Publications Archives

Image quality of ultralow-dose chest CT using deep learning…

Objective: To compare the image quality between the vendor-agnostic and vendor-specific algorithms on ultralow-dose chest CT.

Noise reduction approach in pediatric abdominal CT combining…

To evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning–based noise reduction technique for pediatric abdominal CT, compared with standard iodine concentration single-energy polychromatic CT (SECT).

Incremental Image Noise Reduction in Coronary CT Angiography…

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

Emphysema quantification using low-dose computed…

This study determined the effect of dose reduction and kernel selection on quantifying emphysema using low-dose computed tomography (LDCT) and evaluated the efficiency of a deep learning–based kernel conversion technique in normalizing kernels for emphysema quantification.

Effects of simulated dose variation on contrast-enhanced CT-based radiomic analysis for Non-Small Cell Lung…

To examine the potential effect of CT dose variation on radiomic features in vivo using simulated contrast-enhanced CT dose reduction in patients with non-small lung cell cancer (NSCLC).

Application of Vendor-Neutral Iterative Reconstruction..

To compare image qualities between vendor-neutral and vendor-specific hybrid iterative reconstruction (IR) techniques for abdominopelvic computed tomography (CT) in young patients.

Effect of a novel denoising technique on image quality and diagnostic T accuracy in low-dose CT in patients with suspected appendicitis

To determine the effect of a vendor unspecific, DICOM-based denoising technique on image quality and diagnostic performance in low-dose simulated abdominal computed tomography (CT) examinations in patients with suspected appendicitis.