Koninklijke Philips Corporation N.v.

Accuray Inc.

Chuanyong Bai

Andriy Andreyev

Zhiqiang Hu

Bin Zhang

Jinghan Ye

Xiyun Song

Zhicong Yu

Daniel Gagnon

Shekhar Dwivedi

Amit Jain

Yang-Ming Zhu

Douglas B McKnight

Chi-Hua Tung

Shiyu Xu

Hao Dang

Shushen Lin

Jacob D Shea

Andreas Goedicke

Andre Frank Salomon

Yu-Lung Hsieh

Michael Allen Miller

Hui Wang

Yanfei Mao

Calvin R Maurer

Hao Lai

Sheng Lu

Lingxiong Shao

Wenli Wang

Thomas Christopher Bulgrin

Steven Michael Cochoff

Manoj Narayanan

Mary K Durbin

Dane Pittock

Nathan Serafino

Leonid Romanov

Faguo Yang

Sydney Kaplan

Ilya Brodskiy

Chuang Miao

James Gurian

Georgios Prekas

Robert Zahn

Kushal Shah

Douglas Mcknight

Yang-ming Zhu

Gregory Doughty

Marc Chappo

Jr Calvin R Maurer

A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor () to perform an imaging method (). The method includes: reconstructing emission imaging data to generate an emission image of a lesion; converting intensity values of the emission image to at least one standardized uptake value (SUV value) for the lesion; processing input data using a regression neural network (NN) () to output an SUV correction factor for the lesion, wherein the input data includes at least two of (i) image data comprising the emission image or a feature vector representing the emission image, (ii) the at least one SUV value, (iii) a size of the lesion, and (iv) reconstruction parameters used in the reconstructing; and controlling a display device () to display at least one of (I) the SUV correction factor and (II) a corrected SUV value generated by applying the SUV correction factor to the at least one SUV value.

Artificial intelligence (AI)-based standardized uptake value (SUV) correction and variation assessment for positron emission tomography (PET)

A computer tomography scanner () includes a radiation source () configured to emit x-ray radiation, a detector array () configured to detect x-ray radiation and generate a signal indicative thereof, and a reconstructor () configured to reconstruct the signal and generate sequential spares time line perfusion volumetric image data. The computer tomography scanner further includes a processor () configured to process the sequential spares time line perfusion volumetric image data using a trained neural network of a perfusion data enhancing module () to produce sequential dense time line perfusion volumetric image data.

Low radiation dose computed tomography perfusion (CTP) with improved quantitative analysis

Saturation in at least one saturated ray is in a method including identifying a saturated ray corresponding to a first ray of a radiation source received at a radiation detector after passing through a reference point during a current view of the radiation detector, identifying at least one non-saturated ray corresponding to a second ray of the radiation source received at the radiation detector, and responsive to the identifying, adjusting a value for the saturated ray based on a value of the at least one non-saturated ray. The non-saturated ray can be a ray from an adjacent view of a current rotation, an adjacent view of a previous or subsequent rotation, or a conjugate ray. Methods of selecting a gain level to avoid saturation are also disclosed.

Methods for saturation correction and dynamic gain configuration and apparatuses for performing the same

An image reconstruction method includes: determining singles rates of radiation detectors in a frame of imaging data detected by the radiation detectors; determining an energy correction factor (N) for each radiation detector based on an energy spectrum distribution of gamma rays incident on the radiation detector during acquisition of the frame of imaging data; determining a singles live time correction factor for each radiation detector from the singles rate and the energy correction factor; determining a system coincidence live time correction factor from the system singles rate; for each line of response (LOR) connecting pairs of radiation detectors, determining a live time correction factor for the LOR from the determined singles live time correction factors of the pair of radiation detectors connected by the LOR and the determined system coincidence live time correction factor; and reconstructing the frame of imaging data using the determined LOR live time correction factors.

Dead-time correction method in quantitative positron emission tomography (PET) reconstruction for various objects and radioactivity distributions

A non-spectral computed tomography scanner includes a radiation source configured to emit x-ray radiation, a detector array configured to detect x-ray radiation and generate non-spectral data, and a memory configured to store a spectral image module that includes computer executable instructions including a neural network trained to produce spectral volumetric image data. The neural network is trained with training spectral volumetric image data and training non-spectral data. The non-spectral computed tomography scanner further includes a processor configured to process the non-spectral data with the trained neural network to produce spectral volumetric image data.

Non-spectral computed tomography (CT) scanner configured to generate spectral volumetric image data

Imaging data () are acquired by a PET scanner () or other imaging device. Iterative image reconstruction of the imaging data is performed to generate a reconstructed image (). The iterative image reconstruction includes performing an update step () that includes an edge preserving prior () having a spatially varying edge preservation threshold () whose value at each image voxel depends on a noise metric () in a local neighborhood of the image voxel. The noise metric may be computed as an aggregation of the intensities of neighborhood image voxels of the reconstructed image in the local neighborhood of the image voxel. The edge preserving prior may be a Relative Difference Prior (RDP). For further noise suppression, during the iterative image reconstruction image values of image features of the reconstructed image that have spatial extent smaller than a threshold () may be reduced.

Information-adaptive regularization for iterative image reconstruction

A device () for measuring respiration of a patient includes a positron emission tomography (PET) or single photon emission computed tomography (SPECT) imaging device (). At least one electronic processor () is programmed to: extract a first respiration data signal () from emission imaging data of a patient acquired by the PET or SPECT imaging device; extract a second respiration data signal () from a photoplethysmograph (PPG) signal of the patient; and combine the first and second extracted respiration data signals to generate a respiration signal () indicative of respiration of the patient.

Respiratory gating using pulse oximeters for tomographic imaging

Disclosed herein is an imaging system including a first x-ray source configured to produce first x-ray photons in a first energy range suitable for imaging, project the first x-ray photons onto an area designated for imaging, a rotatable gantry configured to rotate the first x-ray source such that the first x-ray source traverses an angular path, and a data processor having an analytical portion. The analytical portion is configured to collect first data relating to the transmission of the first x-ray photons through the area designated for imaging at a set of image-collection angles along the angular path, collect background data at a set of background-collection angles along the angular path, wherein the system acquires more than one image of the designated area for imaging between background angles. The analytical portion is also configured to remove errors in the first data using the background data, and generate a corrected image based on the removal of errors in the first data.

Sparse background measurement and correction for improving imaging

A spectral computed tomography imaging system () includes a radiation source () configured to emit x-ray radiation and a detector array () configured to detect x-ray radiation and generate spectral data. The spectral imaging system further includes a memory () configured to store a virtual non-contrast image enhancing module () that includes computer executable instructions including a neural network trained to produce image quality enhanced virtual non-contrast images. The neural network is trained with training spectral data and training non-contrast-enhanced images generated from a non-contrast-enhanced scan. The spectral imaging system further includes a processor () configured to process the spectral data with the trained neural network to produce the image quality enhanced virtual non-contrast images.

Image quality improved virtual non-contrast images generated by a spectral computed tomography (CT) scanner

A device () for performing an amyloid assessment includes a radiation detector assembly () including at least one radiation detector (). At least one electronic processor () is programmed to: detect radiation counts over a data acquisition time interval using the radiation detector assembly; compute at least one current count metric from the detected radiation counts; store the at least one current count metric associated with a current test date in a non-transitory storage medium (); and determine an amyloid metric based on a comparison of the at least one current count metric with a count metric stored in the non-transitory storage medium associated with an earlier test date.

Method and apparatus for amyloid screening

A non-transitory computer-readable medium stores instructions readable and executable by a workstation () including at least one electronic processor () to perform an image reconstruction method (). The method includes: determining a weighting parameter () of an edge-preserving regularization or penalty of a regularized image reconstruction of an image acquisition device () for an imaging data set obtained by the image acquisition device; determining an edge sensitivity parameter (&#x3b3;) of the edge-preserving algorithm for the imaging data set obtained by the image acquisition device; and reconstructing the imaging data set obtained by the image acquisition device to generate a reconstructed image by applying the regularized image reconstruction including the edge-preserving regularization or penalty with the determined weighting and edge sensitivity parameters to the imaging data set obtained by the image acquisition device.

Image reconstruction employing tailored edge preserving regularization

An imaging apparatus comprises a rotatable gantry system positioned at least partially around a patient support; a first source of radiation coupled to the rotatable gantry system, the first source of radiation configured for imaging radiation; a second source of radiation coupled to the rotatable gantry system; and a first radiation detector coupled to the rotatable gantry system and laterally movable relative to a central beam of the first source of radiation to receive radiation from at least the first source of radiation over various fields of view. Alternative configurations of the imaging apparatus and methods of using the imaging apparatus are also provided.

Multimodal radiation apparatus and methods

An imaging method () includes: acquiring first training images of one or more imaging subjects using a first image acquisition device (); acquiring second training images of the same one or more imaging subjects as the first training images using a second image acquisition device () of the same imaging modality as the first imaging device; and training a neural network (NN) () to transform the first training images into transformed first training images having a minimized value of a difference metric comparing the transformed first training images and the second training images.

Positron emission tomography (PET) system design optimization using deep imaging

An x-ray imaging apparatus and associated methods are provided to execute multi-pass imaging scans for improved quality and workflow. An imaging scan can be segmented into multiple passes that are faster than the full imaging scan. Data received by an initial scan pass can be utilized early in the workflow and of sufficient quality for treatment setup, including while the another scan pass is executed to generate data needed for higher quality images, which may be needed for treatment planning. In one embodiment, a data acquisition and reconstruction technique is used when the detector is offset in the channel and/or axial direction for a large FOV during multiple passes.

Multi-pass computed tomography scans for improved workflow and performance

A radiological imaging apparatus is provided that includes a radiation source for emitting radiation, a radiation detector positioned to receive radiation emitted by the radiation source and generate radiation data, wherein the radiation data comprises a primary component and a secondary component, and a data processing system. The data processing system is configured to apply image transforms to the primary component using generating functions, build a scatter model basis using the transforms, adjust parameters in the scatter model to fit scatter using the scatter model basis, generate an estimated scatter image by using the fitted scatter model, and modify the radiation data using the scatter image to decrease the scatter in the radiation data thereby generating a scatter corrected image.

Anchored kernel scatter estimate

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Solon, OH, United States of America

Chuanyong Bai