In Vitro Study of the Precision and Accuracy of Measurement of the Vascular Inner Diameter on Computed Tomography Angiography Using Deep Learning Image Reconstruction: Comparison With Filtered Back Projection and Iterative Reconstruction.

Journal: Journal of computer assisted tomography

PMID: 35099132

Abstract

OBJECTIVE: This study aimed to compare the performance of deep learning image reconstruction (DLIR) with that of standard filtered back projection (FBP) and adaptive statistical iterative reconstruction V (ASiR-V) for measurement of the vascular diameter on computed tomography (CT) angiography model.

Authors

Shigeru Suzuki

Department of Radiology, Tokyo Women's Medical University Medical Center East, Arakawa-ku.
Wakana Samejima

Department of Radiology, Tokyo Women's Medical University Medical Center East, Tokyo, Japan.
Shingo Harashima

Department of Radiology, Tokyo Women's Medical University Medical Center East, Tokyo, Japan.
Rika Fukui

Department of Radiology, Tokyo Women's Medical University Medical Center East, Arakawa-ku.

Keywords

Algorithms Computed Tomography Angiography Deep Learning Models, Cardiovascular Phantoms, Imaging Radiographic Image Interpretation, Computer-Assisted

External Resources

View on PubMed Access via DOI PubMed (35099132)

In Vitro Study of the Precision and Accuracy of Measurement of the Vascular Inner Diameter on Computed Tomography Angiography Using Deep Learning Image Reconstruction: Comparison With Filtered Back Projection and Iterative Reconstruction.

Abstract

Authors

Keywords

External Resources

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