Harp Tracking Refinement Using Seeded Region Growing

Liu, Xiaofeng; Murano, Emi Z.; Stone, Maureen; Prince, Jerry L.

doi:10.1109/isbi.2007.356866

Cited by 6 publications

(10 citation statements)

References 6 publications

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“…To address these limitations, we recently developed a region growing HARP refinement (RG-HR) method and successfully applied it to both cardiac and tongue motion tracking [23]. After a seed is manually picked, the method automatically tracks all pixels in an image by using a region growing algorithm.…”

Section: Methodsmentioning

confidence: 99%

“…The previous region growing motion estimation method [23] used an edge criterion alone to decide what point to track next. The inherent problem with this approach is that determination of what point to track next depends on what points are currently on the boundary, and these points are (potentially) far from the seed and have no tight relationships with the seed.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, we reported a region growing refinement method [23], which automatically tracks the whole tissue after a seed point is manually specified, and does this very rapidly. It has been successfully applied to both tongue and cardiac motion tracking.…”

Section: Introductionmentioning

confidence: 99%

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Shortest Path Refinement for Motion Estimation From Tagged MR Images

Liu

Prince

2010

IEEE Trans. Med. Imaging

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Magnetic resonance tagging makes it possible to measure the motion of tissues such as muscles in the heart and tongue. The harmonic phase (HARP) method largely automates the process of tracking points within tagged MR images, permitting many motion properties to be computed. However, HARP tracking can yield erroneous motion estimates due to: (1) large deformations between image frames; (2) through-plane motion; and (3) tissue boundaries. Methods that incorporate the spatial continuity of motion—so-called refinement or floodfilling methods—have previously been reported to reduce tracking errors. This paper presents a new refinement method based on shortest path computations. The method uses a graph representation of the image and seeks an optimal tracking order from a specified seed to each point in the image by solving a single source shortest path problem. This minimizes the potential errors for those path dependent solutions that are found in other refinement methods. In addition to this, tracking in the presence of through-plane motion is improved by introducing synthetic tags at the reference time (when the tissue is not deformed). Experimental results on both tongue and cardiac images show that the proposed method can track the whole tissue more robustly and is also computationally efficient.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Shortest Path Refinement for Motion Estimation From Tagged MR Images

Liu

Prince

2010

IEEE Trans. Med. Imaging

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“…HARP analysis, and other very efficient spectral algorithms for quantitative tag analysis, are often hampered by image noise that affects spatial tag profiles [7], and when this occurs the assumed motion invariance of the harmonic phases of material points does not hold. The HARP tracking may also fail at points that are close to the tissue boundary, and at points approaching or leaving the image plane due to through-plane motion.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al [7] proposed a HARP tracking refinement that is based on using a seeded region growing technique. This technique seeks to solve the problems associated with image motion by starting from a userdefined seed point which is determined to be correctly tracked throughout the entire sequence.…”

Section: Introductionmentioning

confidence: 99%

Accurate modeling of tagged CMR 3D image appearance characteristics to improve cardiac cycle strain estimation

Nitzken

Beache

Elnakib

et al. 2012

2012 19th IEEE International Conference on Image Processing

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To reduce noise within a tag line, unsharpen the tag edges in spatial domain, and amplify the tag-to-background contrast, a 3D energy minimization framework for the enhancement of tagged Cardiac Magnetic Resonance (CMR) image sequences, based on learning first-and second-order visual appearance models, is proposed. The first-order appearance modeling uses adaptive Linear Combinations of Discrete Gaussians (LCDG) to accurately approximate the empirical marginal probability distribution of CMR signals for a given sequence, and separates tag and background submodels. It is also used to classify the tag lines and the background. The second-order model considers image sequences as samples of a translation-and rotation-invariant 3D Markov-Gibbs Random Field (MGRF) with multiple pairwise voxel interactions. A 3D energy function for this model is built by using the analytical estimation of the spatiotemporal geometry and Gibbs potentials of interaction. To improve the strain estimation, by enhancing the tag and background homogeneity and contrast, the given sequence is adjusted using comparisons to the energy minimizer. Special 3D geometric phantoms, motivated by statistical analysis of the tagged CMR data, have been designed to validate the accuracy of our approach. Experiments with the phantoms and eight real data sets have confirmed the high accuracy of the functional parameters that are estimated for the enhanced tagged sequences when using popular spectral techniques, such as spectral Harmonic Phase (HARP).

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An off‐resonance insensitive orthogonal CSPAMM sequence (ORI‐O‐CSPAMM) for the acquisition of CSPAMM and MICSR grids in half scan time

Mella

Wang

Montalba

et al. 2021

Magnetic Resonance in Med

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ORI-O-CSPAMM) for the acquisition of CSPAMM and MICSR grids in half of the acquisition time. Methods: Phantom and mid-level left ventricle short-axis tagged images were acquired using CSPAMM, ORI-CSPAMM, O-CSPAMM, and the proposed ORI-O-CPAMM sequences to interrogate and compare its behavior under off-resonance effects produced by vegetable oil and subcutaneous and epicardial fat. The images were compared in terms of signal and the capacity to obtain complex difference and MICSR images. Results: Like ORI-CSPAMM, the proposed ORI-O-CSPAMM sequence removed almost completely the off-resonance artifacts produced during the tagging preparation. Tagging grids without DC components were obtained with ORI-O-CSPAMM using complex difference and MICSR from only two complementary images, which reduced the scan time to a half compared to CSPAMM and ORI-CSPAMM. The removal of off-resonance effects and the capacity to obtain MICSR images are advantages of ORI-O-CSPAMM over the O-CSPAMM sequence. Conclusion:We developed a novel and fast tagging sequence designed to remove off-resonance effects during the tagging preparation, and to obtain complex difference and MICSR grids in half of the scan time compared to CSPAMM and ORI-CSPAMM sequences, which could allow its application to clinical protocols.

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Harp Tracking Refinement Using Seeded Region Growing

Cited by 6 publications

References 6 publications

Shortest Path Refinement for Motion Estimation From Tagged MR Images

Shortest Path Refinement for Motion Estimation From Tagged MR Images

Accurate modeling of tagged CMR 3D image appearance characteristics to improve cardiac cycle strain estimation

An off‐resonance insensitive orthogonal CSPAMM sequence (ORI‐O‐CSPAMM) for the acquisition of CSPAMM and MICSR grids in half scan time

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