Dynamic MRI and isotope renogram in the functional evaluation of pelviureteric junction obstruction: A comparative study

Case Report; Concomitant Left Nutcracker Syndrome and Right Ureteropelvic Junction Obstruction

We report rare case of concomitant left nutcracker syndrome and right ureteropelvic junction obstruction (UPJO) on adult female patient diagnosed by CT urogram after she presented with intermittent bilateral flank pain. For this we did Anderson-Hynes pyeloplasty for right ureteropelvic obstruction, it was laparoscopic initially but due to difficulty of stenting it is changed to open. She had smooth post-op course then discharged on 3rd post-op day. Symptoms of nutcracker syndrome are not that much bothersome for the patient so we planned to follow her conservatively.

A fully automatic deep learning-based method for segmenting regions of interest and predicting renal function in pediatric dynamic renal scintigraphy

OBJECTIVE

Accurate delineation of renal regions of interest (ROIs) is critical for the assessment of renal function in pediatric dynamic renal scintigraphy (DRS). The purpose of this study was to develop and evaluate a deep learning (DL) model that can fully automatically delineate renal ROIs and calculate renal function in pediatric 99mTechnetium-ethylenedicysteine (99mTc-EC) DRS.

METHODS

This study retrospectively analyzed 1,283 pediatric DRS data at a single center from January to December 2018. These patients were divided into training set (n = 1027), validation set (n = 128), and testing set (n = 128). A fully automatic segmentation of ROIs (FASR) model was developed and evaluated. The pixel values of the automatically segmented ROIs were calculated to predict renal blood perfusion rate (BPR) and differential renal function (DRF). Precision, recall rate, intersection over union (IOU), and Dice similarity coefficient (DSC) were used to evaluate the performance of FASR model. Intraclass correlation (ICC) and Pearson correlation analysis were used to compare the consistency of automatic and manual method in assessing the renal function parameters in the testing set.

RESULTS

The FASR model achieved a precision of 0.88, recall rate of 0.94, IOU of 0.83, and DSC of 0.91. In the testing set, the r values of BPR and DRF calculated by the two methods were 0.94 (P < 0.01) and 0.97 (P < 0.01), and the ICCs (95% confidence interval CI) were 0.94 (0.90-0.96) and 0.94 (0.91-0.96).

CONCLUSION

We propose a reliable and stable DL model that can fully automatically segment ROIs and accurately predict renal function in pediatric 99mTc-EC DRS.

Improving Automatic Renal Segmentation in Clinically Normal and Abnormal Paediatric DCE-MRI via Contrast Maximisation and Convolutional Networks for Computing Markers of Kidney Function

There is a growing demand for fast, accurate computation of clinical markers to improve renal function and anatomy assessment with a single study. However, conventional techniques have limitations leading to overestimations of kidney function or failure to provide sufficient spatial resolution to target the disease location. In contrast, the computer-aided analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) could generate significant markers, including the glomerular filtration rate (GFR) and time-intensity curves of the cortex and medulla for determining obstruction in the urinary tract. This paper presents a dual-stage fully modular framework for automatic renal compartment segmentation in 4D DCE-MRI volumes. (1) Memory-efficient 3D deep learning is integrated to localise each kidney by harnessing residual convolutional neural networks for improved convergence; segmentation is performed by efficiently learning spatial-temporal information coupled with boundary-preserving fully convolutional dense nets. (2) Renal contextual information is enhanced via non-linear transformation to segment the cortex and medulla. The proposed framework is evaluated on a paediatric dataset containing 60 4D DCE-MRI volumes exhibiting varying conditions affecting kidney function. Our technique outperforms a state-of-the-art approach based on a GrabCut and support vector machine classifier in mean dice similarity (DSC) by 3.8% and demonstrates higher statistical stability with lower standard deviation by 12.4% and 15.7% for cortex and medulla segmentation, respectively.

A novel method for estimating the urine drainage time from the renal collecting system

PURPOSE

Partial obstruction of the upper urinary tract is a common urological pathology that leads to progressive atrophy and dysfunction of the kidney. Most methods for evaluating the urine drainage rate, to assess the severity of partial obstruction, involve injection of markers into the blood stream and therefore the filtration rate from the blood effects the drainage rate. This study presents a novel method for assessing the drainage rate from the upper urinary tract by analyzing sequential fluoroscopic images from a routine nephrostogram, in which contrast material is introduced directly into the renal collecting system.

METHODS

Fluoroscopic images from 36 nephrostograms, following percutaneous nephrolithotomy, were retrospectively evaluated, 19 with a dilated renal pelvis. A radiological model for calculating the radiopacity of the renal pelvis, which reflects the amount of contrast material in each sequential image, was developed. Using this model, an algorithm was designed for generating a drainage curve and calculating the "drainage time" t_1/2 in which half of the contrast material has drained from the renal pelvis.

RESULTS

Analysis of images of a step-wedge phantom made of an increasing number of contrast material layers showed that the calculated radiopacity of each step was proportional to the amount of contrast material, independent of the background attenuation. Analysis of the nephrostograms showed that the drainage curves highly fitted an exponential function (R = 0.961), with a significantly higher t_1/2 for dilated cases.

CONCLUSION

The developed method may be used for a quantitative and accurate estimation of the urine drainage rate.

The Accuracy of Renal Function Measurements in Obstructive Hydronephrosis Using Dynamic Contrast-Enhanced MR Renography

OBJECTIVE. The objective of our study was to assess the accuracy of glomerular filtration rate (GFR) evaluation in patients with obstructive hydronephrosis using dynamic contrast-enhanced MR renography (DCE-MRR). MATERIALS AND METHODS. A group of 28 adult volunteers were enrolled in this study: 13 without hydronephrosis, eight with low-grade hydronephrosis, and seven with high-grade hydronephrosis. The GFR obtained from DCE-MRR (GFR_MRR) and the GFR obtained from renal scintigraphy (GFR_RS) were compared with the reference GFR (GFR_Ref) acquired using the two plasma sample method. The correlation and agreement between GFR_MRR and GFR_Ref, GFR_RS and GFR_Ref, and single-kidney GFR_MRR (skGFR_MRR) and single-kidney GFR_RS (skGFR_RS) were assessed. The interrater reliability of DCE-MRR and the interrater reliability of renal scintigraphy (RS) were measured. RESULTS. Both GFR_MRR and GFR_RS correlated well with GFR_Ref. In patients with hydronephrosis, DCE-MRR and RS overestimated GFR by 12.8 ± 13.9 mL/min (mean ± SD) and 11.5 ± 12.3 mL/min, respectively. The skGFR_RS was higher than skGFR_MRR by 5.7 ± 3.8 mL/min in high-grade hydronephrotic kidneys (p = 0.004). Good interrater reliability was observed for skGFR_MRR (intraclass correlation coefficient [ICC] = 0.82-0.92) and skGFR_RS (ICC = 0.79-0.90) for both nonhydronephrotic kidneys and hydronephrotic kidneys. The overall mean SDs of repeated measurements from three investigators were 4.0 and 3.8 mL/min for skGFR_MRR and skGFR_RS, respectively. CONCLUSION. Both DCE-MRR and RS tend to overestimate GFR in patients with hydronephrosis. RS-derived skGFR is slightly higher than that of DCE-MRR in kidneys with high-grade hydronephrosis. DCE-MRR is comparable to RS and may serve as an alternative noninvasive method for GFR measurement.