Siemens Healthcare Gmbh

Siemens Healthineers Ag

Vanderbilt University

Siemens Aktiengesellschaft

Siemens Medical Solutions Usa, Inc.

Thomas Pheiffer

Ankur Kapoor

Tommaso Mansi

Ali Kamen

Dorin Comaniciu

Rui Liao

Jin-hyeong Park

Michael I Miga

Marcel Dominik Nickel

Sasa Grbic

David D Liu

Berthold Kiefer

Michael Suehling

Stephan Kannengiesser

Thomas Engel

Shun P Miao

Stefan Kluckner

Zhoubing Xu

Birgi Tamersoy

Philip Mewes

ALois Regensburger

Robert Grimm

Peter Mountney

Amilcar Alzaga

Michael Sühling

Young-Ho Kim

Heinrich Von Busch

Vincent Ordy

Guillaume Chabin

Pavlo Dyban

Rebekah H Conley

Ingrid M Meszoely

Ioan Marius Popdan

Jarrod Collins

Alin Madalin Draghia

Manasi Datar

Matteo Barbieri

A model dataset is generated based on first image data. The model dataset and second image data map at least a common part of an examination region at a second detail level. The model dataset and the second image data are pre-aligned at a first detail level below the second detail level based on first features that are mapped at the first detail level in the model dataset and the second image data and/or an acquisition geometry of the second image data. The model dataset and the second image data are registered at the second detail level based on second features that are mapped at the second detail level in the model dataset and the second image data. The second class of features is mappable at the second detail level or above. The registered second image data and/or the registered model dataset is provided.

Providing result image data

Systems and methods for performing an assessment of a lesion are provided. A plurality of input medical images of a lesion is received. The plurality of input medical images comprises an initial input medical image and one or more additional input medical images. The initial input medical image comprises a region of interest around the lesion. A mask of the lesion is curated for the initial input medical image based on the region of interest and a set of candidate masks. The region of interest in the initial input medical image is propagated to the one or more additional input medical images based on prior registration transformations. A mask of the lesion is curated for each of the one or more additional input medical images based on the propagated regions of interest and the set of candidate masks. One or more assessments of the lesion are performed based on the mask for the initial input medical image, the masks for the one or more additional input medical images, and prior assessments of lesions. Results of the one or more assessments of the lesion are output.

AI driven longitudinal liver focal lesion analysis

Systems and method for assisted catheter steering are provided. Instructions for steering a catheter within a patient are received. A graph defining paths between a plurality of configurations of a robotic catheter navigation system is constructed based on the received instructions. Each of the plurality of configurations are associated with a respective view of the patient. A path is determined in the graph to a target configuration of the plurality of configurations of the robotic catheter navigation system. The catheter is automatically steered within the patient based on the determined path in the graph to recover the respective view of the patient associated with the target configuration.

Assisted steering of intracardiac echocardiogram catheters

The disclosure relates to techniques for automatically characterizing liver tissue of a patient, comprising receiving morphological magnetic resonance image data set and at least one magnetic resonance parameter map of an imaging region comprising at least partially the liver of the patient, each acquired by a magnetic resonance imaging device, via a first interface. The techniques further include applying a trained function comprising a neural network to input data comprising at least the image data set and the parameter map. At least one tissue score describing the liver tissue is generated as output data, which is provided using a second interface.

Techniques for automatically characterizing liver tissue of a patient

An artificial agent based cognitive operating room system and a method thereof providing automated assistance for a surgical procedure are disclosed. Data related to the surgical procedure from multiple data sources is fused based on a current context. The data includes medical images of a patient acquired using one or more medical imaging modalities. Real-time quantification of patient measurements based on the data from the multiple data sources is performed based on the current context. Short-term predictions in the surgical procedure are forecasted based on the current context, the fused data, and the real-time quantification of the patient measurements. Suggestions for next steps in the surgical procedure and relevant information in the fused data are determined based on the current context and the short-term predictions. The suggestions for the next steps and the relevant information in the fused data are presented to an operator.

System and method for artificial agent based cognitive operating rooms

A method and system for registering a pre-operative 3D medical image volume of a target organ to intra-operative ultrasound images is disclosed. An intra-operative 3D B-mode ultrasound volume and an intra-operative 3D ultrasound elastography volume are acquired. Patient-specific boundary conditions for a biomechanical tissue model of a target organ are determined using the intra-operative 3D B-mode volume. Patient-specific material properties for the biomechanical tissue model of the target organ are determined using the 3D ultrasound elastography volume. The target organ in the pre-operative 3D medical image volume is deformed using the biomechanical tissue model with the patient-specific material properties with the deformation of the target organ in the pre-operative 3D medical image volume constrained by the patient-specific boundary conditions.

System and methods for performing biomechanically driven image registration using ultrasound elastography

Methods and systems are provided for registration of preoperative images to ultrasound images. The preoperative images are segmented using a shape model. An ultrasound procedure is performed to acquire the ultrasound images. The path of an ultrasound transducer used in the ultrasound procedure is tracked. The path is used to deform the segmented preoperative images, providing an alignment. The ultrasound images are registered to the preoperative images using the alignment.

Deformable registration of preoperative volumes and intraoperative ultrasound images from a tracked transducer

A method and system for calculating a volume of resected tissue from a stream of intraoperative images is disclosed. A stream of 2D/2.5D intraoperative images of resected tissue of a patient is received. The 2D/2.5D intraoperative images in the stream are acquired at different angles with respect to the resected tissue. A resected tissue surface is segmented in each of the 2D/2.5D intraoperative images. The segmented resected tissue surfaces are stitched to generate a 3D point cloud representation of the resected tissue surface. A 3D mesh representation of the resected tissue surface is generated from the 3D point cloud representation of the resected tissue surface. The volume of the resected tissue is calculated from the 3D mesh representation of the resected tissue surface.

Method and system for calculating resected tissue volume from 2D/2.5D intraoperative image data

Systems and methods are provided for identifying pathological changes in follow up medical images. Reference image data is acquired. Follow up image data is acquired. A deformation field is generated for the reference image data and the follow up data using a machine-learned network trained to generate deformation fields describing healthy, anatomical deformation between input reference image data and input follow up image data. The reference image data and the follow up image data are aligned using the deformation field. The co-aligned reference image data and follow up image data are analyzed for changes due to pathological phenomena.

Automatic change detection in medical images

A computer-implemented method for performing deformable registration between Magnetic Resonance (MR) and Ultrasound (US) images include receiving an MR volume depicting an organ and segmenting the organ from the MR volume to yield a first 3D point representation of the organ in MR coordinates. Additionally, a US volume depicting an organ is received and the organ is segmented from the US volume to yield a second 3D point representation of the organ in US coordinates. Next, a plurality of point correspondences between the first 3D point representation and the second 3D point representation are determined. Then, a biomechanical model is applied to register the MR volume to the US volume. The plurality of point correspondences are used as displacement boundary conditions for the biomechanical model.

Deformable registration of magnetic resonance and ultrasound images using biomechanical models

For liver modeling from medical scan data, multiple modalities of imaging are used. By using multiple modalities of imaging in combination with generative modeling, a more comprehensive and informed assessment may be performed. The generative modeling may allow feedback of effects of proposed therapy on function of the liver. This feedback is used to update the liver function information based on the imaging. Based on the computerized modeling with information from various imaging modes, an output based on more comprehensive information and patient personalized modeling and feedback may be provided to assist the physician.

Liver disease assessment in medical imaging

A method for image guided surgery includes obtaining intraoperative locations including intraoperative fiducial locations for a non-rigid structure and intraoperative rigid structure locations for associated rigid structures, estimating a gravity deformation for the non-rigid structure based on a first rigid registration for preoperative fiducial locations in a computer model relative to the intraoperative fiducial locations and a second rigid registration for the preoperative rigid structure locations for a rigid structure in the computer model with respect to the intraoperative rigid structure locations, modifying the preoperative fiducial locations using first rigid registration and the gravity deformation, determining a third rigid registration for the modified fiducial locations relative to the intraoperative fiducial locations, calculating a non-rigid transformation for the computer model using the volumetric gravity field deformation and first boundary conditions from errors in the third rigid registration, and displaying image data for the non-rigid structure modified to undergo the non-rigid transformation.

Biomechanical model assisted image guided surgery system and method

In order to improve depth perception for an image displayed during a laparoscopic surgery, a representation of a shadow of a tool included in the image and used in the laparoscopic surgery is identified and introduced into the image. A processor augments a three-dimensional (3D) model including a 3D representation of a surface of an object included in the image, and a representation of the tool by introducing a virtual light source into the 3D model to generate a virtual shadow within the 3D model. The processor subtracts the representation of the shadow out of the augmented 3D model and superimposes the representation of the shadow on the image to be displayed during the laparoscopic surgery.

Virtual shadows for enhanced depth perception

A method for real-time correction of tissue compression for tracked ultrasound includes acquiring an ultrasound image of tissues of interest with an ultrasound probe having a probe surface, where the ultrasound probe is tracked to provide a position and an orientation of the probe surface of the ultrasound probe for the acquired ultrasound image; acquiring intraoperative measurements of the undeformed tissue surface; constructing a generic grid representation of tissues for use with a mathematical model of tissue biomechanics, where the generic grid representation is pre-aligned to the acquired ultrasound image by performing a calibration to the ultrasound probe; determining boundary conditions to the mathematical model represented by the generic grid representation; solving the mathematical model for 3D displacements in the generic grid representation of the tissues; and performing correction of tissue compression by using the reversed and interpolated 3D displacement field on the acquired ultrasound image.

Method and system for real-time compression correction for tracked ultrasound and applications of same

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Langhorne, PA, United States of America

Thomas Pheiffer