Comparative assessment of noise properties for two deep learning CT image reconstruction techniques and filtered back projection.

Journal: Medical physics

Published Date: Sep 12, 2022

Abstract

BACKGROUND: Two deep learning image reconstruction (DLIR) techniques from two different computed tomography (CT) vendors have recently been introduced into clinical practice.

Authors

Hiroki Kawashima

Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, 920-0942, Japan. Electronic address: kawa3@med.kanazawa-u.ac.jp.
Katsuhiro Ichikawa

Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University.
Tadanori Takata

Radiology Division, Kanazawa University Hospital, Takara-Machi, Kanazawa, Japan.
Issei Seto

Department of Radiological Technology, Mitsubishi Kyoto Hospital, Nishikyo-Ku, Kyoto, Japan.

Keywords

Algorithms Deep Learning Image Processing, Computer-Assisted Iodine Phantoms, Imaging Radiation Dosage Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Water

External Resources

View on PubMed Access via DOI PubMed (36047991)

Comparative assessment of noise properties for two deep learning CT image reconstruction techniques and filtered back projection.

Abstract

Authors

Keywords

External Resources

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