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This work aims to evaluate the effect of acquisition parameters of the abdominal computed tomography protocol on image quality and dose, including the contrast-to-noise ratio (CNR), noise, spatial resolution (SR) as well as volumetric computed tomography dose index (CTDIvol). The measurements have been performed on a 16-slice HITACHI scanner using the polymethylmethacrylate phantom for CTDIvol and Catphan 500 for image quality. Two tube voltages of 120 kVp and 100 kVp have been analyzed while varying the tube current settings from 100 to 300 (mA) to measure image quality quantities and dose (CTDIvol). The scanning with 100 kVp compared to 120 kVp has led to a dose reduction up to 38% at mAs ranging from 100 to 300, a decrease in CNR by 28%, with a slight decrease in spatial resolution accompanied by an increase in noise. This work has shown that despite the increase in noise at 100 kVp, it is possible to reduce the dose without affecting the quantities of spatial resolution and the contrast/noise ratio by lowering the tube voltage from 120 kVp to 100 kVp. [ABSTRACT FROM AUTHOR]

Pheochromocytoma, a neuroendocrine tumor, represents a rare medical condition characterized by the excessive secretion of catecholamines. These tumors often exhibit distinctive features on imaging studies, notably appearing hypervascular. Furthermore, they may present as cystic masses with thin walls, a characteristic that becomes more evident following the administration of contrast medium. The cystic form of adrenal pheochromocytoma, as exemplified in our case, is particularly uncommon, thus underscoring the importance of recognizing its atypical presentation. Accurate diagnosis hinges on a thorough understanding of both the clinical manifestations and radiological findings suggestive of pheochromocytoma. However, definitive confirmation typically necessitates histological examination of the surgical specimen post-adrenalectomy. By shedding light on this rare variant, our case emphasizes the critical role of comprehensive diagnostic approaches in managing such complex medical conditions. Additionally, it underscores the significance of multidisciplinary collaboration among clinicians, radiologists, and pathologists to ensure timely and accurate diagnosis, ultimately guiding appropriate treatment strategies and optimizing patient outcomes.

Abstract Background Acute pancreatitis is one of the most common diseases requiring emergency surgery. Rapid and accurate recognition of acute pancreatitis can help improve clinical outcomes. This study aimed to develop a deep learning-powered diagnostic model for acute pancreatitis. Materials and methods In this investigation, we enrolled a cohort of 190 patients with acute pancreatitis who were admitted to Sichuan Provincial People’s Hospital between January 2020 and December 2021. Abdominal computed tomography (CT) scans were obtained from both patients with acute pancreatitis and healthy individuals. Our model was constructed using two modules: (1) the acute pancreatitis classifier module; (2) the pancreatitis lesion segmentation module. Each model’s performance was assessed based on precision, recall rate, F1-score, Area Under the Curve (AUC), loss rate, frequency-weighted accuracy (fwavacc), and Mean Intersection over Union (MIOU). Results Upon admission, significant variations were observed between patients with mild and severe acute pancreatitis in inflammatory indexes, liver, and kidney function indicators, as well as coagulation parameters. The acute pancreatitis classifier module exhibited commendable diagnostic efficacy, showing an impressive AUC of 0.993 (95%CI: 0.978–0.999) in the test set (comprising healthy examination patients vs. those with acute pancreatitis, P

Background: To analyze the value of quantitative parameters of Computed Tomograph (CT) abdominal fat in predicting postoperative intestinal obstruction for gastric cancer. Materials and Methods: A retrospective analysis was conducted on 120 gastric cancer patients treated between January 2017 and December 2021. These patients were divided into two groups: an observation group with postoperative intestinal obstruction (28 patients) and a control group without (92 patients). CT scans were used to measure the Subcutaneous Fat Area (SFA) and Visceral Fat Area(VFA), calculate the SFA-VFA difference, and the VFA/SFA ratio. The receiver operating curve (ROC) was employed to evaluate the predictive efficacy of these CT measurements. Results: The observation group exhibited significantly lower VFA and SFA compared to the control group (P < 0.05), while the differences in VFA/SFA ratio and SFA-VFA were not statistically significant. The area under the ROC curve (AUC) for the combined VFA and SFA in predicting postoperative intestinal obstruction was 0.902, with a 95% confidence interval of 0.859 to 0.956. This combined measure showed higher sensitivity (96.02%) and comparable specificity (85.24%) than single measurements. Logistic regression analysis identified diabetes, malnutrition, C-Reactive Protein (CRP) levels, VFA, and SFA as risk factors for postoperative intestinal obstruction (P < 0.05). Conclusion: The combined quantitative assessment of VFA and SFA using abdominal CT improves the sensitivity of predicting postoperative intestinal obstruction in gastric cancer patients. This complication is multifactorial, emphasizing the importance of a comprehensive approach in the clinical evaluation and management of these patients. [ABSTRACT FROM AUTHOR]

Background: Acute pancreatitis is one of the most common diseases requiring emergency surgery. Rapid and accurate recognition of acute pancreatitis can help improve clinical outcomes. This study aimed to develop a deep learning-powered diagnostic model for acute pancreatitis. Materials and methods: In this investigation, we enrolled a cohort of 190 patients with acute pancreatitis who were admitted to Sichuan Provincial People's Hospital between January 2020 and December 2021. Abdominal computed tomography (CT) scans were obtained from both patients with acute pancreatitis and healthy individuals. Our model was constructed using two modules: (1) the acute pancreatitis classifier module; (2) the pancreatitis lesion segmentation module. Each model's performance was assessed based on precision, recall rate, F1-score, Area Under the Curve (AUC), loss rate, frequency-weighted accuracy (fwavacc), and Mean Intersection over Union (MIOU). Results: Upon admission, significant variations were observed between patients with mild and severe acute pancreatitis in inflammatory indexes, liver, and kidney function indicators, as well as coagulation parameters. The acute pancreatitis classifier module exhibited commendable diagnostic efficacy, showing an impressive AUC of 0.993 (95%CI: 0.978–0.999) in the test set (comprising healthy examination patients vs. those with acute pancreatitis, P < 0.001) and an AUC of 0.850 (95%CI: 0.790–0.898) in the external validation set (healthy examination patients vs. patients with acute pancreatitis, P < 0.001). Furthermore, the acute pancreatitis lesion segmentation module demonstrated exceptional performance in the validation set. For pancreas segmentation, peripancreatic inflammatory exudation, peripancreatic effusion, and peripancreatic abscess necrosis, the MIOU values were 86.02 (84.52, 87.20), 61.81 (56.25, 64.83), 57.73 (49.90, 68.23), and 66.36 (55.08, 72.12), respectively. These findings underscore the robustness and reliability of the developed models in accurately characterizing and assessing acute pancreatitis. Conclusion: The diagnostic model for acute pancreatitis, driven by deep learning, exhibits excellent efficacy in accurately evaluating the severity of the condition. Trial Registration: This is a retrospective study. [ABSTRACT FROM AUTHOR]

Traumatic injury in children often involves traffic accidents, falls from height, bicycle accidents, abuse, and sports. We present our experience with a 6-year-old girl who bruised her abdomen by falling while running with a thermos bottle hanging diagonally across her neck and left shoulder, crossing in front of her body. After the injury, she was rushed to the hospital and plain abdominal computed tomography revealed free air, suggesting intestinal injury due to blunt trauma. She was transferred to our hospital, where abdominal examination revealed a bruise and redness at the umbilicus. There were signs of peritoneal irritation and guarding throughout the abdomen. Upright chest X-ray showed free air in the right subdiaphragmatic region. Laparoscopic inspection revealed one perforation and two serosal injuries. The damaged serosa was repaired, and the perforated intestine was resected and anastomosed. The patient was discharged on the 11th day following an uneventful postoperative course. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate psychological, social, and financial outcomes amongst individuals undergoing a non‐contrast abdominal computed tomography (CT) scan to screen for kidney cancer and other abdominal malignancies alongside the thoracic CT within lung cancer screening. Subjects and Methods: The Yorkshire Kidney Screening Trial (YKST) is a feasibility study of adding a non‐contrast abdominal CT scan to the thoracic CT within lung cancer screening. A total of 500 participants within the YKST, comprising all who had an abnormal CT scan and a random sample of one‐third of those with a normal scan between 14/03/2022 and 24/08/2022 were sent a questionnaire at 3 and 6 months. Outcomes included the Psychological Consequences Questionnaire (PCQ), the short‐form of the Spielberger State–Trait Anxiety Inventory, and the EuroQoL five Dimensions five Levels scale (EQ‐5D‐5L). Data were analysed using regression adjusting for participant age, sex, socioeconomic status, education, baseline quality of life (EQ‐5D‐5L), and ethnicity. Results: A total of 380 (76%) participants returned questionnaires at 3 months and 328 (66%) at 6 months. There was no difference in any outcomes between participants with a normal scan and those with abnormal scans requiring no further action. Individuals requiring initial further investigations or referral had higher scores on the negative PCQ than those with normal scans at 3 months (standardised mean difference 0.28 sd, 95% confidence interval 0.01–0.54; P = 0.044). The difference was greater in those with anxiety or depression at baseline. No differences were seen at 6 months. Conclusion: Screening for kidney cancer and other abdominal malignancies using abdominal CT alongside the thoracic CT within lung cancer screening is unlikely to cause significant lasting psychosocial or financial harm to participants with incidental findings. [ABSTRACT FROM AUTHOR]

To evaluate the performance of machine learning analysis based on proximal femur of abdominal computed tomography (CT) scans in screening for abnormal bone mass in femur. 222 patients aged 50 years or older who underwent abdominal CT and dual-energy X-ray absorptiometry scans within 14 days were retrospectively enrolled. The patients were randomly assigned to a training cohort (n = 155) and a testing cohort (n = 67) in a ratio of 7:3. A total of 2288 candidate radiomic features were extracted from the volume region of interest – the left proximal femur of the abdominal CT scans. The most valuable radiomic features were selected using minimum-Redundancy Maximum-Relevancy and the least absolute shrinkage and selection operator to construct the radiomics model. The predictive performance was assessed with receiver operating characteristic curve. 13 features were chosen to establish the radiomics model. The radiomics model using logistic regression displayed excellent prediction performance in distinguishing normal bone mass and abnormal bone mass, with the area under the curve (AUC), accuracy, sensitivity and specificity of 0.917 (95% CI, 0.867–0.967), 0.826, 0.935 and 0.780 in the training cohort. The testing cohort indicated a better performance with AUC, accuracy, sensitivity and specificity of 0.963 (95% CI, 0.919–0.999), 0.851, 0.923 and 0.889. The radiomics model based on proximal femur of abdominal CT scans had a high predictive performance to identify abnormal bone mass in femur, which can be used as a tool for opportunistic osteoporosis screening. [ABSTRACT FROM AUTHOR]

Although SARS-CoV-2 commonly affects the respiratory system, several other major organ systems can be involved, including the gastrointestinal. The main gastrointestinal manifestations are diarrhoea, nausea, and vomiting. Few cases of acute pancreatitis along with COVID-19 infection have been reported. Current evidence stresses that both clinical and radiographic studies can be suggestive of acute pancreatitis in COVID-19 patients, even without any respiratory symptoms. We report a case of a female patient who presented with severe epigastric pain and vomiting and was diagnosed with acute pancreatitis on a COVID-19 infection. The patient had an otherwise unremarkable medical history. Upper respiratory symptoms were developed on day 2 of her hospital stay. Acute pancreatitis was attributed to the COVID-19 infection, following the exclusion of other common causes of pancreatitis. Our case points out that acute pancreatitis can be developed in asymptomatic COVID-19-infected patients as well as those with a mild form of COVID-19 infection. [ABSTRACT FROM AUTHOR]

Background: There is a wide variety of abdominal painful conditions that necessitate computed tomography (CT) imaging; our study was aimed to demonstrate the utility of non-enhanced computed tomography (NECT) of the abdomen as a standalone study to detect the cause of abdominal pain in patients who are not candidates for iodinated contrast administration. Methods: Nonenhanced CT was performed to one hundred and seventy patients who presented with abdomino-pelvic pain that necessitates abdominal imaging by CT in conjunction with inability to use iodinated contrast media. Results: There was 134/170 patients (~79%) showed clinically relevant radiologic diagnosis. The gastrointestinal system was the most commonly affected system (n= 81/170 ~ 48%). Of the total, 22 patients were presented with right lower quadrant pain and clinically suspected to have appendicitis; 14 of them were diagnosed with acute appendicitis on NECT with sensitivity, specificity and accuracy of 73.6%, 100% and 86.3% respectively. About 20% of patients required urgent management. Final diagnostic confirmation was made operatively in 20/134 cases whereas in remaining cases was based on the presence of specific imaging features, the response to specific therapy, and long-term follow-up. The percent agreement in the study was excellent between the readers; the inter-reader reliability was calculated at 97%. Conclusion: Non-enhanced computed tomography has the potential to detect the cause of abdominal pain in the setting of contraindication to iodinated contrast media in most situations. Using an appropriate checklist, a wide spectrum of clinically-relevant diagnoses could be identified with a significant impact on patient management. [ABSTRACT FROM AUTHOR]

Purpose: To compare fully automated artificial intelligence body composition measures derived from thin (1.25 mm) and thick (5 mm) slice abdominal CT data. Methods: In this retrospective study, fully automated CT-based body composition algorithms for quantifying bone attenuation, muscle attenuation, muscle area, liver attenuation, liver volume, spleen volume, visceral-to-subcutaneous fat ratio (VSR) and aortic calcium were applied to both thin (1.25 × 0.625 mm) and thick (5 × 3 mm) abdominal CT series from two patient cohorts: unenhanced scans in asymptomatic adults undergoing colorectal cancer screening, and post-contrast scans in patients with colorectal cancer. Body composition measures derived from thin and thick slice data were compared, including correlation coefficients and Bland–Altman analysis. Results: A total of 9882 CT scans (mean age, 57.0 years; 4527 women, 5355 men) were evaluated, including 8947 non-contrast and 935 contrast-enhanced CT exams. Very strong positive correlation was observed for all soft tissue measures: muscle attenuation (r2 = 0.97), muscle area (r2 = 0.98), liver attenuation (r2 = 0.99), liver volume (r2 = 0.98) and spleen volume (r2 = 0.99), VSR (r2 = 0.98), and aortic calcium (r2 = 0.92); (p < 0.001 for all). Moderate positive correlation was observed for bone attenuation (r2 = 0.35). Bland–Altman analysis showed strong agreement for muscle attenuation, muscle area, liver attenuation, liver volume and spleen volume. Mean percentage differences amongst body composition measures were less than 5% for VSR (4.6%), muscle area (− 0.5%), liver attenuation (0.4%) and liver volume (2.7%) and less than 10% for muscle attenuation (− 5.5%) and spleen volume (5.1%). For aortic calcium, thick slice overestimated for Agatston scores between 0 and 100 and > 400 burden in 3.1% and 0.3% relative to thin slice, respectively, but underestimated scores between 100 and 400. Conclusion: Automated body composition measures derived from thin and thick abdominal CT data are strongly correlated and show agreement, particularly for soft tissue applications, making it feasible to use either series for these CT-based body composition algorithms. [ABSTRACT FROM AUTHOR]

Goal: In recent years, deep neural networks have consistently outperformed previously proposed methods in the domain of medical segmentation. However, due to their nature, these networks often struggle to delineate desired structures in data that fall outside their training distribution. The goal of this study is to address the challenges associated with domain generalization in CT segmentation by introducing a novel method called BucketAugment for deep neural networks. Methods: BucketAugment leverages principles from the Q-learning algorithm and employs validation loss to search for an optimal policy within a search space comprised of distributed stacks of 3D volumetric augmentations, termed ‘buckets.’ These buckets have tunable parameters and can be seamlessly integrated into existing neural network architectures, offering flexibility for customization. Results: In our experiments, we focus on segmenting kidney and liver structures across three distinct medical datasets, each containing CT scans of the abdominal region collected from various clinical institutions and scanner vendors. Our results indicate that BucketAugment significantly enhances domain generalization across diverse medical datasets, requiring only minimal modifications to existing network architectures. Conclusions: The introduction of BucketAugment provides a promising solution to the challenges of domain generalization in CT segmentation. By leveraging Q-learning principles and distributed stacks of 3D augmentations, this method improves the performance of deep neural networks on medical segmentation tasks, demonstrating its potential to enhance the applicability of such models across different datasets and clinical scenarios.

Intussusception occurs when a part of the intestine enters another segment of the intestine causing bowel obstruction. It is common in children but not in adults with only

Objective. Recognizing the most relevant seven organs in an abdominal computed tomography (CT) slice requires sophisticated knowledge. This study proposed automatically extracting relevant features and applying them in a content-based image retrieval (CBIR) system to provide similar evidence for clinical use. Approach. A total of 2827 abdominal CT slices, including 638 liver, 450 stomach, 229 pancreas, 442 spleen, 362 right kidney, 424 left kidney and 282 gallbladder tissues, were collected to evaluate the proposed CBIR in the present study. Upon fine-tuning, high-level features used to automatically interpret the differences among the seven organs were extracted via deep learning architectures, including DenseNet, Vision Transformer (ViT), and Swin Transformer v2 (SwinViT). Three images with different annotations were employed in the classification and query. Main results. The resulting performances included the classification accuracy (94%–99%) and retrieval result (0.98–0.99). Considering global features and multiple resolutions, SwinViT performed better than ViT. ViT also benefited from a better receptive field to outperform DenseNet. Additionally, the use of hole images can obtain almost perfect results regardless of which deep learning architectures are used. Significance. The experiment showed that using pretrained deep learning architectures and fine-tuning with enough data can achieve successful recognition of seven abdominal organs. The CBIR system can provide more convincing evidence for recognizing abdominal organs via similarity measurements, which could lead to additional possibilities in clinical practice. [ABSTRACT FROM AUTHOR]

Key Clinical Message: In this noteworthy case series regarding pancreatic pseudo tumors, we intend to spread knowledge among physicians for the diagnostic and therapeutic approach and eventual disease prognosis. Inflammatory pseudotumor of pancreatic head greatly mimics pancreatic head tumor. One of them is IgG4‐related pancreatic disease, which is commonly mistaken as neoplastic disease on imaging. In our novel case series, we report three cases of IgG4‐related pancreatic head pseudotumor with patients ranging from 35 to 72 years of age. Patients presented with jaundice and abdominal pain. Alongside initial laboratory workup, abdominal CTs and serum IgG4 levels were also obtained. Imaging features in conjunction with IgG4 levels confirmed the diagnosis of IgG4‐related autoimmune pancreatitis. Pancreatic pseudotumors are notorious for being often reported as real tumors. Through our noteworthy case series, we intend to highlight the imaging features and laboratory markers that are crucial in such cases to avoid invasive procedures. [ABSTRACT FROM AUTHOR]

Different conditions may underlie gastrointestinal bleeding (GIB) in children. The estimated prevalence of GIB in children is 6.4%, with spontaneous resolution in approximately 80% of cases. Nonetheless, the initial approach plays a pivotal role in determining the prognosis. The priority is the stabilization of hemodynamic status, followed by a systematic diagnostic approach. GIB can originate from either upper or lower gastrointestinal tract, leading to a broad differential diagnosis in infants and children. This includes benign and self-limiting disorders, alongside serious conditions necessitating immediate treatment. We performed a nonsystematic review of the literature, in order to describe the variety of conditions responsible for GIB in pediatric patients and to outline diagnostic pathways according to patients' age, suspected site of bleeding and type of bleeding which can help pediatricians in clinical practice. Diagnostic modalities may include esophagogastroduodenoscopy and colonoscopy, abdominal ultrasonography or computed tomography and, when necessary, magnetic resonance imaging. In this review, we critically assess these procedures, emphasizing their respective advantages and limitations concerning specific clinical scenarios. [ABSTRACT FROM AUTHOR]

Objective To investigate the risk of tumor occurrence in the patients with abdominal cyst. Methods A retrospective study was performed on 2 559 patients with cysts from February, 2019 to January, 2020 of Chongqing Municipal Hospital of Traditional Chinese Medicine diagnosed by abdominal CT. The binary logistic regression method was adopted to analyze the risk of tumor occurrence in the patients with cyst diagnosed by abdominal CT. Then whether the occurrence risk of liver cyst, renal cyst and spleen cyst having the differences in age and sex was analyzed. Results Among 2 559 cases, there were 2 405 cases (94.0%) of left renal cyst, 2 272 cases (88.8%) of right renal cyst, 1 167 cases (45.6%) of hepatic cyst, 903 cases (35.3%) of double renal cysts, 625 cases (24.4%) of complicating tumor and 7 cases (0.3%) of splenic cyst. The correlation analysis showed that the double renal cysts were positively correlated with left renal cyst and right renal cyst (r=0.619, 0.688); the regression analysis showed that gender, splenic cyst, renal cyst and hepatic cyst had no statistically significant difference in the risk of tumor occurrence (P>0.05), while the age factor had a statistically significant difference (OR=1, 015, 95%CI 1.008-1.022, P<0.001); gender (OR=0.727, 95% CI 0.570-0.928, P=0.011), age (OR=1.041, 95%CI 1. 031-1.052, P<0.001) and hepatic cyst (OR = 0.001, 95%CI 0.000-0.005, P<0.001) had a statistically significant difference in the occurrence risk of renal cyst; for the occurrence risk of hepatic cyst, only the difference in renal cyst factor had statistical significance (OR=0.001, 95%CI 0.000-0.005, P<0.001). Conclusion Cyst is not a risk factor for tumor. Female is more likely to suffer from renal cyst than male, and the risk of renal cysts occurrence is increased with age increase. [ABSTRACT FROM AUTHOR]

Contrast-enhanced abdominal CT is the gold standard for the diagnosis of acute mesenteric ischemia (AMI). CT findings include several anomalies like bowel wall thickening, thinning, attenuation, decreased enhancement, dilated fluid-filled loops, pneumatosis, and portal venous gas. A rare case of gas found only in the superior mesenteric artery (SMA) is presented. A contrast-enhanced CT scan was performed in emergency on an 80-year-old man with vague and diffuse abdominal pain, which showed findings of occlusive AMI. Gas was found in the context of the SMA and its branches, but not in the mesenteric and portal veins. The patient underwent emergency surgery but he died the next day in the intensive care unit for complications. The rare CT finding of gas in SMA during an AMI should be considered a radiological sign of irreversible intestinal damage: surgical prompt intervention is needed, even if the mortality rate is high.

Here is presented the first case of internal hernia developing from the space between ureter and muscle fascia after robot-assisted radical cystectomy with uretero-cutaneostomy diversion. An 82-year-old man underwent robot-assisted radical cystectomy with uretero-cutaneostomy diversion for high-grade urothelial carcinoma (pT2). On the Postoperative Day 7, the patient presented abdominal pain and nausea. Abdominal computed tomography showed that a part of the small intestine was protruding between the right ureter and the transverse fascia, and was strangulated, causing an obstruction of the intestine. Patient underwent an emergency laparotomy that revealed prolapse and strangulation of the small intestine through the space between the right ureter and the transversalis fascia. The ischemic intestinal tract and ureter were resected. A new right uretero-cutaneostomy diversion anastomosis with use of ureteral stent single J was created. The man was discharged 28 days after surgery, and his clinical course was uneventful through follow-up. [ABSTRACT FROM AUTHOR]

Key Clinical Message In this noteworthy case series regarding pancreatic pseudo tumors, we intend to spread knowledge among physicians for the diagnostic and therapeutic approach and eventual disease prognosis. Abstract Inflammatory pseudotumor of pancreatic head greatly mimics pancreatic head tumor. One of them is IgG4‐related pancreatic disease, which is commonly mistaken as neoplastic disease on imaging. In our novel case series, we report three cases of IgG4‐related pancreatic head pseudotumor with patients ranging from 35 to 72 years of age. Patients presented with jaundice and abdominal pain. Alongside initial laboratory workup, abdominal CTs and serum IgG4 levels were also obtained. Imaging features in conjunction with IgG4 levels confirmed the diagnosis of IgG4‐related autoimmune pancreatitis. Pancreatic pseudotumors are notorious for being often reported as real tumors. Through our noteworthy case series, we intend to highlight the imaging features and laboratory markers that are crucial in such cases to avoid invasive procedures.

The aim of this study was to introduce a patient with mucinous nonneoplastic cyst (MNNC) at an unusual age. MNNCs of the pancreas are uncommon primary tumors, which affect middle-aged women in their fifth decade of life and have significant malignant potential. Therefore, it is important to accurately diagnose and remove them. This case is a 28-year-old woman patient who presented with a pain in the right and upper abdomen from 3 months ago and worsened at night. A cystic lesion was observed near the upper bridge of the left kidney in abdominal ultrasonography. Contrast-enhanced abdominal computed tomography (CT) scan showed a cystic lesion in the trunk and umbilical cord of the pancreas. The patient underwent surgery and the mass was removed and the MNNC was diagnosed. [ABSTRACT FROM AUTHOR]

Intrahepatic foreign bodies are rarely reported. Although rare, a few reports of swallowed foreign bodies straying into the liver from the gastrointestinal tract have been published. Herein, we report a case in which an asymptomatic intrahepatic needle was removed laparoscopically. An 81-year-old woman presented to our hospital with an abnormal shadow on her abdominal X-ray image. Abdominal computed tomography displayed a needle-like shadow obliquely lying in the lateral segment of the left lobe of the liver. No subjective symptoms were reported; however, the patient underwent laparoscopic extraction. The postoperative course was good, and the patient was discharged without any complications. We also present a literature review of 27 patients with intrahepatic foreign bodies, a sewing needle. [ABSTRACT FROM AUTHOR]

Objectives: The diagnosis of patients with chronic constipation is very complicated. This study aimed to develop a simple imaging classification for the diagnosis of chronic constipation by abdominal computed tomography (CT). Methods: Sixty-two patients who underwent abdominal CT in our hospital between January and June 2022 were enrolled. The CT values of the stool in the rectum and cecum were measured in patients with chronic constipation (C group) and in those without (non-C group). Results: A strong correlation was observed between the Bristol Stool Form Scale (BSFS) and the CT value of rectal stool. Furthermore, the rectal stool CT value was significantly higher in patients with chronic constipation than in those without. The CT value of cecal stool did not differ between the two groups. The cecal stool CT value was significantly higher in patients with severe constipation (BSFS 1) than in those with BSFS 2-6. A cutoff CT value of 100 was selected as the optimal value for indicating chronic constipation. Conclusions: Abdominal CT was useful in the diagnosis of chronic constipation. If the patient had constipation, the optimal cutoff CT value was 100.

Pylephlebitis is a complication of intra-abdominal infections. Its occurrence during cholecystitis is a rare situation. We report the case of a 43-year-old female patient who presented with septic thrombosis of the right portal branch following acute calculous cholecystitis diagnosed on abdominal CT. The clinical evolution was favorable under antibiotic therapy and a cholecystectomy was scheduled.

Abstract Background Renal calculi are a common and recurrent urological disease and are usually detected by CT. In this study, we evaluated the diagnostic capability, image quality, and radiation dose of abdominal ultra-low-dose CT (ULDCT) with deep learning reconstruction (DLR) for detecting renal calculi. Methods Sixty patients with suspected renal calculi were prospectively enrolled. Low-dose CT (LDCT) images were reconstructed with hybrid iterative reconstruction (LD-HIR) and was regarded as the standard for stone and lesion detection. ULDCT images were reconstructed with HIR (ULD-HIR) and DLR (ULD-DLR). We then compared stone detection rate, abdominal lesion detection rate, image quality and radiation dose between LDCT and ULDCT. Results A total of 130 calculi were observed on LD-HIR images. Stone detection rates of ULD-HIR and ULD-DLR images were 93.1% (121/130) and 95.4% (124/130). A total of 129 lesions were detected on the LD-HIR images. The lesion detection rate on ULD-DLR images was 92.2%, with 10 cysts

Tuberculosis (TB) is prevalent in underdeveloped and developing countries, mainly in rural areas, with indistinct clinical manifestations. Lungs are the most affected organs; however, tuberculosis may invade almost all human body systems, including the liver. We provided a case study of a young male adult with hepatic tuberculosis. The diagnosis was established by discovering hypodense nodules with rim enhancement in the liver on CT scan and granulomatous inflammation with caseating necrosis on biopsy. Although having unspecified clinical manifestations, novel liver imaging, combined with pathology examination, is capable of depicting the nature of this entity. Regarding the fact that Mycobacterium tuberculosis culturing may be challenging in most settings, imaging plays a vital role in diagnosing, treatment guiding, and patient follow-ups.

Purpose: Liver hepatic vessels segmentation is a crucial step for the diagnosis process in patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal segmental anatomy that helps in the preoperative planning of surgical treatment. Methods: Recently, the convolutional neural networks (CNN) have been proved to be efficient for the task of medical image segmentation. The paper proposes an automatic deep learning‐based system for liver hepatic vessels segmentation of Computed Tomography (CT) datasets from different sources. The proposed work focuses on the combination of different steps; it starts by a preprocessing step to improve the vessels appearance within the liver region of interest in the CT scans. Coherence enhancing diffusion filtering (CED) and vesselness filtering methods are used to improve vessels contrast and intensity homogeneity. The proposed U‐net based network architecture is implemented with modified residual block to include concatenation skip connection. The effect of enhancement using filtering step was studied. Also, the effect of data mismatch used in training and validation is studied. Results: The proposed method is evaluated using many CT datasets. Dice similarity coefficient (DSC) is used to evaluate the method. The average DSC score achieved a score 79%. Conclusions: The proposed approach succeeded to segment liver vasculature from the liver envelope accurately, which makes it as potential tool for clinical preoperative planning. [ABSTRACT FROM AUTHOR]

Information technology has been actively utilized in the field of imaging diagnosis using artificial intelligence (AI), which provides benefits to human health. Readings of abdominal hemorrhage lesions using AI can be utilized in situations where lesions cannot be read due to emergencies or the absence of specialists; however, there is a lack of related research due to the difficulty in collecting and acquiring images. In this study, we processed the abdominal computed tomography (CT) database provided by multiple hospitals for utilization in deep learning and detected abdominal hemorrhage lesions in real time using an AI model designed in a cascade structure using deep learning, a subfield of AI. The AI model was used a detection model to detect lesions distributed in various sizes with high accuracy, and a classification model that could screen out images without lesions was placed before the detection model to solve the problem of increasing false positives owing to the input of images without lesions in actual clinical cases. The developed method achieved 93.22% sensitivity and 99.60% specificity. [ABSTRACT FROM AUTHOR]

Situs inversus totalis (SIT) is a rare autosomal recessive anomaly in which the thoracoabdominal viscerae are laterally transposed, introducing unique challenges in surgical scenarios. Only a few reports have demonstrated the treatment of cholecystitis in situs inversus, much less so in the context of portal vascular anomalies. We present the case of a 41-year-old female presenting to the emergency department with right upper quadrant pain, and subsequently found to have left-sided cholecystitis complicated by SIT with portal venous malformations on magnetic resonance cholangiopancreatography and abdominal computed tomography. Initially, she was referred for open cholecystectomy however due to the lack of symptoms and the presence of a tortuous recanalized portal vein presenting multiple thrombotic complications, an expectant approach was adopted. Thus, imaging remains the gold-standard to diagnose SIT and consideration of all congenital risk factors to cholecystectomy is crucial to avoid post-operative complications. [ABSTRACT FROM AUTHOR]

Purpose: To assess image quality of a deep learning reconstruction (DLR) algorithm across dose levels using a semi-anthropomorphic upper-abdominal phantom, and compare with filtered back projection (FBP) and hybrid iterative reconstruction (IR). Material and methods: CT scans obtained at five dose levels (CTDIvol 5, 10, 15, 20 and 25 mGy) were reconstructed with FBP, hybrid IR (IR50, IR70 and IR90) and DLR of low (DLL), medium (DLM) and high strength (DLH) in 0.625 mm and 2.5 mm slices. CT number, homogeneity, noise, contrast, contrast-to-noise ratio (CNR), noise texture deviation (NTD; a measure of IR-specific artifacts), noise power spectrum (NPS) and task-based transfer function (TTF) were compared between reconstruction algorithms. Results: CT numbers were highly consistent across reconstruction algorithms. Image noise was significantly reduced with higher levels of DLR. Noise texture (NPS and NTD) was with DLR maintained at comparable levels to FBP, contrary to increasing levels of hybrid IR. Images reconstructed with DLR of low and high strength in 0.625 mm slices showed similar noise characteristics to 2.5 mm slice FBP and IR50, respectively. Dose-reduction potential based on image noise with IR50 as reference was estimated to 35% for DLM and 74% for DLH. Conclusions: The novel DLR algorithm demonstrates robust noise reduction with maintained noise texture characteristics despite higher algorithm strength, and may have overcome important limitations of IR. There may be potential for dose reduction and additional benefit from thin-slice reconstruction.

Evaluation of chronic constipation is important, although it is often difficult to satisfactorily treat due to the complex interplay of factors. This study aimed to determine whether the volume of intraluminal contents and lateral diameter of the colon measured from computed tomography (CT) images were correlated with the symptoms of chronic constipation and stool consistency. Consecutive patients who underwent the Constipation Scoring System (CSS), Bristol Stool Form Scale (BSFS) questionnaires and simple abdominal CT were selected retrospectively. The intestinal tract diameter at each site was measured, and the amounts of stool and gas in the intestinal tract were evaluated at five levels. Of the 149 study participants, 54 were males and 95 were females and their mean age was 72.1 years. In the right hemi-colon, CSS5 (Time) correlated significantly with gas volume (p < 0.01). In the left hemi-colon, stool volume correlated significantly with CSS2 (Difficulty), CSS3 (Completeness), CSS5 (Time) and CSS total (p < 0.05). The BSFS negatively correlated with gas volume and diameter in the right hemi-colon and with gas volume in the rectum (p < 0.05). CT findings including stool volume, gas volume and diameter correlated with some constipation symptoms and stool consistency. These findings may be useful in evaluating and treating constipation. [ABSTRACT FROM AUTHOR]

Recent advancements in Computed Tomography (CT) imaging technology have significantly enhanced healthcare delivery. However, public concern regarding the safety and health risks associated with increased exposure to ionizing radiation has escalated. In response, this study performs a quantitative analysis comparing the image quality of the standard CT abdomen protocol (P1 protocols) with alternative optimization techniques. Adjustments to standard CT scan parameters, including tube potential (P2 protocols), effective mAs value (P3), and pitch factor (P4, P5, P6 protocols), were made to evaluate the effectiveness of these optimization strategies. A 64-slice CT scanner (Canon, Japan) and a polymethylmethacrylate (PMMA) textural water phantom was utilized in this study. CT dose descriptors, volume weighted CT Dose Index (CTDI vol) and dose-length product (DLP) alongside evaluation parameters such as Hounsfield Unit (HU) number, noise levels, Signal-to-Noise Ratio (SNR), and Contrast-to-Noise Ratio (CNR) were measured and analysed. Furthermore, this research introduced a Figure of Merit (FOM) based on the ratio of image quality parameters (SNR and CNR) to the CTDI vol , establishing a quantitative measure for optimization. A notable reduction in radiation dose—34.85% below the standard protocol (P1)—was observed in protocol P6, attributed to an increased pitch factor. Conversely, increase the effective mAs value has improved SNR and CNR values. The findings reveal that protocols P1 and P2 deliver the highest FOM for soft tissue and fat imaging, respectively. Protocol P1, with its higher attenuation factor, is optimal for abdominal examinations targeting soft tissues. Meanwhile, protocol P2's increase effective mAs value enhances beam intensity, improving CT numbers for fat tissues with their high-density composition. The study also found a strong correlation between FOM based on SNR and CNR values. In conclusion, adjustments to primary CT parameters significantly influence image quality. The FOM serves as an effective tool for measuring the overall performance of image quality, facilitating post-optimization assessment and contributing to the development of safer, more efficient CT imaging protocols. • CT abdominal protocols were optimized with consideration of primary factors. • Relation between CT number and noise describe in SNR and CNR. • Radiation dose and image quality are greatly affected by the pitch factor and effective mAs value. • Figure-of-merit descriptions serve as valuable optimization tools in CT. [ABSTRACT FROM AUTHOR]

To benchmark image quality and corresponding radiation doses for acute abdominal CT examination across different laboratories and CT manufacturers. An anthropomorphic phantom was scanned once with local abdominal CT protocols at 40 CT scanners, from four vendors, in thirty-three sites. Quantitative image quality was evaluated by CNR and SNR in the liver and kidney parenchyma. Qualitative image quality was assessed by visual grading analysis performed by three experienced radiologists using a five-point Likert scale to score thirteen image quality criteria. The CTDI vol was recorded for each scan. Pearson's correlation coefficient was calculated for the continuous variables, and the intraclass correlation coefficient was used to investigate interrater reliability between the radiologists. CTDI vol ranged from 3.5 to 12 mGy (median 5.3 mGy, third quartile 6.7 mGy). SNR in liver parenchyma ranged from 4.4 to 14.4 (median 8.5), and CNR ranged from 2.7 to 11.2 (median 6.1). A weak correlation was found between CTDI vol and CNR (r = 0.270, p = 0.092). Variations in CNR across scanners at the same dose level CTDI vol were observed. No significant difference in CTDI vol or CNR was found based on scanner installation year. The oldest scanners had a 15 % higher median CTDI vol and a 12 % lower median CNR. The ICC showed acceptable agreement for all dose groups: low (ICC=0.889), medium (ICC=0.767), high (ICC=0.847), and in low (ICC=0.803) and medium (ICC=0.811) CNR groups. There was large variation in radiation dose and image quality across the different CT scanners. Interestingly, the weak correlation between CTDI vol and CNR indicates that higher doses do not consistently improve CNR, indicating a need for systematic assessment and optimization of image quality and radiation doses for the abdominal CT examination. [ABSTRACT FROM AUTHOR]

Introduction: Most trauma centers order abdominal and pelvic computed tomography (CT) as an automatically paired CT for adult blunt trauma evaluation. However, excessive CT utilization adds risks of excessive exposure to ionizing radiation, the need to work up incidental findings (leading to unnecessary and invasive tests), and greater costs. Examining a cohort of adult blunt trauma patients that received paired abdominal and pelvic (A/P) CT, we sought to determine the diagnostic yield of clinically significant injuries (CSI) in the following: 1) the abdomen alone; 2) the pelvis alone; 3) the lumbosacral spine alone; and 4) more than one of these anatomic regions concomitantly. Methods: In this retrospective study, we reviewed the imaging and hospital course of a consecutive sample of blunt trauma activation patients older than 14 years of age who received paired A/PCT during their blunt trauma assessments at an urban Level I trauma center from April through October 2014. Categorization of CSI was determined according to an a priori, expert panel-derivedclassification scheme.Results: The median age of the 689 patients who had A/P CT was 48 years old; 68.1% were male; 64.0% were admitted, and hospital mortality was 3.6%. CSI yields were as follows: abdomen 2.2% (95% confidence interval [CI] [1.3-3.6%]); pelvis 2.9% (95% CI [1.9-4.4%]); lumbosacral spine 0.6% (95% CI [0.2-1.5%]); both abdomen and pelvis 0.3% (95% CI [0.1-1.1%]); both the abdomen andlumbosacral spine 0.6% (0.2-1.5%); both the pelvis and lumbosacral spine 0.1% (0.0-0.8%); all three regions – abdomen, pelvis and lumbosacral spine – 0.1% (0.0-0.8%).Conclusion: Automatic pairing of A/P CT has very low diagnostic yield for CSI in both the abdomenand pelvis. These data suggest a role for selective CT imaging protocols that image these regions individually instead of automatically as a pair.

Background: Renal calculi are a common and recurrent urological disease and are usually detected by CT. In this study, we evaluated the diagnostic capability, image quality, and radiation dose of abdominal ultra-low-dose CT (ULDCT) with deep learning reconstruction (DLR) for detecting renal calculi. Methods: Sixty patients with suspected renal calculi were prospectively enrolled. Low-dose CT (LDCT) images were reconstructed with hybrid iterative reconstruction (LD-HIR) and was regarded as the standard for stone and lesion detection. ULDCT images were reconstructed with HIR (ULD-HIR) and DLR (ULD-DLR). We then compared stone detection rate, abdominal lesion detection rate, image quality and radiation dose between LDCT and ULDCT. Results: A total of 130 calculi were observed on LD-HIR images. Stone detection rates of ULD-HIR and ULD-DLR images were 93.1% (121/130) and 95.4% (124/130). A total of 129 lesions were detected on the LD-HIR images. The lesion detection rate on ULD-DLR images was 92.2%, with 10 cysts < 5 mm in diameter missed. The CT values of organs on ULD-DLR were similar to those on LD-HIR and lower than those on ULD-HIR. Signal-to-noise ratio was highest and noise lowest on ULD-DLR. The subjective image quality of ULD-DLR was similar to that of LD-HIR and better than that of ULD-HIR. The effective radiation dose of ULDCT (0.64 ± 0.17 mSv) was 77% lower than that of LDCT (2.75 ± 0.50 mSv). Conclusion: ULDCT combined with DLR could significantly reduce radiation dose while maintaining suitable image quality and stone detection rate in the diagnosis of renal calculi. [ABSTRACT FROM AUTHOR]

BACKGROUND: Mean aortic and common iliac artery diameters are the best indicators for the diagnosis of aortic and iliac ectasia and aneurysm, as well as the appropriate selection of angiographic catheter size and grafts for endovascular procedures. Currently, there is a lack of evidence regarding the normal abdominal aortic and common iliac artery diameters in Ethiopian adults. This study aimed to assess the mean diameter and associated factors of the abdominal aorta and common iliac arteries on abdominal CT scans of Ethiopian adults visiting Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia. METHODS: Institution-based prospective cross-sectional study was conducted. A convenience sampling method was employed. Data were collected from consecutive eligible adults who came for abdominal CT scans during the study period, using intervieweradministered structured questionnaires. The data was cleaned and analyzed using SPSS version 22. Student t-test and Pearson correlation were used to perform statistical analysis and the results were presented using tables and figures. RESULTS: There were a total of 136 study participants of whom eighty-one(59.6%) were females and fifty-five (40.4%) were males. The mean age was 48.5 ± 13 with a range of 23 to77 years. The mean transverse diameter of the aorta at the aortic hiatus (T12)level was 2.30 ± 0.25cm in males and 2.03±0.19cm in females. The mean transverse diameter of the suprarenal aorta was 2.04 ± 0.21cm in males and 1.83 ± 0.21 cm in females while the infrarenal one was 1.77 ± 0.16cm in males and 1.54 ± 0.15cm in females. Participants who are male and older with large body Surface Area were found to have relatively larger aortic and iliac diameters. CONCLUSION: In this study, the mean diameter of the aorta and common iliac artery was significantly associated with age, sex, and BSA. [ABSTRACT FROM AUTHOR]

Accurate segmentation of the liver and tumors from CT volumes is crucial for hepatocellular carcinoma diagnosis and pre-operative resection planning. Despite advances in deep learning-based methods for abdominal CT images, fully-automated segmentation remains challenging due to class imbalance and structural variations, often requiring cascaded approaches that incur significant computational costs. In this paper, we present the Dual-Encoder Double Concatenation Network (DEDC-Net) for simultaneous segmentation of the liver and its tumors. DEDC-Net leverages both residual and skip connections to enhance feature reuse and optimize performance in liver and tumor segmentation tasks. Extensive qualitative and quantitative experiments on the LiTS dataset demonstrate that DEDC-Net outperforms existing state-of-the-art liver segmentation methods. An ablation study was conducted to evaluate different encoder backbones - specifically VGG19 and ResNet - and the impact of incorporating an attention mechanism. Our results indicate that DEDC-Net, without any additional attention gates, achieves a superior mean Dice Score (DS) of 0.898 for liver segmentation. Moreover, integrating residual connections into one encoder yielded the highest DS for tumor segmentation tasks. The robustness of our proposed network was further validated on two additional, unseen CT datasets: IDCARDb-01 and COMET. Our model demonstrated superior lesion segmentation capabilities, particularly on IRCADb-01, achieving a DS of 0.629. The code implementation is publicly available at this website., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

BACKGROUND: Medical imaging plays a crucial role in modern medicine. In order to provide fast and accurate medical diagnosis, computed tomography (CT) is a commonly used tool in radiological examinations, and 640-slice CT is the most advanced CT imaging modality. OBJECTIVE: To evaluate the radiation dose and the risk under 640-slice abdominal CT examination. METHODS: Examinations were performed using a 640-slice CT scanner on an Alderson-Rando anthropomorphic phantom. The used scanning acquisition parameters were the same as those used on abdominal examination without contrast medium injection in clinical practice. To measure the absorbed doses, optically stimulated luminescence dosimeters (OSLDs) were put into liver, stomach, bladder, gonads, colon, small intestine, bone marrow, and skin. RESULTS: According to the 1990 Recommendations of the International Commission on Radiological Protection (ICRP Publication 60), the calculated effective doses received from this examination were 0.90 mSv in males and 0.89 mSv in females. According to the 2007 Recommendations of the International Commission on Radiological Protection (ICRP Publication 103), the calculated effective dose received from this examination was 0.83 mSv in both sexes. CONCLUSIONS: Radiation doses obtained from the abdominal 640-slice CT examination are lower than the yearly cumulative doses received from natural radiation, revealing there is no deterministic effect and radiation risk is relatively low; therefore, this CT examination is considered safe. [ABSTRACT FROM AUTHOR]

Information technology has been actively utilized in the field of imaging diagnosis using artificial intelligence (AI), which provides benefits to human health. Readings of abdominal hemorrhage lesions using AI can be utilized in situations where lesions cannot be read due to emergencies or the absence of specialists; however, there is a lack of related research due to the difficulty in collecting and acquiring images. In this study, we processed the abdominal computed tomography (CT) database provided by multiple hospitals for utilization in deep learning and detected abdominal hemorrhage lesions in real time using an AI model designed in a cascade structure using deep learning, a subfield of AI. The AI model was used a detection model to detect lesions distributed in various sizes with high accuracy, and a classification model that could screen out images without lesions was placed before the detection model to solve the problem of increasing false positives owing to the input of images without lesions in actual clinical cases. The developed method achieved 93.22% sensitivity and 99.60% specificity.

As children are more vulnerable to radiation-induced cancers and have longer life expectancies, it is essential to implement strict radiation protection measures in pediatric imaging. This study aimed to review radiation dose-minimizing measures in pediatric abdominal computed tomography (CT) examinations. A systematic search across various databases, including Web of Science, PubMed, SpringerLink, ScienceDirect, and Google Scholar, yielded a total of 7,314 articles. The search used keywords that aligned with the objectives of the study. This study included 77 publications after applying the criteria for inclusion and exclusion. We carefully reviewed these selected articles for compliance with the inclusion criteria and excluded them if they did not meet the specified criteria. Only 12 articles fulfilled the strict criteria. An in-depth review of 12 selected articles demonstrated the radiation dose reduction techniques and strategies, which include prefiltering and post-processing algorithms, careful adjustment of exposure parameters such as tube voltage (kVp) and current (mAs), and the establishment of diagnostic reference levels (DRL). Reduction of radiation exposure in pediatric CT imaging demands multifaceted approaches. To reduce the ionizing radiation dose while still obtaining high-quality diagnostic images, healthcare practitioners should adhere to DRL, adjust exposure factors, implement prefiltration, employ AI, and use post-processing algorithms., Competing Interests: Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Sayed et al.)

Objectives: To enhance medical image classification using a Dual-attention ResNet model and investigate the impact of attention mechanisms on model performance in a clinical setting., Methods: We utilized a dataset of medical images and implemented a Dual-attention ResNet model, integrating self-attention and spatial attention mechanisms. The model was trained and evaluated using binary and five-level quality classification tasks, leveraging standard evaluation metrics., Results: Our findings demonstrated substantial performance improvements with the Dual-attention ResNet model in both classification tasks. In the binary classification task, the model achieved an accuracy of 0.940, outperforming the conventional ResNet model. Similarly, in the five-level quality classification task, the Dual-attention ResNet model attained an accuracy of 0.757, highlighting its efficacy in capturing nuanced distinctions in image quality., Conclusions: The integration of attention mechanisms within the ResNet model resulted in significant performance enhancements, showcasing its potential for improving medical image classification tasks. These results underscore the promising role of attention mechanisms in facilitating more accurate and discriminative analysis of medical images, thus holding substantial promise for clinical applications in radiology and diagnostics., Competing Interests: None., (AJTR Copyright © 2024.)

A female patient in her 60s with a history of Parkinson's disease developed epigastric and retrosternal chest pain, with associated vomiting. On examination, she had a distended abdomen with no focal peritonism. A computed tomography (CT) pulmonary angiogram was organized, which demonstrated no evidence of pulmonary emboli, but an incidental finding of gas within the liver peripheries and gastric fundal wall. A plain film abdominal radiograph demonstrated a significantly distended stomach. Thus, acute gastric dilatation was diagnosed. A nasogastric tube was introduced and intravenous fluids were given promptly. An urgent CT scan of abdomen and pelvis with intravenous contrast demonstrated interval reduction with only minor residual gas evident within the left lobe of the liver and gastric fundal wall. We report the case of transient portal venous gas, secondary to acute gastric dilatation, most likely caused by a combination of opioid analgesia and gastric dysmotility from Parkinson's disease. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) scout images, also known as CT localizer radiographs, topograms, or scanograms, are an important, albeit often overlooked part of the CT examination. Scout images may contain important findings outside of the scanned field of view on CT examinations of the abdomen and pelvis, such as unsuspected lung cancer at the lung bases. Alternatively, scout images can provide complementary information to findings within the scanned field of view, such as characterization of retained surgical foreign bodies. Assessment of scout images adds value and provides a complementary "opportunistic" review for interpretation of abdominopelvic CT examinations. Scout image review is a useful modern application of conventional abdominal radiograph interpretation that can help establish a diagnosis or narrow a differential diagnosis. This review discusses the primary purpose and intent of the CT scout images, addresses standard of care and bias related to scout image review, and presents a general systematic approach to assessing scout images with multiple illustrative examples, including potential pitfalls in interpreting scout images. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to compare the rate and clinical yield of computed tomography (CT) imaging between patients presenting with abdominal pain initially seen by a physician in triage (PIT) versus those seen only by physicians working in the main emergency department (ED).Methods: A retrospective study was conducted of all self-arrivals >18 years old presenting to a single ED with abdominal pain. Nine-hundred patients were randomly selected from both the PIT and traditional patient flow groups and rates and yields of CT imaging were compared, both alone and in a model controlling for potential confounders. Predetermined criteria for CT significance included need for admission, consult, or targeted medications.Results: The overall rate of CT imaging (unadjusted) did not differ between the PIT and traditional groups, 48.7% (95% CI 45.4-51.9) vs. 45.1% (95% CI 41.8-48.4), respectively (p = .13). The CT yield for patients seen in in the PIT group was also similar to that of the traditional group: 49.1% (95% CI 44.4-53.8) vs. 50.5% (95% CI 45.6-55.4) (p = .68). In the logistic regression model, when controlling for age, gender, ESI-acuity, race and insurance payor, PIT vs. traditional was not a predictor of CT ordering (OR 1.14, 95% CI 0.94-1.38).Conclusions: For patients with abdominal pain, we found no significant differences in rates of CT ordering or CT yield for patients seen in a PIT vs. traditional models, suggesting the increased efficiencies offered by PIT models do not come at the cost of increased or decreased imaging utilization. [ABSTRACT FROM AUTHOR]