Motion Correction Using Deep Learning Neural Networks - Effects of Data Representation

An in-silico investigation of the effects of ultrasound data representation on the accuracy of the motion prediction made using deep learning neural networks was carried out. The representations studied include: linear (‘envelope’), log compressed, linear with phase and log compressed with phase. A UNet model was trained to predict non-rigid deformation field using a fixed and a moving image pair as the input. The results illustrate that the choice of the representation plays an important role on the accuracy of motion estimation. Specifically, representations with phase information outperform the representations without phase. Furthermore, log-compressed data yielded predictions with higher accuracy than the linear data

3-D In Vitro Acoustic Super-Resolution and Super-Resolved Velocity Mapping Using Microbubbles

Standard clinical ultrasound (US) imaging frequencies are unable to resolve microvascular structures due to the fundamental diffraction limit of US waves. Recent demonstrations of 2D super-resolution both in vitro and in vivo have demonstrated that fine vascular structures can be visualized using acoustic single bubble localization. Visualization of more complex and disordered 3D vasculature, such as that of a tumor, requires an acquisition strategy which can additionally localize bubbles in the elevational plane with high precision in order to generate super-resolution in all three dimensions. Furthermore, a particular challenge lies in the need to provide this level of visualization with minimal acquisition time. In this work, we develop a fast, coherent US imaging tool for microbubble localization in 3D using a pair of US transducers positioned at 90°. This allowed detection of point scatterer signals in 3 dimensions with average precisions equal to 1.9 µm in axial and elevational planes, and 11 µm in the lateral plane, compared to the diffraction limited point spread function full widths at half maximum of 488 µm, 1188 µm and 953 µm of the original imaging system with a single transducer. Visualization and velocity mapping of 3D in vitro structures was demonstrated far beyond the diffraction limit. The capability to measure the complete flow pattern of blood vessels associated with disease at depth would ultimately enable analysis of in vivo microvascular morphology, blood flow dynamics and occlusions resulting from disease states

Avalanche Merging and Continuous Flow in a Sandpile Model

A dynamical transition separating intermittent and continuous flow is observed in a sandpile model, with scaling functions relating the transport behaviors between both regimes. The width of the active zone diverges with system size in the avalanche regime but becomes very narrow for continuous flow. The change of the mean slope, Delta z, on increasing the driving rate, r, obeys Delta z ~ r^{1/theta}. It has nontrivial scaling behavior in the continuous flow phase with an exponent theta given, paradoxically, only in terms of exponents characterizing the avalanches theta = (1+z-D)/(3-D).Comment: Explanations added; relation to other model

Two Stage Sub-Wavelength Motion Correction in Human Microvasculature for CEUS Imaging

The structure of microvasculature cannot be resolved using clinical B-mode or contrast-enhanced ultrasound (CEUS) imaging due to the fundamental diffraction limit at clinical ultrasound frequencies. It is possible to overcome this resolution limitation by localizing individual microbubbles through multiple frames and forming a super-resolved image. However, ultrasound super-resolution creates its unique problems since the structures to be imaged are on the order of 10s of μm. Tissue movement much larger than 10 μm is common in clinical imaging, which can significantly reduce the accuracy of super-resolution images created from microbubble locations gathered through hundreds of frames. This study investigated an existing motion estimation algorithm from magnetic resonance imaging for ultrasound super-resolution imaging. Its correction accuracy is evaluated using simulations with increasing complexity of motion. Feasibility of the method for ultrasound super-resolution in vivo is demonstrated on clinical ultrasound images. For a chosen microvessel, the super-resolution image without motion correction achieved a sub-wavelength resolution; however after the application of proposed two-stage motion correction method the size of the vessel was reduced to half

Acoustic wave sparsely activated localization microscopy (AWSALM): super-resolution ultrasound imaging using acoustic activation and deactivation of nanodroplets

Photo-activated localization microscopy (PALM) has revolutionized the field of fluorescence microscopy by breaking the diffraction limit in spatial resolution. In this study, “acoustic wave sparsely activated localization microscopy (AWSALM),” an acoustic counterpart of PALM, is developed to super-resolve structures which cannot be resolved by conventional B-mode imaging. AWSALM utilizes acoustic waves to sparsely and stochastically activate decafluorobutane nanodroplets by acoustic vaporization and to simultaneously deactivate the existing vaporized nanodroplets via acoustic destruction. In this method, activation, imaging, and deactivation are all performed using acoustic waves. Experimental results show that sub-wavelength micro-structures not resolvable by standard B-mode ultrasound images can be separated by AWSALM. This technique is flow independent and does not require a low concentration of contrast agents, as is required by current ultrasound super resolution techniques. Acoustic activation and deactivation can be controlled by adjusting the acoustic pressure, which remains well within the FDA approved safety range. In conclusion, this study shows the promise of a flow and contrast agent concentration independent super-resolution ultrasound technique which has potential to be faster and go beyond vascular imaging

Order Parameter and Scaling Fields in Self-Organized Criticality

We present a unified dynamical mean-field theory for stochastic self-organized critical models. We use a single site approximation and we include the details of different models by using effective parameters and constraints. We identify the order parameter and the relevant scaling fields in order to describe the critical behavior in terms of usual concepts of non equilibrium lattice models with steady-states. We point out the inconsistencies of previous mean-field approaches, which lead to different predictions. Numerical simulations confirm the validity of our results beyond mean-field theory.Comment: 4 RevTex pages and 2 postscript figure