Dense Non-Rigid Motion Estimation in Sequences of 3D Images Using Differential Constraints

Benayoun, Serge; Nastar, Chahab; Ayache, Nicholas

doi:10.1007/978-3-540-49197-2_40

Cited by 22 publications

(8 citation statements)

References 11 publications

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“…Historically, cardiac motion analysis, especially of the LV, has been the main focus of such approaches. Generic deformable surface models [117]- [119], models exploiting curvature information [120]- [123], 4-D models [124], or models with temporal constraints [125], [126] have been previously described for cardiac motion analysis, and they are all based on surface tracking.…”

Section: Discussionmentioning

confidence: 99%

Estimating motion from MRI data

2003

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Invited Paper-Magnetic resonance imaging (MRI) is an ideal imaging modality to measure blood flow and tissue motion. It provides excellent contrast between soft tissues, and images can be acquired at positions and orientations freely defined by the user. From a temporal sequence of MR images, boundaries and edges of tissues can be tracked by image processing techniques. Additionally, MRI permits the source of the image signal to be manipulated. For example, temporary magnetic tags displaying a pattern of variable brightness may be placed in the object using MR saturation techniques, giving the user a known pattern to detect for motion tracking. The MRI signal is a modulated complex quantity, being derived from a rotating magnetic field in the form of an induced current. Well-defined patterns can also be introduced into the phase of the magnetization, and could be thought of as generalized tags. If the phase of each pixel is preserved during image reconstruction, relative phase shifts can be used to directly encode displacement, velocity and acceleration. New methods for modeling motion fields from MRI have now found application in cardiovascular and other soft tissue imaging. In this review, we shall describe the methods used for encoding, imaging, and modeling motion fields with MRI.

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Section: Discussionmentioning

confidence: 99%

Estimating motion from MRI data

2003

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“…In [1], we mention the particularity of the branching topology and explain why we don't use deformable models or reconstruction methods like [7,4]. Recently, in [5], a branching reconstruction was performed from two dimensional carotid CT images.…”

Section: An Active Branching Modelmentioning

confidence: 99%

“…20 gray level profiles along − → n i are then extracted. For each profile, its Mahalanobis distance ( [7]) from the mean m θ , which is a measure of how well it fits the model, is computed. The point, for which this distance is minimum, is the new M i + dM i .…”

Section: Model Buildingmentioning

confidence: 99%

Active Model Based Carotid Ultrasonic Data Segmentation

Moreau–Gaudry

Cinquin

Baguet

1999

Medical Image Computing and Computer-Assisted Intervention – MICCAI’99

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Abstract.To diagnose cardiovascular diseases, the most significant mortality causes in industrialized countries, the study of the carotid artery wall plays a preferential part. This paper presents a three dimensional active model based carotid ultrasonic data segmentation. A C1 continuous surface, defined by a deformable skeleton and envelope, includes the Y-shape topology and gray level as a priori knowledge. It is automatically fitted to a sequence of 2.5D ultrasonic branching data by an iterative quadratic method. The quality of the fit is illustrated by figures. Result of this 3D fitting can be used as initial position for quantification of the intima-media thickness.

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“…Duncan et al used contour shape descriptors in [1], Ayache; Cohen et al [2] have used superquadratics. The nature of the constraints varies between models and can take a wide range of properties, including differential constraints [3], displacement and velocity constraints [4] as well as other constraints allowing for non-rigid movements.…”

Section: Motivationmentioning

confidence: 99%

Directional Representations of 4D Echocardiography for Temporal Quantification of LV Volume

Angelini

Laine

Takuma

et al. 1999

Medical Image Computing and Computer-Assisted Intervention – MICCAI’99

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Abstract. Real-time acquisition via four-dimensional (3D plus time) ultrasound obviates the need for slice registration and reconstruction, leaving segmentation as the only barrier to an automated, rapid, and clinically applicable calculation of accurate left ventricular cavity volumes and ejection fraction. Speckle noise corrupts ultrasound data by introducing sharp changes in an image intensity profile, while attenuation alters the intensity of equally significant cardiac structures, depending on orientation with respect to the position of the ultrasound beam. These properties suggest that measures based on phase information rather than intensity are appropriate for denoising and boundary (surface) detection. Our method relies on the expansion of temporal volume data on a family of basis functions called Brushlets. These basis functions decompose a signal into distinct patterns of oriented textures. Projected coefficients are associated with distinct "brush strokes" of a particular size (width) and orientation (direction). Brushlet decompositions are invariant to intensity (contrast range) but depend on the spatial frequency content of a signal. Preliminary results of this directional spacefrequency analysis applied to both phantoms and clinical data are presented. The method will be used to clinically evaluate 4D data and to extract and quantify heart LV volumes.

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Dense Non-Rigid Motion Estimation in Sequences of 3D Images Using Differential Constraints

Cited by 22 publications

References 11 publications

Estimating motion from MRI data

Estimating motion from MRI data

Active Model Based Carotid Ultrasonic Data Segmentation

Directional Representations of 4D Echocardiography for Temporal Quantification of LV Volume

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