Koninklijke Philips Corporation N.v.

Zhiqiang Hu

Chuanyong Bai

Andriy Andreyev

Bin Zhang

Jinghan Ye

Xiyun Song

Shekhar Dwivedi

Chi-Hua Tung

Daniel Gagnon

Wenli Wang

Yu-Lung Hsieh

Steffen Renisch

Thomas Blaffert

Shushen Lin

Michael Allen Miller

Yanfei Mao

Thomas Leroy Laurence

Yang-Ming Zhu

Bernd Schweizer

Christian Stehning

Andreas Goedicke

Douglas B McKnight

Andre Frank Salomon

Manoj Narayanan

Lingzhi Hu

Holger Eggers

Lingxiong Shao

Jerome John Griesmer

Peter Bornert

Sharon Xiaorong Wang

Thomas Christopher Bulgrin

Navdeep Ojha

Leonid Romanov

Changhong Dai

Varun Verma

Debashish Pal

Chia-hua Tung

Tianrui Guo

Jeffrey Kaste

James Gurian

Gregory Doughty

Ilya Brodskiy

Faguo Yang

Chia-Hua Tung

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)

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-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

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 () to reconstruct list mode data acquired over a frame acquisition time using a plurality of radiation detectors () in which the events of the list mode data is timestamped. The method includes: for the sub-frame bins of a plurality of sub-frame bins into which the frame acquisition time is divided, determining a sub-frame singles rates map for the plurality of radiation detectors from the list mode data whose time stamps reside in the sub-frame bin; determining a singles rate for the singles events of the list mode data using the sub-frame singles rates maps wherein the singles rates for the singles events are determined at a temporal resolution that is finer than the frame acquisition time; determining correction factors for the list mode data using the determined singles rates for the singles events of the list mode data; and reconstructing the list mode data of the frame acquisition time using the determined correction factors to generate a reconstructed image for the frame acquisition time.

Correction method for quantification accuracy improvement in list mode reconstruction

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 storage medium stores instructions readable and executable by an electronic processor () to perform a method () for estimating singles rates for detectors () of a detector array () of a positron emission tomography (PET) imaging device (). The method includes: obtaining measured delayed coincidence rates for lines of response (LORs) defined by pairs of the detectors of the detector array; and generating estimated singles rates for the detectors of the detector array by an iterative optimization in which each iteration performs an update of estimates of the singles rates for the detectors by operations including: forward projecting the estimates of the singles rates for the detectors to generate estimates of delayed coincidence rates for the LORs; generating quantitative comparisons between the measured delayed coincidence rates for the LORs and the generated estimates of the delayed coincidence rates for the LORs; backprojecting the quantitative comparisons to generate corrections for the singles rates for the detectors; and updating the estimates of the singles rates for the detectors using the generated corrections. The estimated singles rates for the detectors of the detectors array comprise the estimates of the singles rates for the detectors output by the iterative optimization when the iterative optimization satisfies a stopping criterion. This process jointly optimizes the estimated singles rates and randoms estimation.

Relaxed iterative maximum-likelihood expectation maximization for positron emission tomography random coincidence estimation

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

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: generating, from received imaging data, a plurality of intermediate images reconstructed without scatter correction from data partitioned into different energy windows; generating a fraction of true counts and a fraction of scatter events in the generated intermediate images; generating a final reconstructed image from the intermediate images, the fraction of true counts in the intermediate images, and the fraction of scatter counts in the intermediate images; and at least one of controlling the non-transitory computer readable medium to store the final image and control a display device () to display the final image.

Scatter correction for positron emission tomography (PET)

A non-transitory storage medium storing instructions readable and executable by an imaging workstation () including at least one electronic processor () to perform an image reconstruction method (). The method includes: receiving emission imaging data () from an image acquisition device () wherein the emission imaging data has been filtered using an acquisition energy passband (); generating filtered imaging data by filtering the emission imaging data with a second energy passband () that is narrower than an acquisition energy passband; reconstructing the filtered imaging data to generate an intermediate image; estimating one or more scatter correction factors (SCFs) from the intermediate image; and reconstructing the emission imaging data corrected with the estimated SCFs to generate a reconstructed image.

Scatter correction using emission image estimate reconstructed from narrow energy window counts in positron emission tomography

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 machine learning guided image segmentation process is performed by an electronic processor (). Image segmentation () is performed to generate an initial segmented representation () of an anatomical structure in the medical image. Parameters of a geometric shape are fitted () to the anatomical structure in the medical image to produce initial fitted shape parameters (). A classification is assigned for the anatomical structure in the medical image using at least one classifier () operating on the initial fitted shape parameters and the initial segmented representation of the anatomical structure. A final segmented representation () of the anatomical structure in the medical image is generated by operations including repeating () the image segmentation using the classification as prior knowledge. In illustrative embodiments, the anatomical structure is a heart and the geometric shape is an ellipsoid.

Heart segmentation methodology for cardiac motion correction

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Twinsburg, OH, United States of America

Zhiqiang Hu