General Electric Company

Xue Rui

Yannan Jin

Peter Michael Edic

Brian David Yanoff

Geng Fu

Bruno Kristiaan Bernard De Man

Jianjun Guo

Lin Fu

Mingye Wu

Uwe Wiedmann

Biju Jacob

Charles Addison Bouman

Jean-Baptiste Thibault

Jiahua Fan

Ken David Sauer

Zhye Yin

Sangtae Ahn

Sathish Ramani

Somesh Srivastava

Fredrik Gronberg

Xiao Jin

Paul Francis Fitzgerald

Yangyang Yao

A signal processing method is disclosed, which includes detecting a total intensity of X-rays passing through an object comprising multiple materials; obtaining at least one set of basis information of basis material information of the multiple materials and basis component information of photon-electric absorption basis component and Compton scattering basis component of the object; estimating a scatter intensity component of the detected X-rays based on the at least one set of basis information and the detected total intensity; and obtaining an intensity estimate of primary X-rays incident on a detector based on the detected total intensity and the estimated scatter intensity component. An imaging system adopting the above signal processing method is also disclosed.

Signal processing method and imaging system for scatter correction in computed tomography

The present approaches relates to the use of silicon-based energy-discriminating, photon-counting detectors, such as for use in X-ray based imaging including computed tomography. The described approaches address the resolution and classification of X-ray photons affected by Compton scatter, which may be detected as having energy levels below their proper level due to collision or deflection events.

Energy-discriminating photon-counting detector and the use thereof

The present approach relates to the use of detector elements (i.e., reference detector pixels) positioned under septa of an anti-scatter collimator. Signals detected by the reference detector pixels may be used to correct for charging-sharing events with adjacent pixels and/or to characterize or correct for focal spot misalignment either in real time or as a calibration step.

Reference detector elements in conjunction with an anti-scatter collimator

A method is provided that includes acquiring initial PET imaging data. The method also includes acquiring CT imaging data. Further, the method includes training a deep learning model for PET image reconstruction using the initial PET imaging data and the CT imaging data.

Systems and methods for improved PET imaging

A method for imaging an object to be reconstructed includes acquiring projection data corresponding to the object. Furthermore, the method includes generating a measured sinogram based on the acquired projection data and formulating a forward model, where the forward model is representative of a characteristic of the imaging system. In addition, the method includes generating an estimated sinogram based on an estimated image of the object and the forward model and formulating a statistical model based on at least one of pile-up characteristics and dead time characteristics of a detector of the imaging system. Moreover, the method includes determining an update corresponding to the estimated image based on the statistical model, the measured sinogram, and the estimated sinogram and updating the estimated image based on the determined update to generate an updated image of the object. Additionally, the method includes outputting a final image of the object.

System and method for statistical iterative reconstruction and material decomposition

The present approach relates to scatter correction of signals acquired using radiation detectors on a pixel-by-pixel basis. In certain implementations, the systems and methods disclosed herein facilitate scatter correction for signals generated using a detector having segmented detector elements, such as may be present in an energy-resolving, photon-counting CT imaging system.

Scatter correction technique for use with a radiation detector

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.

Tomographic reconstruction based on deep learning

The present approach relates to self-calibration of CT detectors based on detected misalignment of the detector and X-ray source. The present approach make the detector more robust to changes against temperature and focal spot movements. The diagnostic image generated by energy resolving calibrated response signals is able to present enhanced features compared to conventional CT based diagnostic images.

Self-calibrating CT detectors, systems and methods for self-calibration

The present approach relates to the use of reference pixels provided between the primary pixels of a detector panel. Coincidence circuitry or logic may be employed so that the measured signal arising from the same X-ray event may be properly, that is the signal measured at both a reference and primary pixel may be combined so as to provide an accurate estimate of the measured signal, at an appropriate location on the detector panel.

Pixel-design for use in a radiation detector

An imaging method includes executing a low-dose preparatory scan to an object by applying tube voltages and tube currents in an x-ray source, and generating a first image of the object corresponding to the low-dose preparatory scan. The method further includes generating image quality estimates and dose estimates view by view at least based on the first image. The method includes optimizing the tube voltages and the tube currents to generate optimal profiles for the tube voltage and the tube current. At least one of the optimal profiles for the tube voltage and the tube current is generated based on the image quality estimates and the dose estimates. The method includes executing an acquisition scan by applying the tube voltages and the tube currents based on the optimal profiles and generating a second image of the object corresponding to the acquisition scan. An imaging system is also provided.

Imaging method and system of tube voltage and current optimization

The present approach relates to a detector design that allows detector-based wobble using an electronic control scheme. In one implementation, each detector pixel is divided into sub-pixels. The readout of the sub-pixels can be binned with minimal noise penalty to enable the detector wobble without physically shifting the detector or alternating the physical focal spot location, though, as discussed herein alternation of the focal spot location may be used in conjunction with the present approach to further improve radial and longitudinal imaging resolution as well as suppressing artifacts resulted by limited spatial sampling.

Computed tomography with detector wobble

A system and method include acquisition of projection data from a scanned object, the set of projection data comprising a plurality of projection measurements. The system and method also include calculation of a set of modified statistical weights from the projection data, wherein a respective modified statistical weight of the set of modified statistical weights comprises a deviation from an inverse variance of a corresponding projection measurement of the projection data. The system and method further include reconstruction of an image of the scanned object using the set of modified statistical weights as coefficients in an iterative reconstruction algorithm.

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Clifton Park, NY, United States of America

Xue Rui