Deep Learning Image Reconstruction Algorithm for CCTA: Image Quality Assessment and Clinical Application.

Journal: Journal of computer assisted tomography
Published Date: Oct 11, 2023

Abstract

OBJECTIVE: The increasing number of coronary computed tomography angiography (CCTA) requests raised concerns about dose exposure. New dose reduction strategies based on artificial intelligence have been proposed to overcome limitations of iterative reconstruction (IR) algorithms. Our prospective study sought to explore the added value of deep-learning image reconstruction (DLIR) in comparison with a hybrid IR algorithm (adaptive statistical iterative reconstruction-veo [ASiR-V]) in CCTA, even in clinical challenging scenarios, as obesity, heavily calcified vessels and coronary stents.

Authors

  • Federica Catapano
  • Costanza Lisi
    Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
  • Giovanni Savini
    Advanced Imaging and Radiomics Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy.
  • Marzia Olivieri
    Department of neuroscience, Imaging and Clinical Sciences, "G. D'Annunzio" University of Chieti-Pescara, Chieti, Italy.
  • Stefano Figliozzi
    From the Department of Radiology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.
  • Alessandra Caracciolo
    Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
  • Lorenzo Monti
  • Marco Francone
    Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Policlinico Umberto I, V.le Regina Elena 324, 00161, Rome, Italy.

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