Novel near-infrared spectroscopy-intravascular ultrasound-based deep-learning methodology for accurate coronary computed tomography plaque quantification and characterization

Anantharaman Ramasamy; Hessam Sokooti; Xiaotong Zhang; Evangelia Tzorovili; Retesh Bajaj; Pieter Kitslaar; Alexander Broersen; Rajiv Amersey; Ajay Jain; Mick Ozkor; Johan H C Reiber; Jouke Dijkstra; Patrick W Serruys; James C Moon; Anthony Mathur; Andreas Baumbach; Ryo Torii; Francesca Pugliese; Christos V Bourantas

doi:10.1093/ehjopen/oead090

Novel near-infrared spectroscopy-intravascular ultrasound-based deep-learning methodology for accurate coronary computed tomography plaque quantification and characterization

Eur Heart J Open. 2023 Oct 30;3(5):oead090. doi: 10.1093/ehjopen/oead090. eCollection 2023 Sep.

Authors

Anantharaman Ramasamy^{1

2}, Hessam Sokooti³, Xiaotong Zhang⁴, Evangelia Tzorovili⁵, Retesh Bajaj^{1

2}, Pieter Kitslaar^{3

4}, Alexander Broersen⁴, Rajiv Amersey¹, Ajay Jain¹, Mick Ozkor¹, Johan H C Reiber^{3

4}, Jouke Dijkstra⁴, Patrick W Serruys^{6

7}, James C Moon^{1

8}, Anthony Mathur^{1

2}, Andreas Baumbach^{1

2}, Ryo Torii⁹, Francesca Pugliese^{1

2}, Christos V Bourantas^{1

2

8}

Affiliations

¹ Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK.
² Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, Mile End Road, London E1 4NS, UK.
³ Medis Medical Imaging Systems, Leiden, The Netherlands.
⁴ Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
⁵ Pragmatic Clinical Trials Unit, Centre for Evaluation and Methods, Wolfson Institute of Population Health, Queen Mary University of London, London, UK.
⁶ Faculty of Medicine, National Heart and Lung Institute, Imperial College London, Cale Street, London SW3 6LY, UK.
⁷ Department of Cardiology, National University of Ireland, Galway, Ireland.
⁸ Institute of Cardiovascular Sciences, University College London, Gower Street, London WC1E 6BT, UK.
⁹ Department of Mechanical Engineering, University College London, Torrington Place, London WC1E 7JE, UK.

Abstract

Aims: Coronary computed tomography angiography (CCTA) is inferior to intravascular imaging in detecting plaque morphology and quantifying plaque burden. We aim to, for the first time, train a deep-learning (DL) methodology for accurate plaque quantification and characterization in CCTA using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS).

Methods and results: Seventy patients were prospectively recruited who underwent CCTA and NIRS-IVUS imaging. Corresponding cross sections were matched using an in-house developed software, and the estimations of NIRS-IVUS for the lumen, vessel wall borders, and plaque composition were used to train a convolutional neural network in 138 vessels. The performance was evaluated in 48 vessels and compared against the estimations of NIRS-IVUS and the conventional CCTA expert analysis. Sixty-four patients (186 vessels, 22 012 matched cross sections) were included. Deep-learning methodology provided estimations that were closer to NIRS-IVUS compared with the conventional approach for the total atheroma volume (Δ_DL-NIRS-IVUS: -37.8 ± 89.0 vs. Δ_{Conv-NIRS-IVUS}: 243.3 ± 183.7 mm3, variance ratio: 4.262, P < 0.001) and percentage atheroma volume (-3.34 ± 5.77 vs. 17.20 ± 7.20%, variance ratio: 1.578, P < 0.001). The DL methodology detected lesions more accurately than the conventional approach (Area under the curve (AUC): 0.77 vs. 0.67, P < 0.001) and quantified minimum lumen area (Δ_DL-NIRS-IVUS: -0.35 ± 1.81 vs. Δ_{Conv-NIRS-IVUS}: 1.37 ± 2.32 mm², variance ratio: 1.634, P < 0.001), maximum plaque burden (4.33 ± 11.83% vs. 5.77 ± 16.58%, variance ratio: 2.071, P = 0.004), and calcific burden (-51.2 ± 115.1 vs. -54.3 ± 144.4, variance ratio: 2.308, P < 0.001) more accurately than conventional approach. The DL methodology was able to segment a vessel on CCTA in 0.3 s.

Conclusions: The DL methodology developed for CCTA analysis from co-registered NIRS-IVUS and CCTA data enables rapid and accurate assessment of lesion morphology and is superior to expert analysts (Clinicaltrials.gov: NCT03556644).

Keywords: Coronary computed tomography angiography; Deep learning; Intravascular ultrasound; Near-infrared spectroscopy.

Associated data

ClinicalTrials.gov/NCT03556644