Koninklijke Philips Corporation N.V.

General Electric Company

Koninklike Philips N.v.

Koninklijie Philips N.v.

Shekhar Dwivedi

Chuanyong Bai

Zhiqiang Hu

Andriy Andreyev

Bin Zhang

Jinghan Ye

Xiyun Song

Chi-Hua Tung

Yang-Ming Zhu

Rakesh Mullick

Vivek Prabhakar Vaidya

John Patrick Collins

Yu-Lung Hsieh

Michael Allen Miller

Christopher Judson Hardy

Manish Sharma

Volkmar Schulz

Thomas K F Foo

Lingxiong Shao

Douglas B McKnight

Thomas Christopher Bulgrin

Steven Michael Cochoff

Daniel J Blezek

Yannick Berker

Yogish Mallya

Shushen Lin

Yanfei Mao

Narayan Ayyakad Krishnan

Teimuraz Bandzava

Umesha Perdoor Srinivas Adiga

Ilya Brodskiy

James Gurian

Faguo Yang

Venudhar Rao Hajari

Satrajit Misra

Manish Kumar Sharma

PET/MR images are compensated with simplified adaptive algorithms for truncated parts of the body. The compensation adapts to a specific location of truncation of the body or organ in the MR image, and to attributes of the truncation in the truncated body part. Anatomical structures in a PET image that do not require any compensation are masked using a MR image with a smaller field of view. The organs that are not masked are then classified as types of anatomical structures, the orientation of the anatomical structures, and type of truncation. Structure specific algorithms are used to compensate for a truncated anatomical structure. The compensation is validated for correctness and the ROI is filled in where there is missing voxel data. Attenuation maps are generated from the compensated ROI.

Classified truncation compensation

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 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

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 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

An imaging data set () comprising detected counts along lines of response (LORs) is reconstructed () to generate a full-volume image at a standard resolution. A region selection graphical user interface (GUI) () is provided via which a user-chosen region of interest (ROI) is defined in the full-volume image, and this is automatically adjusted by identifying an anatomical feature corresponding to the user-chosen ROI and adjusting the user-chosen ROI to improve alignment with that feature. A sub-set () of the counts of the imaging data set is selected () for reconstructing the ROI, and only the selected sub-set is reconstructed () to generate a ROI image () representing the ROI at a higher resolution than the standard resolution. A fraction of the sub-set of counts may be reconstructed using different reconstruction algorithms () to generate corresponding sample ROI images, and a reconstruction algorithm selection graphical user interface () employs these sample ROI images.

Interactive targeted ultrafast reconstruction in emission and transmission tomography

A diagnostic imaging system retrieves data () from a plurality of accessible data sources, the accessible data sources storing data including physiological data describing a subject to be imaged, a nature of a requested diagnostic image, image preferences of a clinician who requested the diagnostic image, and previously reconstructed images of the requested nature of the subject and/or other subjects, reconstruction parameters and/or sub-routines used to reconstruct the previously reconstructed images. The system analyzes () the retrieved data to automatically generate reconstruction parameters and/or sub-steps specific to the nature of the requested diagnostic image, the subject, and the clinician image preferences. The system controls a display device () to display the generated reconstruction parameters and/or sub-routines to the user for a user selection. The system sets a reconstruction processor system to reconstruct scan data using the selected reconstruction parameters and/or sub-routines.

Irecon: intelligent image reconstruction system with anticipatory execution

A lung segmentation processor () is configured to classify magnetic resonance (MR) images based on noise characteristics. The MR segmenatation processor generates a lung region of interest (ROI) and detailed structure segmentation of the lung from the ROI. The MR segmentation processor performs an iterative normalization and region definition approach that captures the entire lung and the soft tissues within the lung accurately. Accuracy of the segmentation relies on artifact classification coming inherently from MR images. The MR segmentation processor () correlates segmented lung internal tissue pixels with the lung density to determine the attenuation coefficients based on the correlation. Lung densities are computed using MR data obtained from imaging sequences that minimize echo and acquisition times. The densities differentiate healthy tissues and lesions, which an attenuation map processor () uses to create localized attenuation maps for the lung.

Density guided attenuation map generation in PET/MR systems

iRecon: intelligent image reconstruction system with anticipatory execution

An image data processor () includes a structural image data processor () that employs a multi-structure atlas to segment a region of interest from structural image data that includes tissue of interest and that segments the tissue of interests from the region of interest. The image data processor further includes functional image data processor () that identifies the tissue of interest in functional image data based on the segmented tissue of interest. An image data processor includes a multi-structure atlas generator () that generates a multi-structure atlas. The multi-structure atlas physically maps structure to tissue of interest such that locate the structure in structural image data based on the multi-structure atlas localizes the tissue of interest to the region of interest.

Multi-structure atlas and/or use thereof

A diagnostic imaging system utilizing a reduced crystal design pattern is utilized to image a subject and collect event data. The reduced crystal design pattern includes filled crystal locations and empty crystal locations. A processor accounts for empty crystal locations by selecting windows that include nearest neighbor filled crystal locations. The nearest neighbor filled crystal locations include event data which is averaged by the processor and assigned to the empty crystal location. A weighted average based on distance or event strength is incorporated.

Low-cost digital pet design

A cardiac imaging method includes acquiring a projection image representation which includes a myocardium (S). The myocardium is segmented and a mask is generated (S). The mask is optimized (S). A blood pool is determined from the optimized mask (S) and the mask is skeletonized based on a clusterfication of the myocardial slices (S). The center of mass is determined (S) from the blood pool and the skeletonized mask. Myocardial parameters are determined (S) from the skeletonized mask.

Myocardial clusterification and orientation

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Willoughby Hills, OH, United States of America

Shekhar Dwivedi