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16
Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation
- IEEE Transactions on Medical Imaging
"... 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 fiel ..."
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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. Index Terms—Cardiac motion, elastic registration, parametric models, splines, temporal models. I.
A Review of Cardiac Image Registration Methods
, 2002
"... In this paper, the current status of cardiac image registration methods is reviewed. The combination of information from multiple cardiac image modalities, such as magnetic resonance imaging, computed tomography, positron emission tomography, single-photon emission computed tomography, and ultrasoun ..."
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Cited by 29 (0 self)
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In this paper, the current status of cardiac image registration methods is reviewed. The combination of information from multiple cardiac image modalities, such as magnetic resonance imaging, computed tomography, positron emission tomography, single-photon emission computed tomography, and ultrasound, is of increasing interest in the medical community for physiologic understanding and diagnostic purposes. Registration of cardiac images is a more complex problem than brain image registration because the heart is a nonrigid moving organ inside a moving body. Moreover, as compared to the registration of brain images, the heart exhibits much fewer accurate anatomical landmarks. In a clinical context, physicians often mentally integrate image information from different modalities. Automatic registration, based on computer programs, might, however, offer better accuracy and repeatability and save time.
Respiratory motion estimation from slowly rotating x-ray projections, Symposium A Quarterly Journal In Modern Foreign Literatures
, 2004
"... ABSTRACT As radiotherapy has become increasingly conformal, geometric uncertainties caused by breathing and organ motion have become an important issue. Accurate motion estimates may lead to improved treatment planning and dose calculation in radiation therapy. However, respiratory motion is diffic ..."
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Cited by 12 (1 self)
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ABSTRACT As radiotherapy has become increasingly conformal, geometric uncertainties caused by breathing and organ motion have become an important issue. Accurate motion estimates may lead to improved treatment planning and dose calculation in radiation therapy. However, respiratory motion is difficult to study by conventional X-ray CT imaging since object motion causes inconsistent projection views leading to artifacts in reconstructed images. We propose to estimate the parameters of a nonrigid motion model from a set of projection views of the thorax that are acquired using a slowly rotating cone-beam CT scanner, such as a radiotherapy simulator. We use a conventionally reconstructed 3D thorax image, acquired by breath-hold CT, as a reference volume. We represent respiratory motion using a flexible parametric nonrigid motion model based on B-splines. The motion parameters are estimated by optimizing a regularized cost function that includes the squared error between the measured projection views and the reprojections of the deformed reference image. Preliminary 2D simulation results show that there is good agreement between the estimated motion and the true motion.
andN.Ayache, “Registration of 4D cardiac CT sequences under trajectory constraints with multichannel diffeomorphic demons
- IEEE Transactions on Medical Imaging
, 2010
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Consistent estimation of cardiac motions by 4D image registration
- In Proc. MICCAI
, 2005
"... Abstract. A 4D image registration method is proposed for consistent estimation of cardiac motion from MR image sequences. Under this 4D registration framework, all 3D cardiac images taken at different time-points are registered simultaneously, and motion estimated is enforced to be spatiotemporally ..."
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Cited by 6 (1 self)
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Abstract. A 4D image registration method is proposed for consistent estimation of cardiac motion from MR image sequences. Under this 4D registration framework, all 3D cardiac images taken at different time-points are registered simultaneously, and motion estimated is enforced to be spatiotemporally smooth, thereby overcoming potential limitations of some methods that typically estimate cardiac deformation sequentially from one frame to another, instead of treating the entire set of images as a 4D volume. To facilitate our image matching process, an attribute vector is designed for each point in the image to include intensity, boundary and geometric moment invariants (GMIs). Hierarchical registration of two image sequences is achieved by using the most distinctive points for initial registration of two sequences and gradually adding less-distinctive points for refinement of registration. Experimental results on real data demonstrate good performance of the proposed method in registering cardiac images and estimating motions from cardiac image sequences. 1
Regional heart motion abnormality detection via information measures and unscented Kalman filtering
- In: Jiang, T. et al. (Eds.), MICCAI
"... Abstract. This study investigates heart wall motion abnormality detec-tion with an information theoretic measure of heart motion based on the Shannon’s differential entropy (SDE) and recursive Bayesian filtering. Heart wall motion is generally analyzed using functional images which are subject to no ..."
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Cited by 6 (3 self)
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Abstract. This study investigates heart wall motion abnormality detec-tion with an information theoretic measure of heart motion based on the Shannon’s differential entropy (SDE) and recursive Bayesian filtering. Heart wall motion is generally analyzed using functional images which are subject to noise and segmentation inaccuracies, and incorporation of prior knowledge is crucial in improving the accuracy. The Kalman filter, a well known recursive Bayesian filter, is used in this study to esti-mate the left ventricular (LV) cavity points given incomplete and noisy data, and given a dynamic model. However, due to similarities between the statistical information of normal and abnormal heart motions, de-tecting and classifying abnormality is a challenging problem which we proposed to investigate with a global measure based on the SDE. We further derive two other possible information theoretic abnormality de-tection criteria, one is based on Rényi entropy and the other on Fisher information. The proposed method analyzes wall motion quantitatively by constructing distributions of the normalized radial distance estimates of the LV cavity. Using 269×20 segmented LV cavities of short-axis mag-netic resonance images obtained from 30 subjects, the experimental anal-ysis demonstrates that the proposed SDE criterion can lead to significant improvement over other features that are prevalent in the literature re-lated to the LV cavity, namely, mean radial displacement and mean radial velocity. 1
Cardiac Ultrasound Motion Detection by Elastic Registration Exploiting Temporal Coherence
, 2002
"... We propose a new global registration method for estimating the cardiac displacement field in 2D sequences of ultrasound images of the heart. The basic idea is to select a reference irame (e.g., the first image of a cycle) and to map each image in the sequence to it using elastic deformation. What ma ..."
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Cited by 4 (0 self)
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We propose a new global registration method for estimating the cardiac displacement field in 2D sequences of ultrasound images of the heart. The basic idea is to select a reference irame (e.g., the first image of a cycle) and to map each image in the sequence to it using elastic deformation. What makes our method specific is the use of a semi-local parametric model of the deformation (spatiotemporal spline), and the reformulation of the registration task as a global spatio-temporal optimization problem. The scale of the spline model controls the smoothness of the displacement field. Our algorithm uses a multiresolution optimization strategy for higher speed and robustness.
Tracking endocardial motion via multiple model filtering
- IEEE Transactions on Biomedical Engineering
, 2010
"... Abstract—Tracking heart motion plays an essential role in the diagnosis of cardiovascular diseases. As such, accurate characteri-zation of dynamic behavior of the left ventricle (LV) is essential in order to enhance the performance of motion estimation. However, a single Markovian model is not suffi ..."
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Cited by 4 (0 self)
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Abstract—Tracking heart motion plays an essential role in the diagnosis of cardiovascular diseases. As such, accurate characteri-zation of dynamic behavior of the left ventricle (LV) is essential in order to enhance the performance of motion estimation. However, a single Markovian model is not sufficient due to the substantial variability in typical heart motion. Moreover, dynamics of an ab-normal heart could be very different from that of a normal heart. This study introduces a tracking approach based on multiple mod-els, each matched to a different phase of the LV motion. First, the algorithm adopts a graph cut distribution matching method to tackle the problem of segmenting LV cavity from cardiac MR im-ages, which is acknowledged as a difficult problem because of low contrast and photometric similarities between the heart wall and papillary muscles within the LV cavity. Second, interacting multi-ple model (IMM), an effective estimation algorithm for Markovian switching system, is devised subsequent to the segmentations to yield state estimates of the endocardial boundary points. The IMM also yields the model probability indicating the model that most closely matches the LV motion. The proposed method is evalu-ated quantitatively by comparison with independent manual seg-mentations over 2280 images acquired from 20 subjects, which demonstrated competitive results in comparisons with related re-cent methods. Index Terms—Cardiac wall motion estimation, interacting mul-tiple model (IMM) algorithm, MRI, Markovian switching systems, recursive Bayesian filtering. I.
Detection of Left Ventricular Motion Abnormality Via Information Measures and Bayesian Filtering
"... Abstract—We present an original information theoretic measure of heart motion based on the Shannon’s differential entropy (SDE), which allows heart wall motion abnormality detection. Based on functional images, which are subject to noise and segmentation inaccuracies, heart wall motion analysis is a ..."
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Cited by 3 (1 self)
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Abstract—We present an original information theoretic measure of heart motion based on the Shannon’s differential entropy (SDE), which allows heart wall motion abnormality detection. Based on functional images, which are subject to noise and segmentation inaccuracies, heart wall motion analysis is acknowledged as a dif-ficult problem, and as such, incorporation of prior knowledge is crucial for improving accuracy. Given incomplete, noisy data and a dynamic model, the Kalman filter, a well-known recursive Bayesian filter, is devised in this study to the estimation of the left ventricular (LV) cavity points. However, due to similarity between the statisti-cal information of normal and abnormal heart motions, detecting and classifying abnormality is a challenging problem, which we investigate with a global measure based on the SDE. We further derive two other possible information theoretic abnormality de-tection criteria, one is based on Rényi entropy and the other on Fisher information. The proposed methods analyze wall motion quantitatively by constructing distributions of the normalized ra-dial distance estimates of the LV cavity. Using 269 × 20 segmented LV cavities of short-axis MRI obtained from 30 subjects, the ex-perimental analysis demonstrates that the proposed SDE criterion can lead to a significant improvement over other features that are prevalent in the literature related to the LV cavity, namely, mean radial displacement and mean radial velocity. Index Terms—Cardiac wall motion abnormality, computer-aided diagnosis, information theoretic measures, level sets, MRI, recursive Bayesian filtering, Shannon’s differential entropy (SDE). I.
Simultaneous Estimation of Left Ventricular Motion and Material Properties with Maximum a Posteriori Strategy
, 2003
"... In addition to its technical merits as a challenging non-rigid motion and structural integrity analysis problem, quantitative estimation of cardiac regional functions and material characteristics has significant physiological and clinical values. We have earlier developed a stochastic finite element ..."
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Cited by 1 (0 self)
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In addition to its technical merits as a challenging non-rigid motion and structural integrity analysis problem, quantitative estimation of cardiac regional functions and material characteristics has significant physiological and clinical values. We have earlier developed a stochastic finite element framework for the simultaneous estimation of myocardial motion and material parameters from medical image sequences with an extended Kalman filter approach. In this paper, we present a new computational strategy for the framework based upon the maximum a posteriori estimation principles, realized through the extended Kalman smoother, that produce a sequence of kinematics state and material parameter estimates from the entire sequence of observations. The system dynamics equations of the heart is constructed using a biomechanical model with stochastic parameters, and the tissue material and deformation parameters are jointly estimated from the periodic imaging data. Experiments with canine magnetic resonance images have been conducted with very promising results, as validated through comparison to the histological staining of post mortem myocardium.
