Acute Renal Infarction After a Bilateral Aortic-Iliac Stent Thrombosis.

A renal infarction occurs when kidney's arterial blood supply is compromised, causing parenchymal necrosis and loss of function. It is a relatively uncommon complication and its treatment is time-dependent. We present a case where a female patient with a history of bilateral aortic-iliac stenting over 10 years before presented with chest pain, palpitations, and dyspnea associated with hypertension. The patient progressed with an acute worsening of renal function and anuria, with an urgent need for renal replacement therapy. The abdominal CT angiography confirmed a complete chronic stent thrombosis and a recent occlusion of the right renal artery causing an acute renal infarction; however, this exam was performed more than 72 hours after admission. There was no longer indication for reperfusion therapy, taking into account the time course. This case reinforces the importance of a thorough clinical history and awareness of risk factors to raise the suspicion of renal infarction that should lead to an early contrast-enhanced CT scan so that adequate therapy can be performed.

IgG4-related inflammatory pancreatic head pseudotumor mirrors pancreatic head tumor: A novel case series with a review of the literature.

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.

MOTC: Abdominal Multi-objective Segmentation Model with Parallel Fusion of Global and Local Information.

Convolutional Neural Networks have been widely applied in medical image segmentation. However, the existence of local inductive bias in convolutional operations restricts the modeling of long-term dependencies. The introduction of Transformer enables the modeling of long-term dependencies and partially eliminates the local inductive bias in convolutional operations, thereby improving the accuracy of tasks such as segmentation and classification. Researchers have proposed various hybrid structures combining Transformer and Convolutional Neural Networks. One strategy is to stack Transformer blocks and convolutional blocks to concentrate on eliminating the accumulated local bias of convolutional operations. Another strategy is to nest convolutional blocks and Transformer blocks to eliminate bias within each nested block. However, due to the granularity of bias elimination operations, these two strategies cannot fully exploit the potential of Transformer. In this paper, a parallel hybrid model is proposed for segmentation, which includes a Transformer branch and a Convolutional Neural Network branch in encoder. After parallel feature extraction, inter-layer information fusion and exchange of complementary information are performed between the two branches, simultaneously extracting local and global features while eliminating the local bias generated by convolutional operations within the current layer. A pure convolutional operation is used in decoder to obtain final segmentation results. To validate the impact of the granularity of bias elimination operations on the effectiveness of local bias elimination, the experiments in this paper were conducted on Flare21 dataset and Amos22 dataset. The average Dice coefficient reached 92.65% on Flare21 dataset, and 91.61% on Amos22 dataset, surpassing comparative methods. The experimental results demonstrate that smaller granularity of bias elimination operations leads to better performance.

Evaluating Image Quality and Radiation Dose in Low-Dose Thoraco-Abdominal CT Angiography with a Tin Filter for Patients with Aortic Disease.

Background: We aimed to compared radiation exposure and image quality between tin-filter-based and standard dose thoraco-abdominal computed tomography angiography (TACTA) protocols, aiming to address a gap in the existing literature. Methods: In this retrospective study, ninety consecutive patients undergoing TACTA were included. Of these, 45 followed a routine standard-dose protocol (ST100kV), and 45 underwent a low-dose protocol with a tin filter (TF100kV). Radiation metrics were compared. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and figure of merit (FOM) were calculated for the thoracic and abdominal aorta and right common iliac artery. Two independent readers assessed the image noise, image contrast, sharpness, and subjective image quality. Results: The mean dose for the TF100kV group was significantly lower (DLP 128.25 ± 18.18 mGy*cm vs. 662.75 ± 181.29, p < 0.001; CTDIvol 1.83 ± 0.25 mGy vs. 9.28 ± 2.17, p = 0.001), with an effective dose close to 2.3 mSv (2.31 ± 0.33 mSv; p < 0.001). The TF100kV group demonstrated greater dose efficiency (FOM, thoracic aorta: 36.70 ± 22.77 vs. 13.96 ± 13.18 mSv-1, p < 0.001) compared to the ST100kV group. Conclusions: Dedicated low-dose TACTA using a tin filter can significantly reduce the radiation dose while maintaining sufficient diagnostic image quality.

Comparative study of abdominal CT enhancement in overweight and obese patients based on different scanning modes combined with different contrast medium concentrations.

PURPOSE: To compare image quality, iodine intake, and radiation dose in overweight and obese patients undergoing abdominal computed tomography (CT) enhancement using different scanning modes and contrast medium. METHODS: Ninety overweight and obese patients (25 kg/m2≤body mass index (BMI)<  30 kg/m2 and BMI≥30 kg/m2) who underwent abdominal CT-enhanced examinations were randomized into three groups (A, B, and C) of 30 each and scanned using gemstone spectral imaging (GSI) +320 mgI/ml, 100 kVp + 370 mgI/ml, and 120 kVp + 370 mgI/ml, respectively. Reconstruct monochromatic energy images of group A at 50-70 keV (5 keV interval). The iodine intake and radiation dose of each group were recorded and calculated. The CT values, contrast-to-noise ratios (CNRs), and subjective scores of each subgroup image in group A versus images in groups B and C were by using one-way analysis of variance or Kruskal-Wallis H test, and the optimal keV of group A was selected. RESULTS: The dual-phase CT values and CNRs of each part in group A were higher than or similar to those in groups B and C at 50-60 keV, and similar to or lower than those in groups B and C at 65 keV and 70 keV. The subjective scores of the dual-phase images in group A were lower than those of groups B and C at 50 keV and 55 keV, whereas no significant difference was seen at 60-70 keV. Compared to groups B and C, the iodine intake in group A decreased by 12.5% and 13.3%, respectively. The effective doses in groups A and B were 24.7% and 25.8% lower than those in group C, respectively. CONCLUSION: GSI +320 mgI/ml for abdominal CT-enhanced in overweight patients satisfies image quality while reducing iodine intake and radiation dose, and the optimal keV was 60 keV.

Gastrointestinal bleeding in children: diagnostic approach.

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.

Interactive content-based image retrieval with deep learning for CT abdominal organ recognition.

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.

Comparing fully automated AI body composition measures derived from thin and thick slice CT image data.

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.

Short-term psychosocial outcomes of adding a non-contrast abdominal computed tomography (CT) scan to the thoracic CT within lung cancer screening.

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.

Applying Machine Learning Analysis Based on Proximal Femur of Abdominal Computed Tomography to Screen for Abnormal Bone Mass in Femur.

RATIONALE AND OBJECTIVES: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Could CT finding of gas in the sole mesenteric artery be a sign of a severe acute ischemia? Presentation of a rare fatal case and a literature review.

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.

Superior Mesenteric Artery Thrombosis in a Patient With Median Arcuate Ligament Syndrome.

Median arcuate ligament syndrome (MALS) is a rare and controversial vascular compression syndrome. In this condition, the median arcuate ligament compresses the celiac artery, resulting in symptoms such as postprandial abdominal pain, vomiting, and weight loss. Its diagnosis is based on clinical findings in conjunction with supporting radiological features such as elevated flow velocities on Doppler ultrasound and focal indentation of the proximal celiac artery with the typical 'hooked' or 'J'-shaped appearance on conventional angiography or computed tomography angiography (CTA). Herein is the case of a 44-year-old female who presented with early satiety, postprandial abdominal pain, vomiting, and weight loss. A computed tomography mesenteric angiogram (CTMA) showed thickening of the median arcuate ligament with a hooked appearance of the celiac artery and thrombosis of the mid to distal superior mesenteric artery with associated ischemia of a short segment of the jejunum. Subsequent Doppler ultrasound demonstrated elevated peak systolic velocities within the celiac artery over the compressed segment, which varied with respiration (end-inspiration: 234.3 cm/s and end-expiration: 373.5 cm/s).

Robust and efficient abdominal CT segmentation using shape constrained multi-scale attention network.

PURPOSE: Although many deep learning-based abdominal multi-organ segmentation networks have been proposed, the various intensity distributions and organ shapes of the CT images from multi-center, multi-phase with various diseases introduce new challenges for robust abdominal CT segmentation. To achieve robust and efficient abdominal multi-organ segmentation, a new two-stage method is presented in this study. METHODS: A binary segmentation network is used for coarse localization, followed by a multi-scale attention network for the fine segmentation of liver, kidney, spleen, and pancreas. To constrain the organ shapes produced by the fine segmentation network, an additional network is pre-trained to learn the shape features of the organs with serious diseases and then employed to constrain the training of the fine segmentation network. RESULTS: The performance of the presented segmentation method was extensively evaluated on the multi-center data set from the Fast and Low GPU Memory Abdominal oRgan sEgmentation (FLARE) challenge, which was held in conjunction with International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021. Dice Similarity Coefficient (DSC) and Normalized Surface Dice (NSD) were calculated to quantitatively evaluate the segmentation accuracy and efficiency. An average DSC and NSD of 83.7% and 64.4% were achieved, and our method finally won the second place among more than 90 participating teams. CONCLUSIONS: The evaluation results on the public challenge demonstrate that our method shows promising performance in robustness and efficiency, which may promote the clinical application of the automatic abdominal multi-organ segmentation.

AI Model for Detection of Abdominal Hemorrhage Lesions in Abdominal CT Images.

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.

Hepatic vessels segmentation using deep learning and preprocessing enhancement.

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.

Pylephlebitis complicating acute calculous cholecystitis: A case report.

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.

Imaging Classification of Constipation.

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.

Correlation between Constipation Symptoms and Abdominal CT Imaging: A Cross-Sectional Pilot Study.

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.

Evaluation of abdominal gas by plain abdominal radiographs.

BACKGROUND: Our aim was to determine the reliability of plain abdominal radiographs for the evaluation of abdominal gas content in patients with functional digestive symptoms. METHODS: Abdominal CT scan scout views, mimicking a conventional plain abdominal radiograph, were obtained from 30 patients both during episodes of abdominal distension and basal conditions. Physicians (n = 50) were instructed to rate the estimated volume of gas in the 60 images presented in random sequence using a scale graded from 0 to ≥600 ml. KEY RESULTS: The gas volumes estimated in the scout views differed from those measured by CT by a median of 90 (95% CI 70-102) ml, and the misestimation was not related to the absolute volume in the image. The accuracy of the observers, measured by their mean misestimation, was not related to their specialty or the training status (misestimation by 96 (95% CI 85-104) ml in staff vs 78 (70-106) ml in residents; p = 0.297). The accuracy was independent of the order of presentation of the images. Gas volume measured by CT in the images obtained during episodes of abdominal distension differed by a median of 39 (95% CI 29-66) ml from those during basal conditions, and this difference was misestimated by a median of 107 (95% CI 94-119) ml. The accuracy of these estimations was not related to the absolute gas volumes (R = -0.352; p < 0.001) or the magnitude of the differences. CONCLUSIONS & INFERENCES: Plain abdominal radiographs have limited value for the evaluation of abdominal gas volume in patients with functional gut disorders.

Pylephlebitis secondary to inflammatory colitis: A case report.

Pylephlebitis is a thrombotic occlusion of the portal vein or its branches secondary to infection in regions that drain to the portal venous system. Clinical presentation is often atypical, and patients may initially present with non-specific abdominal symptoms. We report a case of pylephlebitis secondary to inflammatory colitis depicted by CT scan in a 35-year-old female admitted for acute abdominal pain associated with vomiting and fever.