Advertisement

Improvement of Spatial Resolution on Coronary CT Angiography by Using Super-Resolution Deep Learning Reconstruction

Published:January 19, 2023DOI:https://doi.org/10.1016/j.acra.2022.12.044

      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 access
      One-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 Radiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Raff GL
        • Gallagher MJ
        • O'Neill WW
        • et al.
        Diagnostic accuracy of noninvasive coronary angiography using 64-slice spiral computed tomography.
        J Am Coll Cardiol. 2005; 46: 552-557
        • Nikolaou K
        • Knez A
        • Rist C
        • et al.
        Accuracy of 64-MDCT in the diagnosis of ischemic heart disease.
        AJR Am J Roentgenol. 2006; 187: 111-117
        • Budoff MJ
        • Dowe D
        • Jollis JG
        • et al.
        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
        • Steigner ML
        • Otero HJ
        • Cai T
        • et al.
        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
        • Dewey M
        • Zimmermann E
        • Deissenrieder F
        • et al.
        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
        • Tatsugami F
        • Matsuki M
        • Inada Y
        • et al.
        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
        • Takagi H
        • Tanaka R
        • Nagata K
        • et al.
        Diagnostic performance of coronary CT angiography with ultra-high-resolution CT: comparison with invasive coronary angiography.
        Eur J Radiol. 2018; 101: 30-37
        • Motoyama S
        • Ito H
        • Sarai M
        • et al.
        Ultra-high-resolution computed tomography angiography for assessment of coronary artery stenosis.
        Circ J. 2018; 82: 1844-1851
        • Lee T-C
        • Zhou J
        • Yu Z
        • et al.
        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
        • Taylor AJ
        • Cerqueira M
        • Hodgson JM
        • et al.
        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)
        • Trattner S
        • Halliburton S
        • Thompson CM
        • et al.
        Cardiac-specific conversion factors to estimate radiation effective dose from dose-length product in computed tomography.
        JACC Cardiovasc Imaging. 2018; 11: 64-74
        • Tatsugami F
        • Higaki T
        • Nakamura Y
        • et al.
        Deep learning-based image restoration algorithm for coronary CT angiography.
        Eur Radiol. 2019; 29: 5322-5329
        • Suzuki S
        • Machida H
        • Tanaka I
        • et al.
        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
        • Park C
        • Choo KS
        • Jung Y
        • et al.
        CT iterative vs deep learning reconstruction: comparison of noise and sharpness.
        Eur Radiol. 2021; 31: 3156-3164
        • Ichikawa Y
        • Kanii Y
        • Yamazaki A
        • et al.
        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
        • Birnbaum BA
        • Hindman N
        • Lee J
        • et al.
        Multi-detector row CT attenuation measurements: assessment of intra- and interscanner variability with an anthropomorphic body CT phantom.
        Radiology. 2007; 242: 109-119
        • Tatsugami F
        • Higaki T
        • Sakane H
        • et al.
        Coronary artery stent evaluation with model-based iterative reconstruction at coronary CT angiography.
        Acad Radiol. 2017; 24: 975-981
        • Monizzi G
        • Sonck J
        • Nagumo S
        • et al.
        Quantification of calcium burden by coronary CT angiography compared to optical coherence tomography.
        Int J Cardiovasc Imaging. 2020; 36: 2393-2402
        • Matsumoto H
        • Watanabe S
        • Kyo E
        • et al.
        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