Over the past two decades, medical imaging and diagnostic techniques have gained
immense attraction due to the rapid development in computing, internet, data storage and …

Shortly after deep learning algorithms were applied to Image Analysis, and more importantly
to medical imaging, their applications increased significantly to become a trend. Likewise …

Objective To assess the feasibility of applying a deep learning-based denoising technique to
coronary CT angiography (CCTA) along with iterative reconstruction for additional noise …

Spectral computed tomography (CT) has attracted much attention in radiation dose
reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray …

Objectives To compare the overall image quality and detectability of significant (malignant
and pre-malignant) liver lesions of low-dose liver CT (LDCT, 33.3% dose) using deep …

Objective To compare the image quality between the vendor-agnostic and vendor-specific
algorithms on ultralow-dose chest CT. Methods Vendor-agnostic deep learning post …

Objectives To evaluate the diagnostic value of deep learning model (DLM) reconstructed
dual-energy CT (DECT) low-keV virtual monoenergetic imaging (VMI) for assessing …

The aim of this review is to provide clinicians and technicians with an overview of the
development of CT protocols in lung cancer screening. CT protocols have evolved from pre …

Objective The purpose of this study was to assess whether a deep learning (DL) algorithm
could enable simultaneous noise reduction and edge sharpening in low-dose lumbar spine …

Assessing the current Artificial intelligence (AI) situation is a crucial step towards its
implementation into radiology practice. The study aimed to assess radiographer willingness …