General Electric Company

Ge Medical Systems Global Technology Company, LLC

Ge Precision Healthcare LLC

G.e. Medical Systems, S.a.

Ge Medical Systems

Centre National De La Recherche Scientifique

Université Pierre Et Marie Curie

Serge Muller

Razvan Iordache

Laurence Vancamberg

Andreas Rick

Sylvain Bernard

Jean-Pierre Saladin

Giovanni Palma

Zhijin Li

Ann-Katherine Carton

Pablo Milioni De Carvalho

Gero L Peters

Clement Jailin

Luc Miotti

Jean Lienard

Randall Payne

Elisabeth Soubelet

Paul Markwardt

Robert Heidsieck

Sylvie Puong

Sylvie Bothorel

Remy Klausz

Aurelie Boudier

Henri Souchay

James Wear

Michel Francois Grimaud

Aurélie Boudier

Guillaume Peter

Fanny Jeunehomme

Laurent Launay

Romain Moulin

Guillaume Morel

Ludovic Avot

Dinko Eduardo Gonzalez Trotter

Fabienne Betting

Marco Blumenthal

Aurelie Roncaglioni

Julie Manzano

Xavier Bouchevreau

Bruno Le Corgne

Serge Calisti

Alain Marie

Axel Crasemann

Jean Pierre Saladin

Vinay N

Celine Pawlak

Marcia C Hill

Ann-katherine Carton

Ruben Sanchez De La Rosa

Muthu Revathy Authavan

Razvan Jordache

Pascal Gatesoupe

Sophie Lavoue

Iordache Razvan

Jérôme Durand

Cybéle Ciofolo

Thierry Salmon Legagneur

Regis Gerard Elle Personnelli

Fran&ccedil;ois Nicolas

Sophie Seggar

Francois Lenfant

Fran&ccedil;ois Nicholas

Caroline DeCock

Luc Mioitti

Sridharan Pradeepkumar

Anis Sahbani

Francois Nicolas

Aurora Talaverano Fuentes

Jean-Luc Baudry

Virginie Corre

Luc Miotto

A breast phantom is provided that has a non-uniform thickness to mimic clinically realistic variations in compressed breast thicknesses when in contact with the compression paddle during a mammography imaging procedure. With the non-uniform thickness, the breast phantom enables a mammography imaging device to be tested for image quality with regard to image correction processes and/or algorithms employed by the mammography imaging device to address non-uniformity in the thickness of the imaged breast. The breast phantom can be made of one or more sections, with one or more of the sections having a non-uniform thickness.

Non-uniform thickness breast imaging phantom for 2D/3D spectral imaging

Various methods and systems are provided for breast positioning assistance during mammography and image guided interventional procedures. In one example, a sensor detection system is utilized to evaluate one or more of a patient position, a breast position, and breast anatomy to determine if the patient and breast are adjusted to desired positions preferred for a desired view and imaging procedure prior to acquiring x-ray images, with real-time feedback provided to guide the user to position the breast and/or the patient based on the evaluation. The evaluation is performed by an artificial intelligence (AI) model stored on the x-ray mammography system that is trained using sensor data obtained from imaging procedures performed using the x-ray mammography system and fashioned into training datasets by a training module located on the x-ray mammography system.

System and method for training sensor-guided x-ray mammography system

Various methods and systems are provided for identifying future occurrences of lesions in a patient. In one example, a method includes feeding first images collected from one or more patients prior to appearance of lesions and second images collected from the one or more patients after appearance of the lesions to a processor to train a model to predict a location of a future lesion. The method further includes inputting third images collected from a new patient to the processor to infer regions for future lesions, and displaying the inferred regions in a probability map at a display unit to indicate areas of increased likelihood of lesion occurrence.

Methods and systems for early detection and localization of a lesion

A system for collecting a biopsy of a lesion within a patient using examination images previously collected from an examination device. An x-ray detector detects energy after passing through the patient. A compression paddle compresses the patient. A processing system generates a biopsy image based on the energy detected by the x-ray detector, accesses the examination images previously collected, and analyzes, while the patient remains compressed, the biopsy image to determine a measured x-coordinate and a measured y-coordinate of the lesion along X and Y axes, respectively. The examination images are analyzed to determine a calculated z-coordinate of the lesion along the Z axis. The location of the lesion along the X, Y, and Z axes is determined based on the measured x-coordinate and measured y-coordinate from the biopsy image and the calculated z-coordinate from examination images. The biopsy may be performed while the patient remains compressed.

Systems and methods for identifying biopsy location coordinates

Various methods and systems are provided for digital mammography imaging procedures. In one example a method for an x-ray system includes acquiring a comparative image of a patient with an x-ray detector, aligning the comparative image with a reference image which includes an ROI, mapping the ROI of the reference image onto the comparative image. When mapping indicates the ROI is outside of frame of the comparative image, the method includes displaying comparative image with instructions indicating a relative position of the ROI.

Methods and systems for digital mammography imaging

Various methods and systems are provided for breast positioning assistance during mammography and image guided interventional procedures. In one example, a sensor detection system is utilized to evaluate one or more of a patient position, a breast position, and breast anatomy to determine if the patient and breast are adjusted to desired positions preferred for a desired view and imaging procedure prior to acquiring x-ray images, with real-time feedback provided to guide the user to position the breast and/or the patient based on the evaluation. The evaluation is performed by an artificial intelligence (AI) model stored on the x-ray mammography system that is trained using sensor data obtained from imaging procedures performed using the x-ray mammography system and fashioned into training datasets by a training module located on the x-ray mammography system. The datasets are supplied to the AI model without any transmission of the datasets exteriorly of the mammography system.

A method for determining a lesion location in a patient for biopsy along X, Y, and Z axes. The method includes positioning the patient in an examination device to collect examination images showing the lesion. The method includes positioning the patient in a biopsy device configured for holding the patient during the biopsy and collecting a biopsy image of the patient using the biopsy device. The method includes analyzing the biopsy image to determine a measured x-coordinate and a measured y-coordinate of the lesion along the X and Y axes, respectively, analyzing the examination images to determine a calculated z-coordinate along the Z axis of the lesion, and determining the location of the lesion based on the measured x-coordinate and the measured y-coordinate from the biopsy image and the calculated z-coordinate determined from the one or more examination images.

A mammography system includes a biopsy guidance system that employs a simplified robot arm support, to enable previously unusable locations for the mounting of the biopsy device directly to the mammography system, such as on a compression paddle, combined with a vision guidance and control system. The vision system operates to determine the position of the biopsy device and the biopsy needle tip, as well as to control the movement/operation of the biopsy guidance system, such as movement to the final end-pose or pre-firing position of the biopsy device to perform the biopsy procedure. The vision system utilizes one or more cameras to visually determine the position the biopsy device relative to a region of interest being biopsied within the required tolerances for the biopsy procedure without the need for precise positional information to be provided by the biopsy guidance system to the mammography system.

Vision-guided biopsy system and method for mammography

The current disclosure provides for mapping medical images to style transferred medical images using deep neural networks, while maintaining clinical quality of the style transferred medical image, thereby enabling a clinician to evaluate medical images in a preferred style without loss of clinically relevant content. In one embodiment the current disclosure provides for a method comprising, acquiring a medical image of an anatomical region of a subject, wherein the medical image is in a first style, selecting a target style, wherein the target style is distinct from the first style, selecting a clinical quality metric, selecting a trained style transfer network based on the target style and the clinical quality metric, mapping the medical image to a style transferred medical image using the trained style transfer network, wherein the style transferred medical image is in the target style, and displaying the style transferred medical image via a display device.

Systems and methods for medical image style transfer using deep neural networks

A system for performing a scan of internal structures of an object/patient is provided. The system includes a radiation source operative to emit a radiation beam, a radiation detector operative to receive the radiation beam and generate an output signal based at least in part on the received radiation beam, and a controller in electronic communication with the radiation source and the radiation detector and operative to generate at least one image of the object/patient. The controller is further operative to determine an offset of the at least one image relative to an image reference and to employ the offset to automatically align the at least one image with the image reference without the need for stopping the operation of the radiation source and detector to reposition the object/patient being scanned.

X-ray image feedback for DXA scan FOV adjustment

Various methods and systems are provided for breast positioning assistance during mammography and image guided interventional procedures. In one example, a vision system is utilized to evaluate one or more of a patient position, a breast position, and breast anatomy to determine if the patient and breast are adjusted to desired positions preferred for a desired view and imaging procedure. Further, based on the evaluation, prior to acquiring x-ray images, real-time feedback may be provided to guide the user to position the breast and/or the patient.

Methods and systems for user and/or patient experience improvement in mammography

Various methods and systems are provided for workflow monitoring during x-ray mammography and related procedures. In one example, a vision system is utilized to monitor an x-ray mammography system, accessories associated with the system, and surrounding environment. Based on the detection and user indications, via a user interface for example, one or more of a current mode of operation of the x-ray system, a current workflow step in the current mode, and one or more errors may be identified using the vision system, and one or more of indications to the user and system adjustments may be performed based on the identification.

Methods and systems for mammography and biopsy workflow optimization

This invention relates to a biopsy method comprising automated moving of at least part of a biopsy sampler within a biopsy volume, to perform a biopsy in a zone of interest in a body, wherein said automated moving is preceded by variable positioning of said biopsy volume.

Biopsy method and associated biopsy device

Embodiments are disclosed herein for providing an immersive reality experience to a patient during a medical procedure. In one example, a method includes determining a location of a stressful object in a medical environment that includes a patient undergoing a medical procedure. The method further includes, upon determining that the stressful object is in a field of view (FOV) of the patient, adjusting display content based on the location of the stressful object within the FOV and outputting the display content for display on an immersive device.

Methods and systems for immersive reality in a medical environment

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Guyancourt, France

Serge Muller