Image2Flow: A proof-of-concept hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data.

Journal: PLoS computational biology
PMID: 38900817

Abstract

Computational fluid dynamics (CFD) can be used for non-invasive evaluation of hemodynamics. However, its routine use is limited by labor-intensive manual segmentation, CFD mesh creation, and time-consuming simulation. This study aims to train a deep learning model to both generate patient-specific volume-meshes of the pulmonary artery from 3D cardiac MRI data and directly estimate CFD flow fields. This proof-of-concept study used 135 3D cardiac MRIs from both a public and private dataset. The pulmonary arteries in the MRIs were manually segmented and converted into volume-meshes. CFD simulations were performed on ground truth meshes and interpolated onto point-point correspondent meshes to create the ground truth dataset. The dataset was split 110/10/15 for training, validation, and testing. Image2Flow, a hybrid image and graph convolutional neural network, was trained to transform a pulmonary artery template to patient-specific anatomy and CFD values, taking a specific inlet velocity as an additional input. Image2Flow was evaluated in terms of segmentation, and the accuracy of predicted CFD was assessed using node-wise comparisons. In addition, the ability of Image2Flow to respond to increasing inlet velocities was also evaluated. Image2Flow achieved excellent segmentation accuracy with a median Dice score of 0.91 (IQR: 0.86-0.92). The median node-wise normalized absolute error for pressure and velocity magnitude was 11.75% (IQR: 9.60-15.30%) and 9.90% (IQR: 8.47-11.90), respectively. Image2Flow also showed an expected response to increased inlet velocities with increasing pressure and velocity values. This proof-of-concept study has shown that it is possible to simultaneously perform patient-specific volume-mesh based segmentation and pressure and flow field estimation using Image2Flow. Image2Flow completes segmentation and CFD in ~330ms, which is ~5000 times faster than manual methods, making it more feasible in a clinical environment.

Authors

  • Tina Yao
    From the Institutes of Health Informatics (T.Y.) and Cardiovascular Science (M.Q., J.A.S., V.M.), University College London, 20c Guilford Street, London WC1N 1DZ, England; Department of Cardiology, Boston Children's Hospital, Boston, Mass (N.S.C., G.F.M., S.G., D.S., R.H.R.); Department of Pediatrics, University of Michigan, Ann Arbor, Mich (A.L.D.); Division of Cardiology, The Children's Hospital of Philadelphia, Philadelphia, Pa (M.A.F.); Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio (R.K.); Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada (C.Z.L.); Department of Pediatrics, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Ill (J.D.R.); Department of Pediatric Cardiology, Emory University School of Medicine, Atlanta, Ga (T.C.S.); and Department of Cardiology, Texas Children's Hospital, Houston, Tex (J.W.).
  • Endrit Pajaziti
    Institute of Cardiovascular Science, University College London, London, United Kingdom.
  • Michael Quail
    UCL Centre for Cardiovascular Imaging, Institute of Cardiovascular Science, University College London, 30 Guildford Street, London, WC1N 1EH, UK.
  • Silvia Schievano
    UCL Great Ormond Street Institute of Child Health, London, UK.
  • Jennifer Steeden
    UCL Centre for Translational Cardiovascular Imaging, University College London, 30 Guilford St, London, WC1N 1EH, UK.
  • Vivek Muthurangu
    UCL Centre for Cardiovascular Imaging, University College London, London, United Kingdom.

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