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Abstract

The high morbidity and mortality of cardiovascular diseases (CVDs) seriously affects the quality of human life. How to asses cardiac function, assist in the diagnosis and treatment of clinical CVDs and evaluate prognosis is a problem to be solved urgently. In response to this issue, based on previous work of Cardiac Cine Magnetic Resonance (CCMR) image segmentation of the left myocardium (LVM), a robust and accurate LVM motion tracking method (DispFlow_UNet_Flow) with using the displacement flow UNet (DispFlow_UNet) and biomechanics-informed variational autoencoder (VAE) is proposed in this paper. The following are the main research contents: (1) building a compressed excitation residual U-net network to accurately segment LVM, calculating the ventricular volume and myocardial mass according to the segmentation results, and then evaluating the overall cardiac function; (2) reconstructing the dense displacement field (DDF) based on the proposed motion tracking method, and obtaining the LVM dense displacement field by combining the LVM segmentation mask; (3) contrasting and evaluating the motion tracking results by using the true displacement vector field of simulated data and clinical data sets. All the results show that the tracking algorithm proposed in this paper has high precision and generalization capability.

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Authors and contacts

Corresponding author:Wang Li-Jia,lijiawangmri@163.com

Funds:Project supported by the Key-Area Research and Development Program of Guangdong Province (Grant Nos. 2019B20230004, 2020B010113015).

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数据集	数据量	相位数	层数	磁共振扫描仪
ACDC	100	12—35	9—10	1.5 T Siemens Area 3.0 T Siemens Trio Tim
临床数据	75	20—28	6—10	1.5 T GE
合成MRI数据	15	30	9—14	无

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第n重交叉验证(Foldn)	Fold1	Fold2	Fold3	Fold4	Fold5
内部数据data_ int	HCM MINFNOR RVA	DCM MINFNOR RVA	DCM HCMNOR RVA	DCM HCMMINF RVA	DCM HCMMINF NOR
外部数据 data_ ext	DCM	HCM	MINF	NOR	RVA
训练集	data_int×0.8
验证集	data_int×0.1
预测集	data_int×0.1 + data_ext×1

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EDV /mL	ESV/mL	SV /mL	EF /%	ED_LVM/L·min^–1	ES_LVM /g
97.94 ±34.73	38.133 ±21.30	61.39 ±24.43	62.90 ±15.3	88.23 ±35.23	84.13±33.22

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方法	Dice		MCD		HD
方法	LVC	LVM	LVC	LVM	LVC	LVM
FFD	0.920 (0.029)	0.797 (0.034)	1.256 (0.387)	1.192 (0.392)	3.431 (0.688)	3.439 (1.181)
DL+L2	0.912 (0.037)	0.800 (0.057)	1.340 (0.428)	1.171 (0.286)	3.699 (1.099)	3.285 (0.717)
DispFlow_UNet	0.925 (0.024)	0.818 (0.054)	1.203 (0.329)	0.971(0.120)	3.347(0.785)	2.906(0.358)
DispFlow_UNet_VAE	0.946(0.016)	0.843(0.031)	0.924(0.279)	0.931(0.239)	2.991(0.960)	3.178 (0.744)

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数据集	Dice			MCD
数据集	LVC	LVM	RVC	LVC	LVM	RVC
ACDC	0.946 (0.016)	0.843 (0.031)	0.876 (0.078)	0.924 (0.279)	0.931 (0.239)	1.348 (0.858)
临床数据	0.882 (0.022)	0.836 (0.059)	0.850 (0.093)	1.874 (0.383)	1.079 (0.262)	1.427 (0.579)

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Vol. 70, Issue 22 - 2021-11-20

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