Siemens Medical Solutions USA, Inc.

University of Virginia Patent Foundation

Siemens Healthineers Ag

University of Cape Town

Bruce S Spottiswoode

Sasa Grbic

Zhoubing Xu

Ludovic Sibille

Juergen Soldner

Ali Kamen

Matthias J Schmand

Sven Zuehlsdorff

Dominik Neumann

Mayank Gupta

Frederick H Epstein

Jicun Hu

Vijay Shah

Günther Platsch

Fernando Vega

Guillaume Chabin

Fei Gao

Ernesta Maria Meintjes

Nicolo Capobianco

Julian Rosenman

Vilim Simcic

Systems and methods for automatically staging non-small cell lung cancer are provided. Patient data relating to a cancer of a patient is received. The patient data comprises one or more medical images and one or more biopsy reports. A T-stage of the cancer is determined based on a location and a size of one or more tumors of the cancer determined using the patient data. An N-stage of the cancer is determined by combining a metastasis evaluation of the cancer to regional lymph nodes determined from the one or more medical images and a metastasis evaluation of the cancer in the regional lymph nodes determined from the one or more biopsy reports. An M-stage of the cancer is determined based on a metastasis evaluation of the cancer to anatomical structures based on the patient data. The T-stage, the N-stage, and the M-stage are output.

Automatic staging of non-small cell lung cancer from medical imaging and biopsy reports

Systems and methods of PET attenuation correction using low-field MR image data includes receiving a first set of image data and a set of low-field magnetic resonance (MR) image data. An attenuation correction map is generated from the low-field MR image data using a first trained neural network. At least one attenuation correction process is applied to the first set of image data based on the attenuation correction map to generate at least one clinical attenuation-corrected image.

Systems and methods for low field MR/PET imaging

The invention describes a method for automatically localizing organ segments in a three-dimensional image comprising the following steps: providing a three-dimensional image showing at least one organ and at least one tubular network comprising a plurality of tubular structures, the organ comprising organ segments; performing automatic separation of the organ from other parts of the image; performing automatic tracing of the tubular network to obtain a branch map; performing automatic analysis of the branch map to identify specific tubular structures; performing automatically assigning regions of the organ to the specific tubular structures to segment the organ into localized organ segments; and outputting the localized organ segments and the traced and analyzed tubular network as image data. The invention further describes a localization arrangement and a medical imaging system.

Method and arrangement for automatically localizing organ segments in a three-dimensional image

A framework for multi-scan image processing. A single real anatomic image of a region of interest is first acquired. One or more emission images of the region of interest are also acquired. One or more synthetic anatomic images may be generated based on the one or more emission images. One or more deformable registrations of the real anatomic image to the one or more synthetic anatomic images are performed to generate one or more registered anatomic images. Attenuation correction may then be performed on the one or more emission images using the one or more registered anatomic images to generate one or more attenuation corrected emission images.

Multi-scan image processing

Systems and methods include control of a nuclear imaging scanner to acquire nuclear imaging scan data of a body, control of a computed tomography scanner to acquire computed tomography scan data of the body, determination of a scanning speed, of the nuclear imaging scanner, associated with each of a plurality of scanning coordinates based on locations of one or more internal volumes associated with radioactivity greater than a threshold level, a classification determined for each of the one or more of the internal volumes indicating a degree of clinical interest based at least in part on the radioactivity associated with the internal volume, and an attenuation coefficient map based on the computed tomography scan data, and control of the nuclear imaging scanner to scan the body over each of the scanning coordinates at the associated scanning speed.

Patient-adaptive nuclear imaging

Systems and methods for detecting and classifying clinical features in medical images are disclosed. Natural language processes are applied to speech received from a dictation system to determine clinical and anatomical information for a medical image being viewed. In some examples, gaze location information identifying an eye position is received, as well as an image position for the medical image being viewed. Features of the medical image are detected and classified based on machine learning models. Anatomical associations are generated based on one or more of the classifications, the anatomical information, the gaze information, and the image position. The machine learning models can be trained based on the anatomical associations. In some examples, reports are generated based on the anatomical associations.

Methods and apparatus for artificial intelligence informed radiological reporting and model refinement

A method for parametric image reconstruction and motion correction using whole-body motion fields includes receiving a nuclear imaging data set including a set of dynamic frames and generating at least one of a whole-body forward motion field and/or a whole-body inverse motion field for at least one frame in the set of frames. An iterative loop is applied to update at least one parameter used in a direct parametric reconstruction and at least one parametric image is generated based on the at least one parameter updated by the iterative loop. The iterative loop includes calculating a frame emission image for the at least one frame, generating a motion-corrected frame emission image based on the at least one whole-body forward motion field or a whole-body inverse motion field, and updating at least one parameter by applying a fit to the motion-corrected frame emission image.

Inter-frame motion correction in whole-body direct parametric image reconstruction

A method and system for determining system-based tumor burden is disclosed. In one aspect, the method includes obtaining the medical image from a source, through an interface. Additionally, the method includes identifying a first region of interest in the medical image. The method also includes selecting from the first region of interest a second region of interest whose tumor burden is to be determined. Furthermore, the method includes defining a segmentation criterion for the second region of interest. The method also includes determining the tumor burden for the second region of interest.

Method and system for determining tumor burden in medical images

A system and method include execution of a first nuclear imaging scan to acquire first nuclear imaging scan data of a body; generation of a target image based on the first nuclear imaging scan data execute a second nuclear imaging scan to acquire second nuclear imaging scan data of the body association of each of a plurality of portions of the second nuclear imaging scan data with a respective one of a plurality of motion phases of the body, generation, for each of the plurality of motion phases of the body, of a binned image based on the portion of the second nuclear imaging scan data associated with the motion phase, performance of motion-correction on each of the plurality of binned images based on the target image to generate a plurality of motion-corrected binned images, and generation of an image based on the target image and the plurality of motion-corrected binned images.

System to improve nuclear image of moving volume

Myocardial tissue tracking techniques are used to project or guide a single manually-defined set of myocardial contours through time. Displacement encoding with stimulated echoes (DENSE), harmonic phase (HARP) and speckle tracking is used to encode tissue displacement into the phase of complex MRI images, providing a time series of these images, and facilitating the non-invasive study of myocardial kinematics. Epicardial and endocardial contours need to be defined at each frame on cine DENSE images for the quantification of regional displacement and strain as a function of time. The disclosed method presents a novel and effective two dimensional semi-automated segmentation technique that uses the encoded motion to project a manually defined region of interest through time. Contours can then easily be extracted for each cardiac phase.

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Knoxville, TN, United States of America

Bruce S Spottiswoode