Local hemodynamic forces play an important role in determining the functional
significance of coronary arterial stenosis and understanding the mechanism of
coronary disease progression. Computational fluid dynamics (CFD) have been
widely performed to simulate hemodynamics non-invasively from coronary computed
tomography angiography (CCTA) images. However, accurate computational analysis
is still limited by the complex construction of patient-specific modeling and
time-consuming computation. In this work, we proposed an end-to-end deep
learning framework, which could predict the coronary artery hemodynamics from
CCTA images. The model was trained on the hemodynamic data obtained from 3D
simulations of synthetic and real datasets. Extensive experiments demonstrated
that the predicted hemdynamic distributions by our method agreed well with the
CFD-derived results. Quantitatively, the proposed method has the capability of
predicting the fractional flow reserve with an average error of 0.5\% and 2.5\%
for the synthetic dataset and real dataset, respectively. Particularly, our
method achieved much better accuracy for the real dataset compared to
PointNet++ with the point cloud input. This study demonstrates the feasibility
and great potential of our end-to-end deep learning method as a fast and
accurate approach for hemodynamic analysis.

本文提出了一种基于深度学习的框架，利用计算机辅助技术快速准确地进行冠状动脉血液动力学分析， 通过迭代学习算法可以用 CCTA 图像预测冠状动脉的血液动力学分布，并与计算流体动力学分析结果相吻合，为冠状动脉疾病发展的机制揭示提供新思路。

Voxel2Hemodynamics：一种端到端的深度学习方法，用于预测冠状动脉血液动力学

Voxel2Hemodynamics: An End-to-end Deep Learning Method for Predicting  Coronary Artery Hemodynamics

Segmentation of the coronary artery is an important task for the quantitative
analysis of coronary computed tomography angiography (CCTA) images and is being
stimulated by the field of deep learning. However, the complex structures with
tiny and narrow branches of the coronary artery bring it a great challenge.
Coupled with the medical image limitations of low resolution and poor contrast,
fragmentations of segmented vessels frequently occur in the prediction.
Therefore, a geometry-based cascaded segmentation method is proposed for the
coronary artery, which has the following innovations: 1) Integrating geometric
deformation networks, we design a cascaded network for segmenting the coronary
artery and vectorizing results. The generated meshes of the coronary artery are
continuous and accurate for twisted and sophisticated coronary artery
structures, without fragmentations. 2) Different from mesh annotations
generated by the traditional marching cube method from voxel-based labels, a
finer vectorized mesh of the coronary artery is reconstructed with the
regularized morphology. The novel mesh annotation benefits the geometry-based
segmentation network, avoiding bifurcation adhesion and point cloud dispersion
in intricate branches. 3) A dataset named CCA-200 is collected, consisting of
200 CCTA images with coronary artery disease. The ground truths of 200 cases
are coronary internal diameter annotations by professional radiologists.
Extensive experiments verify our method on our collected dataset CCA-200 and
public ASOCA dataset, with a Dice of 0.778 on CCA-200 and 0.895 on ASOCA,
showing superior results. Especially, our geometry-based model generates an
accurate, intact and smooth coronary artery, devoid of any fragmentations of
segmented vessels.

提出一个基于几何的级联分割算法用于冠状动脉的连续和准确的三维重建，克服了医疗图像低分辨率和对比度差的局限性，比传统算法提升了分割精度，特别是避免了血管断裂的问题。