Literature DB >> 10072201

Cardiac material markers from tagged MR images.

W S Kerwin1, J L Prince.   

Abstract

Tagged magnetic resonance imaging (MRI) has shown great promise in non-invasive analysis of heart motion. To replace implanted markers as a gold standard, however, tagged MRI must be able to track a sparse set of material points, so-called material markers, with high accuracy. This paper presents a new method for generating accurate motion estimates over a sparse set of material points using standard, parallel-tagged MR images. Each tracked point is located at the intersection of three tag surfaces, each of which is estimated using a thin-plate spline. The intersections are determined by an iterative alternating projections algorithm for which a proof of convergence is provided. The resulting data sets are compatible with applications developed to exploit implanted marker data. One set of these material markers from a normal human volunteer is examined in detail using several methods to visualize the markers. Numerical results that include additional studies are also discussed. Finally, an error analysis is presented using a computer-simulated left ventricle for which material markers are tracked with an RMS error of approximately 0.2 mm for typical imaging parameters and noise levels.

Entities:  

Mesh:

Year:  1998        PMID: 10072201     DOI: 10.1016/s1361-8415(98)80015-7

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  10 in total

1.  Cardiac motion tracking using CINE harmonic phase (HARP) magnetic resonance imaging.

Authors:  N F Osman; W S Kerwin; E R McVeigh; J L Prince
Journal:  Magn Reson Med       Date:  1999-12       Impact factor: 4.668

2.  Incompressible deformation estimation algorithm (IDEA) from tagged MR images.

Authors:  Xiaofeng Liu; Khaled Z Abd-Elmoniem; Maureen Stone; Emi Z Murano; Jiachen Zhuo; Rao P Gullapalli; Jerry L Prince
Journal:  IEEE Trans Med Imaging       Date:  2011-09-19       Impact factor: 10.048

3.  Meshless deformable models for 3D cardiac motion and strain analysis from tagged MRI.

Authors:  Xiaoxu Wang; Ting Chen; Shaoting Zhang; Joël Schaerer; Zhen Qian; Suejung Huh; Dimitris Metaxas; Leon Axel
Journal:  Magn Reson Imaging       Date:  2014-08-23       Impact factor: 2.546

4.  Semi-automatic segmentation for 3D motion analysis of the tongue with dynamic MRI.

Authors:  Junghoon Lee; Jonghye Woo; Fangxu Xing; Emi Z Murano; Maureen Stone; Jerry L Prince
Journal:  Comput Med Imaging Graph       Date:  2014-08-01       Impact factor: 4.790

Review 5.  Myocardial tagging by cardiovascular magnetic resonance: evolution of techniques--pulse sequences, analysis algorithms, and applications.

Authors:  El-Sayed H Ibrahim
Journal:  J Cardiovasc Magn Reson       Date:  2011-07-28       Impact factor: 5.364

6.  Intramyocardial strain estimation from cardiac cine MRI.

Authors:  Ahmed Elnakib; Garth M Beache; Georgy Gimel'farb; Ayman El-Baz
Journal:  Int J Comput Assist Radiol Surg       Date:  2014-12-27       Impact factor: 2.924

7.  Phase Vector Incompressible Registration Algorithm for Motion Estimation From Tagged Magnetic Resonance Images.

Authors:  Fangxu Xing; Jonghye Woo; Arnold D Gomez; Dzung L Pham; Philip V Bayly; Maureen Stone; Jerry L Prince
Journal:  IEEE Trans Med Imaging       Date:  2017-07-04       Impact factor: 10.048

8.  Shortest path refinement for motion estimation from tagged MR images.

Authors:  Xiaofeng Liu; Jerry L Prince
Journal:  IEEE Trans Med Imaging       Date:  2010-03-18       Impact factor: 10.048

Review 9.  Multimodality Imaging in Congenital Heart Disease: an Update.

Authors:  Uyen T Truong; Shelby Kutty; Craig S Broberg; David J Sahn
Journal:  Curr Cardiovasc Imaging Rep       Date:  2012

10.  Myocardial Deformation in Cardiac Amyloid Light-chain Amyloidosis: Assessed with 3T Cardiovascular Magnetic Resonance Feature Tracking.

Authors:  Rui Li; Zhi-Gang Yang; Hua-Yan Xu; Ke Shi; Xi Liu; Kai-Yue Diao; Ying-Kun Guo
Journal:  Sci Rep       Date:  2017-06-19       Impact factor: 4.379

  10 in total

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