Analysis of myocardial motion in tagged MR images using nonrigid image registration

Chandrashekara, R.; Mohiaddin, Raad H.; Rueckert, Daniel

doi:10.1117/12.467075

Cited by 42 publications

(35 citation statements)

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“…Its application to cardiac motion estimation from tagging MRI sequences provided accurate results over-performing the ones reported in [3]. An analytical formulation of the derivative for KNNG αJE estimator allows to speed up notably the optimization process (compared to finite differences) and lower the registration times to manageable values.…”

Section: Discussionmentioning

confidence: 88%

“…Mutual information (MI) based registration methods have been reported to be accurate for estimating cardiac motion fields in the left ventricle (LV) [2,3]. The main drawbacks of previous approaches, like optical flow and tag detection and tracking, have been reported in [2].…”

Section: Introductionmentioning

confidence: 99%

“…Registration based method consist in applying non-rigid transformations to match the SA and LA views of a given cardiac phase, with their counterparts at ED. In [3] the authors propose to maximize a weighted average of the NMIs measured in the SA and LA views separately. This solution attempts to find a compromise between the alignment of each view.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multiview Registration of Cardiac Tagging Mri Images

Oubel

Craene

Gazzola

et al. 2007

2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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This paper introduces a new method based on k-Nearest Neighbors Graphs (KNNG) for bringing into alignment multiple views of the same scene acquired at two different time points. This framework is applied to cardiac motion estimation from tagging MRI sequences. Features acquired in each view are collected in a high dimensional feature space and an efficient estimator of α -Joint Entropy (αJE) is used for selecting the optimal alignment. In order to register 4D datasets, an analytical expression of the αJE estimator was derived, enabling a fast implementation of gradient based optimization. The technique was tested in a set of six sequences and the results compared with respect to manual measurements made at tag crossing points, obtaining good accuracy and low processing times compared to published state of the art methods.

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Section: Discussionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiview Registration of Cardiac Tagging Mri Images

Oubel

Craene

Gazzola

et al. 2007

2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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“…Registration matrices were calculated for each of the tracked views. Additionally for this patient, tagged MR sequences (256x256 matrix, 59 phases, resolution=1.33x1.33x8.0mm, TR=11.0, TE=3.5, tag spacing=8mm) were also acquired from which the myocardial motion was quantified using a non-rigid registration technique [5]. The registration enabled us to relate the position of the measured electrophysiology data to the cardiac motion.…”

Section: Clinical Validationmentioning

confidence: 99%

Application of XMR 2D-3D Registration to Cardiac Interventional Guidance

Rhode

Hill

Edwards

et al. 2003

Lecture Notes in Computer Science

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Abstract. We describe a registration and tracking technique to integrate cardiac x-ray images and cardiac magnetic resonance (MR) images acquired from a combined x-ray and MR interventional suite (XMR). Optical tracking is used to determine the transformation matrices relating MR and x-ray image coordinates. Calibration and tracking enable us to combine x-ray projection images with registered projection MR images from a volume acquisition, and to display 3D reconstructions of catheters within the MR volume. Registration errors were assessed using phantom experiments. Errors in the combined projection images (2D root mean square target registration error -RMS TRE) were found to be 2.4 -4.2mm, and the errors in the integrated volume representation (3D RMS TRE) were found to be 4.6 -5.1mm. These errors are clinically acceptable for alignment of images of the heart and the great vessels. We demonstrate that our technique can be used to guide catheters and to combine electrical and mechanical information in the context of the patient-specific anatomy for a patient undergoing cardiac radio-frequency (RF) ablation.

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“…The dense cardiac motion fields, including displacements, velocities and accelerations throughout the myocardium, can then be estimated from those coarse measurements with a priori constraints [2,3,4,5], for which biomechanical models have been the popular choices because of their physics meaningfulness. A recent effort is particularly related to our current work [6].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Cardiac Electrical Propagation from Medical Image Sequence

Zhang

Wong

Shi

2006

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006

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Abstract.A novel strategy is presented to recover cardiac electrical excitation pattern from tomographic medical image sequences. The geometrical/physical representation of the heart and the dense motion field of the myocardium are first derived from imaging data through segmentation and motion recovery. The myocardial active forces are then calculated through the law of force equilibrium from the motion field, realized with a stochastic multiframe algorithm. Since tissue active forces are physiologically driven by electrical excitations, we can readily relate the pattern of active forces to the pattern of electrical propagation in myocardium, where spatial regularization is enforced. Experiments are conducted on three-dimensional synthetic data and canine magnetic resonance image sequence with favorable results.

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Analysis of myocardial motion in tagged MR images using nonrigid image registration

Cited by 42 publications

References 0 publications

Multiview Registration of Cardiac Tagging Mri Images

Multiview Registration of Cardiac Tagging Mri Images

Application of XMR 2D-3D Registration to Cardiac Interventional Guidance

Estimation of Cardiac Electrical Propagation from Medical Image Sequence

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