Koninklijke Philips Corporation N.v.

Ge Precision Healthcare LLC

General Electric Company

Raz Carmi

Jonathan Sachs

Amir Livne

Galit Sarit Kafri

Liran Goshen

Yariv Grobshtein

Yechiel Lamash

Shai Wald

Michal Maria Merman

Cornelis Reinder Ronda

Naor Wainer

Simha Levene

Mordechay Pinchas Freiman

Amiaz Altman

Galit Naveh

Roman Shiriaev

Amaiz Altman

A computing system includes computer readable medium and a processor. The computer readable medium includes a computer readable medium including a positron emission tomography (PET)-to-single photon emission tomography (SPECT) module with instructions for generating simulated SPECT image data based on first PET image data. The PET image data is acquired prior to a treatment course of a treatment regimen with a SPECT-based treatment/imaging radiopharmaceutical. The processor is configured to obtain the first PET image data, system parameters of a SPECT-CT imaging system utilized to generate corresponding SPECT image data for the treatment regimen and scan parameters utilized by the SPECT-CT imaging system to acquire the corresponding SPECT image data. The processor is further configured to execute the instructions to generate the simulated SPECT image data based on the PET image data and the system parameters and the scan parameters.

System and method for comparing SPECT and PET image data in theranostics

A method includes obtaining emission-tomography functional image data and a corresponding reconstructed anatomical image volume including at least one organ having natural motion; pre-determining a dedicated model for spatial mismatch correction of the at least one organ; performing initial image reconstruction of the emission-tomography functional image data to generate a reconstructed emission-tomography functional image volume utilizing attenuation correction based on the corresponding reconstructed anatomical image volume; and identifying relevant anatomical regions, within both image volumes, where functional image quality may be affected by the natural motion of the at least one organ. The method includes identifying and evaluating potential attenuation-correction image artifacts in the reconstructed emission-tomography functional image volume; estimating model parameters based on confirmed attenuation-correction image artifacts; correcting the corresponding reconstructed anatomical image volume to generate a corrected anatomical image volume; and reconstructing the emission-tomography functional image data utilizing attenuation correction based on the corrected anatomical image volume.

System and method for PET-CT image artifact evaluation and correction

A method includes obtaining functional and anatomical image data sets from a subject acquired at different dates. The method includes receiving a volumetric coordinate of interest in a specified functional and anatomical image data set. The method includes generating a 3D feature matching map for at least one functional feature layer type and for at least one anatomical feature layer type for each non-specified functional and anatomical image data set relative to the specified functional and anatomical image data set utilizing the volumetric coordinate of interest. The method includes generating a best matching coordinate and a corresponding confidence metric value for each 3D feature matching map. The method includes calculating an optimal matching coordinate to the volumetric coordinate of interest based on the best matching coordinates and their corresponding confidence metric values and outputting a respective optimal matching coordinate for each of the non-specified functional and anatomical image data sets.

System and method for follow-up local feature matching based on multiple functional-anatomical feature layers

A computer-implemented method for determining a flow rate for a given vessel includes obtaining, via a processor, dynamic three-dimensional (3D) images of a subject utilizing nuclear medicine imaging. The method also includes obtaining, via the processor, injection parameters for a radiotracer bolus injected into the subject via an automated injector. The method further includes generating, via the processor, time activity curves (TACs) for the radiotracer bolus from the 3D images. The method even further includes estimating, via the processor, the flow rate for the given vessel based on a morphology of the one or more TACs and the injection parameters.

System and method for measuring radiotracer bolus morphology for quantitative analysis

A method and workstation for combining multiple functional imaging datasets into a joint-visualization image. In one aspect, a method of functional imaging includes accessing a plurality of functional imaging datasets acquired from a volume-of-interest, wherein each of the plurality of functional imaging datasets is a different one of a plurality of functional imaging data types. The method includes registering the plurality of functional imaging datasets and determining a visualization priority for each of a plurality of pixels in a joint-visualization image based on a logical comparison of corresponding information in each of the plurality of functional imaging datasets. The method includes generating the joint-visualization image based on the visualization priority and at least a portion of each of the plurality of functional imaging datasets, wherein each of a plurality of pixels in the joint-visualization image represents only a single one of the plurality of functional imaging data types.

Method and workstation for generating a joint-visualization image based on multiple functional imaging datasets

Techniques are provided for improving image data quality, such as in functional imaging follow-up studies, using reconstruction, post-processing, and/or deep-learning enhancement approaches in a way that automatically improves analysis fidelity, such as lesion tracking fidelity. The disclosed approaches may be useful in improving the performance of automatic analysis methods as well as in facilitating reviews performed by clinician.

Image data processing to increase follow-up analysis fidelity

A method includes applying both a first dedicated functional-anatomical registration scheme to a first volume of interest to deform the first volume of interest and a second dedicated functional-anatomical registration scheme to a second volume of interest to deform the second volume of interest, wherein the first volume of interest at least partially encompasses the second volume of interest. The method includes identifying or segmenting relevant organs or anatomical structures related to a first group and a second group in the first volume of interest and the second volume of interest, respectively; generating a spatially smooth-transition weight mask that gives higher weight to image data corresponding to the identified or segmented relevant organs or anatomical structures related to the first group and the second group; and generating a final cohesive registered image volume from the first image volume and the second image volume utilizing the spatially smooth-transition weight mask.

System and method for cohesive multi-regional functional-anatomical medical image registration

A nuclear medicine (NM) multi-head imaging system is provided that includes a gantry, detector units, and at least one processor. The gantry defines a bore. The detector units are mounted to the gantry and configured to rotate as a group with the gantry around the bore in rotational steps, with each detector unit configured to sweep about a corresponding axis and acquire imaging information while sweeping about the corresponding axis. The at least one processor is coupled to the detector units and configured to determine a region of interest (ROI) of the object to be imaged; determine a sweeping configuration based on the size of the ROI; determine a rotational movement configuration for the gantry using the determined sweeping configuration; and control the gantry and the set of detector units to utilize the determined rotational movement and sweeping configurations during acquisition of imaging information.

Systems and methods for image quality enhancement for multi-head camera

A system includes a structural imaging acquisition unit, a functional imaging acquisition unit, and one or more processors. The structural imaging acquisition unit is configured to perform a structural scan to acquire structural imaging information of a patient. The functional imaging acquisition unit is configured to perform a functional scan to acquire functional imaging information of a patient. The one or more processors are configured to generate a tissue-specific anatomical probability map using the structural imaging information; generate a tissue-non-specific anatomical probability map using the structural imaging information; generate local combined anatomical probability weights using the tissue-specific anatomical probability map, the tissue-non-specific anatomical probability map, and the functional image data; re-distribute the functional image data using the local combined anatomical probability weights to provide re-distributed functional volumetric data; and generate an image using the re-distributed functional volumetric data.

Systems and methods for functional imaging

Various methods and systems are provided for comparison of medical scan images during functional imaging evaluation. In one example, structural similarity between a first scan image and a second scan image of a lesion may be determined by implementing a deep learning model including plurality of neural networks trained with base structures and different perturbations of base structures, and ranking structural similarity based on a selected neural network model trained with perturbations of base structures corresponding to the structural difference between the first and second scan images.

Systems and methods for functional imaging follow-up evaluation using deep neural network

A system is provided that includes a gantry, detector units, and at least one processor. The gantry defines a bore. The plural detector units are mounted to the gantry and configured to rotate as a group with the gantry in rotational steps. Each detector unit is configured to acquire imaging information while sweeping about a corresponding axis. The at least one processor is configured to determine a region of interest (ROI) of an object; identify a set of detector units; for the identified set of detector units, determine a sweeping configuration that results in a predetermined percentage of projection pixels receiving information from the ROI; determine a rotational movement configuration for the gantry using the determined sweeping configuration; and control the gantry and the set of detector units to utilize the determined rotational movement and sweeping configurations during acquisition of imaging information.

An image processing system (IPS) and related method. The system comprises an input interface (IN) for receiving an earlier input image (Va) and a later input image (Vb) acquired of an object (OB) whilst a fluid is present within the object (OB). A differentiator (&#x394;) is configured to form a difference image (Vd) from the at least two input images (Va,Vb). An image structure identifier (ID) is operable to identify one or more locations in the difference image. Based on a respective feature descriptor that describes a respective neighborhood around said one or more locations. An output interface (OUT) outputs a feature map (Vm) that includes the one or more locations.

Automatic CT detection and visualization of active bleeding and blood extravasation

Method of data processing for Computed Tomography from a spectral image data set of an imaged zone, an anatomical image data set of the imaged zone, and an anatomical model, comprising: a. Assigning an initial set of values to a regularization scheme, 5 b. Performing a noise reduction scheme on the spectral image data set, wherein said noise reduction scheme is a function of the regularization scheme, of the spectral image data set and of the anatomical image data set, in order to obtain a processed spectral image data set, c. Performing a segmentation of structures of interest using the anatomical data set, the processed spectral image data set, and the anatomical model, in order to obtain a segmentation result, d. Updating the regularization scheme based on the segmentation result, e. Repeating step b to e, wherein the method also comprises a control step, performed when step b and c has both been performed at least once, the control step comprising checking an endloop criterion, and if the endloop criterion is met, outputting at least one of the segmentation result obtained in last iteration of step c and the processed spectral image data set obtained in the last iteration of step b.

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Haifa, Israel

Raz Carmi