PURPOSE: To verify the usefulness of a deep learning model for determining the presence or absence of contrast-enhanced myocardium in late gadolinium-enhancement images in cardiac MRI.
PURPOSE: To verify the optimal imaging conditions for coronary computed tomography angiography (CCTA) examinations when using high-definition (HD) mode and deep learning image reconstruction (DLIR) in combination.
PURPOSE: To investigate whether deep learning with high-pass filtering can be used to effectively reduce motion artifacts in magnetic resonance (MR) images of the liver.
PURPOSE: The aim of this study was to compare the physical properties of small focal spot imaging with deep learning reconstruction (DLR) and small or large focal spot imaging with hybrid iterative reconstruction (IR) in chest-abdominal plain compute...
PURPOSE: Magnetic resonance (MR) images provide essential diagnostic information; however, it is also a very burdensome examination for patients. At our hospital, radiologists make imaging instructions for all MR examination orders, but this is a tim...
PURPOSE: The purposes of this study were to evaluate the low-contrast detectability of CT images assuming hepatocellular carcinoma and to determine whether dose reduction in abdominal plain CT imaging is possible.
PURPOSE: We aimed to investigate the usefulness of iViz air ver.4 Convex (FUJIFILM, Tokyo) as a tool to determine the bladder capacity before prostate radiotherapy by comparing it with the existing BladderScan BVI 6100 (Verathon Inc., Bothell, Washin...
In this study, we compared the image quality of deep learning reconstruction (DLR) with that of conventional image reconstruction methods under the same conditions of reconstruction FOV and acquisition dose assuming abdomen computed tomography (CT) i...
PURPOSE: Using ultra-high-resolution images reconstructed with the Advanced intelligent Clear-IQ Engine (AiCE) lung to measure wall area percentage (WA%), we demonstrated that WA% measured in more distal bronchus has a stronger correlation with respi...