Myocardial motion analysis from B-mode echocardiograms

Sühling, Michael; Arigovindan, Muthuvel; Jansen, Christian; Hunziker, Patrick; Unser, Michaël

doi:10.1109/tip.2004.838709

Cited by 128 publications

(110 citation statements)

References 26 publications

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“…An additional problem is that the borders are usually not well defined in echocardiographic data. Another approach is to use optical flow methods to compute local myocardial movements [26]- [31]. Specific designs for echocardiographic data consider the Rayleigh statistics of the signal in the process [29].…”

mentioning

confidence: 99%

Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation

Ledesma-Carbayo

Kybic

Desco

et al. 2005

IEEE Trans. Med. Imaging

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Abstract-We propose a new spatio-temporal elastic registration algorithm for motion reconstruction from a series of images. The specific application is to estimate displacement fields from two-dimensional ultrasound sequences of the heart. The basic idea is to find a spatio-temporal deformation field that effectively compensates for the motion by minimizing a difference with respect to a reference frame. The key feature of our method is the use of a semi-local spatio-temporal parametric model for the deformation using splines, and the reformulation of the registration task as a global optimization problem. The scale of the spline model controls the smoothness of the displacement field. Our algorithm uses a multiresolution optimization strategy to obtain a higher speed and robustness.We evaluated the accuracy of our algorithm using a synthetic sequence generated with an ultrasound simulation package, together with a realistic cardiac motion model. We compared our new global multiframe approach with a previous method based on pairwise registration of consecutive frames to demonstrate the benefits of introducing temporal consistency. Finally, we applied the algorithm to the regional analysis of the left ventricle. Displacement and strain parameters were evaluated showing significant differences between the normal and pathological segments, thereby illustrating the clinical applicability of our method.

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mentioning

confidence: 99%

Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation

Ledesma-Carbayo

Kybic

Desco

et al. 2005

IEEE Trans. Med. Imaging

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260

181

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“…To circumvent this issue, in [5] Sühling et al proposed a multiscale strategy for locally choosing the most consistent window size. This is based on the possibility of computing the image moments, i.e., the entries of the system matrix M and the vector b, at multiple scales, by using an efficient B-spline coarse-to-fine strategy.…”

Section: Multiscale Choice Of the Window Sizementioning

confidence: 99%

“…We have compared our algorithm with the ones of Sühling et al [5] and Felsberg [3]. Sühling's algorithm exploits the standard intensity conservation assumption and, as in our case, employs an affine displacement model and a multiscale window selection.…”

Section: D Cardiac Ultrasoundmentioning

confidence: 99%

Multiscale optical flow computation from the monogenic signal

Alessandrini

Bernard

Basarab

et al. 2013

IRBM

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. AbstractWe have developed an algorithm for the estimation of cardiac motion from medical images. The algorithm exploits monogenic signal theory, recently introduced as an N-dimensional generalization of the analytic signal. The displacement is computed locally by assuming the conservation of the monogenic phase over time. A local affine displacement model replaces the standard translation model to account for more complex motions as contraction/expansion and shear. A coarse-to-fine B-spline scheme allows a robust and effective computation of the models parameters and a pyramidal refinement scheme helps handle large motions. Robustness against noise is increased by replacing the standard pointwise computation of the monogenic orientation with a more robust least-squares orientation estimate. This paper reviews the results obtained on simulated cardiac images from different modalities, namely 2D and 3D cardiac ultrasound and tagged magnetic resonance. We also show how the proposed algorithm represents a valuable alternative to state-of-the-art algorithms in the respective fields.

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“…Last, several research groups have focused on building new tools for image rendering and analysis for biological data mining. Although this is a vast area of research, I have included references for a small sampling of papers and reviews to provide examples of ongoing research (Lansford et al, 2001;AlKofahi et al, 2002AlKofahi et al, , 2003Jacobs et al, 2003;Megason and Fraser, 2003;Herskovits et al, 2004;Huang and Murphy, 2004;Warner et al, 2004;Ledesma-Carbayo et al, 2005;Liebling et al, 2005;Lin et al, 2005;Peng et al, 2005;Suhling et al, 2005;Toga, 2005;Tyszka et al, 2005a;Forouhar et al, 2006;Luders et al, 2006).…”

Section: Tools For Image Analysismentioning

confidence: 99%

Multimodal imaging of mouse development: Tools for the postgenomic era

Dickinson

2006

Developmental Dynamics

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With the sequence of the mouse genome known, it is now possible to create or identify mutations in every gene to determine the molecules necessary for normal development. Consequently, there is a growing need for advanced phenotyping tools to best understand defects produced by altering gene function. Perhaps nothing is more satisfying than to directly observe a process in action; to disturb it and see for ourselves how the process changes before our very eyes. No doubt, this desire is what drove the invention of the very first microscopes and continues to this day to fuel progress in the field of biological imaging. Because mouse embryos are small and develop embedded within many tissue layers within the nurturing environment of the mother, directly observing the dynamic, micro-and nanoscopic events of early mammalian development has proven to be one of the greater challenges for imaging scientists. Here, I will review some of the imaging methods being used to study mouse development, highlighting the results obtained from imaging.

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Myocardial motion analysis from B-mode echocardiograms

Cited by 128 publications

References 26 publications

Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation

Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation

Multiscale optical flow computation from the monogenic signal

Multimodal imaging of mouse development: Tools for the postgenomic era

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