Integrated Dynamic Shape Tracking and RF Speckle Tracking for Cardiac Motion Analysis

Parajuli, Nripesh; Lu, Allen; Stendahl, John C.; Zontak, Maria; Boutagy, Nabil E.; Eberle, Melissa; Alkhalil, Imran; O’Donnell, Matthew; Sinusas, Albert J.; Duncan, James S.

doi:10.1007/978-3-319-46720-7_50

Cited by 12 publications

(8 citation statements)

References 9 publications

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“…Besides the registration model on brain MR image, there are also several studies that focus on other modality images. Parajuli et al (2017) have proposed a cardiac motion registration model for 4D echocardiography dataset with a Siamese neural network. Krebs et al (2017) presented a deep dual-stream network to learn the artificial agent-based actions in a supervised way, then the agent moves towards the final deformation parameters for inter-subject registration of prostate MR images.…”

Section: Related Workmentioning

confidence: 99%

BIRNet: Brain image registration using dual-supervised fully convolutional networks

Fan

Cao

Yap

et al. 2019

Medical Image Analysis

243

146

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In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance. Since obtaining ground-truth deformation fields for training can be challenging, we design a fully convolutional network that is subject to dual-guidance: (1) Coarse guidance using deformation fields obtained by an existing registration method; and (2) Fine guidance using image similarity. The latter guidance helps avoid overly relying on the supervision from the training deformation fields, which could be inaccurate. For effective training, we further improve the deep convolutional network with gap filling, hierarchical loss, and multi-source strategies. Experiments on a variety of datasets show promising registration accuracy and efficiency compared with state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

BIRNet: Brain image registration using dual-supervised fully convolutional networks

Fan

Cao

Yap

et al. 2019

Medical Image Analysis

243

146

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“…We previously proposed a method that tracks individual points on myocardial surfaces through time (Parajuli et al, 2016). Points on the myocardial surfaces form nodes in a graph and edges exist between points and their spatial neighbors in the next time frame.…”

Section: Temporal Trackingmentioning

confidence: 99%

Flow network tracking for spatiotemporal and periodic point matching: Applied to cardiac motion analysis

Parajuli

et al. 2019

Medical Image Analysis

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The accurate quantification of left ventricular (LV) deformation/strain shows significant promise for quantitatively assessing cardiac function for use in diagnosis and therapy planning . However, accurate estimation of the displacement of myocardial tissue and hence LV strain has been challenging due to a variety of issues, including those related to deriving tracking tokens from images and following tissue locations over the entire cardiac cycle. In this work, we propose a point matching scheme where correspondences are modeled as flow through a graphical network. Myocardial surface points are set up as nodes in the network and edges define neighborhood relationships temporally. The novelty lies in the constraints that are imposed on the matching scheme, which render the correspondences one-to-one through the entire cardiac cycle, and not just two consecutive frames. The constraints also encourage motion to be cyclic, which is an important characteristic of LV motion. We validate our method by applying it to the estimation of quantitative LV displacement and strain estimation using 8 synthetic and 8 open-chested canine 4D echocardiographic image sequences, the latter with sonomicrometric crystals implanted on the LV wall. We were able to achieve excellent tracking accuracy on the synthetic dataset and observed a good correlation with crystal-based strains on the in-vivo data.

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“…Shape tracking and speckle tracking results can be combined using radial basis functions. 26–28 Such a combined method applied to echocardiography images was reported to achieve a better correlation with the golden standard method—Magnetic Resonance Imaging, than the standard speckle tracking. 29 An alternative approach to combining the segmentation information and the elastic image registration was chosen by Mahapatra and Sun.…”

Section: Introductionmentioning

confidence: 99%

Segmentation Enhanced Elastic Image Registration for 2D Speckle Tracking Echocardiography—Performance Study In Silico

2022

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Although the two dimensional Speckle Tracking Echocardiography has gained a strong position among medical diagnostic techniques in cardiology, it still requires further developments to improve its repeatability and reliability. Few works have attempted to incorporate the left ventricle segmentation results in the process of displacements and strain estimation to improve its performance. We proposed the use of mask information as an additional penalty in the elastic image registration based displacements estimation. This approach was studied using a short axis view synthetic echocardiographic data, segmented using an active contour method. The obtained masks were distorted to a different degree, using different methods to assess the influence of the segmentation quality on the displacements and strain estimation process. The results of displacements and circumferential strain estimations show, that even though the method is dependent on the mask quality, the potential loss in accuracy due to the poor segmentation quality is much lower than the potential accuracy gain in cases where the segmentation performs well.

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Integrated Dynamic Shape Tracking and RF Speckle Tracking for Cardiac Motion Analysis

Cited by 12 publications

References 9 publications

BIRNet: Brain image registration using dual-supervised fully convolutional networks

BIRNet: Brain image registration using dual-supervised fully convolutional networks

Flow network tracking for spatiotemporal and periodic point matching: Applied to cardiac motion analysis

Segmentation Enhanced Elastic Image Registration for 2D Speckle Tracking Echocardiography—Performance Study In Silico

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