Image Quality and Lesion Detectability of Lower-Dose Abdominopelvic CT Obtained Using Deep Learning Image Reconstruction.

Journal: Korean journal of radiology

Published Date: Jan 27, 2022

Abstract

OBJECTIVE: To evaluate the image quality and lesion detectability of lower-dose CT (LDCT) of the abdomen and pelvis obtained using a deep learning image reconstruction (DLIR) algorithm compared with those of standard-dose CT (SDCT) images.

Authors

June Park

Department of Nano Semiconductor Engineering, Korea University, Seoul, 02841, South Korea.
Jaeseung Shin
In Kyung Min

Biostatistics Collaboration Unit, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea.
Heejin Bae

Department of Surgery, Yonsei University College of Medicine, Seoul, Republic of Korea.
Yeo-Eun Kim

Department of Radiology, Seoul Medical Center, Seoul, Korea.
Yong Eun Chung

Department of Radiology, Yonsei University College of Medicine, Seoul, 03722, Korea.

Keywords

Aged Deep Learning Female Humans Image Processing, Computer-Assisted Male Middle Aged Radiation Dosage Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (35289146)

Image Quality and Lesion Detectability of Lower-Dose Abdominopelvic CT Obtained Using Deep Learning Image Reconstruction.

Abstract

Authors

Keywords

External Resources

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