Automatic tracking of SPAMM grid and the estimation of deformation parameters from cardiac MR images

Kumar, Sanjay; Goldgof, Dmitry

doi:10.1109/42.276150

Cited by 103 publications

(54 citation statements)

References 17 publications

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“…In the amplitude domain, estimation of motion fields requires detecting the tag lines in every motion frame 38,39 and interpolating the displacement of physical points located between tags. [40][41][42] Such a process is typically time-consuming and generally requires human intervention to correct mistakes in the feature detection step.…”

Section: Iib Regularized Harmonic Phase Motion Trackingmentioning

confidence: 99%

Nonrigid PET motion compensation in the lower abdomen using simultaneous tagged-MRI and PET imaging

et al. 2011

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Purpose: We propose a novel approach for PET respiratory motion correction using tagged-MRI and simultaneous PET-MRI acquisitions. Methods: We use a tagged-MRI acquisition followed by motion tracking in the phase domain to estimate the nonrigid deformation of biological tissues during breathing. In order to accurately estimate motion even in the presence of noise and susceptibility artifacts, we regularize the traditional HARP tracking strategy using a quadratic roughness penalty on neighboring displacement vectors (R-HARP). We then incorporate the motion fields estimated with R-HARP in the system matrix of an MLEM PET reconstruction algorithm formulated both for sinogram and list-mode data representations. This approach allows reconstruction of all detected coincidences in a single image while modeling the effect of motion both in the emission and the attenuation maps. At present, tagged-MRI does not allow estimation of motion in the lungs and our approach is therefore limited to motion correction in soft tissues. Since it is difficult to assess the accuracy of motion correction approaches in vivo, we evaluated the proposed approach in numerical simulations of simultaneous PET-MRI acquisitions using the NCAT phantom. We also assessed its practical feasibility in PET-MRI acquisitions of a small deformable phantom that mimics the complex deformation pattern of a lung that we imaged on a combined PET-MRI brain scanner. Results: Simulations showed that the R-HARP tracking strategy accurately estimated realistic respiratory motion fields for different levels of noise in the tagged-MRI simulation. In simulations of tumors exhibiting increased uptake, contrast estimation was 20% more accurate with motion correction than without. Signal-to-noise ratio (SNR) was more than 100% greater when performing motion-corrected reconstruction which included all counts, compared to when reconstructing only coincidences detected in the first of eight gated frames. These results were confirmed in our proofof-principle PET-MRI acquisitions, indicating that our motion correction strategy is accurate, practically feasible, and is therefore ready to be tested in vivo. Conclusions: This work shows that PET motion correction using motion fields measured with tagged-MRI in simultaneous PET-MRI acquisitions can be made practical for clinical application and that doing so has the potential to remove motion blur in whole-body PET studies of the torso.

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Section: Iib Regularized Harmonic Phase Motion Trackingmentioning

confidence: 99%

Nonrigid PET motion compensation in the lower abdomen using simultaneous tagged-MRI and PET imaging

et al. 2011

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“…All these methods except Huang and Amini (1999), Radeva et al (1997) and Young (1998) suppose that tag lines have been segmented in a preliminary step. A number of methods have been described in the literature to fulfil this time-consuming step, using pattern matching (Denney 1999), snakes (Guttman et al 1994,Radeva et al 1997, or spline modelling (Amini et al 1994, Kumar and Goldgof 1994, with a different level of automation, none being fully automatic at this time.…”

Section: Introductionmentioning

confidence: 99%

Left ventricular motion reconstruction from planar tagged MR images: a comparison

et al. 2000

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Through recent development of MR techniques, it is now possible to assess regional myocardial wall function in a non-invasive way. Using MR tagging, space is marked with planes which deform with the tissue, providing markers for tracking the local motion of the myocardium. Numerous methods to reconstruct the three-dimensional displacement field have been developed. The aim of this article is to provide a framework to quantitatively compare the performance of four methods the authors have developed. Five sets of experiments are described, and their results are reported. Instructions are also provided to perform similar tests on any method using the same data.The experiments show that some characteristic properties of the methods, such as sensitivity to noise or spatial resolution, can be quantitatively classified. Cross-comparison of performances show what range values for these properties can be considered acceptable.

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“…They proposed an improved terminating criterion for the minimization of the intensity energy. Kumar and Goldgof [7] used the active contour (snake) to track the tagged grid in Cardiac MR images automatically.…”

Section: Previous Workmentioning

confidence: 99%

Efficient Nonlinear Finite Element Modeling of Nonrigid Objects via Optimization of Mesh Models

Tsap¹,

Goldgof²,

Sarkar³

et al. 1998

Computer Vision and Image Understanding

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In this paper we propose a new general framework for the application of the nonlinear finite element method (FEM) to nonrigid motion analysis. We construct the models by integrating image data and prior knowledge, using well-established techniques from computer vision, structural mechanics, and computer-aided design (CAD). These techniques guide the process of optimization of mesh models.Linear FEM proved to be a successful physically based modeling tool in solving limited types of nonrigid motion problems. However, linear FEM cannot handle nonlinear materials or large deformations. Application of nonlinear FEM to nonrigid motion analysis has been restricted by difficulties with high computational complexity and noise sensitivity.We tackle the problems associated with nonlinear FEM by changing the parametric description of the object to allow easy automatic control of the model, using physically motivated analysis of the possible displacements to address the worst effects of the noise, applying mesh control strategies, and utilizing multiscale methods. The combination of these methods represents a new systematic approach to a class of nonrigid motion applications for which sufficiently precise and flexible FEM models can be built.The results from the skin elasticity experiments demonstrate the success of the proposed method. The model allows us to objectively detect the differences in elasticity between normal and abnormal skin. Our work demonstrates the possibility of accurate computation of point correspondences and force recovery from range image sequences containing nonrigid objects and large motion.

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Automatic tracking of SPAMM grid and the estimation of deformation parameters from cardiac MR images

Cited by 103 publications

References 17 publications

Nonrigid PET motion compensation in the lower abdomen using simultaneous tagged-MRI and PET imaging

Nonrigid PET motion compensation in the lower abdomen using simultaneous tagged-MRI and PET imaging

Left ventricular motion reconstruction from planar tagged MR images: a comparison

Efficient Nonlinear Finite Element Modeling of Nonrigid Objects via Optimization of Mesh Models

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