Congenital Anomalies of the Upper Urinary Tract: A Comprehensive Review.

The upper urinary tract is the most common human system affected by congenital anomalies. Congenital anomalies of the kidneys and ureters comprise a wide spectrum of disorders ranging from simple variants with no clinical significance to complex anomalies that may lead to severe complications and end-stage renal disease. They may be classified as anomalies of renal form, which are subclassified as structural anomalies (eg, persistent fetal lobulation, hypertrophied column of Bertin, and dromedary hump) and fusion anomalies (eg, horseshoe kidney and pancake kidney); anomalies of renal position (eg, renal malrotation, simple renal ectopia, and crossed renal ectopia) and renal number (eg, renal agenesis and supernumerary kidney); and abnormalities in development of the urinary collecting system (eg, pyelocaliceal diverticulum, megacalycosis, ureteropelvic junction obstruction, duplex collecting system, megaureter, ectopic ureter, and ureterocele). US is usually the first imaging modality used because of its low cost, wide availability, and absence of ionizing radiation. Intravenous urography and voiding cystourethrography are also useful, mainly for characterization of the collecting system and vesicoureteral reflux. However, intravenous urography has been replaced by CT urography and MR urography. These imaging methods not only allow direct visualization of the collecting system but also demonstrate the function of the kidneys, the vascular anatomy, adjacent structures, and complications. Comprehension of congenital anomalies of the upper urinary tract is crucial for an accurate diagnosis and correct management. The authors discuss the spectrum of these anomalies, with emphasis on embryologic development, imaging findings, clinical manifestations, and complications. Online supplemental material is available for this article. ©RSNA, 2021.

Contrast-enhanced ultrasound of the pediatric bowel.

Contrast-enhanced ultrasound (CEUS) has emerged as a valuable modality for bowel imaging in adults and children. CEUS enables visualization of the perfusion of the bowel wall and of the associated mesentery in healthy and disease states. In addition, CEUS images can be used to make quantitative measurements of contrast kinetics, allowing for objective assessment of bowel wall enhancement. Bowel CEUS is commonly applied to evaluate inflammatory bowel disease and to monitor treatment response. It has also been applied to evaluate necrotizing enterocolitis, intussusception, appendicitis and epiploic appendagitis, although experience with these applications is more limited. In this review article, we present the current experience using CEUS to evaluate the pediatric bowel with emphasis on inflammatory bowel disease, extrapolating the established experience from adult studies. We also discuss emerging applications of CEUS as an adjunct or problem-solving tool for evaluating bowel perfusion.

Contrast-enhanced ultrasound of transplant organs - liver and kidney - in children.

Ultrasound (US) is the first-line imaging tool for evaluating liver and kidney transplants during and after the surgical procedures. In most patients after organ transplantation, gray-scale US coupled with color/power and spectral Doppler techniques is used to evaluate the transplant organs, assess the patency of vascular structures, and identify potential complications. In technically difficult or inconclusive cases, however, contrast-enhanced ultrasound (CEUS) can provide prompt and accurate diagnostic information that is essential for management decisions. CEUS is indicated to evaluate for vascular complications including vascular stenosis or thrombosis, active bleeding, pseudoaneurysms and arteriovenous fistulas. Parenchymal indications for CEUS include evaluation for perfusion defects and focal inflammatory and non-inflammatory lesions. When transplant rejection is suspected, CEUS can assist with prompt intervention by excluding potential underlying causes for organ dysfunction. Intracavitary CEUS applications can evaluate the biliary tract of a liver transplant (e.g., for biliary strictures, bile leak or intraductal stones) or the urinary tract of a renal transplant (e.g., for urinary obstruction, urine leak or vesicoureteral reflux) as well as the position and patency of hepatic, biliary and renal drains and catheters. The aim of this review is to present current experience regarding the use of CEUS to evaluate liver and renal transplants, focusing on the examination technique and interpretation of the main imaging findings, predominantly those related to vascular complications.

Frequency of MRI Low Signal Intensity in the Buccal Fat of Fetuses and Speculation as to What It May Reflect.

PURPOSE: We aimed to characterize the fetal buccal fat pad (BFP) on magnetic resonance imaging (MRI) to determine the frequency and types of sequences on which the BFP demonstrates low signal intensity and determine any possible correlation with timing of the MRI during fetal development. MATERIALS AND METHODS: A retrospective review of all fetal MR studies was performed, and a pediatric radiologist blinded to the referring and final fetal diagnosis as well as outcome evaluated the included cases. A positive buccal fat pad sign (BFS) was recorded as present if a round, symmetric, and bilateral area was seen in the submalar region of the face with the following signal characteristics: T1 hyperintensity, low signal on echo planar imaging (EPI), low signal on true fast imaging with steady-state free precession (TRUFI), and with restriction on diffusion-weighted imaging (DWI). RESULTS: A total of one hundred sixty-seven (167) fetal MRI studies: one hundred fourteen (114) body (68%) and fifty-three (53) neuro (32%) scans were reviewed during the study period. The BFS was most commonly seen on EPI (63%) and TRUFI (49%) sequences. Substantial agreement between TRUFI and EPI (κ = 0.68; p < 0.01); moderate agreement between TRUFI and T1 (κ = 0.53; p < 0.01) as well as T1 and EPI (κ = 0.53; p < 0.01), and fair agreement between EPI and Diffusion (κ = 0.28; p < 0.01) was observed. The median gestational age (GA) was 24 weeks (IQR 22-30 weeks). The fetuses with a positive BFS were significantly older (mean GA of 27 weeks or higher) than those without, for each sequence. CONCLUSIONS: The focal low signal in the fetal buccal fat pad, termed the fetal BFS, is a commonly encountered normal finding in the majority of fetal MRI scans on TRUFI and EPI sequences. This finding may be related to the presence and development of brown adipose tissue in the buccal fat pad resulting in T2* effects, but further studies are needed in order to confirm this. Further work can incorporate any of the sensitive sequences demonstrating low signal in brown adipose tissue to map its distribution and development in the fetus and beyond.

Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19.

To tune and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from 4 test sets, including 3 from the United States (patients hospitalized at an academic medical center (N = 154), patients hospitalized at a community hospital (N = 113), and outpatients (N = 108)) and 1 from Brazil (patients at an academic medical center emergency department (N = 303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson R). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. Tuning the deep learning model with outpatient data showed high model performance in 2 United States hospitalized patient datasets (R = 0.88 and R = 0.90, compared to baseline R = 0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (R = 0.86 and R = 0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. A deep learning model that extracts a COVID-19 severity score on CXRs showed generalizable performance across multiple populations from 2 continents, including outpatients and hospitalized patients.

Improvement and Multi-Population Generalizability of a Deep Learning-Based Chest Radiograph Severity Score for COVID-19.

PURPOSE: To improve and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. MATERIALS AND METHODS: A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from four test sets, including 3 from the United States (patients hospitalized at an academic medical center (N=154), patients hospitalized at a community hospital (N=113), and outpatients (N=108)) and 1 from Brazil (patients at an academic medical center emergency department (N=303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson r). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. RESULTS: Tuning the deep learning model with outpatient data improved model performance in two United States hospitalized patient datasets (r=0.88 and r=0.90, compared to baseline r=0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (r=0.86 and r=0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. CONCLUSIONS: Performance of a deep learning-based model that extracts a COVID-19 severity score on CXRs improved using training data from a different patient cohort (outpatient versus hospitalized) and generalized across multiple populations.