Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Aktiengesell Schaft

Siemens Aktiengesellschat

Thomas Redel

Estelle Camus

Puneet Sharma

Lucian Mihai Itu

Tiziano Passerini

Saikiran Rapaka

Dorin Comaniciu

Jan Boese

Chris Schwemmer

Marcus Pfister

Oliver Meissner

Max Schoebinger

Michael Scheuering

Günter Lauritsch

Martin Ostermeier

Bogdan Georgescu

Ali Kamen

Martin Kleen

Viorel Mihalef

Ying Zhu

Michael Zellerhoff

Xudong Zheng

Martin Berger

Michael Maschke

Martin Hoheisel

Philipp Bernhardt

Markus Kowarschik

Klaus Klingenbeck-Regn

Frank Dennerlein

Christopher Rohkohl

Andreas Meyer

Michael Wiets

Sandra Sudarsky

Klaus Klingenbeck

Anton Nekovar

Sajjad Hussain Baloch

Yu Deuerling-Zheng

Alexander Preuhs

Sebastian Bauer

Gary S Martucci

Max Schöbinger

Guenter Lauritsch

Clemens Fiebiger

Klaus Kinnstaetter

Paul Klein

Komal Dutta

Ashraf Mohamed

Durga Namburu

Puthenveettil Nias

Thomas Hartkens

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

A method and system for non-invasive assessment and therapy planning for coronary artery disease from medical image data of a patient is disclosed. Geometric features representing at least a portion of a coronary artery tree of the patient are extracted from medical image data. Lesions are detected in coronary artery tree of the patient and a hemodynamic quantity of interest is computed at a plurality of points along the coronary artery tree including multiple points within the lesions based on the extracted geometric features using a machine learning model, resulting in an estimated pullback curve for the hemodynamic quantity of interest. Post-treatment values for the hemodynamic quantity of interest are predicted at the plurality of points along the coronary artery tree including the multiple points within the lesions for each of one or more candidate treatment options for the patient, resulting in a respective predicted post-treatment pullback curve for the hemodynamic quantity of interest for each of the one or more candidate treatment options. A visualization of a treatment prediction for at least one of the candidate treatment options is displayed.

Non-invasive assessment and therapy guidance for coronary artery disease in diffuse and tandem lesions

Methods for computing hemodynamic quantities include: (a) acquiring angiography data from a patient; (b) calculating a flow and/or calculating a change in pressure in a blood vessel of the patient based on the angiography data; and (c) computing the hemodynamic quantity based on the flow and/or the change in pressure. Systems for computing hemodynamic quantities and computer readable storage media are described.

Computation of hemodynamic quantities from angiographic data

Systems and methods for retraining a trained machine learning model are provided. One or more input medical images are received. Measures of interest for a primary task and a secondary task are predicted from the one or more input medical images using a trained machine learning model. The predicted measures of interest for the primary task and the secondary task are output. User feedback on the predicted measure of interest for the secondary task is received. The trained machine learning model is retrained for predicting the measures of interest for the primary task and the secondary task based on the user feedback on the output for the secondary task.

Performance of machine learning models for automatic quantification of coronary artery disease

Synthetic data-driven hemodynamic determination in medical imaging

A method and system for simulating blood flow in a vessel of a patient to estimate hemodynamic quantities of interest using enhanced blood flow computations based on invasive physiological measurements of the patient is disclosed. Non-invasive patient data including medical image data is received and a patient-specific anatomical model the patient's vessels is generated. Invasive physiological measurements of the patient are received and a computational blood flow model is personalized using the invasive physiological measurements. Blood flow is simulated in the patient-specific anatomical model and one or more hemodynamic quantities of interest are computed using the personalized computational blood flow model.

Method and system for enhancing medical image-based blood flow computations using physiological measurements

A method for determining a clinical characteristic of a body vessel segment including providing, to a computing device, a three-dimensional reconstruction of a body vessel containing the body vessel segment. A segmented angiography recording of the body vessel segment is provided to the computing device. The computing device extracts at least one global feature of the body vessel from the three-dimensional reconstruction and extracts at least one local feature of the body vessel segment from the angiography recording. The clinical characteristic is determined for the body vessel segment as a function of the at least one extracted local feature and the at least one extracted global feature.

Determination of a clinical characteristic using a combination of different recording modalities

A method for planning treatment of a stenosis in a vascular segment includes providing a geometric description of the vascular segment on a computer and determining a course of a hemodynamic parameter of the vascular segment along the vascular segment based on the geometric description provided by the computer. The computer calculates a mathematical derivative of the hemodynamic parameter over the length of the vascular segment along the vascular segment. At least one length section is specified for the vascular segment, and a value of the hemodynamic parameter in a distal end region of the vascular segment is simulated for a treatment device introduced virtually into the specified length section as a function of the mathematical derivative. The treatment of the stenosis including the introduction of the treatment device into the specified length section is planned as a function of the simulated value for the hemodynamic parameter.

Treatment planning for a stenosis in a vascular segment based on virtual hemodynamic analysis

A method and system are provided for at least symbolically reconstructing a reconstruction data set of at least one vessel segment in a vessel tree of a patient. Input data for the reconstruction comprises at least two two-dimensional angiographic projection images taken in different acquisition geometries. At least one first angiographic projection image showing the vessel segment is acquired. An evaluation measure is automatically determined for each first angiographic projection image using three-dimensional preliminary information for the vessel segment. The evaluation measure describes the suitability of the at least one angiographic projection image for reconstructing the reconstruction data set. When a quality criterion evaluating the evaluation measure is not fulfilled, at least one additional acquisition geometry is determined using the three-dimensional preliminary information and/or the evaluation measure. In each additional acquisition geometry, at least one second angiographic projection image is acquired. The reconstruction data set is reconstructed from the at least one second angiographic projection image and/or at least one of the at least one first angiographic projection image fulfilling a suitability criterion evaluating the evaluation measure.

Method for reconstructing a reconstruction data set of a vessel segment

A method and a corresponding system for assessing a haemodynamic parameter for a vascular region of interest of a patient based on angiographic images are provided. After acquiring multiple angiographic images, a three dimensional (3D) representation of at least a first portion of the respective region of interest is performed, and geometric features are extracted from complete or partial views. Additional geometric features are extracted from partial incomplete views. A complete set of 3D geometric features for an anatomical structure, such as a vessel tree, is then generated by combining the extracted geometric features and estimating any missing geometric features. Using the complete set of 3D geometric features, a feature-based assessment of the haemodynamic parameter, such as a fractional flow reserve, is then performed.

Method and system for assessing a haemodynamic parameter

Systems and methods are provided for evaluating the complexity of a stenosis or a section of a vessel. At least one image of the stenosis or the section of the vessel is provided. A geometrical feature value of the stenosis and/or or the section of the vessel is identified from the at least one image. At least one intensity feature value is determined based on a grey level intensity of the stenosis or the section of the vessel from the at least one image. A complexity value relating to the geometrical complexity of the stenosis or the section of the vessel is calculated as a function of the at least one geometrical feature value and the at least one intensity feature value of the stenosis or the section of the vessel.

Determining a complexity value of a stenosis or a section of a vessel

A method for segmenting a two-dimensional angiographic recording of a vessel of a body using a computing apparatus includes providing a three-dimensional reconstruction of the vessel of the body to the computing apparatus. The two-dimensional angiographic recording of the vessel of the body is provided on the computing apparatus. The three-dimensional reconstruction of the vessel of the body is registered with the two-dimensional recording of the vessel of the body. Spatial information of the three-dimensional reconstruction is projected onto the two-dimensional recording, and the two-dimensional recording is segmented using the spatial information projected onto the two-dimensional recording.

Segmenting an angiography using an existing three-dimensional reconstruction

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 for operating an x-ray device, (e.g., a fluoroscope), is described herein. The method includes: creating planning information for a therapeutic intervention into a body vessel segment based on a reconstruction of the body vessel segment; providing the planning information to a processing unit of the x-ray device; providing the reconstruction of the body vessel segment to the processing unit; creating a recording of the body vessel segment introduced into a recording region of the x-ray device; registering the reconstruction of the body vessel segment with the body vessel segment in the recording region of the x-ray device; displaying the recording of the body vessel segment on a display device of the x-ray device; and superimposing a graphical representation of the planning information on the recording displayed on the display device, in order to increase the efficiency of the therapeutic intervention into the body vessel segment.

X-ray recording with superimposed planning information

The disclosure relates to a device and a method for ascertaining at least one individual fluid-dynamic characteristic parameter of a stenosis in a vascular segment having a plurality of serial stenoses, wherein angiography image data of the vascular segment is received from an angiography recording device, geometry data of the vascular segment is ascertained by an analysis device based on the angiography image data and combined into a segment model. At least one division point located between two of the stenoses respectively is ascertained by a dividing device in the segment model, the segment model is subdivided into subsegment models at each of the at least one division points, and the respective fluid-dynamic characteristic parameter is ascertained by a simulation device for at least one of the subsegment models based on respective geometry data of the subsegment model.

Device and method for ascertaining at least one individual fluid-dynamic characteristic parameter of a stenosis in a vascular segment having serial stenoses

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Poxdorf, Germany

Thomas Redel