Comparison of Knowledge-based Iterative Model Reconstruction and Hybrid Reconstruction Techniques for Liver CT Evaluation of Hypervascular Hepatocellular Carcinoma.

Journal: Journal of computer assisted tomography

PMID: 27331929

Abstract

OBJECTIVE: The purpose of this work was to evaluate the image quality, lesion conspicuity, and dose reduction provided by knowledge-based iterative model reconstruction (IMR) in computed tomography (CT) of the liver compared with hybrid iterative reconstruction (IR) and filtered back projection (FBP) in patients with hepatocellular carcinoma (HCC).

Authors

Hyun Jeong Park

From the *Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Republic of Korea, †Department of Radiology and the Institute of Radiation Medicine, Seoul National University Hospital, Seoul, Republic of Korea; and ‡Department of Radiology, Ulsan University Hospital, Ulsan University College of Medicine, Ulsan, Republic of Korea.
Jeong Min Lee
Sung Bin Park
Jong Beum Lee
Yoong Ki Jeong
Jeong Hee Yoon

Keywords

Adult Aged Algorithms Carcinoma, Hepatocellular Computer Simulation Female Humans Knowledge Bases Liver Liver Neoplasms Machine Learning Male Middle Aged Models, Biological Multidetector Computed Tomography Neovascularization, Pathologic Pattern Recognition, Automated Radiographic Image Enhancement Radiographic Image Interpretation, Computer-Assisted Reproducibility of Results Republic of Korea Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (27331929)

Comparison of Knowledge-based Iterative Model Reconstruction and Hybrid Reconstruction Techniques for Liver CT Evaluation of Hypervascular Hepatocellular Carcinoma.

Abstract

Authors

Keywords

External Resources

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