A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.

Journal: The international journal of cardiovascular imaging
Published Date:

Abstract

Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in non-gated images as existing methods to acquire gated or to retrospectively gate IVUS images have failed to dominate in research. We developed a novel deep learning (DL)-methodology for end-diastolic frame detection in IVUS and compared its efficacy against expert analysts and a previously established methodology using electrocardiographic (ECG)-estimations as reference standard. Near-infrared spectroscopy-IVUS (NIRS-IVUS) data were prospectively acquired from 20 coronary arteries and co-registered with the concurrent ECG-signal to identify end-diastolic frames. A DL-methodology which takes advantage of changes in intensity of corresponding pixels in consecutive NIRS-IVUS frames and consists of a network model designed in a bidirectional gated-recurrent-unit (Bi-GRU) structure was trained to detect end-diastolic frames. The efficacy of the DL-methodology in identifying end-diastolic frames was compared with two expert analysts and a conventional image-based (CIB)-methodology that relies on detecting vessel movement to estimate phases of the cardiac cycle. A window of ± 100 ms from the ECG estimations was used to define accurate end-diastolic frames detection. The ECG-signal identified 3,167 end-diastolic frames. The mean difference between DL and ECG estimations was 3 ± 112 ms while the mean differences between the 1st-analyst and ECG, 2nd-analyst and ECG and CIB-methodology and ECG were 86 ± 192 ms, 78 ± 183 ms and 59 ± 207 ms, respectively. The DL-methodology was able to accurately detect 80.4%, while the two analysts and the CIB-methodology detected 39.0%, 43.4% and 42.8% of end-diastolic frames, respectively (P < 0.05). The DL-methodology can identify NIRS-IVUS end-diastolic frames accurately and should be preferred over expert analysts and CIB-methodologies, which have limited efficacy.

Authors

  • Retesh Bajaj
    Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.
  • Xingru Huang
    School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK.
  • Yakup Kilic
    Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.
  • Ajay Jain
    Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.
  • Anantharaman Ramasamy
    Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.
  • Ryo Torii
    Department of Mechanical Engineering, University College London, London, UK.
  • James Moon
    Imperial College London, National Heart and Lung Institute, Hammersmith Hospital, United Kingdom
  • Tat Koh
    Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.
  • Tom Crake
    Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.
  • Maurizio K Parker
    Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK.
  • Vincenzo Tufaro
    Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.
  • Patrick W Serruys
    NHLI, Imperial College London, London, United Kingdom.
  • Francesca Pugliese
    Centre for Advanced Cardiovascular Imaging, NIHR Cardiovascular Biomedical Research Unit at Barts, Barts and the London School of Medicine, Queen Mary University of London, UK.
  • Anthony Mathur
    Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.
  • Andreas Baumbach
    Kirchhoff-Institute for Physics, Heidelberg University, Im Neuenheimer Feld 227, D-69120 Heidelberg, Germany.
  • Jouke Dijkstra
    Department of Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, The Netherlands.
  • Qianni Zhang
    Queen Mary University of London, London, UK.
  • Christos V Bourantas
    Institute of Cardiovascular Science, University College London, United Kingdom (K.D.K., A.S., J.B.A., L.C., C.M., A.N.B., T.K., C.V.B., R.H.D., M.F., J.C.M.).