Energy-integrating detector based ultra-high-resolution CT with deep learning reconstruction for the assessment of calcified lesions in coronary artery disease.

Journal: Journal of cardiovascular computed tomography

PMID: 39379200

Abstract

BACKGROUND: The aim of this study to compare of the image quality of calcified lesions in coronary artery disease between deep learning reconstruction (DLR) and model-based iterative reconstruction (MBIR) on energy-integrating detector (EID) based ultra-high-resolution CT (UHRCT).

Authors

Misato Sone

Department of Radiology, Iwate Medical University, 2-1-1, Idaidori, Yahaba, 028-3695, Iwate, Japan.
Makoto Orii

Department of Radiology, Iwate Medical University, 2-1-1, Idaidori, Yahaba, 028-3695, Iwate, Japan. kori931@gmail.com.
Yoshitaka Ota

Division of Central Radiology, Iwate Medical University Hospital, Iwate, Japan.
Takuya Chiba

Center for Radiological Science, Iwate Medical University, 2-1-1, Idaidori, Yahaba, 028-3695, Japan.
Joanne D Schuijf

Canon Medical Systems Europe, Amstelveen, Netherlands.
Naruomi Akino

Canon Medical Systems Co. Ltd., Otawara, Japan.
Kunihiro Yoshioka

Department of Radiology, Iwate Medical University School of Medicine, Iwate, Japan.

Keywords

Aged Computed Tomography Angiography Coronary Angiography Coronary Artery Disease Coronary Vessels Deep Learning Female Humans Male Middle Aged Multidetector Computed Tomography Phantoms, Imaging Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Reproducibility of Results Retrospective Studies Severity of Illness Index Vascular Calcification

External Resources

View on PubMed Access via DOI PubMed (39379200)

Energy-integrating detector based ultra-high-resolution CT with deep learning reconstruction for the assessment of calcified lesions in coronary artery disease.

Abstract

Authors

Keywords

External Resources

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