Low-Dose Abdominal CT Using a Deep Learning-Based Denoising Algorithm: A Comparison with CT Reconstructed with Filtered Back Projection or Iterative Reconstruction Algorithm.

Journal: Korean journal of radiology

Published Date: Mar 1, 2020

Abstract

OBJECTIVE: To compare the image quality of low-dose (LD) computed tomography (CT) obtained using a deep learning-based denoising algorithm (DLA) with LD CT images reconstructed with a filtered back projection (FBP) and advanced modeled iterative reconstruction (ADMIRE).

Authors

Yoon Joo Shin

Department of Radiology, Konkuk University Medical Center, Seoul, Korea.
Won Chang
Jong Chul Ye
Eunhee Kang
Dong Yul Oh

Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea.
Yoon Jin Lee

Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea.
Ji Hoon Park

Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea.
Young Hoon Kim

Department of Surgery, College of Medicine, Ulsan University, Asan Medical Center, Seoul, Korea.

Keywords

Abdomen Adult Aged Area Under Curve Body Mass Index Deep Learning Female Humans Male Middle Aged Radiation Dosage Radiographic Image Interpretation, Computer-Assisted ROC Curve Signal-To-Noise Ratio Tomography, X-Ray Computed Young Adult

External Resources

View on PubMed Access via DOI PubMed (32090528)

Low-Dose Abdominal CT Using a Deep Learning-Based Denoising Algorithm: A Comparison with CT Reconstructed with Filtered Back Projection or Iterative Reconstruction Algorithm.

Abstract

Authors

Keywords

External Resources

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