159 results about "Tomographic reconstruction" patented technology

A touch-sensitive apparatus comprises a panel configured to conduct signals from a plurality of peripheral incoupling points to a plurality of peripheral outcoupling points. Actual detection lines are defined between pairs of incoupling and outcoupling points to extend across a surface portion of the panel. The signals may be in the form of light, and objects touching the surface portion may affect the light via frustrated total internal reflection (FTIR). A signal generator is coupled to the incoupling points to generate the signals, and a signal detector is coupled to the out-coupling points to generate an output signal. A data processor operates on the output signal to enable identification of touching objects. The output signal is processed (40) to generate a set of data samples, which are indicative of detected energy for at least a subset of the actual detection lines. The set of data samples is processed (42) to generate a set of matched samples, which are indicative of estimated detected energy for fictitious detection lines that have a location on the surface portion that matches a standard geometry for tomographic reconstruction. The set of matched samples is processed (44, 46) by tomographic reconstruction to generate data indicative of a distribution of an energy-related parameter within at least part of the surface portion.

A tomographic reconstruction method and system incorporating Bayesian estimation techniques to inspect and classify regions of imaged objects, especially objects of the type typically found in linear, areal, or 3-dimensional arrays. The method and system requires a highly constrained model M that incorporates prior information about the object or objects to be imaged, a set of prior probabilities P(M) of possible instances of the object; a forward map that calculates the probability density P(D|M), and a set of projections D of the object. Using Bayesian estimation, the posterior probability p(M|D) is calculated and an estimated model MEST of the imaged object is generated. Classification of the imaged object into one of a plurality of classifications may be performed based on the estimated model MEST, the posterior probability p(M|D) or MAP function, or calculated expectation values of features of interest of the object.

The present discussion relates to the use of deep learning techniques to accelerate iterative reconstruction of images, such as CT, PET, and MR images. The present approach utilizes deep learning techniques so as to provide a better initialization to one or more steps of the numerical iterative reconstruction algorithm by learning a trajectory of convergence from estimates at different convergence status so that it can reach the maximum or minimum of a cost function faster.

A dynamic SPECT camera is provided, comprising, a plurality of single-pixel detectors, a timing mechanism, in communication with each single-pixel detector, configured for enabling time-binning of the radioactive emissions impinging upon each single-pixel detector to time periods not greater than substantially 30 seconds, and a position-tracker, configured for providing information on the position and orientation of each detecting unit, with respect to the overall structure, substantially at all times, during the individual motion, the dynamic SPECT camera being configured for acquiring a tomographic reconstruction image of a region of interest of about 15×15×15 cubic centimeters, during an acquisition time of 30 seconds, at a spatial resolution of at least 10×10×10 cubic millimeter. The dynamic camera is configured for very short damping time, and may further acquire images in a stationary mode, with no motion. It is further configured for time binning at dynamically varying time-bin lengths, dynamically determining a spectral energy bin for each detecting unit, and employing an anatomic construction of voxels in the imaging and reconstruction.

A method of obtaining a spatial model of a property of part of a subsurface formation located between underground seismic receivers in which at least two sets of pairs of seismic receivers are utilized and one pair of receivers is used to record a signal from a seimic source and obtaining a response by solving (s11(−t){circle around (x)}s21(t))=r11,21(t){circle around (x)}(s11(−t){circle around (x)}s21(t)), wherein the symbol {circle around (x)} denotes convolution and wherein s11(−t) is the time-reverse of the signal s11(t). A path-related attribute is selected from transmission response r11,21(t) that corresponds to the property of the subsurface formation and a tomographic reconstruction technique is applied to the path-related attribute to obtain the spatial model of the property of part of the subsurface formation.

A dynamic SPECT camera is provided, comprising, a plurality of single-pixel detectors, a timing mechanism, in communication with each single-pixel detector, configured for enabling time-binning of the radioactive emissions impinging upon each single-pixel detector to time periods not greater than substantially 30 seconds, and a position-tracker, configured for providing information on the position and orientation of each detecting unit, with respect to the overall structure, substantially at all times, during the individual motion, the dynamic SPECT camera being configured for acquiring a tomographic reconstruction image of a region of interest of about 15×15×15 cubic centimeters, during an acquisition time of 30 seconds, at a spatial resolution of at least 10×10×10 cubic millimeter. The dynamic camera is configured for very short damping time, and may further acquire images in a stationary mode, with no motion. It is further configured for time binning at dynamically varying time-bin lengths, dynamically determining a spectral energy bin for each detecting unit, and employing an anatomic construction of voxels in the imaging and reconstruction.

A dynamic SPECT camera is provided, comprising, a plurality of single-pixel detectors, a timing mechanism, in communication with each single-pixel detector, configured for enabling time-binning of the radioactive emissions impinging upon each single-pixel detector to time periods not greater than substantially 30 seconds, and a position-tracker, configured for providing information on the position and orientation of each detecting unit, with respect to the overall structure, substantially at all times, during the individual motion, the dynamic SPECT camera being configured for acquiring a tomographic reconstruction image of a region of interest of about 15×15×15 cubic centimeters, during an acquisition time of 30 seconds, at a spatial resolution of at least 10×10×10 cubic millimeter. The dynamic camera is configured for very short damping time, and may further acquire images in a stationary mode, with no motion. It is further configured for time binning at dynamically varying time-bin lengths, dynamically determining a spectral energy bin for each detecting unit, and employing an anatomic construction of voxels in the imaging and reconstruction.

Several related inventions for estimating scattered radiation in radiographic projections are disclosed. Several of the inventions use scatter kernels of various forms, including symmetric and asymmetric forms. The inventions may be used alone or in various combinations with one another. The resulting estimates of scattered radiation may be used to correct the projections, which can improve the results of tomographic reconstructions. Still other inventions of the present application generate estimates of scattered radiation from shaded or partially shaded regions of a radiographic projection, which may be used to correct the projections or used to adjust the estimates of scattered radiation generated according to inventions of the present application that employ kernels.

A model-based approach to estimating wall positions for a building is developed and tested using simulated data. It borrows two techniques from geophysical inversion problems, layer stripping and stacking, and combines them with a model-based estimation algorithm that minimizes the mean-square error between the predicted signal and the data. The technique is designed to process multiple looks from an ultra wideband radar array. The processed signal is time-gated and each section processed to detect the presence of a wall and estimate its position, thickness, and material parameters. The floor plan of a building is determined by moving the array around the outside of the building. In this paper we describe how the stacking and layer stripping algorithms are combined and show the results from a simple numerical example of three parallel walls.

A touch-sensitive apparatus comprises a panel for conducting signals from incoupling points to outcoupling points. Actual detection lines are defined between pairs of incoupling and outcoupling points to extend across the panel. A signal generator is coupled to the incoupling points to generate the signals, e.g. light, and a signal detector is coupled to the outcoupling points to generate an output signal. A data processor processes the output signal to generate a set of data samples, which are non-uniformly arranged in a two-dimensional sample space. Each data sample is indicative of detected energy on a detection line and is defined by a signal value and first and second dimension values in the sample space. The first and second dimension values define the location of the detection line on the surface portion. Adjustment factors are obtained for the data samples, each adjustment factor being representative of the local density of data samples in the sample space. The data samples are processed by tomographic reconstruction, while applying the adjustment factors, to generate a reconstructed distribution of an energy-related parameter

The present approach relates to the use of machine learning and deep learning systems suitable for solving large-scale, space-variant tomographic reconstruction and / or correction problems. In certain embodiments, a tomographic transform of measured data obtained from a tomography scanner is used as an input to a neural network. In accordance with certain aspects of the present approach, the tomographic transform operation(s) is performed separate from or outside the neural network such that the result of the tomographic transform operation is instead provided as an input to the neural network. In addition, in certain embodiments, one or more layers of the neural network may be provided as wavelet filter banks.

An interactive touchscreen system comprising a conductive panel having a surface portion adapted to receive touch interactions, the conductive panel operatively coupled to a tomograph configured for determining conductivity of the conductive panel and further adapted for incrementally improving a reconstructed tomogram estimation by integrating the output of a tomographic reconstruction algorithm having as input a unreconstructed tomogram delta, the reconstructed tomogram estimation being representative of the panel conductivity properties at locations spatially correlated to a plurality of regions notionally defined over the surface portion and thereform indicative of the touch interactions, the tomograph further processing and outputting said reconstructed tomogram estimation and an indication thereof.

A tomographic reconstruction method and system incorporating Bayesian estimation techniques to inspect and classify regions of imaged objects, especially objects of the type typically found in linear, areal, or 3-dimensional arrays. The method and system requires a highly constrained model M that incorporates prior information about the object or objects to be imaged, a set of prior probabilities P(M) of possible instances of the object; a forward map that calculates the probability density P(D|M), and a set of projections D of the object. Using Bayesian estimation, the posterior probability p(M|D) is calculated and an estimated model MEST of the imaged object is generated. Classification of the imaged object into one of a plurality of classifications may be performed based on the estimated model MEST, the posterior probability p(M|D) or MAP function, or calculated expectation values of features of interest of the object.

Objects touching a surface portion attenuate transmitted signals. A data processor processes an output signal from a detector coupled to the outcoupling points, to generate a set of data samples indicative of detected energy for the actual detection lines. The set of data samples is further processed to generate a set of matched samples indicative of estimated detected energy for fictitious detection lines that extend across the surface portion in parallel groups at a plurality of different angles. The individual spacing between the fictitious detection lines in each group and the individual difference in angle between said groups are selected such that the set of matched samples transforms to Fourier coefficients arranged as data points on a pseudo-polar grid in a Fourier domain. The set of matched samples is processed by tomographic reconstruction to generate a two-dimensional distribution of an interaction parameter within the surface portion.

A touch-sensitive apparatus comprises a panel configured to conduct signals from a plurality of peripheral incoupling points to a plurality of peripheral outcoupling points. Actual detection lines are defined between pairs of incoupling and outcoupling points to extend across a surface portion of the panel. The signals may be in the form of light, and objects touching the surface portion may affect the light via frustrated total internal reflection (FTIR). A signal generator is coupled to the incoupling points to generate the signals, and a signal detector is coupled to the outcoupling points to generate an output signal. A data processor operates on the output signal to enable identification of touching objects. The output signal is processed (40) to generate a set of data samples, which are indicative of detected energy for at least a subset of the actual detection lines. The set of data samples is processed (42) to generate a set of matched samples, which are indicative of estimated detected energy for fictitious detection lines that have a location on the surface portion that matches a standard geometry for tomographic reconstruction. The set of matched samples is processed (44, 46) by tomographic reconstruction to generate data indicative of a distribution of an energy-related parameter within at least part of the surface portion.

A signal processor in a touch-sensitive apparatus generates a 2D representation of touch interaction on a touch surface by tomographic processing. The signal processor generates observed values for detection lines that correspond to signal propagation paths across the touch surface. The observed values correspond to sampling points in a sample space defined by a first dimension representing a rotation angle of the detection line on the touch surface and a second dimension representing a distance of the detection line from a predetermined origin on the touch surface. The signal processor processes the observed values, by interpolation in the sample space, to generate estimated values for matched sampling points in the sample space using a tomographic reconstruction function.

A touch-sensitive apparatus comprises a panel for conducting signals from incoupling points to outcoupling points. Detection lines are defined between pairs of incoupling and outcoupling points. A signal generator is coupled to the incoupling points to generate the signals. A data processor processes the output signal to generate a set of data samples, which are non-uniformly arranged in a two-dimensional sample space. Each data sample is indicative of detected energy on a detection line and is defined by a signal value and first and second dimension values defining the location of the detection line on the surface portion. Adjustment factors are obtained for the data samples, each adjustment factor being representative of the local density of data samples in the sample space. The data samples are processed by tomographic reconstruction, while applying the adjustment factors, to generate a reconstructed distribution of an energy-related parameter within the surface portion.

Several related inventions for estimating scattered radiation in radiographic projections are disclosed. Several of the inventions use scatter kernels of various forms, including symmetric and asymmetric forms. The inventions may be used alone or in various combinations with one another. The resulting estimates of scattered radiation may be used to correct the projections, which can improve the results of tomographic reconstructions. Still other inventions of the present application generate estimates of scattered radiation from shaded or partially shaded regions of a radiographic projection, which may be used to correct the projections or used to adjust the estimates of scattered radiation generated according to inventions of the present application that employ kernels.

The invention relates to a method for detecting guide waves of a steel storage tank bottom plate, comprising the following steps in sequence; (1) selecting the number of ultrasonic probe arrays (5), and symmetrically distributing the ultrasonic probe arrays (5) along the steel storage tank bottom plate (3); (2) arranging two signal processing devices (4) on each ultrasound probe array (5), and arranging the ultrasound probe array (5) on the steel storage tank bottom plate (3) through a wedge block in a coupling way; (3) selecting a ultrasonic wave length which corresponds to the thickness of the steel storage tank bottom plate (3) and selecting a method for exciting Lamb waves; (4) transforming a travel time matrix with Radon algorithmic function to generate Lamb wave travel time projections of different incidence angles, which are used as the projection data for the subsequent tomography reconstruction; (5) reconstructing a tomography in the projection data with the filtered back projection algorithm; (6) analyzing the tomography to find the position of the defect and grade the defect level; and (7) if the defect existing, changing the positions of ultrasonic probes clockwise, repeating the steps (1), (2), (3), (4), (5) and (6), comparing the repeated detection and tomography reconstruction results, and eliminating the effect of noise and other factors if the position of the defect and the morphology existing.

A method relates to the use of deep learning techniques, which may be implemented using trained neural networks (50), to estimate various types of missing projection or other unreconstructed data. Similarly, the method may also be employed to replace or correct corrupted or erroneous projection data as opposed to estimating missing projection data.

The present invention is a tomographic reconstruction algorithm, which is highly effective improving image quality and accuracy by reducing or eliminating artifacts within images produced by limited data tomography. Using algebraic reconstruction techniques (ART), depending on whether or not an object has higher or lower densities, a current threshold value is set to either a high or low threshold parameter and then decreased or increased, respectively, to reduce or eliminate the artifacts in a reconstructed image.

A signal processor in a touch-sensitive apparatus generates a 2D representation of touch interaction on a touch surface by tomographic processing. The signal processor generates observed values for detection lines that correspond to signal propagation paths across the touch surface. The observed values correspond to sampling points in a sample space defined by a first dimension representing a rotation angle of the detection line on the touch surface and a second dimension representing a distance of the detection line from a predetermined origin on the touch surface. The signal processor processes the observed values, by interpolation in the sample space, to generate estimated values for matched sampling points in the sample space using a tomographic reconstruction function.

Motion of an object of interest, such as a cell, has a variable velocity that can be varied on a cell-by-cell basis. Cell velocity is controlled in one example by packing cells into a capillary tube, or any other linear substrate that provides optically equivalent 360 degree viewing access, so that the cells are stationary within the capillary tube, but the capillary tube is translated and rotated mechanically through a variable motion optical tomography reconstruction cylinder. The capillary tube motion may advantageously be controlled in a start-and-stop fashion and translated and rotated at any velocity for any motion interval, under the control of a computer program. As such, there are several configurations of the optical tomography system that take advantage of this controlled motion capability. Additionally, the use of polarization filters and phase plates to reduce light scatter and diffraction background noise is described.

Methods and systems for obtaining improved computed tomographic reconstructions are provided. A camera obtains one or more images of a patient while a tomographic scan is performed. A CT scanner obtains one or more tomographic projections. The total number of the one or more tomographic projections obtained during the CT scan is determined. For each of the one or more patient images obtained, a processor correlates the patient image with the one or more tomographic projections, calculates a position of the patient and determines if the calculated position of the patient is greater than one or more predetermined constants. If it is determined that the calculated position of the patient is greater than one or more predetermined constants and a total number of the one or more tomographic projections obtained is less than a predetermined number, separate projections are substituted in the place of the one or more tomographic projections that were correlated to the patient image. If it is determined that the calculated position of the patient is greater than one or more predetermined constants and a total number of the one or more tomographic projections is greater than a predetermined number, the tomographic scan is aborted or the tomographic reconstruction is reconstructed on a reduced arc.

A method of obtaining a spatial model of a property of part of a subsurface formation located between underground seismic receivers in which at least two sets of pairs of seismic receivers are utilized and one pair of receivers is used to record a signal from a seimic source and obtaining a response by solving (s11(−t){circle over (x)}s21(t))=r11,21(t){circle over (x)}(s11(−t){circle over (x)}s21(t)), wherein the symbol {circle over (x)} denotes convolution and wherein s11(−t) is the time-reverse of the signal s11(t). A path-related attribute is selected from transmission response r11,21(t) that corresponds to the property of the subsurface formation and a tomographic reconstruction technique is applied to the path-related attribute to obtain the spatial model of the property of part of the subsurface formation.

Concealed object detection using electromagnetic and acoustic multistatic imaging systems and methods. A method of simultaneously screening plural subjects for concealed objects includes transmitting a signal into a screening area where there is at least one subject to be screened having an associated object, receiving a reflected signal from the object when the object is located within the screening area, processing the reflected signal using multistatic Fourier space sampling and tomographic reconstruction to generate a three-dimensional image of the object and displaying the three-dimensional image. The transmitting and receiving are performed using a multi-directional array including at least three sensors. An object detection system includes a screening area, a multi-directional array including at least three sensors, a processor configured to execute multistatic Fourier space sampling and tomographic reconstruction and a display.

An operator shielded X-ray imaging system has sufficiently low mass (less than 300 kg) and is compact enough to enable portability by reducing operator shielding requirements to a minimum shielded volume. The resultant shielded volume may require a relatively small mass of shielding in addition to the already integrally shielded X-ray source, intensifier, and detector. The system is suitable for portable imaging of well cores at remotely located well drilling sites. The system accommodates either small samples, or small cross-sectioned objects of unlimited length. By rotating samples relative to the imaging device, the information required for computer aided tomographic reconstruction may be obtained. By further translating the samples relative to the imaging system, fully three dimensional (3D) tomographic reconstructions may be obtained of samples having arbitrary length.

A method of reconstructing tomography images comprising: acquiring data on individual radiation events; distributing a weight of the individual radiation events along a line of flight associated with the event determined from the acquired data; and iteratively reconstructing the image based on the individually reprojected data.

A method and apparatus for automated reconstruction of a 3D computed tomography (CT) volume from a small number of X-ray images is disclosed. A sparse 3D volume is generated from a small number of x-ray images using a tomographic reconstruction algorithm. A final reconstructed 3D CT volume is generated from the sparse 3D volume using a trained deep neural network. A 3D segmentation mask can also be generated from the sparse 3D volume using the trained deep neural network.

The invention relates to systems and methods for tomographic imaging in diffuse media employing a fast reconstruction technique. A hybrid Fourier approach is presented that enables the fast tomographic reconstruction of large datasets. In certain embodiments, the invention features methods of in vivo fluorescence molecular tomographic (FMT) reconstruction of signals, reporters and / or agents (i.e., contrast agents or probes) in a diffusive medium (e.g., a mammalian subject). The method preserves the three-dimensional fluorophore distribution and quantitative nature of the FMT approach while substantially accelerating its computation speed, allowing FMT imaging of larger anatomies.