International Business Machines Corporation

Matthieu M Simeoni

Paul T Hurley

Giovanni Cherubini

Sanaz Kazemi

Sepand Kashani

Lucien Roquette

A method for computing a tissue reflectivity function (TRF) is provided. This method comprises accessing radio-frequency (RF) data and computing a point spread function (PSF), to obtain the TRF. The RF data accessed are data obtained by beamforming time signals from an array of transducers of an ultrasound device. Next, for each pair (r, s) of given pairs of points in the imaging plane, the PSF is computed as a sum of contributions from at least some of the transducers. Each contribution is computed by evaluating a transducer's transfer function, based on two outputs of a respective time-of-flight function. Such outputs are obtained by evaluating the time-of-flight function at a first point r and at a second point s of each pair, respectively. Finally, the RF function can be deconvoluted, based on the computed PSF, so as to obtain the TRF.

Efficient computation of spatially varying ultrasound analytical point spread functions

A method of beamforming datasets from a tomographic detection system. The system comprises scintillation detectors that are arranged in D detector pairs, D&#x2265;1, wherein the detectors are adapted to count radiation hits. According to the method in one aspect, a tomographic dataset is received for each detector pair coordinates (&#x3b8;, p) of a detector pair d of the D detector pairs, so as to obtain a plurality of tomographic datasets. Each of said datasets is associated with respective detector pair coordinates (&#x3b8;, p). Then, for each point y of interest, the received datasets are coherently combined by weighting the datasets according to respective beamforming weights(y)=&#x3c9;(&#x3b8;, p; y), based on said respective detector pair coordinates (&#x3b8;, p) and coordinates of said each point y of interest. This way, a signal focusing on said each point y is obtained. Related tomographic detection systems and computer program products may be also presented.

Beamforming for tomographic detection systems

The present invention is directed to methods and apparatuses for beamforming signals or compute beamformed signals. The present approach is to determine a series of beams from or for a set of devices configured for receiving signals from and/or transmitting signals to one or more regions of interest in an n-dimensional space, with n=2 or 3. Each of the devices has a known position pwithin said n-dimensional space. Signals are to be respectively transmitted or received non-uniformly in this space, i.e., according to the particular regions of interest. During a first phase, operations are performed in order to successively obtain a spatial filter function {circumflex over (&#x3c9;)}(r), a beamforming function &#x3c9;(p), and beamforming weights &#x3c9;(p). The spatial filter function {circumflex over (&#x3c9;)}(r) matches projections of the regions of interest onto an n&#x2212;1-dimensional sphere centered on said set of devices. The function {circumflex over (&#x3c9;)}(r), however, extends over the n-dimensional space and does not restrict to the n&#x2212;1-dimensional sphere. During a second phase, delays and gains are suitably introduced in the signals, by weighting time-series according to beamforming weights &#x3c9;(p) obtained during the first phase.

Methods and apparatuses for versatile beamforming

The present invention is notably directed to a computer-implemented method for image reconstruction. The method comprises: accessing elements that respectively correspond to measurement values, which can be respectively mapped to measurement nodes; and performing message passing estimator operations to obtain estimates of random variables associated with variable nodes, according to a message passing method in a bipartite factor graph. In this message passing method: the measurement values are, each, expressed as a term that comprises linear combinations of the random variables; each message exchanged between any of the measurement nodes and any of the variable nodes is parameterized by parameters of a distribution of the random variables; and performing the message passing estimator operations further comprises randomly mapping measurement values to the measurement nodes, at one or more iterations of the message passing method. Finally, image data are obtained from the obtained estimates of the random variables, which image data are adapted to reconstruct an image. The present invention is further directed to related systems and methods using the above image reconstruction method.

Image reconstruction using approximate message passing methods

The present invention is notably directed to computer-implemented methods and systems for recovering an image. Present methods comprise: accessing signal data representing signals; identifying subsets of points arranged so as to span a region of interest as current subsets of points; reconstructing an image based on current subsets of points, by combining signal data associated to the current subsets of points; detecting one or more signal features in a last image reconstructed; for each of the detected one or more signal features, modifying one or more subsets of the current subsets, so as to increase, for each of the modified one or more subsets, a relative number of points at a location of said each of the detected one or more signal features. The relative number of points of a given subset at a given location may be defined as the number of points of said given subset at the given location divided by the total number of points of said given subset, whereby new current subsets of points are obtained; and repeating the above steps of reconstructing, detecting and modifying, as necessary to obtain a reconstructed image that satisfies a given condition.

Iterative image subset processing for image reconstruction

Embodiments include methods for calibrating sensors of one or more sensor arrays. Aspects include accessing one or more beamforming matrices respectively associated to the one or more sensor arrays. Source intensity estimates are obtained for a set of points in a region of interest, based on measurement values as obtained after beamforming signals from the one or more sensor arrays based on the one or more beamforming matrices, assuming fixed amplitude and phase of gains of sensors of the one or more sensor arrays. Estimates of amplitude and phase of the sensor gains are obtained based on: measurement values as obtained before beamforming; and the previously obtained source intensity estimates. The obtained estimates of amplitude and phase can be used for calibrating sensors.

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Zurich, Switzerland

Matthieu M Simeoni