An unsupervised learning approach to ultrasound strain elastography with spatio-temporal consistency

Quasi-static ultrasound elastography (USE) is an imaging modality that measures deformation (i.e. strain) of tissue in response to an applied mechanical force. In USE, the strain modulus is traditionally obtained by deriving the displacement field estimated between a pair of radio-frequency data. In this work we propose a recurrent network architecture with convolutional long-short-term memory decoder blocks to improve displacement estimation and spatio-temporal continuity between time series ultrasound frames. The network is trained in an unsupervised way, by optimising a similarity metric between the reference and compressed image. Our training loss is also composed of a regularisation term that preserves displacement continuity by directly optimising the strain smoothness, and a temporal continuity term that enforces consistency between successive strain predictions. In addition, we propose an open-access in vivo database for quasi-static USE, which consists of radio-frequency data sequences captured on the arm of a human volunteer. Our results from numerical simulation and in vivo data suggest that our recurrent neural network can account for larger deformations, as compared with two other feed-forward neural networks. In all experiments, our recurrent network outperformed the state-of-the-art for both learning-based and optimisationbased methods, in terms of elastographic signal-to-noise ratio, strain consistency, and image similarity. Finally, our open-source code provides a 3D-slicer visualisation module that can be used to process ultrasound RF frames in real-time, at a rate of up to 20 frames per second, using a standard GPU

An unsupervised learning-based shear wave tracking method for ultrasound elastography

Shear wave elastography involves applying a non-invasive acoustic radiation force to the tissue and imaging the induced deformation to infer its mechanical properties. This work investigates the use of convolutional neural networks to improve displacement estimation accuracy in shear wave imaging. Our training approach is completely unsupervised, which allows to learn the estimation of the induced micro-scale deformations without ground truth labels. We also present an ultrasound simulation dataset where the shear wave propagation has been simulated via finite element method. Our dataset is made publicly available along with this paper, and consists in 150 shear wave propagation simulations in both homogenous and hetegeneous media, which represents a total of 20,000 ultrasound images. We assessed the ability of our learning-based approach to characterise tissue elastic properties (i.e., Young's modulus) on our dataset and compared our results with a classical normalised cross-correlation approach

An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation

Quasi-static ultrasound elastography (USE) is an imaging modality that consists of determining a measure of deformation (i.e. strain) of soft tissue in response to an applied mechanical force. The strain is generally determined by estimating the displacement between successive ultrasound frames acquired before and after applying manual compression. The computational efficiency and accuracy of the displacement prediction, also known as time-delay estimation, are key challenges for real-time USE applications. In this paper, we present a novel deep-learning method for efficient time-delay estimation between ultrasound radio-frequency (RF) data. The proposed method consists of a convolutional neural network (CNN) that predicts a displacement field between a pair of pre- and post-compression ultrasound RF frames. The network is trained in an unsupervised way, by optimizing a similarity metric between the reference and compressed image. We also introduce a new regularization term that preserves displacement continuity by directly optimizing the strain smoothness. We validated the performance of our method by using both ultrasound simulation and in vivo data on healthy volunteers. We also compared the performance of our method with a state-of-the-art method called OVERWIND [17]. Average contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of our method in 30 simulation and 3 in vivo image pairs are 7.70 and 6.95, 7 and 0.31, respectively. Our results suggest that our approach can effectively predict accurate strain images. The unsupervised aspect of our approach represents a great potential for the use of deep learning application for the analysis of clinical ultrasound data

Random parking, Euclidean functionals, and rubber elasticity

We study subadditive functions of the random parking model previously analyzed by the second author. In particular, we consider local functions $S$ of subsets of $\mathbb{R}^d$ and of point sets that are (almost) subadditive in their first variable. Denoting by $\xi$ the random parking measure in $\mathbb{R}^d$, and by $\xi^R$ the random parking measure in the cube $Q_R=(-R,R)^d$, we show, under some natural assumptions on $S$, that there exists a constant $\bar{S}\in \mathbb{R}$ such that % $$\lim_{R\to +\infty} \frac{S(Q_R,\xi)}{|Q_R|}\,=\,\lim_{R\to +\infty}\frac{S(Q_R,\xi^R)}{|Q_R|}\,=\,\bar{S}$$ % almost surely. If $\zeta \mapsto S(Q_R,\zeta)$ is the counting measure of $\zeta$ in $Q_R$, then we retrieve the result by the second author on the existence of the jamming limit. The present work generalizes this result to a wide class of (almost) subadditive functions. In particular, classical Euclidean optimization problems as well as the discrete model for rubber previously studied by Alicandro, Cicalese, and the first author enter this class of functions. In the case of rubber elasticity, this yields an approximation result for the continuous energy density associated with the discrete model at the thermodynamic limit, as well as a generalization to stochastic networks generated on bounded sets.Comment: 28 page

Determination of the magnetization profile of Co/Mg periodic multilayers by magneto-optic Kerr effect and X-ray magnetic resonant reflectivity

The resonant magnetic reflectivity of Co/Mg multilayers around the Co L2,3 absorption edge is simulated then measured on a specifically designed sample. The dichroic signal is obtained when making the difference between the two reflectivities measured with the magnetic field applied in two opposite directions parallel to the sample surface. The simulations show that the existence of magnetic dead layers at the interfaces between the Co and Mg layers leads to an important increase of the dichroic signal measured in the vicinity of the third Bragg peak that otherwise should be negligible. The measurements are in agreement with the model introducing 0.25 nm thick dead layers. This is attributed to the Co atoms in contact with the Mg layers and thus we conclude that the Co-Mg interfaces are abrupt from the magnetic point of view.Comment: 8 page

Predictions from Heavy New Physics Interpretation of the Top Forward-Backward Asymmetry

We derive generic predictions at hadron colliders from the large forward-backward asymmetry observed at the Tevatron, assuming the latter arises from heavy new physics beyond the Standard Model. We use an effective field theory approach to characterize the associated unknown dynamics. By fitting the Tevatron t \bar t data we derive constraints on the form of the new physics. Furthermore, we show that heavy new physics explaining the Tevatron data generically enhances at high invariant masses both the top pair production cross section and the charge asymmetry at the LHC. This enhancement can be within the sensitivity of the 8 TeV run, such that the 2012 LHC data should be able to exclude a large class of models of heavy new physics or provide hints for its presence. The same new physics implies a contribution to the forward-backward asymmetry in bottom pair production at low invariant masses of order a permil at most.Comment: 11 pages, 6 figures. v2: added remarks on EFT validity range, dijet bounds and UV completions; matches published versio

Generation of Vorticity and Velocity Dispersion by Orbit Crossing

We study the generation of vorticity and velocity dispersion by orbit crossing using cosmological numerical simulations, and calculate the backreaction of these effects on the evolution of large-scale density and velocity divergence power spectra. We use Delaunay tessellations to define the velocity field, showing that the power spectra of velocity divergence and vorticity measured in this way are unbiased and have better noise properties than for standard interpolation methods that deal with mass weighted velocities. We show that high resolution simulations are required to recover the correct large-scale vorticity power spectrum, while poor resolution can spuriously amplify its amplitude by more than one order of magnitude. We measure the scalar and vector modes of the stress tensor induced by orbit crossing using an adaptive technique, showing that its vector modes lead, when input into the vorticity evolution equation, to the same vorticity power spectrum obtained from the Delaunay method. We incorporate orbit crossing corrections to the evolution of large scale density and velocity fields in perturbation theory by using the measured stress tensor modes. We find that at large scales (k~0.1 h/Mpc) vector modes have very little effect in the density power spectrum, while scalar modes (velocity dispersion) can induce percent level corrections at z=0, particularly in the velocity divergence power spectrum. In addition, we show that the velocity power spectrum is smaller than predicted by linear theory until well into the nonlinear regime, with little contribution from virial velocities.Comment: 27 pages, 14 figures. v2: reorganization of the material, new appendix. Accepted by PR

Cosmological Consequences of Nearly Conformal Dynamics at the TeV scale

Nearly conformal dynamics at the TeV scale as motivated by the hierarchy problem can be characterized by a stage of significant supercooling at the electroweak epoch. This has important cosmological consequences. In particular, a common assumption about the history of the universe is that the reheating temperature is high, at least high enough to assume that TeV-mass particles were once in thermal equilibrium. However, as we discuss in this paper, this assumption is not well justified in some models of strong dynamics at the TeV scale. We then need to reexamine how to achieve baryogenesis in these theories as well as reconsider how the dark matter abundance is inherited. We argue that baryonic and dark matter abundances can be explained naturally in these setups where reheating takes place by bubble collisions at the end of the strongly first-order phase transition characterizing conformal symmetry breaking, even if the reheating temperature is below the electroweak scale $\sim 100$ GeV. We also discuss inflation as well as gravity wave smoking gun signatures of this class of models.Comment: 22 pages, 7 figure

A Novel Intraoperative Ultrasound Probe for Transsphenoidal Surgery: First-in-human study.

Background. Ultrasound has been explored as an alternative, less bulky, less time-consuming and less expensive means of intraoperative imaging in pituitary surgery. However, its use has been limited by the size of its probes relative to the transsphenoidal corridor. We developed a novel prototype that is more slender than previously reported forward-viewing probes and, in this report, we assess its feasibility and safety in an initial patient cohort. Method. The probe was integrated into the transsphenoidal approach in patients with pituitary adenoma, following a single-centre prospective proof of concept study design, as defined by the Innovation, Development, Exploration, Assessment and Long-Term Study (IDEAL) guidelines for assessing innovation in surgery (IDEAL stage 1 - Idea phase). Results. The probe was employed in 5 cases, and its ability to be used alongside the standard surgical equipment was demonstrated in each case. No adverse events were encountered. The average surgical time was 20 minutes longer than that of 30 contemporaneous cases operated without intraoperative ultrasound. Conclusion. We demonstrate the safety and feasibility of our novel ultrasound probe during transsphenoidal procedures to the pituitary fossa, and, as a next step, plan to integrate the device into a surgical navigation system (IDEAL Stage 2a - Development phase)