Improved focusing in HIFU and SWL

Improved focusing in HIFU and SWL

Improved focusing in HIFU and SWL In order to achieve improved focusing during the high-focused ultrasound therapy or lithotripsy we: use the bent ray model for sound propagation in non-homogenious media; take into account variable sound speed and temperature; do adaptive focusing given a feedback (MRI or utrasound); optimize the focusing taking into account discrete set of emitters. Projects3D automated whole breast ultrasound imaging Improved focusing in HIFU and SWL 3D automatic breast boundary detection Beamforming in acoustic tomography Ultrasound tomography with learned dictionaries Ultrasound tomography for breast screening Remote Temperature Monitoring in Furnaces and Boilers Temperature and flow estimation in the atmosphere Compact loudspeaker arrays for directional sound reproduction Automatic multi-channel room acoustics correction Sound field rendering ⇦ ALL PROJECTS Interested in working together? We are currently looking for partners that will actively support our development. Contact...
3D automatic breast boundary detection

3D automatic breast boundary detection

3D automatic breast boundary detection Breast screening with an ultrasound tomography scanner [1] involves immersion of the breast in the water. The breast is scanned along the coronal axis from the chest wall to the nipple region. To improve the visualization of the generated 3D image, it is desirable to remove the water background. To this end, the 3D boundary of the breast must be accurately estimated. We developed an algorithm based on active contours that automatically detects the boundary of a breast using a 3D coronal stack of ultrasound attenuation images obtained from an ultrasound tomography scanner. Our approach builds upon a method described in [2] which uses parametric active contours that are represented with exponential B-splines. The authors in [2] demonstrate that their active contour approach can approximate any blob-like objects with good accuracy, and can perfectly reproduce spheres and ellipsoids. The optimization process is remarkably fast and good approximation can be obtained using only a few control points. The technique is appealing in that the smoothness of the object boundary is implicitly taken into account. We made a number of modification to the original technique such as: 1)  mirroring of the input image stack around a chest wall; 2) adapting the region of interest; 3) making an iterative optimization algorithm that successively optimizes the boundary  etc. As a result our algorithm is fully automated, fast, and robust to artifacts that may arise from the ultrasound tomography reconstruction process. Results We demonstrate the effectiveness of the proposed technique using clinical data sets obtained from SoftVue scanner developed by Delphinus Medical Technologies. References [1] Duric, N., Littrup, P.,...
Beamforming in acoustic tomography

Beamforming in acoustic tomography

Beamforming in acoustic tomography Overview Beamforming is a signal processing technique used with arrays of transmitters or receivers that control the directionality of a radiation pattern. When receiving a signal, the so called receive beamforming can increase the receiver sensitivity in the direction of wanted signals and decrease the sensitivity in the direction of interference and noise. When transmitting a signal, the so called transmit beamforming can increase the power along the chosen direction. Therefore, using beamforming we can enhance the signal in a particular direction and suppress noise and reflections coming from other directions. Our patented beamforming method uses transmit and receive beamforming optimized by taking into account transducers’ characteristics. The method shows to be very efficient in inhomogeneous and absorbing media and it reduces many ultrasounds artifacts. We were the first ones to propose a beamforming technique in an acoustic tomogrpahy setup. Results In the images below, we see a coronal slice of a breast the is scanned by an ultrasound tomography scanner. On the left image, the effect of a strong surface wave that is traveling on the surface of the breast is present (black region on the breast boundary). On the right image, we see that the effect of the surface wave disappeared. Moreover, the latter has as a positive consequence on the whole reconstruction, and not only in the region where the strong surface wave is present. Projects3D automated whole breast ultrasound imaging Improved focusing in HIFU and SWL 3D automatic breast boundary detection Beamforming in acoustic tomography Ultrasound tomography with learned dictionaries Ultrasound tomography for breast screening Remote Temperature Monitoring in Furnaces and Boilers Temperature and flow estimation in the atmosphere...
Ultrasound tomography with learned dictionaries

Ultrasound tomography with learned dictionaries

Ultrasound tomography with learned dictionaries Overview The images produced by MRI scan and sound speed images depict similar structures in the breast. Although these two imaging modalities rely on totally different physical principles (magnetism versus acoustics) they trace similar structures because both water content (measured by MRI) and sound speed (measured by UST) increase with tissue density. The high degree of spatial correlation of MR and sound speed images is therefore largely driven by similar sensitivity to changes in tissue density. However, UST imaging is an ill-conditioned problem, which requires a proper regularization approach to assure a reliable and accurate reconstruction. Assumption that an image has a sparse representation in an overcomplete dictionary can be used as an efficient regularization constraint for image reconstruction from ill-conditioned systems. Reconstruction Steps To obtain the dictionary adapted to the statistical properties of medical breast images (MRI and UST), we have applied the maximum likelihood dictionary learning method on the CURE database of MRI breast scans. The main steps of the algorithm can be outlined as follows. Learn dictionary on high resolution MRI breast images Ultrasound artifacts are not in the dictionary Find a sparse image representation in the dictionary Results Projects3D automated whole breast ultrasound imaging Improved focusing in HIFU and SWL 3D automatic breast boundary detection Beamforming in acoustic tomography Ultrasound tomography with learned dictionaries Ultrasound tomography for breast screening Remote Temperature Monitoring in Furnaces and Boilers Temperature and flow estimation in the atmosphere Compact loudspeaker arrays for directional sound reproduction Automatic multi-channel room acoustics correction Sound field rendering ⇦ ALL PROJECTS Interested in working together? We are currently looking for...
Ultrasound tomography for breast screening

Ultrasound tomography for breast screening

Ultrasound tomography for breast screening Ultrasound Tomography Setup Ultrasound tomography aims at recovering the parameters of an unknown medium by studying the characteristics of sound propagated through the medium. Instead of modeling the forward problem using the wave equation, travel-time tomography employs the principles of geometrical acoustics to estimate the sound speed distribution. It is based on the fact that acoustic energy travels along the lines perpendicular to the equal-phase wavefronts which is a valid assumption at high frequencies. In contrast to the problems with straight-line propagations such as X-ray tomography, the ultrasound propagation paths are not straight in an inhomogeneous medium and depend on the sound speed distribution. Therefore, travel-times are a nonlinear function of the unknown sound speed values. Reconstruction Steps The scanner consists of a circular array of transmitters and receivers which encloses the object to be imaged. By solving a nonlinear system of equations, the reconstruction algorithm estimates the sound speed of the object using the set of travel-time measurements. The main difficulty in this inverse problem is to ensure the convergence and robustness to noise.  We used a gradient method to find a solution for which the corresponding travel-times are closest to the measured travel-times in the least squares sense. To this end, first the gradient of the cost function is derived using Fermat’s Principle. Then, the iterative nonlinear conjugate gradient algorithm solves the minimization problem. This is combined with the backtracking line search method to efficiently find the step size in each iteration. This approach is guaranteed to converge to a local minimum of the cost function where the convergence point depends on the initial guess. Moreover, the method has the potential...