Multiframe Temporal Estimation of Cardiac Nonrigid Motion

A robust, flexible system for tracking the point to point nonrigid motion of the left ventricular (LV) endocardial wall in image sequences has been developed. This system is unique in its ability to model motion trajectories across multiple frames. The foundation of this system is an adaptive transversal filter based on the recursive least-squares algorithm. This filter facilitates the integration of models for periodicity and proximal smoothness as appropriate using a contour-based description of the object’s boundaries. A set of correspondences between contours and an associated set of correspondence quality measures comprise the input to the system. Frame-to-frame relationships from two different frames of reference are derived and analyzed using synthetic and actual images. Two multiframe temporal models, both based on a sum of sinusoids, are derived. Illustrative examples of the system’s output are presented for quantitative analysis. Validation of the system is performed by comparing computed trajectory estimates with the trajectories of physical markers implanted in the LV wall. Sample case studies of marker trajectory comparisons are presented. Ensemble statistics from comparisons with 15 marker trajectories are acquired and analyzed. A multiframe temporal model without spatial periodicity constraints was determined to provide excellent performance with the least computational cost. A multiframe spatiotemporal model provided the best performance based on statistical standard deviation, although at significant computational expense.National Heart, Lung, and Blood InstituteAir Force of Scientific ResearchNational Science FoundationOffice of Naval ResearchR01HL44803F49620-99-1-0481F49620-99-1-0067MIP-9615590N00014-98-1-054

Respiratory Motion Estimation from Slowly Rotating X-Ray Projections

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.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85878/1/Fessler197.pd

Spatio-Temporal Nonrigid Registration for Ultrasound Cardiac Motion Estimation

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

Cardiac motion estimation from medical images: a regularisation framework applied on pairwise image registration displacement fields

Accurate cardiac motion estimation from medical images such as ultrasound is important for clinical evaluation. We present a novel regularisation layer for cardiac motion estimation that will be applied after image registration and demonstrate its effectiveness. The regularisation utilises a spatio-temporal model of motion, b-splines of Fourier, to fit to displacement fields from pairwise image registration. In the process, it enforces spatial and temporal smoothness and consistency, cyclic nature of cardiac motion, and better adherence to the stroke volume of the heart. Flexibility is further given for inclusion of any set of registration displacement fields. The approach gave high accuracy. When applied to human adult Ultrasound data from a Cardiac Motion Analysis Challenge (CMAC), the proposed method is found to have 10% lower tracking error over CMAC participants. Satisfactory cardiac motion estimation is also demonstrated on other data sets, including human fetal echocardiography, chick embryonic heart ultrasound images, and zebrafish embryonic microscope images, with the average Dice coefficient between estimation motion and manual segmentation at 0.82–0.87. The approach of performing regularisation as an add-on layer after the completion of image registration is thus a viable option for cardiac motion estimation that can still have good accuracy. Since motion estimation algorithms are complex, dividing up regularisation and registration can simplify the process and provide flexibility. Further, owing to a large variety of existing registration algorithms, such an approach that is usable on any algorithm may be useful

Multiframe Temporal Estimation of Cardiac Nonrigid Motion

A robust, flexible system for tracking the point to point nonrigid motion of the left ventricular (LV) endocardial wall in image sequences has been developed. This system is unique in its ability to model motion trajectories across multiple frames. The foundation of this system is an adaptive transversal filter based on the recursive least-squares algorithm. This filter facilitates the integration of models for periodicity and proximal smoothness as appropriate using a contour-based description of the object's boundaries. A set of correspondences between contours and an associated set of correspondence quality measures comprise the input to the system. Frame-to-frame relationships from two different frames of reference are derived and analyzed using synthetic and actual images. Two multiframe temporal models, both based on a sum of sinusoids, are derived. Illustrative examples of the system's output are presented for quantitative analysis. Validation of the system is performed by compa..