Image2Flow: A hybrid image and graph convolutional neural network for
  rapid patient-specific pulmonary artery segmentation and CFD flow field
  calculation from 3D cardiac MRI data
Tina Yao Endrit Pajaziti Michael Quail Silvia Schievano Jennifer A Steeden Vivek Muthurangu
Computational fluid dynamics (CFD) can be used for 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 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 85/10/15 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. Image2Flow was evaluated in terms of segmentation and accuracy of CFD
predicted was assessed using node-wise comparisons. Centerline comparisons of
Image2Flow and CFD simulations performed using machine learning segmentation
were also performed.
  Image2Flow achieved excellent segmentation accuracy with a median Dice score
of 0.9 (IQR: 0.86-0.92). The median node-wise normalized absolute error for
pressure and velocity magnitude was 11.98% (IQR: 9.44-17.90%) and 8.06% (IQR:
7.54-10.41), respectively. Centerline analysis showed no significant difference
between the Image2Flow and conventional CFD simulated on machine
learning-generated volume-meshes.
  This proof-of-concept study has shown it is possible to simultaneously
perform patient specific volume-mesh based segmentation and pressure and flow
field estimation. Image2Flow completes segmentation and CFD in ~205ms, which
~7000 times faster than manual methods, making it more feasible in a clinical
environment.