This study explores the clinical utility of dual-energy computed tomography (DECT) in discriminating thrombus types in ischemic stroke.

The aim of this study is to investigate the clinical value of liver fat fraction assessed by CT(CT-LFF) and abdominal fat components. We focus on predicting liver metastasis (LM) after colorectal cancer (CRC) surgery.

To assess the diagnostic accuracy of O-RADS Ultrasound (O-RADS US) v2022, O-RADS US v2020, and IOTA SR, and to evaluate whether combining imaging findings with tumor markers enhances the diagnosis of adnexal masses.

Both CT angiography and CT perfusion involve the administration and tracking of a contrast medium bolus for different purposes. In this study, we aim to compare the diagnostic accuracy and subjective image quality of CTP-angiographic reconstructions with conventional CTA for occlusion detection in ischemic stroke patients.

There is a wide and ever-increasing range of imaging indications for thoracic computed tomography (CT). Identifying and appropriately triaging cardiovascular findings is often challenging, especially in non-gated or unenhanced studies. The authors provide a pictorial review of clinically relevant abnormalities of the main intrathoracic vessels (aorta, superior vena cava, pulmonary arteries and coronary arteries), for radiologists reporting non-gated enhanced or unenhanced CT of the thorax.

Newly onset conduction disturbances with the need for permanent pacemaker (PPM) implantation remain the most common complication of transcatheter aortic valve replacement (TAVR). The objective was to evaluate the predictive value of clinical, ECG and new pre-procedural CT-imaging parameters for the requirement of PPM-implantation after TAVR.

This work aims to provide an overview of the current reality of clinical practice in interventional radiology in Spain, evaluating clinical activity, interventional radiologists' opinion on the importance of playing a proactive clinical role, and the barriers they encounter in doing so.

To assess the impact of a faculty-mentored, resident-as-teacher theoretical-practical breast MRI (B-MRI) course on the reporting completeness and accuracy of "trainee" radiology residents (RRs) with basic or no experience in conventional breast imaging (CBI) and the personal skills and critical thinking of experienced, "teacher" RRs.

Four-Dimensional Computed Tomography Angiography (4D CTA) seems a promising technique for capturing vessel motion of cerebral arteries, which may help to assess pathological conditions such as intracranial aneurysms. The goal of our current observational study is to capture the lumen diameter of cerebral arteries during three subsequent cardiac cycles with 4D CTA and to assess vessel motion, anticipating consistent expansion patterns within each cardiac cycle. Eighteen adult patients with unruptured and untreated intracranial aneurysms were recruited at Radboud University Medical Center. Three cardiac cycles were captured, on a wide detector CT system, using ECG-gated 4D CTA. To reduce the impact of small head movements during the acquisition, a rigid-body registration was employed. Three 10 mm segments of cerebral arteries were selected. The total deformation of the vessel lumen was calculated using a deformable registration algorithm and was used as a substitute measure for vessel motion. No pulsations could be registered, which was probably caused by pulsation motion below threshold of detection in combination with insufficient Signal-to-Noise Ratio. Further studies need to investigate if large intracranial structures can be evaluated and if using a novel scanner with a high spatial resolution would result in reproducible measurements of arteries this size.

Multiparametric magnetic resonance imaging (mpMRI) techniques, including intravoxel incoherence motion (IVIM), iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantification sequence (IDEAL IQ), T2* mapping and T2 mapping, were employed to develop and validate a predictive model for non-alcoholic steatohepatitis (NASH) diagnosis and liver fibrosis (LF) staging in rats. The combined model was interpreted using SHapley Additive exPlanations (SHAP) values for model interpretation.

We aimed to develop and externally validate a tool for predicting short-term functional outcome after lumbar fusion surgery.

Photon-counting (PC) CT has the potential to improve diagnostic confidence and image quality of CT angiography (CTA) in patients with peripheral artery disease (PAD).

This study aimed to examine the changes in brain diffusion in patients with heart failure (HF) by measuring the apparent diffusion coefficient (ADC) and to investigate the relationship between these changes and cardiac injury features.

Diagnostic delays are common in eosinophilic gastroenteritis (EGE). This study aimed to evaluate the value of imaging modalities in facilitating the diagnosis of EGE.

We aimed to modify LI-RADS version 2018 to improve sensitivity and determine the value of the combination of high alpha-fetoprotein (AFP) levels for small HCC (sHCC, ≤ 30 mm) diagnosis.

To compare the coating damage and shape retention quality of microcatheter by steam (ST), hot air gun (HA), and HA + ST shaping.

Large language models (LLMs) promise to streamline radiology reporting. With the release of OpenAI's GPT-4o (Generative Pre-trained Transformers-4 omni), which processes not only text but also speech, multimodal LLMs might now also be used as medical speech recognition software for radiology reporting in multiple languages. This proof-of-concept study investigates the feasibility of using GPT-4o for automated voice-to-text transcription of radiology reports in English and German.

This study aims to evaluate the diagnostic performance of a commercial, fully-automated, artificial intelligence (AI) driven software tool in identifying and grading prostate lesions in prostate MRI, using histopathological findings as the reference standard, while contextualizing its performance within the framework of PI-RADS v2.1 criteria.

To examine the impact of deep learning-augmented contrast enhancement on image quality and diagnostic accuracy of poorly contrasted CT angiography in patients with suspected stroke.