Analytic signal phase-based myocardial motion estimation in tagged MRI sequences by a bilinear model and motion compensation

Wang, Liang; Basarab, Adrian; Girard, Patrick; Croisille, Pierre; Clarysse, Patrick; Delachartre, Philippe

doi:10.1016/j.media.2015.06.005

Cited by 13 publications

(14 citation statements)

References 40 publications

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“…1 and Exp. 5 show that without topology preservation in the functional (5), the residuum was about 0.1 with and without the low-rank constraint. Applying low-rank, the Jacobian determinant had higher values but still suffered from heavy distortions.…”

Section: Resultsmentioning

confidence: 97%

Robust cardiac motion estimation using ultrafast ultrasound data: a low-rank topology-preserving approach

Avilés

Widlak²,

Casals

et al. 2017

Phys. Med. Biol.

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Cardiac motion estimation is an important diagnostic tool for detecting heart diseases and it has been explored with modalities such as MRI and conventional ultrasound (US) sequences. US cardiac motion estimation still presents challenges because of complex motion patterns and the presence of noise. In this work, we propose a novel approach to estimate cardiac motion using ultrafast ultrasound data. Our solution is based on a variational formulation characterized by the L -regularized class. Displacement is represented by a lattice of b-splines and we ensure robustness, in the sense of eliminating outliers, by applying a maximum likelihood type estimator. While this is an important part of our solution, the main object of this work is to combine low-rank data representation with topology preservation. Low-rank data representation (achieved by finding the k-dominant singular values of a Casorati matrix arranged from the data sequence) speeds up the global solution and achieves noise reduction. On the other hand, topology preservation (achieved by monitoring the Jacobian determinant) allows one to radically rule out distortions while carefully controlling the size of allowed expansions and contractions. Our variational approach is carried out on a realistic dataset as well as on a simulated one. We demonstrate how our proposed variational solution deals with complex deformations through careful numerical experiments. The low-rank constraint speeds up the convergence of the optimization problem while topology preservation ensures a more accurate displacement. Beyond cardiac motion estimation, our approach is promising for the analysis of other organs that exhibit motion.

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Section: Resultsmentioning

confidence: 97%

Robust cardiac motion estimation using ultrafast ultrasound data: a low-rank topology-preserving approach

Avilés

Widlak²,

Casals

et al. 2017

Phys. Med. Biol.

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“…The decomposition property of analytic signals has been successfully applied to 2D ultrasound envelope detection [24] and myocardium motion estimation of 2D MRI images [25]. In this study, the decomposition property of a 3D complex analytic signal is adopted to extract the temporal information of 4D spatial-temporal DCE-MRI data.…”

Section: Methodsmentioning

confidence: 99%

A Clifford Analytic Signal-Based Breast Lesion Segmentation Method for 4D Spatial-Temporal DCE-MRI Sequences

et al. 2020

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Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has been increasingly used for lesion detection in breast cancer diagnosis for its capability to provide spatial-temporal information. However, the massive and complex 4D spatial-temporal DCE-MRI data make the diagnosis process lengthy and error-prone. Moreover, normal fibroglandular tissue is occasionally enhanced through background parenchymal enhancement (BPE), which can degrade the performance of current algorithms. We propose a new method using a 3D Clifford analytic signal (CAS) approach for breast lesion segmentation of DCE-MRI data. A 2D temporal image is constructed from all the 2D DCE-MRI slices at different scanning time points on a given transverse plane, according to the CAS approach. Then, a 3D Clifford temporal image (CTI) is constructed by successively stacking temporal images. The proposed CTI can distinguish lesion regions both visually and quantitatively compared to the traditional DCE-MRI subtraction image. Finally, we employ a fully convolutional network (FCN) model for breast lesion segmentation using the CTI as one of the inputs. Experimental results on an independent public dataset (TCIA QIN breast DCE-MRI) and a private household breast DCE-MRI dataset (TBD) show that the proposed method can achieve superior performance over current methods, both qualitatively and quantitatively. INDEX TERMS Breast DCE-MRI, breast lesion segmentation, fully convolutional network, clifford analytic signal, clifford temporal image.

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“…These methods use Gabor 3D filters and the same general equations of the optical flow for the computation of phase features. However, unlike the differential methods based on the invariance of the intensity, these approaches rely on the invariance of the phase gradient over time for the estimation of the velocity of the displacement [37,38]. The main limitations of these spatio-temporal approaches lie in the difficulty of respecting the optical flow assumptions and the relatively high number of used filters, making the estimation process very complex [39].…”

Section: Optical Flow Methodsmentioning

confidence: 99%

The Assessment of left ventricular Function in MRI using the detection of myocardial borders and optical flow approaches: A Review

Benameur

Kraim

Arous

et al. 2017

ijcp

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The evaluation of left ventricular wall motion in Magnetic Resonance Imaging (MRI) clinical practice is based on a visual assessment of cine-MRI sequences. In fact, clinical interpreters (radiologists) proceed with a global visual evaluation of multiple cine-MRI sequences acquired in the three standard views. In addition, some functional parameters are quantified following a manual or a semi-automatic contouring of the myocardial borders. Although these parameters give information about the functional state of the left ventricle, they are not able to provide the location and the extent of wall motion abnormalities, which are associated with many cardiovascular diseases. In the past years, several approaches were developed to overcome the limitations of the classical evaluation techniques of left ventricular function. The aim of this article is to present an overview of the different methods and to summarize the relevant techniques based on myocardial contour detection and optical flow for regional assessment of left ventricular abnormalities.

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Analytic signal phase-based myocardial motion estimation in tagged MRI sequences by a bilinear model and motion compensation

Cited by 13 publications

References 40 publications

Robust cardiac motion estimation using ultrafast ultrasound data: a low-rank topology-preserving approach

Robust cardiac motion estimation using ultrafast ultrasound data: a low-rank topology-preserving approach

A Clifford Analytic Signal-Based Breast Lesion Segmentation Method for 4D Spatial-Temporal DCE-MRI Sequences

The Assessment of left ventricular Function in MRI using the detection of myocardial borders and optical flow approaches: A Review

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