.3 months; standard, 4.2 months), 10 were awake (median age: 4D, 8.9 years; standard, 8.6 years). Overall image quality, diagnostic confidence, respiratory motion artifacts, as well as sharpness and contrast of the aorta, kidneys, and ureters were rated significantly better for 4D free-breathing compared with standard breath-hold by both readers ( P ranging from <0.0001 to 0.005). Vertical kidney motion was significantly reduced in 4D free-breathing for the right and the left kidney (both P < 0.001). There was a significantly smaller variance concerning the differences between the 2 readers for vpDRF in 4D MRU ( P = 0.0003). In contrast, no significant difference could be demonstrated for volume ( P = 0.05), vDRF ( P = 0.93), and pDRF ( P = 0.14). CONCLUSIONS: We demonstrated the feasibility of applying a 4D free-breathing variable density stack-of-stars imaging for functional MRU in young pediatric patients with improved image quality, fewer motion artifacts, and improved functional analyzability.

Discrepant guidelines in the evaluation of hematuria.

PURPOSE: To assess discrepancies in current imaging recommendations for hematuria among North American societies: American College of Radiology (ACR), American Urological Association (AUA), and Canadian Urological Association (CUA). METHODS: The latest available ACR Appropriateness Recommendations, AUA guidelines, and CUA guidelines were reviewed. AUA and CUA guidelines imaging recommendations by variants and level of appropriateness were converted to match the style of ACR. Imaging recommendations including modality, anatomy, and requirement for contrast were recorded. RESULTS: Clinical variants included microhematuria without risk factors, microhematuria with risk factors, gross hematuria, and microhematuria during pregnancy. CUA recommends ultrasound kidneys as the first-line imaging study in the first 3 variants; pregnancy is not explicitly addressed. For hematuria without risk factors, ACR does not routinely recommend imaging, while AUA recommends shared decision-making to decide repeat urinalysis versus cystoscopy with ultrasound kidneys. For hematuria with risk factors and gross hematuria, ACR recommends CT urography; MR urography can also be considered in gross hematuria. AUA further stratifies intermediate- and high-risk patients, for which ultrasound kidneys and CT urography are recommended, respectively. For pregnancy, ACR and AUA both recommend ultrasound kidneys, though AUA additionally recommends consideration of CT or MR urography after delivery. CONCLUSION: There is no universally agreed upon algorithm for diagnostic evaluation. Discrepancies centered on the role of upper tract imaging with ultrasound versus CT. Prospective studies and/or repeat simulation studies that apply newly updated guidelines are needed to further clarify the role of imaging, particularly for patients with microhematuria with no and intermediate risk factors.

Evaluation of image quality and radiation dose in computed tomography urography following tube voltage optimisation.

INTRODUCTION: Computed tomography urography (CTU) comprehensively evaluates the urinary tract. However, the procedure is associated with a high radiation dose due to multiple scan series and therefore requires optimisation. The study performed CTU protocol optimisation based on a reduction in tube voltage (kV) using quality assurance (QA) phantom and clinical images and evaluated image quality and radiation dose. METHODS: The study was prospectively conducted on patients referred for CTU. The patients were grouped into A and B and were scanned with the standard protocol, a protocol used for the routine CTU at the CT centre before optimisation, and optimised protocol, a protocol with reduced kV respectively. The protocols were first tried on a quality assurance (QA) phantom before being applied to patients, and image quality was assessed based on signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). In addition, the clinical images were assessed based on the visibility of the anatomical criteria for CT images by five observers with >5 years of experience. The data were analysed using both visual grading characteristic (VGC) curves and statistical package for social sciences (SPSS) version 22.0. RESULTS: The dose was significantly lower in the optimised protocol with a 10 % reduction in both volume computed tomography dose index and (CTDIvol) and dose length product (DLP) for the phantom images, and a 26 % reduction in CTDIvol and 28 % in DLP for the clinical images. However, there was no significant difference in image quality noted between the standard and optimised protocols based on the quantitative and qualitative image quality evaluation using both the QA phantom and clinical images. CONCLUSION: The findings revealed a significant dose reduction in the optimised protocol. Further, image quality in standard and optimised protocols did not differ significantly based on quantitative and qualitative methods. IMPLICATION FOR PRACTICE: kV optimisation in contrast-enhanced procedures provides dose reduction and should be encouraged in the medical imaging departments.

Non-contrast-enhanced magnetic resonance urography for measuring split kidney function in pediatric patients with hydronephrosis: comparison with renal scintigraphy.

BACKGROUND: Split kidney function (SKF) is critical for treatment decision in pediatric patients with hydronephrosis and is commonly measured using renal scintigraphy (RS). Non-contrast-enhanced magnetic resonance urography (NCE-MRU) is increasingly used in clinical practice. This study aimed to investigate the feasibility of using NCE-MRU as an alternative to estimate SKF in pediatric patients with hydronephrosis, compared to RS. METHODS: Seventy-five pediatric patients with hydronephrosis were included in this retrospective study. All patients underwent NCE-MRU and RS within 2 weeks. Kidney parenchyma volume (KPV) and texture analysis parameters were obtained from T2-weighted (T2WI) in NCE-MRU. The calculated split KPV (SKPV) percent and texture analysis parameters percent of left kidney were compared with the RS-determined SKF. RESULTS: SKPV showed a significant positive correlation with SKF (r = 0.88, p < 0.001), while inhomogeneity was negatively correlated with SKF (r = - 0.68, p < 0.001). The uncorrected and corrected prediction models of SKF were established using simple and multiple linear regression. Bland-Altman plots demonstrated good agreement of both predictive models. The residual sum of squares of the corrected prediction model was lower than that of the uncorrected model (0.283 vs. 0.314) but not statistically significant (p = 0.662). Subgroup analysis based on different MR machines showed correlation coefficients of 0.85, 0.95, and 0.94 between SKF and SKPV for three different scanners, respectively (p < 0.05 for all). CONCLUSIONS: NCE-MRU can be used as an alternative method for estimating SKF in pediatric patients with hydronephrosis when comparing with RS. Specifically, SKPV proves to be a simple and universally applicable indicator for predicting SKF.