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Impact of an intra-cycle motion correction algorithm on overall evaluability and diagnostic accuracy of computed tomography coronary angiography

  • Computed Tomography
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Abstract

Objectives

The aim of this study was to evaluate the impact of a novel intra-cycle motion correction algorithm (MCA) on overall evaluability and diagnostic accuracy of cardiac computed tomography coronary angiography (CCT).

Methods

From a cohort of 900 consecutive patients referred for CCT for suspected coronary artery disease (CAD), we enrolled 160 (18 %) patients (mean age 65.3 ± 11.7 years, 101 male) with at least one coronary segment classified as non-evaluable for motion artefacts. The CCT data sets were evaluated using a standard reconstruction algorithm (SRA) and MCA and compared in terms of subjective image quality, evaluability and diagnostic accuracy.

Results

The mean heart rate during the examination was 68.3 ± 9.4 bpm. The MCA showed a higher Likert score (3.1 ± 0.9 vs. 2.5 ± 1.1, p < 0.001) and evaluability (94%vs.79 %, p < 0.001) than the SRA. In a 45-patient subgroup studied by clinically indicated invasive coronary angiography, specificity, positive predictive value and accuracy were higher in MCA vs. SRA in segment-based and vessel-based models, respectively (87%vs.73 %, 50%vs.34 %, 85%vs.73 %, p < 0.001 and 62%vs.28 %, 66%vs.51 % and 75%vs.57 %, p < 0.001). In a patient-based model, MCA showed higher accuracy vs. SCA (93%vs.76 %, p < 0.05).

Conclusions

MCA can significantly improve subjective image quality, overall evaluability and diagnostic accuracy of CCT.

Key Points

• Cardiac computed tomographic coronary angiography (CCT) allows non-invasive evaluation of coronary arteries

• Intra-cycle motion correction algorithm (MCA) allows for compensation of coronary motion

• An MCA improves image quality, CCT evaluability and diagnostic accuracy

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Abbreviations

CAD:

Coronary artery disease

CCT:

Cardiac computed tomography coronary angiography

CNR:

Contrast to noise ratio

DLP:

Dose length product

DSCT:

Dual source computed tomography

ED:

Effective dose

HR:

Heart rate

ICA:

Invasive coronary angiography

MCA:

Intra-cycle motion correction algorithm

ROI:

Region of interest

SD:

Standard deviation

SRA:

Standard reconstruction algorithm

SNR:

Signal to noise ratio

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Acknowledgements

The scientific guarantor of this publication is Gianluca Pontone. The authors of this manuscript declare relationships with the following companies: Gianluca Pontone has been a speaker for GE Healthcare, Heartflow, Medtronic, Bayer and a consultant for GE Healthcare and Heartflow. Daniele Andreini has been in a speaker and consultant for GE Healthcare. The authors state that this work has not received any funding. No complex statistical methods were necessary for this paper. Institutional Review Board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. Study subjects or cohorts have not been previously reported. Methodology: retrospective, observational, performed at one institution.

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Authors and Affiliations

  1. Centro Cardiologico Monzino, IRCCS, Via C. Parea 4, 20138, Milan, Italy

    Gianluca Pontone, Daniele Andreini, Erika Bertella, Andrea Baggiano, Saima Mushtaq, Monica Loguercio, Chiara Segurini, Edoardo Conte, Virginia Beltrama, Andrea Annoni, Alberto Formenti, Maria Petullà, Piero Montorsi, Daniela Trabattoni, Antonio L. Bartorelli & Mauro Pepi

  2. Department of Cardiovascular Sciences and Community Health, University of Milan, Milan, Italy

    Daniele Andreini, Piero Montorsi & Antonio L. Bartorelli

  3. Department of Cardiology, University of Foggia, Foggia, Italy

    Andrea I. Guaricci

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  1. Gianluca Pontone
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Correspondence to Gianluca Pontone.

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Pontone, G., Andreini, D., Bertella, E. et al. Impact of an intra-cycle motion correction algorithm on overall evaluability and diagnostic accuracy of computed tomography coronary angiography. Eur Radiol 26, 147–156 (2016). https://doi.org/10.1007/s00330-015-3793-1

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  • DOI: https://doi.org/10.1007/s00330-015-3793-1

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