Technion Research & Development Foundation Limited

Huawei Technologies Co., Limited

Michael Zibulevsky

Alexander Bronstein

Joseph Shtok

Michael Elad

Oleg Michailovich

Yoni Choukroun

Grigoriy Zurakhov

Ortal Senouf

Sanketh Vedula

An unconstrained saddle point of a function is obtained by computing a combination of a first subspace for minimization, and a second subspace for maximization. A combination of a current location including a first and second current location within the first and second subspace is iteratively selected. From the current location, a combination of a step-size including a first and second step-size along a first and second direction of the first and second subspace, is computed. The first and second step-size is to a next first and second location within the first and second subspace. The current location is set to a next location including the next first and second location. The combination of the first and second subspace is according to the next location. The iterations terminate when the next location meets a requirement denoting the unconstrained saddle point. The location indicating the unconstrained saddle point is provided.

Computation of a saddle-point

There is provided an ultrasound device comprising: ultrasound transducer(s), memory storing code, hardware processor(s) coupled to the ultrasound transducer(s) and the memory for executing stored code comprising: activating the transducer(s) for simultaneously transmitting at least one line with a predefined focus width, and receiving an indication of a plurality of narrow-focused received lines, inputting the indication of the narrow-focused received lines into a convolutional neural network (CNN) trained on pairs of ultrasound imaging data capturing rapid motion of the certain tissue of sample patient(s), including a single-line ultrasound imaging data captured based on a single line in response to a transmitted single narrow-focused pulse, and a multiple-line ultrasound imaging data captured based on receiving narrow-focused lines in response to simultaneously transmitted at least one line, outputting adjusted narrow-focused received lines, and computing an adjusted ultrasound image according to the adjusted narrow-focused received lines for presentation on a display.

Systems and methods for ultrasonic imaging

A method of projection domain processing based on a local transform and shrinkage for use in reconstructing digital images from a set of projections, the method including providing a target image of a target object, providing projection data of the target object, producing filtered projection data by applying a sparsifying transform and a shrinkage function to the projection data, followed by an inverse of the sparsifying transform, producing a restored image by applying a reconstruction transform to the filtered projection data, comparing the restored image to the target image, and producing an optimized projection domain shrinkage function by adapting the shrinkage function to minimize differences between the restored image and the target image. Related apparatus and methods are also described.

Image reconstruction in computed tomography

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Jerusalem, Israel

Michael Zibulevsky