Cardiac motion analysis from magnetic resonance imaging: Cine magnetic resonance versus tagged magnetic resonance

Bajo, Andrea; Ledesma-Carbayo, María J.; Marta, Cristina Santa; David, Esther Pérez; Garcı́a-Fernández, Miguel Ángel; Desco, Manuel; Santos, A.

doi:10.1109/cic.2007.4745426

Cited by 1 publication

(1 citation statement)

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“…MR imaging methods for quantification of intramyocardial wall motion can be loosely classified into two approaches, i.e., those relying on specially developed MR imaging pulse sequences, like MR Tagging [9, 10], to help in the estimation of myocardial motion, and those relying on image analysis techniques to extract motion estimates from conventional cine MR sequences. Recent work has also compared the results obtained from the analysis of tagged vs. cine MR sequences [11]. For the sake of brevity, we limit our review of related work to the latter.…”

Section: Introductionmentioning

confidence: 99%

Estimating myocardial motion by 4D image warping

Sundar

Litt

Shen

2009

Pattern Recognition

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A method for spatio-temporally smooth and consistent estimation of cardiac motion from MR cine sequences is proposed. Myocardial motion is estimated within a 4-dimensional (4D) registration framework, in which all 3D images obtained at different cardiac phases are simultaneously registered. This facilitates spatio-temporally consistent estimation of motion as opposed to other registration-based algorithms which estimate the motion by sequentially registering one frame to another. To facilitate image matching, an attribute vector (AV) is constructed for each point in the image, and is intended to serve as a “morphological signature” of that point. The AV includes 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 to refine the registration. Experimental results on real data demonstrate good performance of the proposed method for cardiac image registration and motion estimation. The motion estimation is validated via comparisons with motion estimates obtained from MR images with myocardial tagging.

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Section: Introductionmentioning

confidence: 99%