The development and widespread adoption of iterative reconstruction (IR) algorithms for CT
have greatly facilitated the contemporary practice of radiation dose reduction during …

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 …

Purpose To describe a large, publicly available dataset comprising computed tomography
(CT) projection data from patient exams, both at routine clinical doses and simulated lower …

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 …

In 1971, the first patient CT examination by Ambrose and Hounsfield paved the way for not
only volumetric imaging of the brain but of the entire body. From the initial 5-minute scan for …

Objectives To compare the image quality and hepatic metastasis detection of low-dose deep
learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative …

Photon-counting CT (PCCT) is an emerging advanced CT technology that differs from
conventional CT in its ability to directly convert incident x-ray photon energies into electrical …

Objectives To compare deep learning (True Fidelity, TF) and partial model based Iterative
Reconstruction (ASiR-V) algorithm for image texture, low contrast lesion detectability and …

Purpose To investigate performance in detectability of small (≤ 1 cm) low-contrast
hypoattenuating focal lesions by using filtered back projection (FBP) and iterative …

Purpose To estimate the ability of lower dose levels and iterative reconstruction (IR) to
display hepatic metastases that can be detected by radiologists. Materials and Methods …