Contrast CT Images Research Articles

Exploring the Impact of Model Complexity on Laryngeal Cancer Detection.

Background: Laryngeal cancer accounts for a third of all head and neck malignancies, necessitating timely detection for effective treatment and enhanced patient outcomes. Machine learning shows promise in medical diagnostics, but the impact of model complexity on diagnostic efficacy in laryngeal cancer detection can be ambiguous. Methods: In this study, we examine the relationship between model sophistication and diagnostic efficacy by evaluating three approaches: Logistic Regression, a small neural network with 4 layers of neurons and a more complex convolutional neural network with 50 layers and examine their efficacy on laryngeal cancer detection on computed tomography images. Results: Logistic regression achieved 82.5% accuracy. The 4-Layer NN reached 87.2% accuracy, while ResNet-50, a deep learning architecture, achieved the highest accuracy at 92.6%. Its deep learning capabilities excelled in discerning fine-grained CT image features. Conclusion: Our study highlights the choices involved in selecting a laryngeal cancer detection model. Logistic regression is interpretable but may struggle with complex patterns. The 4-Layer NN balances complexity and accuracy. ResNet-50 excels in image classification but demands resources. This research advances understanding affect machine learning model complexity could have on learning features of laryngeal tumor features in contrast CT images for purposes of disease prediction.

Morphological evaluation for the narrowest section of the patent ductus arteriosus in infants by CT: a crucial point for device closure.

The minimum diameter of the patent ductus arteriosus measured in the lateral angiographic view is usually used to determine the device size. Sometimes the device can be easily removed from the patent ductus arteriosus, even if it appears to be the optimum size. From 2016 to 2021, 29 patients who underwent contrast-enhanced CT prior to patent ductus arteriosus closure included. Morphological evaluation of the narrowest part of the patent ductus arteriosus was performed on contrast-enhanced CT. We also examined whether there were differences in morphology depended on Krichenko classification, age, and the diameter of the narrowest portion of the patent ductus arteriosus. At the time of treatment, the median age was 4.8 (range, 1-52) months, and the median weight was 5.0 (2.5-12.7) kg. The median minimum vertical diameter of patent ductus arteriosus was 2.9 (1.6-6.6) mm. The narrowest patent ductus arteriosus part in the contrast CT imaging showed horizontal-to-vertical diameter ratios in the range of 1.0-1.7, with no case where the vertical diameter was larger than the horizontal diameter. The median horizontal-to-vertical diameter ratio by Krichenko type was: A, 1.22; C, 1.29; E, 1.62(p = 0.017). When classifying the patients into a group aged under six months (n = 21) and a group aged six months or older (n = 8), the respective median horizontal-to-vertical diameter ratio was 1.34 and 1.15 (p = 0.027). The vertical patent ductus arteriosus diameter was not correlated with the elliptical shape. Most patent ductus arteriosus cases have a horizontally oriented elliptical shape in this study. This characteristic showed high reproducibility and is important information that angiography cannot evaluate.

Gamma‐ray‐induced modification of poly(ethylene terephthalate) and polypropylene solid materials with halogen‐containing olefins for X‐ray CT image contrast enhancement

AbstractIt is known that X‐ray CT of organic polymer materials usually provides low‐contrast images because of the low X‐ray absorption of these materials. In this paper, we describe a new method to enhance the contrast of X‐ray CT images of organic polymer materials. We demonstrate that gamma‐ray irradiation of poly (ethylene terephthalate) (PET) fibers in dichloromethane containing 2‐bromoethyl methacrylate, 2‐chloroethyl methacrylate, or 4‐bromostyrene results in the grafting of halogen‐containing oligomeric chains onto the PET fibers. Focused ion beam scanning electron microscopy (FIB‐SEM) and time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS) analyses revealed a homogeneous distribution of bromine atoms within the 2‐bromoethyl methacrylate‐modified PET fibers, not only on the surface. This uniform incorporation translated to a significant improvement in X‐ray CT image contrast compared with pristine fibers. Similar treatment of polypropylene pellets also enhanced the contrast of their X‐ray CT images. This is a new simple and effective method to enhance the X‐ray CT image contrast and potentially applicable to various polymer materials including polymer composites and porous polymer materials, readily allowing the observation of their 3D microstructures finely.

CT myelography by intrathecal injection of contrast medium though percutaneous administration route visualizes compressed cervical spinal cord in a mouse

BackgroundChronic compressive myelopathy (CCM) is a major cause of spinal cord disorders in the elderly, in which the spinal cord is compressed by bony or soft tissue structures. Although computed tomography myelography (CTM) has been clinically used for the diagnosis of CCM, a method of CTM in rodents remains to be developed. New methodA 50 μl Hamilton syringe attached to a disposable needle was percutaneously inserted into the subarachnoid space (cisterna magna) between the occipital bone and C1 lamina in an anesthetized adult mouse, followed by the injection of contrast medium and CT imaging. ResultsCTM clearly visualized the shape of the spinal cord of intact mice and tiptoe-walking Yoshimura (Twy) mice without any health issues. Comparison with existing method(s)Unlike histology, the current method functions in live mice, directly depicts the compressed spinal cord, and provides clinically related image information. Furthermore, the intrathecal administration of contrast medium through the percutaneous route makes CTM less invasive and takes less time than a conventional intrathecal injection method. ConclusionsThe CTM method used in the present study enables clear visualization of the shape of the dural sac and spinal cord and is useful when conducting experiments on CCM and other spinal diseases in rodents.

Integrating muscle fiber orientation from visible human data into radiotherapy target volumes.

Objective.A major challenge in treatment of tumors near skeletal muscle is defining the target volume for suspected tumor invasion into the muscle. This study develops a framework that generates radiation target volumes with muscle fiber orientation directly integrated into their definition. The framework is applied to nineteen sacral tumor patients with suspected infiltration into surrounding muscles.Approach.To compensate for the poor soft-tissue contrast of CT images, muscle fiber orientation is derived from cryo-images of two cadavers from the human visible project (VHP). The approach consists of (a) detecting image gradients in the cadaver images representative of muscle fibers, (b) mapping this information onto the patient image, and (c) embedding the muscle fiber orientation into an expansion method to generate patient-specific clinical target volumes (CTV). The validation tested the consistency of image gradient orientation across VHP subjects for the piriformis, gluteus maximus, paraspinal, gluteus medius, and gluteus minimus muscles. The model robustness was analyzed by comparing CTVs generated using different VHP subjects. The difference in shape between the new CTVs and standard CTV was analyzed for clinical impact.Main results.Good agreement was found between the image gradient orientation across VHP subjects, as the voxel-wise median cosine similarity was at least 0.86 (for the gluteus minimus) and up to 0.98 for the piriformis. The volume and surface similarity between the CTVs generating from different VHP subjects was on average at least 0.95 and 5.13 mm for the Dice Similarity Coefficient and the Hausdorff 95% Percentile Index, showing excellent robustness. Finally, compared to the standard CTV with different margins in muscle and non-muscle tissue, the new CTV margins are reduced in muscle tissue depending on the chosen clinical margins.Significance.This study implements a method to integrate muscle fiber orientation into the target volume without the need for additional imaging.

New liver window width in detecting hepatocellular carcinoma on dynamic contrast-enhanced computed tomography with deep learning reconstruction

Changing a window width (WW) alters appearance of noise and contrast of CT images. The aim of this study was to investigate the impact of adjusted WW for deep learning reconstruction (DLR) in detecting hepatocellular carcinomas (HCCs) on CT with DLR. This retrospective study included thirty-five patients who underwent abdominal dynamic contrast-enhanced CT. DLR was used to reconstruct arterial, portal, and delayed phase images. The investigation of the optimal WW involved two blinded readers. Then, five other blinded readers independently read the image sets for detection of HCCs and evaluation of image quality with optimal or conventional liver WW. The optimal WW for detection of HCC was 119 (rounded to 120 in the subsequent analyses) Hounsfield unit (HU), which was the average of adjusted WW in the arterial, portal, and delayed phases. The average figures of merit for the readers for the jackknife alternative free-response receiver operating characteristic analysis to detect HCC were 0.809 (reader 1/2/3/4/5, 0.765/0.798/0.892/0.764/0.827) in the optimal WW (120 HU) and 0.765 (reader 1/2/3/4/5, 0.707/0.769/0.838/0.720/0.791) in the conventional WW (150 HU), and statistically significant difference was observed between them (p < 0.001). Image quality in the optimal WW was superior to those in the conventional WW, and significant difference was seen for some readers (p < 0.041). The optimal WW for detection of HCC was narrower than conventional WW on dynamic contrast-enhanced CT with DLR. Compared with the conventional liver WW, optimal liver WW significantly improved detection performance of HCC.

Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm.

CT-image segmentation for liver and hepatic vessels can facilitate liver surgical planning. However, time-consuming process and inter-observer variations of manual segmentation have limited wider application in clinical practice. Our study aimed to propose an automated deep learning (DL) segmentation algorithm for liver and hepatic vessels on portal venous phase CT images. This retrospective study was performed to develop a coarse-to-fine DL-based algorithm that was trained, validated, and tested using private 413, 52, and 50 portal venous phase CT images, respectively. Additionally, the performance of the DL algorithm was extensively evaluated and compared with manual segmentation using an independent clinical dataset of preoperative contrast-enhanced CT images from 44 patients with hepatic focal lesions. The accuracy of DL-based segmentation was quantitatively evaluated using the Dice Similarity Coefficient (DSC) and complementary metrics [Normalized Surface Dice (NSD) and Hausdorff distance_95 (HD95) for liver segmentation, Recall and Precision for hepatic vessel segmentation]. The processing time for DL and manual segmentation was also compared. Our DL algorithm achieved accurate liver segmentation with DSC of 0.98, NSD of 0.92, and HD95 of 1.52mm. DL-segmentation of hepatic veins, portal veins, and inferior vena cava attained DSC of 0.86, 0.89, and 0.94, respectively. Compared with the manual approach, the DL algorithm significantly outperformed with better segmentation results for both liver and hepatic vessels, with higher accuracy of liver and hepatic vessel segmentation (all p<0.001) in independent 44 clinical data. In addition, the DL method significantly reduced the manual processing time of clinical postprocessing (p<0.001). The proposed DL algorithm potentially enabled accurate and rapid segmentation for liver and hepatic vessels using portal venous phase contrast CT images.

Clinical validation of commercial deep-learning based auto-segmentation models for organs at risk in the head and neck region: a single institution study.

To evaluate organ at risk (OAR) auto-segmentation in the head and neck region of computed tomography images using two different commercially available deep-learning-based auto-segmentation (DLAS) tools in a single institutional clinical applications. Twenty-two OARs were manually contoured by clinicians according to published guidelines on planning computed tomography (pCT) images for 40 clinical head and neck cancer (HNC) cases. Automatic contours were generated for each patient using two deep-learning-based auto-segmentation models-Manteia AccuContour and MIM ProtégéAI. The accuracy and integrity of autocontours (ACs) were then compared to expert contours (ECs) using the Sørensen-Dice similarity coefficient (DSC) and Mean Distance (MD) metrics. ACs were generated for 22 OARs using AccuContour and 17 OARs using ProtégéAI with average contour generation time of 1 min/patient and 5 min/patient respectively. EC and AC agreement was highest for the mandible (DSC 0.90 ± 0.16) and (DSC 0.91 ± 0.03), and lowest for the chiasm (DSC 0.28 ± 0.14) and (DSC 0.30 ± 0.14) for AccuContour and ProtégéAI respectively. Using AccuContour, the average MD was<1mm for 10 of the 22 OARs contoured, 1-2mm for 6 OARs, and 2-3mm for 6 OARs. For ProtégéAI, the average mean distance was<1mm for 8 out of 17 OARs, 1-2mm for 6 OARs, and 2-3mm for 3 OARs. Both DLAS programs were proven to be valuable tools to significantly reduce the time required to generate large amounts of OAR contours in the head and neck region, even though manual editing of ACs is likely needed prior to implementation into treatment planning. The DSCs and MDs achieved were similar to those reported in other studies that evaluated various other DLAS solutions. Still, small volume structures with nonideal contrast in CT images, such as nerves, are very challenging and will require additional solutions to achieve sufficient results.

Difficult Intravenous Access Requiring Ultrasound in the Emergency Department: Associations With Delays in Care and Areas for Quality Improvement.

Background Patients with difficult intravenous access (DIVA) requiring ultrasound-guided intravenous (USGIV) access have been associated with delays in treatment, imaging, and disposition in academic emergency department (ED) patient populations. Our objective in this study was to characterize differences in time to intravenous access, imaging, and disposition between patients with DIVA versus those without DIVA requiring USGIV access in a community ED while also assessing for DIVA-associated comorbidities. Methods A cross-sectional, observational analysis was performed on admitted ED patients evaluated from September 2to September 31, 2022, at a community ED. Patients with DIVA were defined as patients with two failed attempts at traditional intravenous placement. These patients require USGIV placement per institutional protocol. Patients younger than 18 years of age, trauma admissions, repeated visits from the same patient, patients with missing data, and direct hospital admissions were excluded. Continuous variables were recorded with medians and included ED throughput measures of time to vascular access, contrast CT imaging, and disposition. Differences in median times between DIVA patients versus non-DIVA patients were assessed with the Mann-Whitney U-test. Categorical data involving comorbidities were reported as percentages, and differences in proportions between DIVA versus non-DIVA patients were assessed via chi-square tests. Multivariate logistic regression analysis evaluated for correlations between DIVA and times to access, contrast CT imaging, disposition, and significant covariates while adjusting for demographic information. Results A total of 1250 patients were included in this investigation (5.8% associated with DIVA requiring USGIV access). The median age of all subjects was 69 (interquartile range = 58, 79) with no significant difference between the DIVA and non-DIVA groups. Patients with DIVA were more likely to be female in comparison to patients without DIVA (65.3% and 51.2%, respectively, p < 0.05). Patients with a history of end-stage renal disease (ESRD) (p < 0.001), intravenous drug use (IVDU) (p < 0.001), and venous thromboembolism (p < 0.05) had statistically significant associations with DIVA. On regression analysis, patients with DIVA were more likely to have a history of ESRD with an odds ratio (OR) of 3.56 (95% confidence interval (CI): 1.62-7.81) and a history of IVDU with an OR of 14.29 (95% CI: 5.17-39.54). Patients with DIVA were associated with statistically significant greater median times to vascular access, contrast CT imaging, and disposition (p < 0.001 for time to access and disposition and p < 0.01 for time to contrast CT imaging). Conclusion In this study, DIVA cases requiring USGIV access were positively associated with significantly longer times to access, contrast CT imaging, and disposition compared to patients without DIVA at our community ED. Comorbidities such as IVDU and ESRD had statistically significant associations with DIVA requiring USGIV access.

Incremental prognostic value of intensity-weighted regional calcification scoring using contrast CT imaging in TAVR.

Aortic valve calcification scoring plays an important role in predicting outcomes of transcatheter aortic valve replacement (TAVR). However, the impact of relative calcific density and its causal effect on peri-procedural complications due to sub-optimal valve expansion remains limited. This study aims to investigate the prognostic power of quantifying regional calcification in the device landing zone in the context of peri-procedural events and post-procedural complications based on pre-operative contrast computed tomography angiography (CCTA) images. Assess the effect of calcification on post-procedural device expansion and final configuration. We introduce a novel patient invariant topographic scheme for quantifying the location and relative density of landing zone calcification. The calcification was detected on CCTA images based on a recently developed method using automatic minimization of the false positive rate between aortic lumen and calcific segments. Multinomial logistic regression model evaluation and ROC curve analysis showed excellent classification power for predicting paravalvular leakage [area under the curve (AUC) = 0.8; P < 0.001] and balloon pre-dilation (AUC = 0.907; P < 0.001). The model exhibited an acceptable classification ability for left bundle branch block (AUC = 0.748; P < 0.001) and balloon post-dilation (AUC = 0.75; P < 0.001). Notably, all evaluated models were significantly superior to alternative models that did not include intensity-weighted regional volume scoring. TAVR planning based on contrast computed tomography images can benefit from detailed location, quantity, and density contribution of calcific deposits in the device landing zone. Those parameters could be employed to stratify patients who need a more personalized approach during TAVR planning, predict peri-procedural complications, and indicate patients for follow-up monitoring.

CT-based radiomics model for the prediction of genomic alterations in renal cell carcinoma (RCC)

Abstract Background Radiogenomics is an emerging tool with applications in screening for molecular biomarkers in diagnostic and prognostic assessment through the extraction of quantitative data from medical images (Shui et al., Front Oncol 2021). In this study, we aim to develop machine learning (ML) models to predict the most common genomic alterations present in our subset of renal cell carcinoma patients (pts). Methods Retrospectively, pts with tissue genomic testing from CT-guided biopsy samples were identified. Genomic testing was done via GEM ExTra assay, a CAP-accredited, CLIA-certified test encompassing tumor whole exome sequencing and whole transcriptome sequencing (TGen; Phoenix, AZ). Biopsy sample collection sites were identified from pre-biopsy contrast CT images, and the lesions were segmented with ITK-SNAP software, from which 510 radiomic features were extracted with Pyradiomics. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select the most relevant features. Logistic regression (LR) and support vector machine (SVM) classifiers were built for the prediction of PBRM1, VHL, and SETD2 gene alterations. Multiple metrics were used to evaluate the predictive performance via leave-one-out cross-validation, including the area under the receiver operating characteristic (AUROC) and the area under the precision-recall curve (AUPRC). Feature importance was evaluated with Shapley additive explanations (SHAP) method. Results A total of 14 RCC pts (10:4 M:F) with genomic testing from CT-guided biopsies were identified. The majority of pts were White (85.7%) and had clear cell histology (71.4%). The most common locations for the CT-guided biopsy were lung (18.2%), soft tissue (13.6%), kidney (9.1%), and bone (9.1%). The most common alterations were seen in PBRM1 (50%), VHL (43.9%), and SETD2 (35.7%) genes. The PBRM1 gene was predicted with the highest AUROC (0.84) and AUPRC (0.88) with the SVM classifier, followed by the SETD2 gene (AUROC=0.78 and AUPRC=0.66) with LR classifier and VHL gene (AUROC=0.56 and AUPRC=0.65) with SVM classifier. Notably, all three models showed good sensitivity in classifying gene mutation status (PBRM1: 0.86; VHL: 0.88; SETD2: 0.89). Among all radiomic features, first-order features, Gray Level Size Zone Matrix features, and Gray Level Dependence Matrix features were found to be the most important features for predictions. Conclusions Using a CT-based radiomics analysis of the biopsy area, we showed that SVM and LR prediction models could predict PBRM1, VHL, and SETD2 mutations with high accuracy. These models may assist in identifying potentially actionable alterations and yield ease in treatment selection for RCC. Further extensive studies are warranted to validate our findings and improve our model. CDMRP DOD Funding: no

Predicting EGFR Mutation in Lung Adenocarcinoma: Development and Validation of the EGFR Mutation Predictive Score (EMPS) in Bali, Indonesia.

The examination of epidermal growth factor receptor (EGFR) mutations may not be routinely available to all patients due to the limited availability and the expensive price of the examination, especially in area with limited resources such as in Indonesia. Therefore, we aimed to build a nomogram to predict the EGFR mutation in patients with lung adenocarcinoma by incorporating significant clinical and radiological parameters. We conducted an age-matched case-control study using 160 treatment-naïve patients [80 patients with EGFR-mutated (EGFRmut) and 80 with EGFR-wild-type (EGFRwt)] with pathologically confirmed lung adenocarcinomas with tumor specimens available for genetic analysis taken from 2017 through 2021 in Bali, Indonesia. Radiomics features were extracted from contrast CT images. The cut-off of the tumor diameter was defined using Receiver Operating Characteristic Curve. A conditional logistic regression model was constructed to identify significant risk factors, and a nomogram was developed for predicting the risk of EGFR mutation. A cohort was done to validate the nomogram. Being female, never-smoker, having a smaller tumor diameter (<48.5mm), located in the upper lobe, have bubble-like lucency and air-bronchogram in the chest CT scan were identified as independent risk factors of EGFR mutation at the multivariate logistic regression model. The forming normogram model produced an area under the curve of 0.993 (95 % CI = 0.98-1.00) and 0.91 (95 % CI = 0.84-0.99) in development and validation group, respectively. The calibration curve showed good agreement between predicted and actual probability. At the cut-off point of the normogram score 246 shows a sensitivity of 97.5%, a specificity of 98.8%, a positive predictive value of 99.0%, and a negative predictive value of 96.8%. Our study indicated that the EGFR Mutation Normogram could provide a non-invasive way to predict the risk of EGFR mutation in patients with lung adenocarcinoma in clinical practice. This normogram need to be validated in other area in Indonesia.

Multi-enzyme mimetic iridium nanozymes-based thrombus microenvironment-modulated nanoplatform for enhanced thrombolytic therapy

Intravenous injection of thrombolytic drugs is the most feasible strategy for the clinical treatment of thrombotic diseases, however, its applications are yet limited by narrow therapeutic index, short half-life, and hemorrhagic risk. Inspired by the excessive ROS and inflammatory factors at the thrombus site, we manufactured a thrombus microenvironment-modulated nanosystem, which achieved the targeted delivery of thrombolytic drugs and ROS scavenging agents. Ir monomers (Ir-PVP) with enzyme-mimetic activity were prepared and then wrapped with lumbrokinase (LK) by HA materials of thrombo-inflammatory affinity, forming multifunctional thrombolytic agents Ir-LK@HA. Ir-LK@HA could prolong the biological half-life of LK and target the thrombus site to release LK with reduced bleeding risk. Moreover, Ir-NPs, as both CT imaging contrast agent and ROS scavenger, not only allowed us to observe the progression of thrombolytic treatment through CT imaging in real time, but also played an important role in elimination of excessive ROS at thrombus site. Taken together, the in vitro and in vivo experiments illustrated that Ir-LK@HA with CT function displayed an excellent efficiency in thrombolysis, anti-inflammation and ROS scavenging. Our work may encourage further exploration of thrombus microenvironment-modulated theranostic platforms.

#5276 DEVELOPMENT OF A MULTIMODAL “KIDNEY AGE” PREDICTION BASED ON AUTOMATIC SEGMENTATION CT IMAGE IN PATIENTS WITH NORMAL RENAL FUNCTION

Abstract Background and Aims Renal function, for decades, has been of interest to clinicians and researchers to describe. For example, serum creatinine (Scr) and estimated glomerulus filtration rate (eGFR) is familiar but also limited in many circumstances. Meanwhile, the physiological volumes of the kidney cortex and medulla are presumed to change with age and have been proven to change with decreasing kidney function. Method We recruited 182 patients with normal Scr levels and contrasted CT images between Oct. 2021 and Feb 2022 in Peking Union Medical College Hospital (PUMCH) with their demographic and clinical data. The automatic segmentation method was modified from U-NET and used for both cortex and medullary separation and their volume calculation, respectively. We combined the kidney volume and clinical data as multimodal features of the machine learning model. All the data were separated into a training dataset (PUMCH training set, 80%) and a test dataset (PUMCH test set, 20%). Besides, we included patients with the same inclusion criteria but with diabetes (PUMCH-DM test set) and diabetic nephropathy (PUMCH-DN test set) for internal comparison to verify the possible clinical value of “KIDNEY AGE” (K-AGE). Data from DongZhiMen Hospital (DZMH test set) was used as separate external validation sets to evaluate model generalizability. Results The PUMCH training set included 146 participants with a mean age of 47.5±7.4 years. 58.9% were female, and the mean Scr is 63.5±12.3 μmol/L. The PUMCH test set included 36 participants with a mean age of 47.1±7.9 years, 52.7% were female, and the mean Scr was 66.9±13.0 μmol/L. For segmented kidneys, volume differences between non-contrast-enhanced CT and non-contrast-enhanced CT in the left and right kidneys were not evident. The multimodal neural network predicted: K-AGE approximately close to the patient's actual physiological age, with 92% prediction within the 95% confidential interval (Figure 1). We also compared the K-AGE prediction in PUMCH, PUMCH-DM, and PUMCH-DN test sets by applying U-NET-nonCon segmentation algorithm in non-contrast CT images. The mean absolute error increases along with the disease process (control group 5.00, diabetes group 6.99, diabetic nephropathy group 9.32) (Figure 2). Conclusion We established a machine learning model for predicting the K-AGE of normal Scr patients.

Prediction of Tumour Site in Larynx Contrast CT Images by Radiomic Feature Analysis

Prediction of Tumour Site in Larynx Contrast CT Images by Radiomic Feature Analysis

Intravenous Versus Rectal Contrast in CT Imaging for Abdominal Gunshot Wounds.

CT imaging with rectal contrast historically has been a useful tool to help identify potential colon/rectal injuries; however, recent trends have shown less utilization of rectal contrast, in favor of IV contrast CT imaging alone. A retrospective review of patients with abdominal gunshot wounds was carried out to compare the two CT imaging techniques. An analysis of patients with colorectal injuries was conducted. Patients with IV contrast had a sensitivity of 84% and specificity of 96.8%. The PPV was 87.5% and NPV was 95.8%. In the IV and rectal contrast group, the sensitivity was 88.9% and specificity was 90.5%. The PPV was 80% and NPV was 95%. The proportion of missed injuries between the two was not statistically significant, p=0.18. The study suggests that while CT imaging with rectal contrast confidently identifies colon/rectal injuries, there are often secondary findings that will correctly prompt surgical exploration.

MP16-20 ASYMPTOMATIC SPONTANEOUS STONE PASSAGE IS FREQUENT IN RECURRENT KIDNEY STONE FORMERS: RESULTS OF THE PROSPECTIVE, RANDOMIZED NOSTONE TRIAL

You have accessJournal of UrologyCME1 Apr 2023MP16-20 ASYMPTOMATIC SPONTANEOUS STONE PASSAGE IS FREQUENT IN RECURRENT KIDNEY STONE FORMERS: RESULTS OF THE PROSPECTIVE, RANDOMIZED NOSTONE TRIAL Kevin Stritt, Olivier Bonny, Daniel Fuster, Andreas Christe, and Beat Roth Kevin StrittKevin Stritt More articles by this author , Olivier BonnyOlivier Bonny More articles by this author , Daniel FusterDaniel Fuster More articles by this author , Andreas ChristeAndreas Christe More articles by this author , and Beat RothBeat Roth More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003236.20AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Kidney stones are a common healthcare problem, and both prevalence and incidence are increasing worldwide. Kidney stone recurrences are frequent. Still, a number of kidney stones pass without symptoms and are not noticed by the patient. Data are limited regarding the frequency of asymptomatic spontaneous stone passage. The aim of the study is to evaluate the frequency of asymptomatic spontaneous stone passage and its predictive factors. METHODS: We analyzed the data from the NOSTONE trial, a prospective, multicenter, double-blind, placebo-controlled randomized trial to assess the efficacy of hydrochlorothiazide (HCT) in the prevention of recurrence in patients with recurrent calcium containing kidney stones. We identified all asymptomatic spontaneous stone passages by comparing the total number of kidney stones on low-dose non-intravenous contrast CT imaging at the beginning and at the end of the 3-year follow-up, taking into account the symptomatic spontaneous stone passages and the surgical stone removals. Association of independent variables identified in the data set and the number of asymptomatic spontaneous stone passages using linear regression analyses. RESULTS: A total of 416 patients were randomized and included in the NOSTONE trial, 5 outliers were removed, therefore we analyzed 411 patients. Median follow-up was 35 months (IQR: 29 – 41); median patient age was 49 years (IQR: 39 – 55); 84/411 (20%) patients were female. A total of 476 stone events occurred in 224 of 411 patients (55%); 245/476 (52%) were symptomatic spontaneous stone passages, 73/476 (15%) were surgical stone removals, and 158/476 (33%) were asymptomatic spontaneous stone passages. The median size of asymptomatic stones (2.4 mm; IQR: 1.95-3.4) and the size of symptomatic stones (2.15 mm; IQR: 1.68-2.79) that passed spontaneously were not significantly different (p=0.366). The number of asymptomatic spontaneous stone passages was significantly influenced by a higher number of stones on CT imaging at randomization (p=0.001) and a lower total stone volume (p=0.001). CONCLUSIONS: Stone recurrence in recurrent calcium containing kidney stone formers is high. A relevant number of stones, however, pass spontaneously, many of these being asymptomatic (39%). Predictive factors for asymptomatic spontaneous stone passages are the number and the total volume of stones. Our data presented should encourage urologists to counsel patients about the possibility of a conservative approach if kidney stones are present. Source of Funding: none © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e210 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Kevin Stritt More articles by this author Olivier Bonny More articles by this author Daniel Fuster More articles by this author Andreas Christe More articles by this author Beat Roth More articles by this author Expand All Advertisement PDF downloadLoading ...

An iterative method for simultaneous reduction on beam-hardening and scatter artifacts in x-ray CT

Beam-hardening and scatter are two significant factors leading to contrast reduction and gray value inaccuracy in CT images. The cupping artifacts and obscure boundaries in reconstructed images are also caused mainly by both beam-hardening and scattering. It is difficult to get high-quality CT images with only one of them to make correction. In this paper, we proposed an x-ray CT polychromatic attenuation model with scatter effect, and an iterative method for simultaneous reduction on beam-hardening and scatter artifacts. In this model, the measurements of the detector comprise two parts: an attenuation term and a scatter term. The former is defined by an exponential rational fraction to fit the traditional attenuation process, and the latter is defined by a convolutional scatter intensity. The coefficients of the rational fraction in the attenuation term and the scatter term kernel are all calculated from a calibration phantom which is scanned to get corresponding equations. Based on the polychromatic attenuation model, we proposed an iterative artifacts reduction method combining deconvolution technique with linearized back-projection (iDLB method). This method makes the nonlinear polychromatic attenuation model become easily solvable by linearizing transformation, which simplifies the residuals allocation process. Experiments of both numerical simulation and practical data show the iDLB method has the ability to reduce beam-hardening and scatter artifacts simultaneously, improve the contrast of CT images, and it is highly parallelized for lower computational cost.

Novel setup for rapid phase contrast CT imaging of heavy and bulky specimens.

This work introduces a novel setup for computed tomography of heavy and bulky specimens at the SYRMEP beamline of the Italian synchrotron Elettra. All the key features of the setup are described and the first application to off-center computed tomography scanning of a human chest phantom (approximately 45 kg) as well as the first results for vertical helical acquisitions arediscussed.

Automatic abdominal segmentation using novel 3D self-adjustable organ aware deep network in CT images

CT scan is an important reference means of disease diagnosis in practice. Automatic segmentation of organ regions can save a lot of time and labor costs, and allow doctors to produce more intuitive observations of the organization of the human body. However, automatic multi-organ segmentation in CT images remains challenging due to the complicated anatomical structures and low tissue contrast in CT images. Traditional segmentation methods are relatively inefficient for organ segmentation with large abdominal deformation, small volume, and blurry tissue boundaries, and the traditional network architectures are rarely designed to meet the requirements of lightweight and efficient clinical practice. In this paper, we propose a novel segmentation network named Self-Adjustable Organ Attention U-Net (SOA-Net) to overcome these limitations. To be a pragmatic solution for effective segmentation method, the SOA-Net includes multi-branches feature attention (MBFA) module and the feature attention aggregation (FAA) module. These two modules have multiple branches with different kernel sizes to capture different scales feature information based on multiple scales of the target organs. An adjustable attention is used on these branches to generate different sizes of the receptive fields in the fusion layer. On the whole, SOA-Net is a 3D self-adjustable organ aware deep network which can adaptively adjust their attention and receptive field sizes based on multiple scales of the target organs to realize the efficient segmentation of multiple abdominal organs. We evaluate our method on AbdomenCT-1K and AMOS2022 datasets and the final experiments proved that our model achieves the best segmentation performance compared with the state-of-the-art segmentation networks. (Our code will be publicly available soon).