Efficient Image Registration Network for Non-Rigid Cardiac Motion Estimation

2021 ◽  
pp. 14-24
Author(s):  
Jiazhen Pan ◽  
Daniel Rueckert ◽  
Thomas Küstner ◽  
Kerstin Hammernik
Keyword(s):  
Image Registration ◽  
Motion Estimation ◽  
Cardiac Motion

scholarly journals Non-rigid image registration using a modified fuzzy feature-based inference system for 3D cardiac motion estimation

2021 ◽  
Vol 205 ◽  
pp. 106085
Author(s):  
Monire Sheikh Hosseini ◽  
Mahammad Hassan Moradi ◽  
Mahdi Tabassian ◽  
Jan D'hooge
Keyword(s):  
Image Registration ◽  
Motion Estimation ◽  
Cardiac Motion ◽  
Inference System ◽  
Feature Based

Spatiotemporal non-rigid image registration for 3D ultrasound cardiac motion estimation

2007 ◽  
Author(s):  
D. Loeckx ◽  
J. Ector ◽  
F. Maes ◽  
J. D'hooge ◽  
D. Vandermeulen ◽  
...  
Keyword(s):  
Image Registration ◽  
Motion Estimation ◽  
3D Ultrasound ◽  
Cardiac Motion

scholarly journals Cardiac motion estimation from medical images: a regularisation framework applied on pairwise image registration displacement fields

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hadi Wiputra ◽  
Wei Xuan Chan ◽  
Yoke Yin Foo ◽  
Sheldon Ho ◽  
Choon Hwai Yap
Keyword(s):  
Image Registration ◽  
Motion Estimation ◽  
Medical Images ◽  
Fetal Echocardiography ◽  
Tracking Error ◽  
Cardiac Motion ◽  
Ultrasound Images ◽  
Displacement Fields ◽  
Estimation Algorithms ◽  
Spatio Temporal

Abstract Accurate cardiac motion estimation from medical images such as ultrasound is important for clinical evaluation. We present a novel regularisation layer for cardiac motion estimation that will be applied after image registration and demonstrate its effectiveness. The regularisation utilises a spatio-temporal model of motion, b-splines of Fourier, to fit to displacement fields from pairwise image registration. In the process, it enforces spatial and temporal smoothness and consistency, cyclic nature of cardiac motion, and better adherence to the stroke volume of the heart. Flexibility is further given for inclusion of any set of registration displacement fields. The approach gave high accuracy. When applied to human adult Ultrasound data from a Cardiac Motion Analysis Challenge (CMAC), the proposed method is found to have 10% lower tracking error over CMAC participants. Satisfactory cardiac motion estimation is also demonstrated on other data sets, including human fetal echocardiography, chick embryonic heart ultrasound images, and zebrafish embryonic microscope images, with the average Dice coefficient between estimation motion and manual segmentation at 0.82–0.87. The approach of performing regularisation as an add-on layer after the completion of image registration is thus a viable option for cardiac motion estimation that can still have good accuracy. Since motion estimation algorithms are complex, dividing up regularisation and registration can simplify the process and provide flexibility. Further, owing to a large variety of existing registration algorithms, such an approach that is usable on any algorithm may be useful.

scholarly journals Conditional Shape Models for Cardiac Motion Estimation

2010 ◽  
pp. 452-459 ◽  
Author(s):  
Coert Metz ◽  
Nora Baka ◽  
Hortense Kirisli ◽  
Michiel Schaap ◽  
Theo van Walsum ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Cardiac Motion ◽  
Shape Models

Cardiac motion estimation using pyramid, warping, and cost volume neural network

2021 ◽  
Author(s):  
Catharine V. Graves ◽  
Ramon A. Moreno ◽  
Marina F. S. Rebelo ◽  
Adriano Bordignom ◽  
Cesar H. Nomura ◽  
...  
Keyword(s):  
Neural Network ◽  
Motion Estimation ◽  
Cardiac Motion

scholarly journals Anatomy-Aware Cardiac Motion Estimation

2020 ◽  
pp. 150-159
Author(s):  
Pingjun Chen ◽  
Xiao Chen ◽  
Eric Z. Chen ◽  
Hanchao Yu ◽  
Terrence Chen ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Cardiac Motion
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