Utilization of abdominal radiography in the emergency department: Appropriateness, interpretation, radiation protection and costs.

INTRODUCTION: The use of abdominal radiography (AXR) apparently continues to be widespread despite its limited indications, the potential radiation and unnecessary costs associated. In addition, the interpretation and its report seem variable and not always performed by a radiologist. Our objective is to analyze the use, adequacy and usefulness of AXR in the emergency of a tertiary referral hospital. MATERIAL AND METHODS: We retrospectively reviewed all the AXR performed in January 2020 in the emergency of our centre, as well as the patient's demographics and medical records, technical quality of the radiographs, indications according to the SERAM (Spanish Society of Radiology) Appropriateness Guidelines, presence of a formal radiology report, and impact on the clinical management of the patient. Of all non-appropriated AXR we calculated the radiation received by the patients and its extra costs. RESULTS: In January 2020, 429 AXR (9.1% of all radiographies) were performed in the emergency of our centre. The most frequent indication was abdominal pain (40%, n = 176), followed by low back pain (21.4%, n = 92). 12.4% of AXR requested did not include any clinical information. Most of the AXR (79.6%) had sufficient technical quality. 61.3% (n = 263) of the AXR performed were not indicated, assuming an average unjustified radiation dose per patient of 0.50 ±â€¯0.33 mSv, and a total additional cost of 6575;. Only 6% of the inadequate AXRs led to a change in the clinical management of the patient, compared to 29% of the adequate AXR (p < 0.001). Only 3% of the AXR had a formal radiology report. CONCLUSIONS: AXR is still common in the emergency setting, although most of them might be inadequate according to the SERAM Appropriateness Guidelines. Its use should be optimized to avoid unnecessary radiation and costs. Radiologists must have a more active participation in the management of AXR.

Assessing the inter-observer and intra-observer reliability of radiographic measurements for size-specific dose estimates.

BACKGROUND: Calculating size-specific dose estimates (SSDEs) requires measurement of the patient's anteroposterior (AP) and lateral thickness based on computed tomography (CT) images. However, these measurements can be subject to variation due to inter-observer and intra-observer differences. This study aimed to investigate the impact of these variations on the accuracy of the calculated SSDE. METHODS: Four radiographers with 1-10 years of experience were invited to measure the AP and lateral thickness on 30 chest, abdomen, and pelvic CT images. The images were sourced from an internet-based database and anonymized for analysis. The observers were trained to perform the measurements using MicroDicom software and asked to repeat the measurements 1 week later. The study was approved by the institutional review board at Taibah University, and written informed consent was obtained from the observers. Statistical analyses were performed using Python libraries Pingouin (version 0.5.3), Seaborn (version 0.12.2), and Matplotlib (version 3.7.1). RESULTS: The study revealed excellent inter-observer agreement for the calculated effective diameter and AP thickness measurements, with Intraclass correlation coefficients (ICC) values of 0.95 and 0.96, respectively. The agreement for lateral thickness measurements was lower, with an ICC value of 0.89. The second round of measurements yielded nearly the same levels of inter-observer agreement, with ICC values of 0.97 for the effective diameter, 1.0 for AP thickness, and 0.88 for lateral thickness. When the consistency of the observer was examined, excellent consistency was found for the calculated effective diameter, with ICC values ranging from 0.91 to 1.0 for all observers. This was observed despite the lower consistency in the lateral thickness measurements, which had ICC values ranging from 0.78 to 1.0. CONCLUSIONS: The study's findings suggest that the measurements required for calculating SSDEs are robust to inter-observer and intra-observer differences. This is important for the clinical use of SSDEs to set diagnostic reference levels for CT scans.

A rare association of liver abscess and rhabdomyolysis induced by Klebsiella oxytoca.

INTRODUCTION: We report the case of a 76-year-old male who was hospitalized with severe dehydration, pain in the hepatic region, and weakness in the limbs. METHODOLOGY: A contrast-enhanced abdomen CT and a contrast-enhanced ultrasound identified a large liver abscess. The patient underwent percutaneous drainage of the abscess. RESULTS: The culture examination, analyzed by multiplex polymerase chain reaction test, showed the presence of Klebsiella oxytoca. The laboratory report identified a resistance mechanism involving a plasmid-mediated SHV-1 extended-spectrum-beta-lactamase (ESBL). CONCLUSIONS: K. oxytoca is a Gram-negative bacterium and is potentially associated with a large variety of infections. The association between the liver abscess by K. oxytoca and rhabdomyolysis had not yet been described in the literature.

Abdominal radiographic features of anticoagulant rodenticide toxicity in 14 dogs and 2 cats.

Background: Anticoagulant rodenticide toxicity is commonly encountered in veterinary practice that can result in internal bleeding. We have observed dogs with retroperitoneal hemorrhage secondary to anticoagulant rodenticide toxicity. However, abdominal radiographic changes in dogs with rodenticide toxicity have not been studied and retroperitoneal hemorrhage secondary to rodenticide toxicity has rarely been reported. Aim: The objective is to describe abdominal radiographic features of anticoagulant rodenticide toxicity and concurrent thoracic radiographic changes in dogs and cats. Methods: Dogs and cats diagnosed with rodenticide toxicity and with available abdominal radiographs were included in this retrospective analysis. Board-certified radiologists reviewed the abdominal and thoracic radiographs. Evaluation of abdominal radiographic changes included assessment of peritoneal or retroperitoneal effusion, subcutaneous hemorrhage, and internal hemorrhage of abdominal organs. Results: Fourteen dogs and two cats with confirmed rodenticide toxicity were included in the study. In dogs, retroperitoneal effusion (28.6%) was the most commonly observed abdominal radiographic change, followed by peritoneal effusion (14.3%). Thoracic radiographic changes in dogs included pleural effusion (63.6%) and mediastinal widening (63.6%) as the most common findings, followed by pulmonary hemorrhage (36.4%) and tracheal narrowing (36.4%). Subcutaneous hemorrhage or edema (9.1%) was also noted. No abdominal radiographic changes consistent with hemorrhage secondary to rodenticide toxicity were noted in the two cats. Conclusion: Based on our findings, it is suggested that rodenticide toxicity may result in retroperitoneal effusion even in the absence of thoracic disease. Therefore, abdominal radiographs may be valuable when suspecting hemorrhage due to coagulopathy. However, abdominal radiographic changes associated with rodenticide toxicity are considered rare in cats.

Submillisievert Abdominal Photon-Counting CT versus Energy-integrating Detector CT for Urinary Calculi Detection: Impact on Diagnostic Confidence.

Background Contrast-unenhanced abdominal CT is the imaging standard for urinary calculi detection; however, studies comparing photon-counting detector (PCD) CT and energy-integrating detector (EID) CT dose-reduction potentials are lacking. Purpose To compare the radiation dose and image quality of optimized EID CT with those of an experimental PCD CT scan protocol including tin prefiltration in patients suspected of having urinary calculi. Materials and Methods This retrospective single-center study included patients who underwent unenhanced abdominal PCD CT or EID CT for suspected urinary caliculi between February 2022 and March 2023. Signal and noise measurements were performed at three anatomic levels (kidney, psoas, and obturator muscle). Nephrolithiasis and/or urolithiasis presence was independently assessed by three radiologists, and diagnostic confidence was recorded on a five-point scale (1, little to no confidence; 5, complete confidence). Reader agreement was determined by calculating Krippendorff α. Results A total of 507 patients (mean age, 51.7 years ± 17.4 [SD]; 317 male patients) were included (PCD CT group, 229 patients; EID CT group, 278 patients). Readers 1, 2, and 3 detected nephrolithiasis in 129, 127, and 129 patients and 94, 94, and 94 patients, whereas the readers detected urolithiasis in 113, 114, and 114 patients and 152, 153, and 152 patients in the PCD CT and EID CT groups, respectively. Regardless of protocol (PCD CT or EID CT) or calculus localization, near perfect interreader agreement was found (α ≥ 0.99; 95% CI: 0.99, 1). There was no evidence of a difference in reader confidence between PCD CT and EID CT (median confidence, 5; IQR, 5-5; P ≥ .57). The effective doses were 0.79 mSv (IQR, 0.63-0.99 mSv) and 1.39 mSv (IQR, 1.01-1.87 mSv) for PCD CT and EID CT, respectively. Despite the lower radiation exposure, the signal-to-noise ratios at the kidney, psoas, and obturator levels were 30%, 23%, and 17% higher, respectively, in the PCD CT group (P < .001). Conclusion Submillisievert abdominal PCD CT provided high-quality images for the diagnosis of urinary calculi; radiation exposure was reduced by 44% with a higher signal-to-noise ratio than with EID CT and with no evidence of a difference in reader confidence. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Nezami and Malayeri in this issue.

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique.

Abdominal multi-organ segmentation is one of the most important topics in the field of medical image analysis, and it plays an important role in supporting clinical workflows such as disease diagnosis and treatment planning. In this study, an efficient multi-organ segmentation method called Swin-PSAxialNet based on the nnU-Net architecture is proposed. It was designed specifically for the precise segmentation of 11 abdominal organs in CT images. The proposed network has made the following improvements compared to nnU-Net. Firstly, Space-to-depth (SPD) modules and parameter-shared axial attention (PSAA) feature extraction blocks were introduced, enhancing the capability of 3D image feature extraction. Secondly, a multi-scale image fusion approach was employed to capture detailed information and spatial features, improving the capability of extracting subtle features and edge features. Lastly, a parameter-sharing method was introduced to reduce the model's computational cost and training speed. The proposed network achieves an average Dice coefficient of 0.93342 for the segmentation task involving 11 organs. Experimental results indicate the notable superiority of Swin-PSAxialNet over previous mainstream segmentation methods. The method shows excellent accuracy and low computational costs in segmenting major abdominal organs.

Clinical feasibility of deep learning based synthetic contrast enhanced abdominal CT in patients undergoing non enhanced CT scans.

Our objective was to develop and evaluate the clinical feasibility of deep-learning-based synthetic contrast-enhanced computed tomography (DL-SynCCT) in patients designated for nonenhanced CT (NECT). We proposed a weakly supervised learning with the utilization of virtual non-contrast CT (VNC) for the development of DL-SynCCT. Training and internal validations were performed with 2202 pairs of retrospectively collected contrast-enhanced CT (CECT) images with the corresponding VNC images acquired from dual-energy CT. Clinical validation was performed using an external validation set including 398 patients designated for true nonenhanced CT (NECT), from multiple vendors at three institutes. Detection of lesions was performed by three radiologists with only NECT in the first session and an additionally provided DL-SynCCT in the second session. The mean peak signal-to-noise ratio (PSNR) and structural similarity index map (SSIM) of the DL-SynCCT compared to CECT were 43.25 ± 0.41 and 0.92 ± 0.01, respectively. With DL-SynCCT, the pooled sensitivity for lesion detection (72.0% to 76.4%, P < 0.001) and level of diagnostic confidence (3.0 to 3.6, P < 0.001) significantly increased. In conclusion, DL-SynCCT generated by weakly supervised learning showed significant benefit in terms of sensitivity in detecting abnormal findings when added to NECT in patients designated for nonenhanced CT scans.

Low-iodine-dose computed tomography coupled with an artificial intelligence-based contrast-boosting technique in children: a retrospective study on comparison with conventional-iodine-dose computed tomography.

BACKGROUND: Low-iodine-dose computed tomography (CT) protocols have emerged to mitigate the risks associated with contrast injection, often resulting in decreased image quality. OBJECTIVE: To evaluate the image quality of low-iodine-dose CT combined with an artificial intelligence (AI)-based contrast-boosting technique in abdominal CT, compared to a standard-iodine-dose protocol in children. MATERIALS AND METHODS: This single-center retrospective study included 35 pediatric patients (mean age 9.2 years, range 1-17 years) who underwent sequential abdominal CT scans-one with a standard-iodine-dose protocol (standard-dose group, Iobitridol 350 mgI/mL) and another with a low-iodine-dose protocol (low-dose group, Iohexol 240 mgI/mL)-within a 4-month interval from January 2022 to July 2022. The low-iodine CT protocol was reconstructed using an AI-based contrast-boosting technique (contrast-boosted group). Quantitative and qualitative parameters were measured in the three groups. For qualitative parameters, interobserver agreement was assessed using the intraclass correlation coefficient, and mean values were employed for subsequent analyses. For quantitative analysis of the three groups, repeated measures one-way analysis of variance with post hoc pairwise analysis was used. For qualitative analysis, the Friedman test followed by post hoc pairwise analysis was used. Paired t-tests were employed to compare radiation dose and iodine uptake between the standard- and low-dose groups. RESULTS: The standard-dose group exhibited higher attenuation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of organs and vessels compared to the low-dose group (all P-values < 0.05 except for liver SNR, P = 0.12). However, noise levels did not differ between the standard- and low-dose groups (P = 0.86). The contrast-boosted group had increased attenuation, CNR, and SNR of organs and vessels, and reduced noise compared with the low-dose group (all P < 0.05). The contrast-boosted group showed no differences in attenuation, CNR, and SNR of organs and vessels (all P > 0.05), and lower noise (P = 0.002), than the standard-dose group. In qualitative analysis, the contrast-boosted group did not differ regarding vessel enhancement and lesion conspicuity (P > 0.05) but had lower noise (P < 0.05) and higher organ enhancement and artifacts (all P < 0.05) than the standard-dose group. While iodine uptake was significantly reduced in low-iodine-dose CT (P < 0.001), there was no difference in radiation dose between standard- and low-iodine-dose CT (all P > 0.05). CONCLUSION: Low-iodine-dose abdominal CT, combined with an AI-based contrast-boosting technique exhibited comparable organ and vessel enhancement, as well as lesion conspicuity compared to standard-iodine-dose CT in children. Moreover, image noise decreased in the contrast-boosted group, albeit with an increase in artifacts.

Evaluation of Incidental Findings on Abdominopelvic CT: Potential Benefit of Photon-counting Detector CT Over Conventional Single-energy CT.

PURPOSE: To assese of potential benefint of photon-counting detector CT (PCD-CT) over conventional single-energy CT (CSE-CT) on accurate diagnosis of incidental findings with high clinical significance (IFHCS). MATERIALS AND METHODS: This retrospective study included 365 patients who initially underwent abdominopelvic contrast-enhanced CT (AP-CECT) without non-enhancement (PCD-CT: 187 and CSE-CT: 178). We selected IFHCS and evaluated their diagnosability using CE-CT alone. IFHCSs that could not be diagnosed with only CE-CT were evaluated using additional PCD-CT postprocessing techniques, including virtual non-contrast image, low keV image, and iodine map. A PCD-CT scanner (NAEOTOM Alpha, Siemens Healthineer, Erlangen, Germany) was used. RESULTS: Thirty-nine IFHCSs (PCD-CT: 22 and CSE-CT: 17) were determined in this study. Seven IFHCSs in each group were able to diagnose with only CE-CT. Fifteen IFHCSs were able to diagnose using the additional PCD-CT postprocessing technique, which was useful for detecting and accurately diagnosing 68.2% (15/22) of lesions and 65% (13/20) of patients. All IFHCSs were accurately diagonosed with PCD-CT. CONCLUSION: PCD-CT was useful for characterizing IFHCSs that are indeterminate at CSE-CT. PCD-CT offered potential benefit of PCD-CT over conventional single-energy CT on evaluation of IFHCS on only abdominopelvic CT.

Dual fluid silhouette in X-ray of the abdomen: a diagnostic flag for neurogenic bladder with urinary ascites.

A neonate presented with abdominal distension and decreased urinary output. X-ray revealed dual abdominal fluid condition-ascites with a distended bladder, along with vertebral anomalies. The possibility of urinary ascites and neurogenic bladder was kept, which was further confirmed on evaluation. Here, we emphasise the crucial role of abdominal X-ray as a diagnostic tool in uncovering this intricate medical puzzle. By detailing the clinical presentation, diagnostic approach and treatment strategy, the report contributes insights into the rare and complex abdominal condition.

Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT.

BACKGROUND: To assess the improvement of image quality and diagnostic acceptance of thinner slice iodine maps enabled by deep learning image reconstruction (DLIR) in abdominal dual-energy CT (DECT). METHODS: This study prospectively included 104 participants with 136 lesions. Four series of iodine maps were generated based on portal-venous scans of contrast-enhanced abdominal DECT: 5-mm and 1.25-mm using adaptive statistical iterative reconstruction-V (Asir-V) with 50% blending (AV-50), and 1.25-mm using DLIR with medium (DLIR-M), and high strength (DLIR-H). The iodine concentrations (IC) and their standard deviations of nine anatomical sites were measured, and the corresponding coefficient of variations (CV) were calculated. Noise-power-spectrum (NPS) and edge-rise-slope (ERS) were measured. Five radiologists rated image quality in terms of image noise, contrast, sharpness, texture, and small structure visibility, and evaluated overall diagnostic acceptability of images and lesion conspicuity. RESULTS: The four reconstructions maintained the IC values unchanged in nine anatomical sites (all p > 0.999). Compared to 1.25-mm AV-50, 1.25-mm DLIR-M and DLIR-H significantly reduced CV values (all p < 0.001) and presented lower noise and noise peak (both p < 0.001). Compared to 5-mm AV-50, 1.25-mm images had higher ERS (all p < 0.001). The difference of the peak and average spatial frequency among the four reconstructions was relatively small but statistically significant (both p < 0.001). The 1.25-mm DLIR-M images were rated higher than the 5-mm and 1.25-mm AV-50 images for diagnostic acceptability and lesion conspicuity (all P < 0.001). CONCLUSIONS: DLIR may facilitate the thinner slice thickness iodine maps in abdominal DECT for improvement of image quality, diagnostic acceptability, and lesion conspicuity.

Optimizing twin-beam dual-energy CT reconstruction: Quantitative consistency and stability assessment in reference to 120 kV: An observational study.

The split filter CT can filter X-ray beam. Theoretically, the split filter CT not only provides a good low-energy beam, but also provides a more robust CT value. The aim of this study was to compare conventional single-energy computed tomography (SECT) and twin-beam dual-energy (TBDE) CT regarding the quantitative consistency and stabilities of HU measurements at different abdominal organs. Forty-four patients were prospectively enrolled to randomly receive SECT and TBDE protocols at either body part of a thorax-abdominal examination. Their overlapping scan coverage was subjected to further image analysis. For TBDE scans, composed images(c-images) and virtual monoenergetic images (VMIs) at 60, 70, 80, and 90 kiloelectron volt (keV) were reconstructed. The attenuations were measured at 5 abdominal organs and compared between SECT and TBDE to characterize quantitative consistency by intraclass correlation coefficients (ICCs), whereas their standard deviations were used to assess the Hounsfield Unit (HU) stability. The c-images, 70 keV and 80 keV VMIs from TBDE provided consistent HU values (all ICCs > 0.8) with the SECT measurements; moreover, these TBDE images had superior HU stability over SECT images in all abdominal measurements except for fat tissue. The best HU stability can be achieved in 80 keV VMIs with the lowest noise level. The c-images and VMIs derived from TBDE can produce consistent values as SECT. The 80 keV images displayed better HU stability and a lower noise level across various abdominal organs.

Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks.

BACKGROUND: Abdominal CT scans are vital for diagnosing abdominal diseases but have limitations in tissue analysis and soft tissue detection. Dual-energy CT (DECT) can improve these issues by offering low keV virtual monoenergetic images (VMI), enhancing lesion detection and tissue characterization. However, its cost limits widespread use. PURPOSE: To develop a model that converts conventional images (CI) into generative virtual monoenergetic images at 40 keV (Gen-VMI40keV) of the upper abdomen CT scan. METHODS: Totally 444 patients who underwent upper abdominal spectral contrast-enhanced CT were enrolled and assigned to the training and validation datasets (7:3). Then, 40-keV portal-vein virtual monoenergetic (VMI40keV) and CI, generated from spectral CT scans, served as target and source images. These images were employed to build and train a CI-VMI40keV model. Indexes such as Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) were utilized to determine the best generator mode. An additional 198 cases were divided into three test groups, including Group 1 (58 cases with visible abnormalities), Group 2 (40 cases with hepatocellular carcinoma [HCC]) and Group 3 (100 cases from a publicly available HCC dataset). Both subjective and objective evaluations were performed. Comparisons, correlation analyses and Bland-Altman plot analyses were performed. RESULTS: The 192nd iteration produced the best generator mode (lower MAE and highest PSNR and SSIM). In the Test groups (1 and 2), both VMI40keV and Gen-VMI40keV significantly improved CT values, as well as SNR and CNR, for all organs compared to CI. Significant positive correlations for objective indexes were found between Gen-VMI40keV and VMI40keV in various organs and lesions. Bland-Altman analysis showed that the differences between both imaging types mostly fell within the 95% confidence interval. Pearson's and Spearman's correlation coefficients for objective scores between Gen-VMI40keV and VMI40keV in Groups 1 and 2 ranged from 0.645 to 0.980. In Group 3, Gen-VMI40keV yielded significantly higher CT values for HCC (220.5HU vs. 109.1HU) and liver (220.0HU vs. 112.8HU) compared to CI (p < 0.01). The CNR for HCC/liver was also significantly higher in Gen-VMI40keV (2.0 vs. 1.2) than in CI (p < 0.01). Additionally, Gen-VMI40keV was subjectively evaluated to have a higher image quality compared to CI. CONCLUSION: CI-VMI40keV model can generate Gen-VMI40keV from conventional CT scan, closely resembling VMI40keV.

Prevalence and clinical significance of incidental findings in chest and abdominopelvic CT scans of trauma patients; A cross-sectional study.

BACKGROUND: Imaging may inadvertently reveal pathologies unrelated to their performing purpose, known as incidental findings (IF). This study aimed to assess the prevalence, clinical significance, and documentation of IFs in chest and abdominopelvic computed tomography (CT) scans of trauma patients. METHODS: This observational study was conducted at two urban level-1 trauma centers from March 2019 through April 2022. Official radiology reports of trauma patients who underwent chest and/or abdominopelvic CT scans at the emergency department (ED) were explored, and IF were extracted. Predictive factors of the presence of IFs and their documenting were investigated. RESULTS: Out of 656 chest and 658 abdominopelvic CT scans, 167 (25.37%) and 212 (32.31%) scans harbored at least one IF, respectively. Patients with IFs tended to be of higher age and female in both chest (age: 48 [IQR: 35-62] vs. 34 [IQR: 25-42.5]; female: 31.14% vs 14.66%, p < 0.001 for both) and abdominopelvic CT scans (age: 41 [IQR: 30-57.5] vs 33 [IQR: 25-43], female: 26.42% vs. 13.96%, p < 0.001 for both). As for documentation of significant IFs, only 49 of 112 chest IFs (43.8%) and 55 of 176 abdominopelvic IFs (31.3%) were documented. Investigating factors associated with documentation of clinically significant IFs, shorter length of hospital stay (1.5 (IQR: 0-4) vs. 3 (IQR: 2-8), p = 0.003), and discharging by ED physicians (documentation rate: 13.2% vs 42.6%, p < 0.001) were associated with poorer documentation of IFs only in abdominopelvic scans. CONCLUSION: CT imaging in ED trauma patients often reveals incidental findings, especially in older patients. Over 50% of these findings are clinically significant, yet they are frequently ignored and not documented. Physicians need to be more vigilant in recognizing and documenting these incidental findings and informing patients of the need for further evaluation.

Abdominal CT findings characteristic of Castleman disease: multi-centre review of 76 adult cases with abdominopelvic nodal involvement.

OBJECTIVE: Characterize the CT findings of abdominopelvic Castleman disease, including a new observation involving the perinodal fat. METHODS: Multi-centre search at 5 institutions yielded 76 adults (mean age, 42.1 ± 14.3 years; 38 women/38 men) meeting inclusion criteria of histopathologically proven Castleman disease with nodal involvement at abdominopelvic CT. Retrospective review of the dominant nodal mass was assessed for size, attenuation, and presence of calcification, and for prominence and soft-tissue infiltration of the perinodal fat. Hypervascular nodal enhancement was based on both subjective and objective comparison with aortic blood pool attenuation. RESULTS: Abdominal involvement was unicentric in 48.7% (37/76) and multicentric in 51.3% (39/76), including 31 cases with extra-abdominal involvement. Histopathologic subtypes included hyaline vascular variant (HVV), plasma cell variant (PCV), mixed HVV/PCV, and HHV-8 variant in 39, 25, 3 and 9 cases, respectively. The dominant nodal mass measured 4.4 ± 1.9 cm and 3.2 ± 1.7 cm in mean long- and short-axis, respectively, and appeared hypervascular in 58.6% (41/70 with IV contrast). Internal calcification was seen in 22.4% (17/76). Infiltration of the perinodal fat, with or without hypertrophy, was present in 56.6% (43/76), more frequent with hypervascular vs non-hypervascular nodal masses (80.5% vs 20.7%; P < .001). Among HVV cases, 76.9% were unicentric, 71.1% appeared hypervascular, and 69.2% demonstrated perinodal fat infiltration. CONCLUSION: Hypervascular nodal masses demonstrating prominence and infiltration of perinodal fat at CT can suggest the specific diagnosis of Castleman disease, especially the HVV. ADVANCES IN KNOWLEDGE: Abdominopelvic nodal masses that demonstrate hypervascular enhancement and prominent infiltration of the perinodal fat at CT can suggest the diagnosis of Castleman disease, but nonetheless requires tissue sampling.

Deep Learning Models for Abdominal CT Organ Segmentation in Children: Development and Validation in Internal and Heterogeneous Public Datasets.

BACKGROUND. Deep learning abdominal organ segmentation algorithms have shown excellent results in adults; validation in children is sparse. OBJECTIVE. The purpose of this article is to develop and validate deep learning models for liver, spleen, and pancreas segmentation on pediatric CT examinations. METHODS. This retrospective study developed and validated deep learning models for liver, spleen, and pancreas segmentation using 1731 CT examinations (1504 training, 221 testing), derived from three internal institutional pediatric (age ≤ 18 years) datasets (n = 483) and three public datasets comprising pediatric and adult examinations with various pathologies (n = 1248). Three deep learning model architectures (SegResNet, DynUNet, and SwinUNETR) from the Medical Open Network for Artificial Intelligence (MONAI) framework underwent training using native training (NT), relying solely on institutional datasets, and transfer learning (TL), incorporating pretraining on public datasets. For comparison, TotalSegmentator, a publicly available segmentation model, was applied to test data without further training. Segmentation performance was evaluated using mean Dice similarity coefficient (DSC), with manual segmentations as reference. RESULTS. For internal pediatric data, the DSC for TotalSegmentator, NT models, and TL models for normal liver was 0.953, 0.964-0.965, and 0.965-0.966, respectively; for normal spleen, 0.914, 0.942-0.945, and 0.937-0.945; for normal pancreas, 0.733, 0.774-0.785, and 0.775-0.786; and for pancreas with pancreatitis, 0.703, 0.590-0.640, and 0.667-0.711. For public pediatric data, the DSC for TotalSegmentator, NT models, and TL models for liver was 0.952, 0.871-0.908, and 0.941-0.946, respectively; for spleen, 0.905, 0.771-0.827, and 0.897-0.926; and for pancreas, 0.700, 0.577-0.648, and 0.693-0.736. For public primarily adult data, the DSC for TotalSegmentator, NT models, and TL models for liver was 0.991, 0.633-0.750, and 0.926-0.952, respectively; for spleen, 0.983, 0.569-0.604, and 0.923-0.947; and for pancreas, 0.909, 0.148-0.241, and 0.699-0.775. The DynUNet TL model was selected as the best-performing NT or TL model considering DSC values across organs and test datasets and was made available as an open-source MONAI bundle (https://github.com/cchmc-dll/pediatric_abdominal_segmentation_bundle.git). CONCLUSION. TL models trained on heterogeneous public datasets and fine-tuned using institutional pediatric data outperformed internal NT models and Total-Segmentator across internal and external pediatric test data. Segmentation performance was better in liver and spleen than in pancreas. CLINICAL IMPACT. The selected model may be used for various volumetry applications in pediatric imaging.

Optimizing CT Abdomen-Pelvis Scan Radiation Dose: Examining the Role of Body Metrics (Waist Circumference, Hip Circumference, Abdominal Fat, and Body Mass Index) in Dose Efficiency.

Objective: This study investigates the correlation between patient body metrics and radiation dose in abdominopelvic CT scans, aiming to identify significant predictors of radiation exposure. Methods: Employing a cross-sectional analysis of patient data, including BMI, abdominal fat, waist, abdomen, and hip circumference, we analyzed their relationship with the following dose metrics: the CTDIvol, DLP, and SSDE. Results: Results from the analysis of various body measurements revealed that BMI, abdominal fat, and waist circumference are strongly correlated with increased radiation doses. Notably, the SSDE, as a more patient-centric dose metric, showed significant positive correlations, especially with waist circumference, suggesting its potential as a key predictor for optimizing radiation doses. Conclusions: The findings suggest that incorporating patient-specific body metrics into CT dosimetry could enhance personalized care and radiation safety. Conclusively, this study highlights the necessity for tailored imaging protocols based on individual body metrics to optimize radiation exposure, encouraging further research into predictive models and the integration of these metrics into clinical practice for improved patient management.

Optimization of smoothing parameter for block matching and 3D filtering algorithm in low-dose chest and abdominal computed tomography images.

Computed tomography (CT), known for its exceptionally high accuracy, is associated with a substantial dose of ionizing radiation. Low-dose protocols have been devised to address this issue; however, a reduction in the radiation dose can lead to a deficiency in the number of photons, resulting in quantum noise. Thus, the aim of this study was to optimize the smoothing parameter (σ-value) of the block matching and 3D filtering (BM3D) algorithm to effectively reduce noise in low-dose chest and abdominal CT images. Acquired images were subsequently analyze using quantitative evaluation metrics, including contrast to noise ratio (CNR), coefficient of variation (CV), and naturalness image quality evaluator (NIQE). Quantitative evaluation results demonstrated that the optimal σ-value for CNR, CV, and NIQE were 0.10, 0.11, and 0.09 in low-dose chest CT images respectively, whereas those in abdominal images were 0.12, 0.11, and 0.09, respectively. The average of the optimal σ-values, which produced the most improved results, was 0.10, considering both visual and quantitative evaluations. In conclusion, we demonstrated that the optimized BM3D algorithm with σ-value is effective for noise reduction in low-dose chest and abdominal CT images indicating its feasibility of in the clinical field.

Effect of arm position on image quality and radiation dose during thorax and abdomen computed tomography scans.

INTRODUCTION: During Computed Tomography (CT) scans of the Thorax-Abdomen-Pelvis (TAP) the patient's arms should be positioned above the head to obtain optimal image quality and expose the patient to the lowest possible radiation dose. This may be impossible with patients with shoulder problems leading to arms being positioned in other ways. This study aimed to investigate differences in objective image quality and estimated effective dose (E), when positioning the arms below shoulder level in CT-TAP. METHODS: An anthropomorphic phantom with cadaver arms was used. Four arm positions were tested: Along the torso (A), on the pelvis (B), on a pillow on the pelvis (C), and one arm on pillow on the pelvis and the other arm on the pelvis (D). A Siemens SOMATOM Definition Flash CT-scanner with CareDose 4D was used. The dose length product was read to estimate E. Image quality was assessed objectively by measuring noise within the region of interest in the liver and urinary bladder. RESULTS: Significant differences in E between all arm positions were seen (p = 0.005). The lowest E was obtained in position C, reducing E by 8.42%. Position A provided the best image quality but the highest E. CONCLUSION: This study showed no unequivocal optimal positioning of arms in CT-TAP. Position A provided the best object image quality, while position C yielded the lowest E. These results may impact the planning of diagnostic CT where positioning of arms may influence optimal image quality and radiation dose. IMPLICATION FOR PRACTICE: This study illustrates tendencies for objective image quality and E when arms are positioned below shoulder level. Further research is needed to assess the clinical relevance with the diagnostic potential.

Use of a 3D camera for automated patient positioning for chest-abdomen-pelvis CT scans: Effect on positioning accuracy and patient dose.

INTRODUCTION: 3D positioning cameras that automate the positioning of patients with respect to the CT isocentre have been developed and are in common use in CT departments. This study aimed to compare the performance of radiographers and a 3D camera system with respect to positioning accuracy and the effect on patient radiation dose for chest-abdomen-pelvis scans. METHODS: Patient positioning and dose data obtained from a dose management system was evaluated over a two-month period for patients positioned with (CAMon) and without (CAMoff) the positioning camera. Median vertical and lateral offset values were compared between the groups whilst doses were evaluated as a function of patient water equivalent diameter (WED) for the thorax and abdomen-pelvis acquisitions for both cohorts. RESULTS: Radiographers demonstrated high levels of positioning accuracy, however significant improvements in median vertical offset were identified for the CAMon cohort for both thorax (8 mm vs. 17 mm (p = 0.001)) and abdomen-pelvis (7 mm vs. 16 mm (p = 0.003)) scans. The percentage of patients positioned within 5 mm of the isocentre was 39.0% and 16.1% for the CAMon and CAMoff cohorts. For CAMoff scans, 77.4% of patients were positioned below the isocentre, but this was reduced to 45.8% for CAMon scans. No significant changes in dose as a function of WED were identified related to the camera use (thorax: p = 0.569, abdomen-pelvis: p = 0.760). CONCLUSION: Use of a 3D camera delivered significant improvements in the accuracy and reproducibility of patient positioning when compared with radiographers. IMPLICATIONS FOR PRACTICE: Improvements in positioning accuracy were observed at the research site and hence positioning camera use has the potential to become standard practice in CT to help ensure appropriate doses are delivered to patients according to their size.