Iterative reconstruction (IR) algorithms are the most widely used CT noise-reduction method
to improve image quality and have greatly facilitated radiation dose reduction within the …

Abstract Purpose of Review Deep Learning reconstruction (DLR) is the current state-of-the-
art method for CT image formation. Comparisons to existing filter back-projection, iterative …

Background An iterative reconstruction (IR) algorithm was introduced for clinical photon-
counting detector (PCD) CT. Purpose To investigate the image quality and the optimal …

Purpose To characterize the noise and spatial resolution properties of a commercially
available deep learning‐based computed tomography (CT) reconstruction algorithm …

Photon-counting detector (PCD) CT is an emerging technology that has led to continued
innovation and progress in diagnostic imaging after it was approved by the US Food and …

Purpose We aimed to thoroughly characterize image quality of a novel deep learning image
reconstruction (DLIR), and investigate its potential for dose reduction in abdominal CT in …

Optimizing the CT acquisition parameters to obtain diagnostic image quality at the lowest
possible radiation dose is crucial in the radiosensitive pediatric population. The image …

The aim of this study was to characterize image quality and to determine the optimal strength
levels of a novel iterative reconstruction algorithm (quantum iterative reconstruction, QIR) for …

Hepatocellular carcinoma (HCC) is the sixth-most common cancer in the world, and hepatic
dynamic CT studies are routinely performed for its evaluation. Ongoing studies are …

Innovations in CT have been impressive among imaging and medical technologies in both
the hardware and software domain. The range and speed of CT scanning improved from the …