1 |
尹立雪.心腔内血液流场及流体力学状态的可视化观察及量化评价[J].中华医学超声杂志(电子版),2009,6(3):427-431. 10.3969/j.issn.1672-6448.2009.03.002
|
|
YIN L X. Visualised observation and quantixed evaluation on heart flow field and hydrodynamic status [J]. Chinese Journal of Medical Ultrasound (Electronic Version), 2009, 6(3): 427-431. 10.3969/j.issn.1672-6448.2009.03.002
|
2 |
OHTSUKI S, TANAK M. The flow velocity distribution from the doppler information on a plane in three-dimensional flow [J]. Journal of Visualization, 2006, 9(1): 69-82. 10.1007/bf03181570
|
3 |
UEJIMA T, KOIKE A, SAWADA H, et al. A new echocardiographic method for identifying vortex flow in the left ventricle: numerical validation [J]. Ultrasound in Medicine and Biology, 2010, 36(5): 772-788. 10.1016/j.ultrasmedbio.2010.02.017
|
4 |
GARCIA D, ÁLAMO J C DEL, TANNÉ D, et al. Two-dimensional intraventricular flow mapping by digital processing conventional color-Doppler echocardiography images [J]. IEEE Transactions on Medical Imaging, 2010, 29(10): 1701-1713. 10.1109/tmi.2010.2049656
|
5 |
ITATANI K, OKADA T, UEJIMA T, et al. Intraventricular flow velocity vector visualization based on the continuity equation and measurements of vorticity and wall shear stress [J]. Japanese Journal of Applied Physics, 2013, 52(7): 1044-1055. 10.7567/jjap.52.07hf16
|
6 |
谢盛华,尹立雪,甘建红,等.彩色多普勒图像心脏流场流线可视化方法[J].计算机辅助设计与图形学学报,2014,26(2):287-292. 10.3969/j.issn.1003-9775.2014.02.015
|
|
XIE S H, YIN L X, GAN J H, et al. Visualization method of plane streamlines in cardiac flow field based on color Doppler image information [J]. Journal of Computer-Aided Design and Computer Graphics, 2014, 26(2): 287-292. 10.3969/j.issn.1003-9775.2014.02.015
|
7 |
ASAMI R, TANAKA T, KAWABATA K I, et al. Accuracy and limitations of vector flow mapping: left ventricular phantom validation using stereo particle image velocimetory [J]. Journal of Echocardiography, 2017, 15(2): 57-66. 10.1007/s12574-016-0321-5
|
8 |
TANAKA T, ASAMI R, KAWABATA K I, et al. A posteriori accuracy estimation of ultrasonic Vector-Flow Mapping (VFM) [J]. Journal of Visualization, 2017, 20(3): 607-623. 10.1007/s12650-016-0413-3
|
9 |
ZHUANG Z M, LIU G B, DING W L, et al. Cardiac VFM visualization and analysis based on YOLO deep learning model and modified 2D continuity equation [J]. Computerized Medical Imaging and Graphics, 2020, 82: Article No.101732. 10.1016/j.compmedimag.2020.101732
|
10 |
SUN D, YANG X, LIU M Y, et al. PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IIEEE, 2018: 8934-8943. 10.1109/cvpr.2018.00931
|
11 |
尹立雪.现代超声心脏电生理学[M].北京:人民军医出版社,2007:167-178.
|
|
YIN L X. Modern Ultrasonic in Cardiac Electrophysiology [M]. Beijing: People’s Military Medical Press, 2007: 167-178.
|
12 |
KOBAYSHI I, MORI S, ARAKAWA M, et al. Spiral complex movements of the heart wall at the beginning of myocardial contraction detected by high frame speckle tracking [C]// Proceedings of the 2018 IEEE International Ultrasonics Symposium. Piscataway: IEEE, 2018: 1-4. 10.1109/ultsym.2018.8579988
|
13 |
CALVO M, BONNET J L, LE ROLLE V, et al. Evaluation of three-dimensional accelerometers for the study of left ventricular contractility [C]// Proceedings of the 2018 Computing in Cardiology Conference. Piscataway: IEEE, 2018: 1-4. 10.22489/cinc.2018.176
|
14 |
ZENG Q Y, WANG L, YANG Y L, et al. Comparison of multidirectional myocardial deformation result from pressure and volume overload in compensatory stage [J]. IEEE Access, 2018, 6: 76429-76436. 10.1109/access.2018.2882438
|
15 |
RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation [C]// Proceedings of the 2015 International Conference on Medical Image Computing and Computer-Assisted Intervention, LNCS9351. Berlin: Springer, 2015: 234-241.
|
16 |
LECLERC S, SMISTAD E, PEDROSA J, et al. Deep learning for segmentation using an open large-scale dataset in 2D echocardiography [J]. IEEE Transactions on Medical Imaging, 2019, 38(9): 2198-2210. 10.1109/tmi.2019.2900516
|
17 |
PENG B, XIAN Y H, ZHANG Q, et al. Neural-network-based motion tracking for breast ultrasound strain elastography: an initial assessment of performance and feasibility [J]. Ultrasonic Imaging, 2020, 42(2): 74-91. 10.1177/0161734620902527
|
18 |
DOSOVITSKIY A, FISCHER P, ILG E, et al. FlowNet: learning optical flow with convolutional networks [C]// Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2015: 2758-2766. 10.1109/iccv.2015.316
|
19 |
GROVER P. 5-regression-loss-functions-all-machine-learners-should-know: choosing the right loss function for fitting a model [EB/OL]. [2021-03-08]. .
|
20 |
施仲伟.超声心动图在心血管疾病诊断中的应用[J].临床荟萃,2009,24(19):1671-1674.
|
|
SHI Z W. Application of echocardiography in diagnosis of cardiovascular diseases [J]. Clinical Focus, 2009, 24(19): 1671-1674.
|
21 |
LEDESMA-CARBAYO M J, KYBIC J, DESCO M, et al. Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation [J]. IEEE Transactions on Medical Imaging, 2005, 24(9): 1113-1126. 10.1109/tmi.2005.852050
|