Cross-sectional angle prediction of lipid-rich and calcified tissue on computed tomography angiography images.

Journal: International journal of computer assisted radiology and surgery
PMID: 38478204

Abstract

PURPOSE: The assessment of vulnerable plaque characteristics and distribution is important to stratify cardiovascular risk in a patient. Computed tomography angiography (CTA) offers a promising alternative to invasive imaging but is limited by the fact that the range of Hounsfield units (HU) in lipid-rich areas overlaps with the HU range in fibrotic tissue and that the HU range of calcified plaques overlaps with the contrast within the contrast-filled lumen. This paper is to investigate whether lipid-rich and calcified plaques can be detected more accurately on cross-sectional CTA images using deep learning methodology.

Authors

  • Xiaotong Zhang
    Dalian University of Technology, Dalian 116024, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian 116024, China. Electronic address: zxt.dut@hotmail.com.
  • Alexander Broersen
    Department of Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, the Netherlands.
  • Hessam Sokooti
    Division of Image Processing of the Leiden University Medical Center, Leiden, The Netherlands.
  • Anantharaman Ramasamy
    Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.
  • Pieter Kitslaar
    Medis Medical Imaging Systems Leiden the Netherlands.
  • Ramya Parasa
    Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK.
  • Medeni Karaduman
    Cardiology, Van Yuzuncu Yil University, Van, Turkey.
  • Amear Souded Ali Jan Mohammed
    School of Engineering and Material Science, 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.).
  • Jouke Dijkstra
    Department of Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, The Netherlands.

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