Rationale and Objectives
Our objective was to compare the image quality of coronary CT angiography reconstructed
with super-resolution deep learning reconstruction (SR-DLR) and with hybrid iterative
reconstruction (IR) images.
Materials and Methods
This retrospective study included 100 patients who underwent coronary CT angiography
using a 320-detector-row CT scanner. The CT images were reconstructed with hybrid
IR and SR-DLR. The standard deviation of the CT number was recorded and the CT attenuation
profile across the left main coronary artery was generated to calculate the contrast-to-noise
ratio (CNR) and measure the edge rise slope (ERS). Overall image quality was evaluated
and plaque detectability was assessed on a 4-point scale (1 = poor, 4 = excellent).
For reference, invasive coronary angiography of 14 patients was used.
Results
The mean image noise on SR-DLR was significantly lower than on hybrid IR images (15.6
vs 22.9 HU; p < 0.01). The mean CNR was significantly higher and the ERS was steeper on SR-DLR-
compared to hybrid IR images (CNR: 32.4 vs 20.4, p < 0.01; ERS: 300.0 vs 198.2 HU/mm, p < 0.01). The image quality score was better on SR-DLR- than on hybrid IR images (3.6
vs 3.1; p < 0.01). SR-DLR increased the detectability of plaques with < 50% stenosis (p < 0.01).
Conclusion
SR-DLR was superior to hybrid IR with respect to the image noise, the sharpness of
coronary artery margins, and plaque detectability.
Key Words
Abbreviations:
CCTA (coronary computed tomography angiography), U-HRCT (ultra-high-resolution computed tomography), SR-DLR (super-resolution-deep learning reconstruction), DCNN (deep convolutional neural network), NR (normal resolution), IR (iterative reconstruction), LMA (left main coronary artery), CNR (contrast-to-noise ratio), ERS (edge rise slope)To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Academic RadiologyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Diagnostic accuracy of noninvasive coronary angiography using 64-slice spiral computed tomography.J Am Coll Cardiol. 2005; 46: 552-557
- Accuracy of 64-MDCT in the diagnosis of ischemic heart disease.AJR Am J Roentgenol. 2006; 187: 111-117
- Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial.J Am Coll Cardiol. 2008; 52: 1724-1732
- Narrowing the phase window width in prospectively ECG-gated single heart beat 320-detector row coronary CT angiography.Int J Cardiovasc Imaging. 2009; 25: 85-90
- Noninvasive coronary angiography by 320-row computed tomography with lower radiation exposure and maintained diagnostic accuracy: comparison of results with cardiac catheterization in a head-to-head pilot investigation.Circulation. 2009; 120: 867-875
- Feasibility of low-volume injections of contrast material with a body weight-adapted iodine-dose protocol in 320-detector row coronary CT angiography.Acad Radiol. 2010; 17: 207-211
- Diagnostic performance of coronary CT angiography with ultra-high-resolution CT: comparison with invasive coronary angiography.Eur J Radiol. 2018; 101: 30-37
- Ultra-high-resolution computed tomography angiography for assessment of coronary artery stenosis.Circ J. 2018; 82: 1844-1851
- Deep learning enabled wide-coverage high-resolution cardiac CT.SPIE Med Imaging. 2022; (San Diego, California, United States)https://doi.org/10.1117/12.2611817
- ACCF/SCCT/ACR/AHA/ASE/ASNC/NASCI/SCAI/SCMR 2010 Appropriate use criteria for cardiac computed tomography. A report of the american college of cardiology foundation appropriate use criteria task force, the society of cardiovascular computed tomography, the American college of radiology, the American heart association, the American society of echocardiography, the American society of nuclear cardiology, the North American society for cardiovascular imaging, the society for cardiovascular angiography and interventions, and the society for cardiovascular magnetic resonance.J Cardiovasc Comput Tomogr. 2010; 4 (407 e1-33)
- Cardiac-specific conversion factors to estimate radiation effective dose from dose-length product in computed tomography.JACC Cardiovasc Imaging. 2018; 11: 64-74
- Deep learning-based image restoration algorithm for coronary CT angiography.Eur Radiol. 2019; 29: 5322-5329
- Vascular diameter measurement in CT angiography: comparison of model-based iterative reconstruction and standard filtered back projection algorithms in vitro.AJR Am J Roentgenol. 2013; 200: 652-657
- CT iterative vs deep learning reconstruction: comparison of noise and sharpness.Eur Radiol. 2021; 31: 3156-3164
- Deep learning image reconstruction for improvement of image quality of abdominal computed tomography: comparison with hybrid iterative reconstruction.Jpn J Radiol. 2021; 39: 598-604
- Multi-detector row CT attenuation measurements: assessment of intra- and interscanner variability with an anthropomorphic body CT phantom.Radiology. 2007; 242: 109-119
- Coronary artery stent evaluation with model-based iterative reconstruction at coronary CT angiography.Acad Radiol. 2017; 24: 975-981
- Quantification of calcium burden by coronary CT angiography compared to optical coherence tomography.Int J Cardiovasc Imaging. 2020; 36: 2393-2402
- Standardized volumetric plaque quantification and characterization from coronary CT angiography: a head-to-head comparison with invasive intravascular ultrasound.Eur Radiol. 2019; 29: 6129-6139
Article info
Publication history
Published online: January 19, 2023
Accepted:
December 28,
2022
Received in revised form:
December 27,
2022
Received:
October 11,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
