Latest AI and machine learning research in radiology for healthcare professionals.
The objective of the study is to assess the impact of super-resolution deep-learning reconstruction (SR-DLR) on image quality in low-dose thin-slice pediatric abdominal CT. Thirty-eight children (< 10 years; median age, 2.0 [IQR: 0.0-3.8] years) who had available low-radiation abdominal CT data were retrospectively analyzed. The mean size-specific dose estimate was 1.71 ± 0.38 mGy. From the raw da...
OBJECTIVE: The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to rise, underscoring the need for tools to stratify individual risk of disease progression. We evaluated whether logistic regression models augmented by deep learning-based predictions (DLPs) can improve the B-mode ultrasound-based identification of at-risk MASLD, defined as patients with incre...
BACKGROUND: Magnetic Resonance Imaging (MRI)-only radiotherapy (RT) is increasingly adopted, but still lacks standardized commissioning procedures. Th...
BACKGROUND AND OBJECTIVES: Differentiation of Alzheimer's disease dementia (ADD) and dementia with Lewy bodies (DLB) remains a challenge. Free-water i...
PURPOSE: To analyze image quality over 3 levels (low, medium, and high) of deep learning image reconstruction (DLIR), evaluate whether edge detail is ...
BACKGROUND: Recognized worldwide as a major health threat and Sustainable Development Goal priority, breast cancer places an especially heavy clinical...
OBJECTIVE: Recently, deep learning (DL)-based reconstruction methods have been introduced into clinical magnetic resonance imaging (MRI) systems to en...
Accelerated Single Photon Emission Computed Tomography (SPECT) imaging, achieved by reducing either the number of projection angles or the acquis...
Generalisation of synthetic CT (sCT) generation to diagnostic MRI data in the spine faces many challenges, particularly when aiming at accurate visual...
BACKGROUND: There has been a trend toward exclusive use of high MRI field strength (1.5 T or above) for stereotactic neurosurgery imaging. However, lo...
Coronary Computed Tomography Angiography (CCTA) is a non-invasive imaging technique used to visualize the coronary arteries and diagnose coronary arte...
Functional magnetic resonance imaging (fMRI) derived functional connectivity (FC) is represented as graphs and as correlation or covariance matrices t...
Digital twin technology has emerged as a transformative innovation in healthcare, offering virtual replicas of physical entities at patient-level, equ...
OBJECTIVE: To determine the added value of post-hoc convolutional neural network (CNN)-based denoising for low-radiation coronary CT angiography (CCTA...
Foundation models (FMs), large neural networks pretrained on extensive and diverse datasets, have revolutionized artificial intelligence and demonstra...
Coronary computed tomography angiography (CCTA) provides qualitative and quantitative characteristics of atherosclerotic plaques, quantified percentag...
Early and reliable detection of brain tumors from magnetic resonance imaging (MRI) is essential for timely clinical diagnosis and treatment planning. ...
With global population aging and increased life expectancy, bone diseases affect a substantial proportion of individuals worldwide, imposing a signifi...
This review systematically synthesizes and critically appraises domestic and international advances in pulmonary function tests (PFT) research and cli...
Tuberculosis (TB) remains a major global public health threat and continues to be one of the leading causes of death from infectious diseases worldwid...