Renal Computed Tomography Research Articles

Segmentation-based quantitative measurements in renal CT imaging using deep learning

BackgroundRenal quantitative measurements are important descriptors for assessing kidney function. We developed a deep learning-based method for automated kidney measurements from computed tomography (CT) images.MethodsThe study datasets comprised potential kidney donors (n = 88), both contrast-enhanced (Dataset 1 CE) and noncontrast (Dataset 1 NC) CT scans, and test sets of contrast-enhanced cases (Test set 2, n = 18), cases from a photon-counting (PC)CT scanner reconstructed at 60 and 190 keV (Test set 3 PCCT, n = 15), and low-dose cases (Test set 4, n = 8), which were retrospectively analyzed to train, validate, and test two networks for kidney segmentation and subsequent measurements. Segmentation performance was evaluated using the Dice similarity coefficient (DSC). The quantitative measurements’ effectiveness was compared to manual annotations using the intraclass correlation coefficient (ICC).ResultsThe contrast-enhanced and noncontrast models demonstrated excellent reliability in renal segmentation with DSC of 0.95 (Test set 1 CE), 0.94 (Test set 2), 0.92 (Test set 3 PCCT) and 0.94 (Test set 1 NC), 0.92 (Test set 3 PCCT), and 0.93 (Test set 4). Volume estimation was accurate with mean volume errors of 4%, 3%, 6% mL (contrast test sets) and 4%, 5%, 7% mL (noncontrast test sets). Renal axes measurements (length, width, and thickness) had ICC values greater than 0.90 (p < 0.001) for all test sets, supported by narrow 95% confidence intervals.ConclusionTwo deep learning networks were shown to derive quantitative measurements from contrast-enhanced and noncontrast renal CT imaging at the human performance level.Relevance statementDeep learning-based networks can automatically obtain renal clinical descriptors from both noncontrast and contrast-enhanced CT images. When healthy subjects comprise the training cohort, careful consideration is required during model adaptation, especially in scenarios involving unhealthy kidneys. This creates an opportunity for improved clinical decision-making without labor-intensive manual effort.Key PointsTrained 3D UNet models quantify renal measurements from contrast and noncontrast CT.The models performed interchangeably to the manual annotator and to each other.The models can provide expert-level, quantitative, accurate, and rapid renal measurements.Graphical

Gram-Scale Synthesis of Renal-Clearable Tantalum Nanodots with High Water Solubility for Computed Tomography Imaging In Vivo.

Tantalum (Ta) emerges as a promising element for advanced computed tomography (CT) imaging probes owing to its high X-ray attenuation coefficient and excellent biocompatibility. Nevertheless, the synthesis of renally clear Ta-based imaging probes through simple methods remains a significant challenge. Herein, we introduce a simple and gram-scale approach for the synthesis of renal-clearable Ta nanodots with high water solubility for CT imaging in vivo. The Ta nanodots, coordination polymers, are fabricated via coordination reactions involving Ta(OH)5, citric acid (CA), and hydrogen peroxide. The Ta nanodots exhibit an ultrasmall hydrodynamic diameter (2.8 nm), high water solubility (1.88 g/mL, 688 mg Ta/mL), superior X-ray absorption capacity, gram-scale production capability (>10 g in lab synthesis), renal-clearable ability, and good biocompatibility. The Ta nanodots possess superior CT imaging efficacy across diverse tube voltages, enabling highly sensitive gastrointestinal CT imaging, renal CT imaging, and CT angiography (CTA). Moreover, Ta nanodots maintain robust CT imaging capabilities even at high X-ray energies, and Ta nanodots-based spectral CT achieves metallic artifacts-minimized CTA. The proposed Ta nanodots present substantial potential as a potent CT imaging probe for diagnosing various diseases.

Kidney Tumor Segmentation Based on DWR-SegFormer

Kidney cancer is a malignant tumor with a high mortality rate. The accurate segmentation of tumors from computed tomography (CT) scans can assist physicians in clinical diagnosis. We introduced a new segmentation network called DWR-SegFormer to address the challenge of accurately segmenting kidney tumors in CT images. The method involved binarizing the label maps of clear cell renal cell carcinoma and papillary renal cell carcinoma CT images for identification, and the cancer lesion area was obtained by the label so that the model could accurately identify the area and enhance the feature extraction ability. Secondly, an optimized segmentation model combining a DWR attention mechanism and SegFormer network was constructed. MiT-B0 was used as the encoder of the model to establish long-distance feature dependencies and effectively extract feature information at different resolutions. The decoder with a multi-branch DWR module was implemented to utilize multi-scale feature information effectively and enhance segmentation accuracy. Comparing the experimental results with other existing models shows that the model significantly outperformed the comparison methods in all evaluation metrics on the CT image dataset of clear cell renal cancer. Furthermore, the experimental findings highlight the robustness of the proposed model across other datasets.

AI-based segmentation of renal enhanced CT images for quantitative evaluate of chronic kidney disease

To quantitatively evaluate chronic kidney disease (CKD), a deep convolutional neural network-based segmentation model was applied to renal enhanced computed tomography (CT) images. A retrospective analysis was conducted on a cohort of 100 individuals diagnosed with CKD and 90 individuals with healthy kidneys, who underwent contrast-enhanced CT scans of the kidneys or abdomen. Demographic and clinical data were collected from all participants. The study consisted of two distinct stages: firstly, the development and validation of a three-dimensional (3D) nnU-Net model for segmenting the arterial phase of renal enhanced CT scans; secondly, the utilization of the 3D nnU-Net model for quantitative evaluation of CKD. The 3D nnU-Net model achieved a mean Dice Similarity Coefficient (DSC) of 93.53% for renal parenchyma and 81.48% for renal cortex. Statistically significant differences were observed among different stages of renal function for renal parenchyma volume (VRP), renal cortex volume (VRC), renal medulla volume (VRM), the CT values of renal parenchyma (HuRP), the CT values of renal cortex (HuRC), and the CT values of renal medulla (HuRM) (F = 93.476, 144.918, 9.637, 170.533, 216.616, and 94.283; p < 0.001). Pearson correlation analysis revealed significant positive associations between glomerular filtration rate (eGFR) and VRP, VRC, VRM, HuRP, HuRC, and HuRM (r = 0.749, 0.818, 0.321, 0.819, 0.820, and 0.747, respectively, all p < 0.001). Similarly, a negative correlation was observed between serum creatinine (Scr) levels and VRP, VRC, VRM, HuRP, HuRC, and HuRM (r = − 0.759, − 0.777, − 0.420, − 0.762, − 0.771, and − 0.726, respectively, all p < 0.001). For predicting CKD in males, VRP had an area under the curve (AUC) of 0.726, p < 0.001; VRC, AUC 0.765, p < 0.001; VRM, AUC 0.578, p = 0.018; HuRP, AUC 0.912, p < 0.001; HuRC, AUC 0.952, p < 0.001; and HuRM, AUC 0.772, p < 0.001 in males. In females, VRP had an AUC of 0.813, p < 0.001; VRC, AUC 0.851, p < 0.001; VRM, AUC 0.623, p = 0.060; HuRP, AUC 0.904, p < 0.001; HuRC, AUC 0.934, p < 0.001; and HuRM, AUC 0.840, p < 0.001. The optimal cutoff values for predicting CKD in HuRP are 99.9 Hu for males and 98.4 Hu for females, while in HuRC are 120.1 Hu for males and 111.8 Hu for females. The kidney was effectively segmented by our AI-based 3D nnU-Net model for enhanced renal CT images. In terms of mild kidney injury, the CT values exhibited higher sensitivity compared to kidney volume. The correlation analysis revealed a stronger association between VRC, HuRP, and HuRC with renal function, while the association between VRP and HuRM was weaker, and the association between VRM was the weakest. Particularly, HuRP and HuRC demonstrated significant potential in predicting renal function. For diagnosing CKD, it is recommended to set the threshold values as follows: HuRP < 99.9 Hu and HuRC < 120.1 Hu in males, and HuRP < 98.4 Hu and HuRC < 111.8 Hu in females.

Are renal stone protocol computed tomography reports giving us enough information?

Non-contrast computed tomography (CT) is the gold-standard diagnostic test for urolithiasis. Little is published regarding which information needs to be included in the report for it to be most useful to the healthcare team for efficient triage and high-quality patient care. This study aimed to assess the quality and variability of CT scan reporting at a single Canadian tertiary academic medical center. We completed a retrospective review of 100 consecutive renal colic CT scans. Descriptive statistics were used to report the frequency with which specific elements commonly used by urologists to triage and treat patients were included in radiology reports. Our sample had a mean age of 51.4±13.1 years. Stone size was universally reported for obstructing stones but was less frequently reported for non-obstructing stones (100% vs. 86.8%). A similar trend was observed for the exact stone number (100% vs. 93.4%). Non-obstructing stones were more likely than obstructing stones to be reported in one dimension (77.5% vs. 47%). Obstructing stones were reported in three dimensions 27% of the time. CT reports commonly include the presence or absence of hydronephrosis status (98%) but are less likely to include renal size (32%) and periureteral stranding (16%). Hounsfield units (HU) were reported in 3% of the reports, but skin-to-stone distance (SSD) and radiation dose were never reported. Reports routinely included assessments of stone size, location, and number (although not uniformly). HU, SSD, and radiation dose were rarely reported. This provides insight into opportunities for standardized reporting to optimize knowledge transfer that may result in clinical efficiency and improved quality of patient care.

Renal Lesions with Low-level Enhancement on Contrast-enhanced CT Promotes Early Detection of Drug-induced Kidney Injury in Patients Administered Anticancer Drugs.

Some patients with cancer-administered anti-cancer drugs may develop renal lesions with low-level enhancement on follow-up abdominal computed tomography (CT). To explore the clinical significance of renal lesions with low-level enhancement on CT after exposure to anti-cancer drugs. Medical records of patients with cancer who developed renal lesions on CT after exposure to anti-cancer drugs were retrospectively reviewed. Renal lesions were scored according to the extent of involvement, CT attenuation values of lesions and normal parenchyma were measured on precontrast CT and three phases of contrast-enhanced CT, and changes in serum creatinine (SCr) from one week before exposure to drugs to one week before and after the appearance of renal lesions were recorded. This study included 54 patients (86 lesions). Lesions were slightly lower density on pre-contrast CT, and less enhancing than normal renal parenchyma, especially in the delayed phase. Lesions were wedge-shaped, and involved the renal pyramid and associated renal cortex, as well as, were single or multiple, and occurred in the unilateral or bilateral kidneys. There were patchy and cord-like shadows of increased density in adjacent perirenal adipose tissue. During follow-up, lesions disappeared in 15 patients and persisted in 39 patients without significant progression. There were significant differences in renal lesions and normal renal parenchyma CT attenuation values in each phase of contrast-enhanced CT. Change in SCr level was significantly positively correlated with lesion score. Renal lesions with low-level enhancement on CT suggest early drug-induced kidney injury. These findings will inform clinical decision-making.

A Multicentric Audit to Reevaluate the Guidelines Adherence in Computed Tomography of Kidneys, Ureters, and Bladder (CT-KUB) X-ray Imaging in Jordan.

Background With the increasing use of imaging techniques involving ionizing radiation, the area of the body scanned should be restricted to what is required to answer the clinical question. Therefore, this is a retrospective audit that intends to evaluate the presence of overscanning in renal computed tomography (CT) scan images during the process of evaluation for urinary symptoms. Objective This study aims to reduce the unnecessary scan length and exposure to radiation in patients who undergo CT scans for urinary symptoms. Materials and Methods In two months duration, patients from different clinics underwent CT imaging, and the resulting radiographic images were collected and analyzed. Overscanning was defined to be more than 10% of the total scan. Subsequently, the total length of the CT scan was measured which is used to measure the unnecessary overscan above the highest kidney margin as a percentage of the total length. Results Out of the 88 patients who were evaluated, 100% did not meet the guidelines for renal CT imaging and were exposed to a high radiation dose. However, the minimum percentage of overscanned patients was 20-40%. Conclusion A significant number of scans demonstrated surplus overscanning above the highest kidney. Therefore, recognizing the suitable anatomical landmarks for scanning and establishing a follow-up audit are suggested measures to minimize the noxious effects of radiation exposure.

Incidental Indeterminate Renal Lesions: Distinguishing Non-Enhancing from Potential Enhancing Renal Lesions Using Iodine Quantification on Portal Venous Dual-Layer Spectral CT.

The purpose of our study is to determine a threshold for iodine quantification to distinguish definitely non-enhancing benign renal lesions from potential enhancing masses on portal venous dual-layer spectral computed tomography (CT) to reduce the need for additional multiphase CT. In this single-center retrospective study, patients (≥18 years) scanned between April 2021 and January 2023 following the local renal CT protocol were included. Exclusion criteria were patients without renal lesions, lesions smaller than 10 mm, only fat-containing lesions, abscesses or infarction, follow-up after radiofrequent ablation, wrong scan protocol, or artefacts. Scans were performed on a dual layer detector-based spectral CT (CT 7500, Philips Healthcare, Best, The Netherlands). Iodine concentration (mgI/mL) in renal lesions was determined using spectral data. Analyses were performed for all lesions and for lesions of >30 HU on portal venous CT. Enhancement on multiphase CT (≥20 ΔHU from true unenhanced (TUE) to portal venous phase (PVP) CT) was used as reference standard. To determine thresholds for iodine concentration receiver operating characteristic (ROC) curves, area under the curve (AUC) and 95% confidence intervals were calculated. To obtain thresholds for definite (non-)enhancement, 100% sensitivity with maximum specificity and 100% specificity with maximum sensitivity were noted. Data were measured using one reader. To assess interobserver agreement, a second reader performed measurements on the PVP CT scans. A total of 103 patients (62 years ± 14, 68 men) were included. We measured 328 renal lesions, 56 enhancing lesions (17%) in 38 patients and 272 non-enhancing lesions (83%) in 86 patients. The threshold for non-enhancing lesions was 0.76 mgI/mL or lower (100% sensitivity, 76% specificity). The threshold for a definite enhancing mass was 1.69 mgI/mL or higher (100% specificity, 78% sensitivity). A total of 77% of indeterminate lesions (>30 HU on PVP CT) in our study could be definitely characterized. Renal lesions can be definitively classified as non-enhancing or enhancing on PVP spectral CT using thresholds of 0.76 mgI/mL or 1.69 mgI/mL, respectively, eliminating the need for multiphase imaging.

Renal protection CT protocol using low-dose and low-concentration iodine contrast medium in at-risk patients of HCC and with chronic kidney disease: a randomized controlled non-inferiority trial

BackgroundAlthough efforts have been made to reduce the dose of Contrast Medium (CM) to improve patient safety, there are ongoing concerns regarding its potential effects on image quality and diagnostic performance. Moreover, research is lacking to establish a lower limit for safe and effective CM dose reduction. To determine whether the image quality of contrast-enhanced liver computed tomography (CT) using a reduced amount of iodinated CM was similar to that of standard liver CT.MethodsWe enrolled participants at risk for hepatocellular carcinoma with decreased estimated glomerular filtration rates (< 60 mL/min/1.73m2). Participants were randomly assigned to the standard group or the renal protection protocol (RPP) group. In the standard group, images were reconstructed using hybrid iterative reconstruction (iDose), while in the RPP group, low monoenergetic (50-keV) images and deep learning (DL)-based iodine-boosting reconstruction were used. Four radiologists independently assessed image quality and lesion conspicuity.ResultsFifty-two participants were assigned to the standard (n = 25) or RPP (n = 27) groups. The iodine load was significantly lower in the RPP group than in the standard group (301.5 ± 1.71 vs. 524 ± 7.37 mgI/kg, P < 0.001). The 50-keV and DL-based iodine-boosting images from the RPP group exhibited higher image contrast than those from the standard group during arterial (3.60 ± 0.65, 3.75 ± 0.60, and 3.09 ± 0.43, respectively) and portal venous phases (4.01 ± 0.49, 3.86 ± 0.42, and 3.21 ± 0.31, respectively) (P < 0.05 for all). Overall image quality was superior in the RPP group (P < 0.05 for all). No significant difference in lesion conspicuity was observed (P > 0.017).ConclusionsThe reduction in image contrast and overall image quality caused by decreased CM can be restored using either low monoenergetic imaging or DL-based iodine-boosting reconstruction.Trial registrationclinicaltrials.gov, NCT04024514, Registered July 18, 2019, prospectively registered, https://classic.clinicaltrials.gov/ct2/show/NCT04024514.

Comparison of true non-contrast and virtual non-contrast images in the characterization of renal lesions using detector-based spectral CT.

Renal lesions are sometimes incidentally detected during computed tomography (CT) examinations in which an unenhanced series is not included, preventing the lesions from being fully characterized. The aim of this study was to investigate the feasibility to use virtual non-contrast (VNC) images, acquired using a detector-based dual-energy CT, for the characterization of renal lesions. Twenty-seven patients (12 women) underwent a renal CT scan, including a non-contrast, an arterial, and a venous phase contrast-enhanced series, using a detector-based dual-energy CT scanner. VNC images were reconstructed from the venous contrast-enhanced series. The mean attenuation values of 65 renal lesions in both the VNC and true non-contrast (TNC) images were measured and compared quantitatively. Three radiologists blindly assessed all lesions using either VNC or TNC images in combination with contrast-enhanced images. Sixteen patients had cystic lesions, five had angiomyolipoma (AML), and six had suspected renal cell carcinomas (RCC). Attenuation values in VNC and TNC images were strongly correlated (ρ = 0.7, mean difference -6.0 ± 13 HU). The largest differences were found for unenhanced high-attenuation lesions. Radiologists classified 86% of the lesions correctly using VNC images. In 70% of the patients, incidentally detected renal lesions could be accurately characterized using VNC images, resulting in less patient burden and a reduction in radiation exposure. This study shows that renal lesions can be accurately characterized using VNC images acquired by detector-based dual-energy CT, which is in agreement with previous studies using dual-source and rapid X-ray tube potential switching technique.

MR Virtual Biopsy of Solid Renal Masses: An Algorithmic Approach.

Between 1983 and 2002, the incidence of solid renal tumors increased from 7.1 to 10.8 cases per 100,000. This is in large part due to the increase in the volume of ultrasound and cross-sectional imaging, although a majority of solid renal tumors are still found incidentally. Ultrasound and computed tomography (CT) have been the mainstay of renal mass screening and diagnosis but recent advances in magnetic resonance (MR) technology have made this the optimal choice when diagnosing and staging renal tumors. Our purpose in writing this review is to survey the modern MR imaging approach to benign and malignant solid renal tumors, consolidate the various imaging findings into an easy-to-read reference, and provide an imaging-based, algorithmic approach to renal mass characterization for clinicians. MR is at the forefront of renal mass characterization, surpassing ultrasound and CT in its ability to describe multiple tissue parameters and predict tumor biology. Cutting-edge MR protocols and the integration of diagnostic algorithms can improve patient outcomes, allowing the imager to narrow the differential and better guide oncologic and surgical management.

Impact of deep learning-based image reconstruction on image quality and lesion visibility in renal computed tomography at different doses.

Numerous computed tomography (CT) image reconstruction algorithms have been developed to improve image quality, and high-quality renal CT images are crucial to clinical diagnosis. This study evaluated the image quality and lesion visibility of deep learning-based image reconstruction (DLIR) compared with adaptive statistical iterative reconstruction-Veo (ASiR-V) in contrast-enhanced renal CT at different reconstruction strengths and doses. From January 2020 to May 2021, we prospectively included 101 patients who underwent renal contrast-enhanced CT scanning (69 at 120 kV; 32 at 80 kV). All image data were reconstructed with ASiR-V (30% and 70%) and DLIR at low, medium, and high reconstruction strengths (DLIR-L, DLIR-M, and DLIR-H, respectively). The CT number, noise, noise reduction rate (NRR), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), overall image quality, and the proportion of acceptable images were compared. Lesions of DLIR groups were evaluated, and the conspicuity-to-noise ratio (C/N) was calculated. Quantitative values (noise, SNR, CNR, and NRR) significantly differed between all reconstructions at 120 and 80 kV (P<0.001) and between each reconstruction, except ASiR-V 70% vs. DLIR-M. At 120 kV, the overall image quality and the proportion of acceptable images significantly differed between all reconstructions (P<0.001) and between each reconstruction, except ASiR-V 30% vs. DLIR-L and ASiR-V 70% vs. DLIR-M. At 80 kV, the overall image quality significantly differed between all reconstructions (P<0.001) and between each reconstruction, except between ASiR-V 30%, ASiR-V 70%, and DLIR-L. Quantitative and qualitative values were highest in DLIR-H, while the values were close in DLIR-H (80 kV) vs. ASiR-V 70% (120 kV) and DLIR-M (80 kV) vs. ASiR-V 30% (120 kV). The lesion conspicuity and noise significantly differed in DLIR at 120 kV and 80 kV (P<0.001). C/N significantly differed in DLIR at 120 kV (P<0.001) but not at 80 kV. DLIR-L and DLIR-M exhibited much-improved lesion display (C/N >1), and DLIR-H exhibited much-improved noise (C/N <1) at 120 kV. DLIR significantly improved the image quality and lesion visibility of renal CT compared with ASiR-V, even at a low dose.

A framework to distinguish healthy/cancer renal CT images using the fused deep features.

Cancer happening rates in humankind are gradually rising due to a variety of reasons, and sensible detection and management are essential to decrease the disease rates. The kidney is one of the vital organs in human physiology, and cancer in the kidney is a medical emergency and needs accurate diagnosis and well-organized management. The proposed work aims to develop a framework to classify renal computed tomography (CT) images into healthy/cancer classes using pre-trained deep-learning schemes. To improve the detection accuracy, this work suggests a threshold filter-based pre-processing scheme, which helps in removing the artefact in the CT slices to achieve better detection. The various stages of this scheme involve: (i) Image collection, resizing, and artefact removal, (ii) Deep features extraction, (iii) Feature reduction and fusion, and (iv) Binary classification using five-fold cross-validation. This experimental investigation is executed separately for: (i) CT slices with the artefact and (ii) CT slices without the artefact. As a result of the experimental outcome of this study, the K-Nearest Neighbor (KNN) classifier is able to achieve 100% detection accuracy by using the pre-processed CT slices. Therefore, this scheme can be considered for the purpose of examining clinical grade renal CT images, as it is clinically significant.

Establishment of diagnostic reference levels in low-dose renal computed tomography.

Increased radiation doses from computed tomography (CT) examinations is well known with proven risks of inducing cancers for effective dose >100 mSv (according to some studies >50 mSvs). To establish the diagnostic reference level (DRL) for low-dose renal CT examinations in the evaluation of renal stones. Patient demographics, CT parameters, and dosimetric indices (CTDIvol and dose length product [DLP]) were collected from 12 tertiary hospitals that routinely perform renal CT in the detection and evaluation of renal stones over a period of 12 weeks. Data obtained from 1418 average-sized patients in each category were recorded. The median values of dosimetric indices for each site were calculated. The DRL values were defined as the 75th percentile of the distribution of the median values of CTDIvol and DLP. There were no significant differences between patient demographics. Mean kVp and mAs for protocols were 121.67 ± 11.56 and 226.91 ± 78.44, respectively. The CTDIvol values were in the range of 2-36.2 mGy, while the DLP values were in the range of 43-1942 mGy.cm. The DRL for the CTDIvol was 16.15 mGy and for the DLP 851.77 mGy.cm. The local median values of CDTIvol and DLP are higher than DRL in two hospitals. Comparison of local median values of CDTIvol and DLP with DRL suggests the needs of an optimization strategy in some hospitals.

Impact of deep learning-based image reconstruction on image quality compared with adaptive statistical iterative reconstruction-Veo in renal and adrenal computed tomography.

OBJECTIVE: To evaluate image quality of deep learning-based image reconstruction (DLIR) in contrast-enhanced renal and adrenal computed tomography (CT) compared with adaptive statistical iterative reconstruction-Veo (ASiR-V).METHODS: We prospectively recruited 52 patients. All images were reconstructed with ASiR-V 30%, ASiR-V 70%, and DLIR at low, medium, and high reconstruction strengths. CT number, noise, noise reduction rate, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured and calculated within the region of interest (ROI) on subcutaneous fat, bilateral renal cortices, renal medulla, renal arteries, and adrenal glands. For qualitative analyses, the differentiation of the renal cortex and medulla, conspicuity of the adrenal gland boundary, sharpness, artifacts, and subjective noise were assessed. The overall image quality was calculated on a scale from 0 (worst) to 15 (best) based on the five values above and the score≥9 was acceptable.RESULTS: CT number does not significantly differ between the reconstruction datasets. Noise does not significantly differ between ASiR-V 30% and DLIR-L, but it is significantly lower using ASiR-V 70%, DLIR-M, and DLIR-H. The noise reduction rate relative to ASiR-V 30% is significantly different between the DLIR groups and ASiR-V 70%, and DLIR-H yields the highest noise reduction rate (61.6%). SNR and CNR are higher for DLIR-M, DLIR-H, and ASiR-V 70% than for ASiR-V 30% and DLIR-L. DLIR-H shows the best SNR and CNR. The overall image quality yields the same pattern for DLIR-H, with the highest score. Percentages of cases with overall image quality score≥9 are 100% (DLIR-H), 94.23% (DLIR-M), 90.38% (ASiR-V70%), 67.31% (DLIR-L), and 63.46% (ASiR-V30%), respectively.CONCLUSIONS: DLIR significantly improved the objective and subjective image quality of renal and adrenal CTs, yielding superior noise reduction compared with ASiR-V.

Mayer-Rokitansky-Küster-Hauser Syndrome

The association of renal abnormalities with Mullerian anomalies is well recognized and assessment of the renal tract forms part of the routine assessment of patients presenting with Mullerian anomalies. This is a case of a 20-year-old nulliparous female who had abdominopelvic ultrasonography on account of primary amenorrhea showing bilateral ectopic kidney fused in the pelvis, complete absence of the uterus, the cervix, and the upper part of the vagina; while normal sized ovaries seen. These were confirmed on computed tomography (CT). Renal CT angiograms showed a renal artery arising anteriorly from the abdominal aorta just above its bifurcation into the common iliac arteries. An interesting feature in this patient was the existence of the median sacral artery in the blood supply of the fused pelvic kidney. No skeletal anomalies were visualized in dorsolumbar spine or pelvis.

Diagnostic sensitivity and specificity of CT angiography for renal artery stenosis in children.

Children with suspected renal artery stenosis (RAS) are screened with renal Doppler ultrasonography or computed tomography (CT) angiography/magnetic resonance (MR) angiography depending on institutional preference. CT angiography produces images with superior resolution, allowing higher quality multiplanar two-dimensional reformats and three-dimensional reconstructions. However, there is a paucity of data in the literature regarding the utility and diagnostic performance of renal CT angiography in pediatric RAS. The objective of this study is to retrospectively review our experience with renal CT angiography in the diagnosis of pediatric RAS relative to digital subtraction angiography (DSA) as the reference standard. All patients 0-18years of age who underwent CT angiography for evaluation of RAS as a cause of hypertension between January 2012 and May 2019 were identified for the study. A total of 131 patients were identified, 23 of whom had DSA correlation. Twenty-three patients (17 boys, 6 girls) with a mean age of 6years 3months (range: 3months to 14years 7months) were included in this study. Of the 59 renal arteries studied by DSA, 22 were abnormal on CT angiography and 20 were abnormal on DSA. Of the 59 renal arteries, CT angiography was true positive in 18 and true negative in 35. The sensitivity and specificity of CT angiography for RAS diagnosis were 90.0% and 89.7%, respectively. CT angiography identified all cases of main RAS. Renal CT angiography has a high sensitivity and specificity for pediatric RAS diagnosis in patients referred for DSA.

Estimation of Renal Function Using Unenhanced Computed Tomography in Upper Urinary Tract Stones Patients.

Objectives: The aim of this study was to determine whether unenhanced computed tomography (CT) imaging can estimate differential renal function (DRF) in patients with chronic unilateral obstructive upper urinary tract stones.Materials and Methods: This was a single-center retrospective study of 76 patients. All the patients underwent unenhanced CT and nuclear renography (RG) at an interval of 4 to 6 weeks due to chronic unilateral obstructive urinary stones. Renal CT measurements (RCMs), including residual parenchymal volume (RPV) and volumetric CT texture analysis parameters, were obtained through a semiautomatic method. Percent RCMs were calculated and compared with renal function determined by RG.Results: The strongest Pearson coefficient between percent RCM and DRF was reflected by RPV (r = 0.957, P < 0.001). Combinations of RPV and other parameters did not significantly improve the correlation compared with RPV alone (r = 0.957 vs. r = 0.957, 0.957, 0.887, 0.815, and 0.956 for combination with Hounsfield unit, parenchymal voxel, skewness, kurtosis, and entropy, respectively; all P < 0.001). Percent RPV was subsequently introduced into linear regression, and the equation y = −2.66 + 1.07* × (P < 0.001) was derived to calculate predicted DRF. No statistically difference was found between predicted DRF using the equation and observed DRF according to RG (P = 0.959).Conclusion: Unenhanced CT imaging can estimate DRF in patients with chronic unilateral obstructive upper urinary tract stones, and RG might not be necessary as a conventional method in clinical.

Tissue-specific and interpretable sub-segmentation of whole tumour burden on CT images by unsupervised fuzzy clustering

Background:Cancer typically exhibits genotypic and phenotypic heterogeneity, which can have prognostic significance and influence therapy response. Computed Tomography (CT)-based radiomic approaches calculate quantitative features of tumour heterogeneity at a mesoscopic level, regardless of macroscopic areas of hypo-dense (i.e., cystic/necrotic), hyper-dense (i.e., calcified), or intermediately dense (i.e., soft tissue) portions. Method:With the goal of achieving the automated sub-segmentation of these three tissue types, we present here a two-stage computational framework based on unsupervised Fuzzy C-Means Clustering (FCM) techniques. No existing approach has specifically addressed this task so far. Our tissue-specific image sub-segmentation was tested on ovarian cancer (pelvic/ovarian and omental disease) and renal cell carcinoma CT datasets using both overlap-based and distance-based metrics for evaluation. Results:On all tested sub-segmentation tasks, our two-stage segmentation approach outperformed conventional segmentation techniques: fixed multi-thresholding, the Otsu method, and automatic cluster number selection heuristics for the K-means clustering algorithm. In addition, experiments showed that the integration of the spatial information into the FCM algorithm generally achieves more accurate segmentation results, whilst the kernelised FCM versions are not beneficial. The best spatial FCM configuration achieved average Dice similarity coefficient values starting from 81.94±4.76 and 83.43±3.81 for hyper-dense and hypo-dense components, respectively, for the investigated sub-segmentation tasks. Conclusions:The proposed intelligent framework could be readily integrated into clinical research environments and provides robust tools for future radiomic biomarker validation.

Establishment of a novel system for the preoperative prediction of adherent perinephric fat (APF) occurrence based on a multi-mode and multi-parameter analysis of dual-energy CT.

Adherent perinephric fat (APF) is evaluated preoperatively with the Mayo adhesive probability (MAP) scoring system using conventional single-form computed tomography (CT) images. An objective or quantitative indicator for predicting APF is urgently needed for clinical application. A total of 150 patients with renal tumours who underwent laparoscopic partial nephrectomy (LPN) were retrospectively enrolled and divided into the APF group (n=100) and the non-APF group (n=50) according to surgical results. All patients underwent a renal contrast-enhanced dual-energy CT (DECT) scan. The obtained CT DICOM data were transmitted to the DECT post-processing workstation and adopted virtual non-contrast (VNC), Rho/Z Maps, and Monoenergetic Plus (mono+) modes separately to undergo a multi-parameter analysis. A logistic stepwise investigation was utilized to analyse the related risk factors. The cutoff value was determined by the Youden index. Fifty patients were prospectively enrolled to validate the constructed model. The area under the curve (AUC), sensitivity, specificity and accuracy of the model were calculated. The study demonstrated that age, sex, body mass index (BMI), smoking status, tumour diameter, exophytic status, degree of malignancy and posterior perinephric fat thickness were related to the occurrence of APF (P<0.05). Model 1 was selected with the contrast material (CM) parameter (cutoff point 0.5), model 2 was selected with the effective atomic number (Zeff) parameter (cutoff point 6.5), and model 3 was selected with the slope K (K) parameter (cutoff point -0.95). The AUC, sensitivity, specificity and accuracy of model 1 were 0.94, 0.94, 0.93 and 0.94, respectively; for model 2, they were 0.94, 0.93, 0.93 and 0.96, respectively; and for model 3, they were 0.92, 0.92, 0.93 and 0.92, respectively. Multi-mode and multi-parameter models of DECT can effectively be used to predict the occurrence of APF.