Rationale and Objectives
To explore the value of an artificial intelligence (AI)-based application for identifying
plaque-specific stenosis and obstructive coronary artery disease from monoenergetic
spectral reconstructions on coronary computed tomography angiography (CTA).
Materials and Methods
This retrospective study enrolled 71 consecutive patients (52 men, 19 women; 63.3
± 10.7 years) who underwent coronary CTA and invasive coronary angiography for diagnosing
coronary artery disease. The conventional 120 kVp images and eight different virtual
monoenergetic images (VMIs) (from 40 keV to 140 keV at increment of 10 keV) were reconstructed.
An AI system automatically detected plaques from the conventional 120 kVp images and
VMIs and calculated the degree of stenosis, which was further compared to invasive
coronary angiography. The assessment was performed at a segment, vessel, and patient
level.
Results
Vessel and segment-based analyses showed comparable diagnostic performance between
conventional CTA images and VMIs from 50 keV to 90 keV. For vessel-based analysis,
the sensitivity, specificity, positive predictive value, negative predictive value
and diagnostic accuracy of conventional CTA were 74.3% (95% CI: 64.9%-82.0%), 85.6%
(95% CI: 77.0%-91.4%), 84.3% (95% CI: 75.2%-90.7%), 76.1% (95% CI: 67.1%-83.3%) and
79.8% (95% CI: 73.7%-84.9%), respectively; the average sensitivity, specificity, positive
predictive value, negative predictive value and diagnostic accuracy values of the
VMIs ranging from 50 keV to 90 keV were 71.6%, 90.7%, 87.5%, 64.1% and 81.6%, respectively.
For plaque-based assessment, diagnostic performance of the average VMIs ranging from
50 keV to 100 keV showed no significant statistical difference in diagnostic accuracy
compared to those of conventional CTA images in detecting calcified (91.4% vs. 93.8%,
p > 0.05), noncalcified (92.6% vs. 85.2%, p > 0.05) or mixed (80.2% vs. 81.2%, p > 0.05) stenosis, although the specificity was slightly higher (53.4% vs. 40.0%,
p > 0.05) in detecting stenosis caused by mixed plaques. For VMIs above 100 keV, the
diagnostic accuracy dropped significantly.
Conclusion
Our study showed that the performance of an AI-based application employed to detect
significant coronary stenosis in virtual monoenergetic reconstructions ranging from
50 keV to 90 keV was comparable to conventional 120 kVp reconstructions.
Key Words
Abbreviations:
CTA (Computed tomography angiography), CAD (Coronary artery disease), AI (Artificial intelligence), VMI (Virtual monoenergetic images), ICA (Invasive coronary angiography), SDCT (Spectral detector CT), 3D (3-Dimensional), MPR (Multiple planner reformat), CPR (Curve plannar reformat), CACS (Coronary artery calcium score), DE-CCTA (Dual-energy coronary CT angiography)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
- The diagnostic accuracy of 256-row computed tomographic angiography compared with invasive coronary angiography in patients with suspected coronary artery disease.Eur Heart J. 2010; 31: 1916-1923
- 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
- Diagnostic accuracy of 320-row multidetector computed tomography coronary angiography in the non-invasive evaluation of significant coronary artery disease.Eur Heart J. 2010; 31: 1908-1915
- Chest lesion CT radiological features and quantitative analysis in RT-PCR turned negative and clinical symptoms resolved COVID-19 patients.Quant Imaging Med Surg. 2020; 10: 1307-1317
- Artificial intelligence in medical imaging: a radiomic guide to precision phenotyping of cardiovascular disease.Cardiovasc Res. 2020; 116: 2040-2054
- Artificial intelligence in precision cardiovascular medicine.J Am Coll Cardiol. 2017; 69: 2657-2664
- Improvement of image quality in unenhanced dual-layer CT of the head using virtual monoenergetic images compared with polyenergetic single-energy CT.Invest Radiol. 2017; 52: 470-476
- Optimization of kiloelectron volt settings in cerebral and cervical dual-energy CT angiography determined with virtual monoenergetic imaging.Acad Radiol. 2014; 21: 431-436
- Virtual monochromatic imaging in patients with intermediate to high likelihood of coronary artery disease: impact of coronary calcification.Acad Radiol. 2016; 23: 1490-1497
- Impact of monochromatic coronary computed tomography angiography from single-source dual-energy CT on coronary stenosis quantification.J Cardiovasc Comput Tomogr. 2016; 10: 135-140
- Effect of energy difference in the evaluation of calcification size and luminal diameter in calcified coronary artery plaque using spectral CT.Jpn J Radiol. 2020; 38: 1142-1149
- Incremental improvement of diagnostic performance of coronary CT angiography for the assessment of coronary stenosis in the presence of calcium using a dual-layer spectral detector CT: validation by invasive coronary angiography.Int J Cardiovasc Imaging. 2021; 37: 2561-2572
- Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network.Nat Commun. 2020; 11: 4829
- Efficient processing of deep neural networks: a tutorial and survey.Proceedings of the IEEE. 2017; 12: 2295-2329
- Predictors of inaccurate coronary arterial stenosis assessment by CT angiography.JACC Cardiovasc Imaging. 2013; 6: 963-972
- Calcified vascular plaque specimens: assessment with cardiac dual-energy multidetector CT in anthropomorphically moving heart phantom.Radiology. 2008; 249: 119-126
- Virtual monochromatic spectral imaging with fast kilovoltage switching: improved image quality as compared with that obtained with conventional 120-kVp CT.Radiology. 2011; 259: 257-262
- Monoenergetic extrapolation of cardiac dual energy CT for artifact reduction.Acta Radiol. 2015; 56: 413-418
- Deep learning analysis in coronary computed tomographic angiography imaging for the assessment of patients with coronary artery stenosis.Comput Methods Programs Biomed. 2020; 196105651
- Diagnostic performance of deep learning-based vascular extraction and stenosis detection technique for coronary artery disease.Br J Radiol. 2020; 9320191028
- Plaque burden assessment and attenuation measurement of carotid atherosclerotic plaque using virtual monoenergetic images in comparison to conventional polyenergetic images from dual-layer spectral detector CT.Eur J Radiol. 2020; 132109302
- Improved depiction of atherosclerotic carotid artery stenosis in virtual monoenergetic reconstructions of venous phase dual-layer computed tomography in comparison to polyenergetic reconstructions.Eur J Radiol. 2018; 100: 36-42
Article info
Publication history
Published online: December 08, 2021
Accepted:
October 28,
2021
Received in revised form:
October 19,
2021
Received:
August 3,
2021
Identification
Copyright
© 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
