Computed tomography (CT) is the standard imaging modality for evaluating mild traumatic brain injury (mTBI) in emergency settings. However, most CT scans reveal no intracranial injury, raising concerns about unnecessary radiation exposure, healthcare costs, and diagnostic delays. Glial Fibrillary Acidic Protein (GFAP) and Ubiquitin C-terminal Hydrolase-L1 (UCH-L1) are promising biomarkers for mTBI assessment. The i-STAT® TBI Plasma cartridge enables rapid assessment of these biomarkers. This study evaluated clinical performance of the TBI Plasma cartridge, focusing on its potential to reduce unnecessary CT utilization and improve diagnostic turnaround times. A single-centre observational study was conducted in an academic emergency department. Adult patients presenting with head trauma and a GCS of 15 were enrolled. TBI biomarker testing was performed and compared to CT findings. In 141 adult patients, the TBI plasma cartridge demonstrated 100% sensitivity, 34.1% specificity, 6.3% positive predictive value (PPV), and 100% negative predictive value (NPV). Among patients <90 and<65years old, specificity was 34.6% and 60.7% respectively. In patients with non-elevated biomarkers, CT imaging could have been avoided in 32.6% of patients, saving an estimated 72.8h. Diagnostic wait time associated with TBI biomarker testing was 14.3h (4.6%) less than waiting for CT results alone. The TBI plasma cartridge demonstrated excellent sensitivity and NPV in identifying patients without intracranial injury on CT. These findings suggest the potential for the TBI cartridge to efficiently and safely rule-out unnecessary CT imaging and reduce turnaround times for diagnostic test results, which may facilitate improved ED throughput.

The nasal cavities and paranasal sinuses of cattle are complex anatomical structures that undergo significant changes throughout growth and maturation. Computed tomography (CT) is increasingly used as a routine imaging modality in small animals but remains less common in cattle due to technical limitations. However, it provides valuable diagnostic insight into the nasal cavities and sinuses, which are difficult to assess and often involved in disease processes. This study aimed to provide a detailed description of these structures using CT in cattle of different ages in order to provide a valuable tool to clinicians. CT bone window scans of thirteen bovine heads aged 5 days to 5 years and belonging to two breeds, Holstein and Blonde d'Aquitaine, were obtained in ventral recumbency. CT and gross anatomical data were correlated with CT images and cross-sectional anatomy from the head of three 5-year-old cows. Relevant anatomical structures were identified and labelled on each CT image and anatomical cross section in three different planes. CT scans produced an excellent definition of the bony and air-filled structures and allowed us to successfully identify most of the bovine head bone structures. The results revealed a progressive pneumatization of the paranasal sinuses with age, as well as an increased ossification and complexity of the nasal turbinates. Age-related differences were particularly evident in the extent and shape of the frontal sinus. This study highlights the importance of considering age when interpreting CT images of the bovine head, especially in clinical and research contexts. The findings provide a reference for normal anatomical variation across different life stages and contribute to improved diagnostic accuracy in bovine veterinary imaging.

Clinical value of peritoneal signs and computed tomography in hemodynamically stable patients with abdominal gunshot wounds.

Three-dimensional finite element (FE) models derived from Computed Tomography (CT) images predict hip fractures better than areal bone mineral density measurements from Dual-energy X-ray Absorptiometry (DXA). Yet, these results have not justified the adoption of CT in clinical practice, and only 2D DXA images are clinically available. Statistical shape and appearance models can be used to reconstruct three-dimensional FE models from 2D DXA images. While ex vivo validations have been performed on 3D reconstructed DXA-based FE models, it is not clear how well 3D reconstructed DXA-based FE models can predict fractures compared to CT-based models. The aim of this study was thus to evaluate the ability of one such methodology, namely DXA2FEM, to predict fractures in a clinical cohort of pair-matched fractured and control subjects, for whom both DXA and CT images were available. 3D FE models of the femur were built from both DXA and CT, and FE simulations were run reproducing a sideways fall in 28 different femoral configurations. An absolute risk of fracture (ARF0) was then computed based on the FE-predicted femoral strength values. DXA- and CT-derived models were compared with respect to geometry, density distribution, and FE-predicted proximal femoral strength. DXA-derived 3D FE models had an average point-to-surface distance of -2mm from CT-based models, whereas the Young's moduli were 29% higher. ARF0 by CT reported statistically significantly better diagnostic accuracy (0.83, 95% CI 0.75 to 0.91) than standard hip DXA (0.69, 95% CI 0.6 to 0.8) or FRAX (0.69, 95% CI 0.57 to 0.81). The diagnostic accuracy of ARF0 by DXA was between ARF0 by CT and standard hip DXA/FRAX (0.74, 95% CI 0.62 to 0.86), albeit neither difference was statistically significant in the analysed cohort.

Evaluation of statistical low-contrast detectability (SLCD) with various confidence levels in CT images and their comparison with human observers.

Multi-dimensional CT feature screening, construction, and validation of a clinical diagnostic model for thyroid eye disease.

Overall risk of cancer incidence attributable to adult body CT examinations: impact of a seven-year continuous quality improvement program.

Risk factors and strategies to limit contrast media extravasation during computed tomography: A scoping review.

In radiotherapy, medical image segmentation is performed to achieve a more structured view of the patient’s anatomical region. Automatic segmentation methods aim to eliminate the significant time investment required by the manual and semi-automatic segmentation processes that are most commonly used today. The present work describes an innovative seed-based automatic segmentation method for computed tomography (CT) images, known as LUNg Automatic Seeding and Segmentation (LUNAS). The study compares LUNAS with other segmentation algorithms from the Lung CT Segmentation Challenge (LCTSC), which are based on neural networks and multi-atlas approaches. The findings indicate that LUNAS achieved a Dice accuracy metric of 0.96 and 0.97 for the left and right lungs, respectively, matched the top-performing DL methods in the competition. Additionally, other state-of-the-art methods were evaluated for comparison, including one seed-based method similar to LUNAS, as well as a deep learning method. Using three other public thoracic CT image datasets, a detailed and fair analysis was performed to compare the algorithms indicating the effectiveness of LUNAS. LUNAS also has the ability to segment other regions, such as the trachea, bones, and skin. • LUNAS achieves state-of-the-art accuracy in lung CT segmentation, outper-forming traditional methods and matching deep learning approaches. • The method employs a novel automatic seed generation strategy combined with the Relaxed Oriented Image Foresting Transform (ROIFT). • LUNAS provides robust segmentation without requiring GPU acceleration, making it computationally efficient. • The method demonstrates superior adaptability, performing well on multiple publicly available thoracic CT datasets. • The methodology is adaptable to other anatomical structures such as trachea, bones, and skin.

A scoping review of radiation dose and image quality in paediatric CT: Towards safe imaging protocols and diagnostic reference levels in Jordan.

Optimizing Screening of Hepatocellular Carcinoma with Computed Tomography and MR Imaging: Emerging Research, Updated Guidelines, and New Technologies.

Understanding the anatomy of the paranasal sinuses and nasal cavity in alpacas (Vicugna pacos) is limited by scarce published data. This prospective cadaveric study aimed to enhance the interpretation of clinical cross-sectional imaging and expand knowledge of normal anatomy using computed tomography (CT), magnetic resonance imaging (MRI), macroscopic cross-sections, and 3D reconstructions (Amira v5.3.3; Thermo Fisher Scientific, Waltham, MA, USA). Seven alpacas (median age 3.9 years; five males, two females) underwent CT and MRI. Frozen anatomical slices were obtained from three heads, and sinus fenestration from one. All specimens had a conchal (dorsal and middle), maxillary, frontal, and ethmoidal sinus. The 6/7 specimens presented a sphenoidal sinus, and 5/7 specimens presented bilateral lacrimal sinuses. Notably, the ventral conchal and palatine sinuses were absent in all specimens. The frontal sinus was divided into a smaller, non-concamerated, medial compartment and a large, diversely concamerated, lateral compartment in all specimens, except one in which only a common frontal sinus was present. The sphenoidal sinus was divided by a variably thickened septum in 3/6 heads. The ethmoidal bone contained air-filled cells divided into lateral and medial groups. The lateral cells presented a lengthier rostro-caudal extension and connected to the frontal, lacrimal, and maxillary sinuses, whereas the medial cells were shorter and surrounded the middle conchal sinus.

Artificial intelligence model for cardiovascular disease risk prediction in breast cancer patients using electronic health records and computed tomography scans.

Glial Fibrillary Acidic Protein and Ubiquitin C-Terminal Hydrolase L1 for Acute Traumatic Intracranial Lesions: The PIONEER Diagnostic Accuracy Study.

Retrospective Diagnosis of Mineralizing Lenticulostriate Vasculopathy in Childhood Arterial Ischemic Stroke.

To assess the relationship between the condyle and fossa in idiopathic condylar resorption (ICR) patients with severe Class II skeletal deformities who achieved stable functional and esthetic outcomes. ICR patients receiving orthodontic-orthognathic treatment with more than 2 years of follow-up were included in this retrospective study. Post-treatment changes in Y-axis-B of less than 2 mm were considered indicative of achieving a stable treatment outcome. Anterior joint space (AJS), superior joint space (SJS), and posterior joint space (PJS) on computed tomography (CT) and magnetic resonance images (MRI) were measured. After screening, a total of 20 patients with 40 condyles were included. 12 patients were included in the Stable group and eight patients were included in the Relapse group. In the Stable group, mean AJS, SJS, and PJS linear measurements during follow-up on CT images were 1.82 ± 0.34, 1.78 ± 0.38, and 1.81 ± 0.33 mm, respectively. Ratio of AJS:SJS:PJS was 1.0:0.97:1.0. Mean AJS, SJS, and PJS linear measurements on MRI images were 1.71 ± 0.50, 1.82 ± 0.50, and 1.70 ± 0.51 mm, respectively. Ratio of AJS:SJS:PJS was 1.0:1.06:1.0. In the Relapse group, no constant ratio of joint spaces was found on CT or MRI images. A relationship exists between the condyle and fossa in ICR patients with stable treatment outcomes, as the AJS:SJS:PJS ratio was close to 1:1:1. This condyle-fossa relationship provides clinicians with an assessment criterion and may be an alternative treatment goal for patients with ICR.

Full-wave modeling of transcranial ultrasound using volume-surface integral equations and CT-derived heterogeneous skull data.

Towards novel methods of dosimetry in pediatric and neonatal head computed tomography: Comparative study using two novel and cost effective fabricated in-house phantoms.

This study aimed to obtain morphometric data of the pituitary gland and sella turcica in Hamdani sheep using computed tomography (CT) and to assess sexual dimorphism. CT scans of skulls from 16 healthy adult Hamdani sheep (8 males, 8 females) were analysed using RadiAnt DICOM Viewer (64-bit). Morphometric measurements of the pituitary gland were obtained from transverse (length, height, width), dorsal (length, width), and sagittal (length, height) sections, and these values were used to calculate the volume of the gland. Additionally, using the pituitary gland measurements, the transverse index (TI), sagittal index (SI), and dorsal index (DI) were calculated. Furthermore, nine osteometric parameters including surface area and 12 ratio values were obtained from sagittal images of the sella turcica. Statistical analysis showed that both the linear dimensions and volume of the pituitary gland and sella turcica were greater in males. The pituitary gland volume was 1.211±0.166cm3 in females and 1.911±0.281cm3 in males. In both sexes, age and body weight positively correlated with most pituitary gland measurements. However, in female Hamdani sheep, most of the correlations between linear measurements and index values were negative. Similarly, significant osteometric measurements of the sella turcica in both sexes showed positive correlations, while the height/width ratio generally had negative correlations with age and body weight. CT imaging revealed marked morphometric differences between sexes in Hamdani sheep, confirming sexual dimorphism. These findings may contribute to future research in areas such as anatomical reference, sex determination, clinical diagnosis, pituitary and sella turcica pathology, veterinary surgery, and zooarchaeology.

The combined assessment of multiple abdominal imaging traits in relation to type 2 diabetes remains incompletely characterised. The study examines these relationships on computed tomography (CT) scans from a large-scale, racially diverse, disease-focused medical biobank. Deep learning algorithms were applied to patients with abdominal CT scans in the Penn Medicine BioBank to quantify image-derived phenotypes, including spleen-hepatic attenuation difference (SHAD) for hepatic steatosis (HS), liver and spleen volumes (SV), abdominal visceral and subcutaneous adipose tissue (VAT and SAT, respectively) and visceral-to-subcutaneous ratio (VSR). One thousand five hundred and ninety-four patients (62 years, 49.4% male, 59.3% White), comprising 950 nondiabetics and 644 diabetics, were included in analysis with diabetes status determined by a 6.5% haemoglobin A1c cutoff. Diabetic patients had greater HS (SHAD -4.49 vs. -6.88 Hounsfield units, p = 1.34 × 10-8), steatosis prevalence (41.8% vs. 27.7%, p = 4.85 × 10-9) and VSR (0.62 vs. 0.55, p = 1.69 × 10-3) than nondiabetics. In multivariate analyses adjusting for age, sex, race and body mass index (BMI), diabetes was independently associated with SHAD (odds ratios [OR] 1.04, 95% confidence interval [1.02-1.05]), SV (OR 4.53 [1.89-10.99]) and VSR (OR 2.87, [1.96-4.20]). Combined regression analysis showed no relationship between splenomegaly and type 2 diabetes once controlling for hepatic factors (OR 1.08, [0.95-1.23]), but uncovered a stronger VSR correlation (OR 1.40, [1.20-1.63]) than BMI (OR 1.14, [1.01-1.29]). Hepatic steatosis, hepatomegaly and visceral adiposity on CT are associated with type 2 diabetes. Hepatic changes may influence spleen size effects on diabetes. VSR can serve as an alternative to traditional obesity metrics to accurately reflect diabetes risk.