Koninklijke Philips Corporation N.v.

Mckesson Automated Healthcare, Inc.

Bin Zhang

Zhiqiang Hu

Andriy Andreyev

Chuanyong Bai

Jinghan Ye

Xiyun Song

Chi-Hua Tung

Shekhar Dwivedi

Yu-Lung Hsieh

Shushen Lin

Yang-Ming Zhu

Andreas Goedicke

Andre Frank Salomon

John Patrick Collins

Michael Allen Miller

Manoj Narayanan

Patrick Olivier

Yanfei Mao

Manoj Wangu

Douglas B McKnight

Thomas Christopher Bulgrin

Amy Perkins

Christopher Gorman

Ilya Brodskiy

Karl Appareti

James Gurian

Faguo Yang

Kushal Shah

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 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

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 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

The present disclosure describes a pressure support therapy device valve that enables a subject to safely supplement therapy pressure support with low flow oxygen while using a heated (or non-heated) tube. The valve conducts a pressurized flow of breathable gas and the low flow oxygen to the heated tube. The valve includes electrical components configured to pass power from the pressure support therapy device to the heated tube, and a plunger biased to close a flow path from the pressure support therapy device when a pressurized flow of breathable gas is not provided (or is below a predetermined pressure threshold). Closing the flow path stops oxygen gas from flowing back through the valve toward the pressurized gas source.

Pressure support system valve

A non-transitory computer-readable medium stores instructions readable and executable by a workstation () operatively connected to a display device () and including at least one electronic processor () to perform an image acquisition and reconstruction method (). The method includes: retrieving a non-voxel-based reconstructed image comprising non-voxel image elements from a picture and archiving communication system (PACS) database () to the workstation; at the workstation, generating at least one voxel-based resampled image from the non-voxel-based reconstructed image; and displaying the at least one voxel-based reconstructed image on the display device.

Real time reconstruction-native image element resampling for high definition image generation and processing

A non-transitory computer readable medium storing instructions readable and executable by an imaging workstation () including at least one electronic processor () to perform a dataset generation method () operating on emission imaging data acquired of a patient for one or more axial frames at a corresponding one or more bed positions, the method comprising: (a) identifying a frame of interest from the one or more axial frames; (b) generating simulated lesion data by simulating emission imaging data for the frame of interest of at least one simulated lesion placed in the frame of interest; (c) generating simulated frame emission imaging data by simulating emission imaging data for the frame of interest of the patient; (d) determining a normalization factor comprising a ratio of the value of a quantitative metric for the simulated patient data and the value of the quantitative metric for the emission imaging data acquired of the same patient for the frame of interest; and (e) generating a hybrid data set comprising the emission imaging data acquired of the patient for the one or more axial frames at the corresponding one or more bed positions with the frame of interest replaced by a combination of the simulated lesion data scaled by the normalization factor and the emission imaging data acquired of the patient for the frame of interest.

Generation of accurate hybrid datasets for quantitative molecular imaging

A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor () to perform an image reconstruction method (). The method includes: performing iterative image reconstruction of imaging data acquired using an image acquisition device (); selecting an update image from a plurality of update images produced by the iterative image reconstruction; processing the selected update image to generate a hot spot artifact map; and suppressing hot spots identified by the generated hot spot artifact map in a reconstructed image output by the iterative image reconstruction.

Learning-based voxel evolution for regularized reconstruction

Iterative reconstruction () of imaging data is performed to generate a sequence of update images () terminating at a reconstructed image. During the iterative reconstruction, at least one of an update image and a parameter of the iterative reconstruction is adjusted using an adjustment process separate from the iterative reconstruction. In some embodiments using an edge-preserving regularization prior (), the adjustment process () adjusts an edge preservation threshold to reduce gradient steepness above which edge preservation applies for later iterations compared with earlier iterations. In some embodiments, the adjustment process includes determining () for each pixel, voxel, or region of a current update image whether its evolution prior to the current update image) satisfies an artifact feature criterion. A local noise suppression operation () is performed on the pixel, voxel, or region if the evolution satisfies the artifact feature criterion and is not performed otherwise.

Iterative image reconstruction with dynamic suppression of formation of noise-induced artifacts

The following relates to noise filtering in nuclear imaging systems. In one aspect, a fully automatic noise filtering system is provided in a nuclear imaging device. In some embodiments, a filter parameter selection engine is applied to an image environment of a medical image to calculate an image filter configuration for the medical image wherein the image environment includes values for one or more of an imaging subject of the medical image, an imaging device used to acquire the medical image, and a medical context of the medical image.

Smart filtering for PET imaging including automatic selection of filter parameters based on patient, imaging device, and/or medical context information

A non-transitory computer-readable medium storing instructions readable and executable by a workstation () including at least one electronic processor () to perform a quality control (QC) method (). The method includes: receiving a current QC data set acquired by a pixelated detector () and one or more prior QC data sets acquired by the pixelated detector; determining stability levels of detector pixels () of the pixelated detector over time from the current QC data set and the one or more prior QC data sets; labeling a detector pixel of the pixelated detector as dead when the stability level determined for the detector pixel is outside of a stability threshold range; and displaying, on a display device () operatively connected with the workstation, an identification () of the detector pixels labelled as dead.

Handling detector pixel performance variation in digital positron emission tomography

A nuclear medicine image reconstruction method generates a reconstructed image () by performing iterative image in reconstruction () on nuclear medicine imaging data (). The iterative image reconstruction produces a sequence of update images (). During the iterative image reconstruction, a standardized uptake value (SUV) transform () is applied to convert an update image () to an update SUV image (). The SUV transform scales values of voxels of the update image to SUV values using scaling factors including at least a body size metric and a dose metric. During the iterative image reconstruction, at least one parameter used in an image update of the iterative image reconstruction is adjusted using the update SUV image. For example, a parameter of a prior or filter () incorporated into an image reconstruction update step () or used in filtering of an update image () may be adjusted.

Standardized uptake value (SUV) guided reconstruction control for improved outcome robustness in positron emission tomography (PET) imaging

A respiratory motion signal generation method operates on emission data () of an imaging subject in an imaging field of view (FOV) acquired by a positron emission tomography (PET) or single photon emission computed tomography (SPECT) imaging device (). An array of regions () is defined in the imaging FOV without reference to anatomy of the imaging subject. For each region of the array of regions defined in the imaging FOV, an activity position versus time curve () is computed from the emission data acquired by the PET or SPECT imaging device. Frequency-selective filtering of the activity position versus time curves is performed to generate filtered activity position versus time curves. At least one motion signal () is generated by combining the filtered activity position versus time curves of at least a selected sub-set of the regions.

Noise-robust real-time extraction of the respiratory motion signal from PET list-data

A positron emission tomography (PET) detector array includes an enclosing radiation detector array () comprising radiation detector elements () effective for detecting 511 keV radiation emanating from inside the radiation detector array. The radiation detector pixels of the cylindrical radiation detector array include both higher speed radiation detector elements () and lower speed radiation detector elements (). The lower speed radiation detector pixels have a temporal resolution that is coarser than a temporal resolution of the higher speed radiation detector pixels.

Hybrid TOF and non-TOF PET systems with joint TOF and non-TOF image reconstruction

Image processing performed by a computer () includes iterative image reconstruction or refinement () that produces a series of update images ending in an iteratively reconstructed or refined image. A difference image () is computed between a first update image () and a second update image () of the series. The difference image is converted to a feature image () and is used in the iterative processing () or in post-processing () performed on the iteratively reconstructed or refined images or images from different reconstruction or refinement techniques. In another embodiment, first and second image reconstructions () are performed to generate respective first and second reconstructed images (). A difference image () is computed between two images each selected from the group: the first reconstructed image, an update image of the first reconstruction, the second reconstructed image, and an update image of the second reconstruction. A feature image is generated from the difference image and used to combine the first and second reconstructed images.

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Cleveland, OH, United States of America

Bin Zhang