Adrian Basarab

Myocardial Motion Estimation From Medical Images Using the Monogenic Signal

We present a method for the analysis of heart motion from medical images. The algorithm exploits monogenic signal theory, recently introduced as an N-dimensional generalization of the analytic signal. The displacement is computed locally by assuming the conservation of the monogenic phase over time. A local affine displacement model is considered to account for typical heart motions as contraction/expansion and shear. A coarse-to-fine B-spline scheme allows a robust and effective computation of the models parameters, and a pyramidal refinement scheme helps to handle large motions. Robustness against noise is increased by replacing the standard point-wise computation of the monogenic orientation with a robust least-squares orientation estimate. Given its general formulation, the algorithm is well suited for images from different modalities, in particular for those cases where time variant changes of local intensity invalidate the standard brightness constancy assumption. This paper evaluates the methods feasibility on two emblematic cases: cardiac tagged magnetic resonance and cardiac ultrasound. In order to quantify the performance of the proposed method, we made use of realistic synthetic sequences from both modalities for which the benchmark motion is known. A comparison is presented with state-of-the-art methods for cardiac motion analysis. On the data considered, these conventional approaches are outperformed by the proposed algorithm. A recent global optical-flow estimation algorithm based on the monogenic curvature tensor is also considered in the comparison. With respect to the latter, the proposed framework provides, along with higher accuracy, superior robustness to noise and a considerably shorter computation time.

Frequency Domain Compressive Sampling for Ultrasound Imaging

Compressed sensing or compressive sampling is a recent theory that originated in the applied mathematics field. It suggests a robust way to sample signals or images below the classic Shannon-Nyquist theorem limit. This technique has led to many applications, and has especially been successfully used in diverse medical imaging modalities such as magnetic resonance imaging, computed tomography, or photoacoustics. This paper first revisits the compressive sampling theory and then proposes several strategies to perform compressive sampling in the context of ultrasound imaging. Finally, we show encouraging results in 2D and 3D, on high- and low-frequency ultrasound images.

A method for vector displacement estimation with ultrasound imaging and its application for thyroid nodular disease

Ultrasound elastography is a promising imaging technique that can assist in diagnosis of thyroid cancer. However, the complexity of the tissue movements under freehand compression requires the use of a parametric displacement model and a specific estimation method adapted to sub-pixel motion. Therefore, the aim of this study was to develop a motion estimation method for ultrasound elastography and test its performances compared to a classical block matching technique. The proposed method, referred to as Bilinear Deformable Block Matching (BDBM), uses a bilinear model with eight parameters for controlling the local mesh deformation. In addition, a technique of motion initialization based on a triangle scan of the images adapted to ultrasound elastography is proposed. The BDBM method includes an iterative multi-scale process. This iterative approach is shown to decrease the absolute error of the displacement estimation by a factor of 1.4 when passing from 1 to 2 iterations. The method was tested on simulated images and the results show that absolute displacement estimation error was reduced by a factor of 4 compared to classical block matching. We applied the BDBM method on three experimental sets of data. In the first data set, a phantom designed for ultrasound elastography was used. The two other sets of data involve the thyroid gland and were acquired using freehand tissue compression by ultrasound probe of a clinical ultrasound scanner modified for research. A similarity measurement based on local cross-correlation shows that, for experimental data, the BDBM method outperforms the usual block matching.

Phase-based block matching applied to motion estimation with unconventional beamforming strategies

A phase-based block matching method adapted to motion estimation with unconventional beamforming strategies is presented. The unconventional beamforming technique used allows us to obtain 2-D RF images with axial and lateral modulations. Based on these images, we propose a method that uses phase images instead of amplitude images. This way of proceeding allows us to provide an analytical solution to the local displacement estimation so that no minimization of a classical cost function is used for the local estimation. For this reason, the local estimator is directly applied to signals, without the need to process a complex cross-correlation function, as is done with most of the phase shift estimators. In this paper, the method is applied to elastography. Results with simulated data show that a downsampling of axial and lateral modulated signals leads to very little change in the accuracy and in the spatial resolution of the proposed method. For example, for decimation factors of 2 in the axial direction and of 4 in the lateral direction, the mean axial absolute error is 3 mum. The same estimation with original images provides a mean axial error of 0.7 mum. The accuracy of the lateral motion is unchanged in this case. The accuracy of our method with downsampled signals is an important issue in the purpose of a real-time implementation. With experimental data, for the same level of estimation error, classical block matching using the maximum of cross correlation as a local estimator requires images that are 36 times larger (in number of pixels) and consequently a computational time roughly 10 times longer. Our phase block matching is also shown to provide 10 percent less error than a motion estimation method based on seeking the zero of the complex correlation function phase. Finally, it is shown that given the separability of the local estimator that we propose, our method can be applied on both n-D signals and classical RF ultrasound images. The phase block matching method presented was implemented in real time on an ultrasound research scanner.

Compressive sensing in medical ultrasound

One of the fundamental theorem in information theory is the so-called sampling theorem also known as Shannon-Nyquist theorem. This theorem aims at giving the minimal frequency needed to sample and reconstruct perfectly an analog band-limited signal. Compressive sensing (or compressed sensing, compressive sampling) or CS in short is a recent theory that allows, if the signal to be reconstructed satisfies a number of conditions, to decrease the amount of data needed to reconstruct the signal. As a result this theory can be used for at least two purposes: i) accelerate the acquisition rate without decreasing the reconstructed signal quality (e.g. in terms of resolution, SNR, contrast ...) ii) improve the image quality without increasing the quantity of needed data. Even if medical ultrasound is a domain where several potential applications can be highlighted, the use of this theory in this domain is extremely recent. In this paper we review the basic theory of compressive sensing. Then, a review of the existing CS studies in the field of medical ultrasound is given: reconstruction of sparse scattering maps, pre-beamforming channel data, post-beamforming signals and slow time Doppler data. Finally the open problems and challenges to be tackled in order to make the application of CS to medical US a reality will be given.

Compressed sensing of ultrasound images: Sampling of spatial and frequency domains

This paper proposes a comparison between an established (used in magnetic resonance imaging) and a innovative compressed sensing (CS) approach, both adapted to ultrasound (US) imaging. Two undersampling patterns suited to US imaging were investigated in each approach on simulated and in vivo radio-frequency US images. Reconstructions of simulated and in vivo US images using CS show minimal information loss. The best strategy (minimising the errors of reconstruction) was a uniform random sampling in the two directions of the spatial RF US image associated with the reconstruction of its k-space.

Transverse oscillations for tissue motion estimation.

This paper gives an overview of the methods developed for tissue motion estimation using transverse oscillation images (TO). TO images are specific radiofrequency ultrasound images featuring oscillations in both spatial directions. The initial studies on TO were published in the late 1990s. This paper reviews the main ideas and applications behind this motion estimation approach. First the origin and motivation of TO is briefly reviewed. Then the beamforming methods that lead to TO images are given, detailing the receive-only approach and the transmit-and-receive approach using synthetic aperture data. The different medical applications where TO has been used are discussed (blood flow, elastography and echocardiography), showing how it can improve motion estimation. Finally, the future perspectives of TO are outlined.

Analytic Estimation of Subsample Spatial Shift Using the Phases of Multidimensional Analytic Signals

In this correspondence, a method of analytic subsample spatial shift estimation based on an a priori n-D signal model is proposed. The estimation uses the linear phases of n analytic signals defined with the multidimensional Hilbert transform. This estimation proposes: i) an analytic solution to the n -D shift estimation and ii) an estimation without processing complex cross-correlation function or cross-spectra between signals contrary to most phase shift estimators. The method provides better performance in estimating subsample shifts than two classical estimators, one using the maximum of cross-correlation function and the other seeking the zero of the complex correlation function phase. Two delay estimators using the in-phase and quadrature-phase components of signals are also compared to our estimator. Like most estimators using the complex signal phases, the estimator proposed herein presents the advantage of unaltered accuracy when low sampled signals are used. Moreover, we show that this method can be applied to motion tracking with ultrasound images. Thus, included in a block-based motion estimation method and tested with ultrasound data, this estimator provides an analytical solution to the translation estimation problem.

Compressive Deconvolution in Medical Ultrasound Imaging

The interest of compressive sampling in ultrasound imaging has been recently extensively evaluated by several research teams. Following the different application setups, it has been shown that the RF data may be reconstructed from a small number of measurements and/or using a reduced number of ultrasound pulse emissions. Nevertheless, RF image spatial resolution, contrast and signal to noise ratio are affected by the limited bandwidth of the imaging transducer and the physical phenomenon related to US wave propagation. To overcome these limitations, several deconvolution-based image processing techniques have been proposed to enhance the ultrasound images. In this paper, we propose a novel framework, named compressive deconvolution, that reconstructs enhanced RF images from compressed measurements. Exploiting an unified formulation of the direct acquisition model, combining random projections and 2D convolution with a spatially invariant point spread function, the benefit of our approach is the joint data volume reduction and image quality improvement. The proposed optimization method, based on the Alternating Direction Method of Multipliers, is evaluated on both simulated and in vivo data.

A New Technique for the Estimation of Cardiac Motion in Echocardiography Based on Transverse Oscillations: A Preliminary Evaluation In Silico and a Feasibility Demonstration In Vivo

Quantification of regional myocardial motion and deformation from cardiac ultrasound is fostering considerable research efforts. Despite the tremendous improvements done in the field, all existing approaches still face a common limitation which is intrinsically connected with the formation of the ultrasound images. Specifically, the reduced lateral resolution and the absence of phase information in the lateral direction highly limit the accuracy in the computation of lateral displacements. In this context, this paper introduces a novel setup for the estimation of cardiac motion with ultrasound. The framework includes an unconventional beamforming technique and a dedicated motion estimation algorithm. The beamformer aims at introducing phase information in the lateral direction by producing transverse oscillations. The estimator directly exploits the phase information in the two directions by decomposing the image into two 2-D single-orthant analytic signals. An in silico evaluation of the proposed framework is presented on five ultra-realistic simulated echocardiographic sequences, where the proposed motion estimator is contrasted against other two phase-based solutions exploiting the presence of transverse oscillations and against block-matching on standard images. An implementation of the new beamforming strategy on a research ultrasound platform is also shown along with a preliminary in vivo evaluation on one healthy subject.