Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Aktiengesellschaft

Chris Schwemmer

Max Schoebinger

Puneet Sharma

Thomas Redel

Dorin Comaniciu

Tiziano Passerini

Lucian Mihai Itu

Saikiran Rapaka

Michael Wels

Felix Lades

Christian Hopfgartner

Shaohua Kevin Zhou

Bernhard Geiger

David D Liu

Li Zhang

Michael Suehling

Günter Lauritsch

Daguang Xu

Noha Youssry El-Zehiry

Dominik Neumann

Nathan S Lay

Serkan Cimen

Vincent Ordy

Philipp Wolber

Suha Ayman

Ashish Jaiswal

Rumman Khan

Tommaso Mansi

Sasa Grbic

Michael Scheuering

Ingmar Voigt

Christopher Rohkohl

Razvan Ionasec

Helene C Houle

Julian Krebs

Ulrich Hartung

Rainer Kaergel

Max Schöbinger

Ruth J Soenius

Kerstin Mueller

Susanne Schmolke

Joel Mancina

Xinyun Li

Piotr Bialecki

At least one example embodiment provides an improved handling of image data sets of patients.

Methods for operating an evaluation system for medical image data sets, evaluation systems, computer programs and electronically readable storage mediums

At least one example embodiment provides a computer-implemented method for evaluating at least one image data set of an imaging region of a patient, wherein at least one evaluation information describing at least one medical condition in an anatomical structure of the imaging region is determined.

Computer-implemented methods and evaluation systems for evaluating at least one image data set of an imaging region of a patient, computer programs and electronically readable storage mediums

In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution. Combinations of one or more of uncertainty, use of synthetic training data, and therapy prediction may be provided.

Data-driven plaque determination in medical imaging

An embodiment of a method includes providing a first result list indicating a plurality of first anatomic structures and indicating, for each respective first anatomic structure of the plurality of first anatomic structures, a corresponding first severity indicator; providing a second result list indicating, for each respective second anatomic structure of the plurality of the second anatomic structures, a corresponding second severity indicator; providing a relationship matrix indicating a level of interrelatedness between the first anatomic structures and the second anatomic structures; and generating, based on the first result list provided, on the second result list and on the relationship matrix provided, a concordance visualization indicating a respective level of concordance between at least one of the first anatomic structures and the corresponding first severity indicator, and indicating a respective level of concordance between at least one of the second anatomic structures and the corresponding second severity indicator.

Automatically generating synchronized view and level of concordance of diagnostic exams

Synthetic data-driven hemodynamic determination in medical imaging

A method, for two-dimensional mapping of anatomical structures of a patient, includes acquiring three-dimensional image data of anatomical structures of a patient; adapting a virtual network structure to a spatial course of the anatomical structures; defining a user-defined map projection for projection of two-dimensional pixel positions of an image to be output onto a geometric figure around a center of the anatomical structures for which mapping onto a two-dimensional space is defined; ascertaining points of intersection of radially extending half lines assigned to the two-dimensional pixel positions of the image to be output with the virtual network structure; and ascertaining the image to be output based upon image intensity values assigned to the points of intersection ascertained. A method for two-dimensional mapping of the tree-like elongated structure of the patient; a method for simultaneous mapping of a tree-like elongated structure; and corresponding apparatuses are also described.

Planar visualization of anatomical structures

A method for interactively generating a geometric model of a volume object on the basis of three-dimensional image data of an examination region of interest of an examination subject is described. According to an embodiment, a representation of the volume object is determined on the basis of three-dimensional image data and a two-dimensional representation is determined on the basis of the determined representation using a preferably non-linear planar reformation of the three-dimensional object. Subsequently, boundary indicators which define the surface profile of the volume object are edited in the two-dimensional representation. Following the editing, a three-dimensional representation of the edited boundary indicators is generated by back-transforming the edited boundary indicators into three-dimensional space. Finally, a model-based representation of the volume object is generated in three-dimensional space on the basis of the edited boundary indicators. A volume object modeling device is also described. In addition, a medical imaging device is described.

Model-based generation and representation of three-dimensional objects

A method and system for determining hemodynamic indices, such as fractional flow reserve (FFR), for a location of interest in a coronary artery of a patient is disclosed. Medical image data of a patient is received. Patient-specific coronary arterial tree geometry of the patient is extracted from the medical image data. Geometric features are extracted from the patient-specific coronary arterial tree geometry of the patient. A hemodynamic index, such as FFR, is computed for a location of interest in the patient-specific coronary arterial tree based on the extracted geometric features using a trained machine-learning based surrogate model. The machine-learning based surrogate model is trained based on geometric features extracted from synthetically generated coronary arterial tree geometries.

Method and system for purely geometric machine learning based fractional flow reserve

A method and apparatus for whole body bone removal and vasculature visualization in medical image data, such as computed tomography angiography (CTA) scans, is disclosed. Bone structures are segmented in the a 3D medical image, resulting in a bone mask of the 3D medical image. Vessel structures are segmented in the 3D medical image, resulting in a vessel mask of the 3D medical image. The bone mask and the vessel mask are refined by fusing information from the bone mask and the vessel mask. Bone voxels are removed from the 3D medical image using the refined bone mask, in order to generate a visualization of the vessel structures in the 3D medical image.

Method and system for whole body bone removal and vascular visualization in medical image data

A method and system for transcatheter aortic valve implantation (TAVI) planning is disclosed. An anatomical surface model of the aortic valve is estimated from medical image data of a patient. Calcified lesions within the aortic valve are segmented in the medical image data. A combined volumetric model of the aortic valve and calcified lesions is generated. A 3D printed model of the heart valve and calcified lesions is created using a 3D printer. Different implant device types and sizes can be placed into the 3D printed model of the aortic valve and calcified lesions to select an implant device type and size for the patient for a TAVI procedure. The method can be similarly applied to other heart valves for any type of heart valve intervention planning.

Method and system for advanced transcatheter aortic valve implantation planning

A method for ascertaining a fluid-dynamic characteristic value of a resilient vascular tree, through which a fluid flows in a pulsating manner, is provided. At least one 2D projection, respectively, of the resilient vascular tree is generated by a projection device from different angles of projection, and a digital 3D reconstruction of the vascular tree is generated by an analysis device based on of the 2D projections. A geometry of at least one vessel of the resilient vascular tree is estimated based on the 3D reconstruction, and at least one fluid state in the resilient vascular tree is ascertained from the geometry and predetermined resilient properties of the resilient vascular tree. The at least one fluid-dynamic characteristic value is calculated as a function of the at least one fluid state.

Fluid-dynamic analysis of a vascular tree using angiography

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Forchheim, Germany

Chris Schwemmer