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Hemodynamic analysis of vessels using recurrent neural network

A technology of hemodynamics and blood vessels, applied in biological neural network models, neural learning methods, neural architectures, etc.

Pending Publication Date: 2020-12-11
SIEMENS HEALTHCARE GMBH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] Furthermore, currently no suitable machine learning approach has been proposed for this task
This may be due to the need to develop algorithms using big data, which is not readily available

Method used

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  • Hemodynamic analysis of vessels using recurrent neural network
  • Hemodynamic analysis of vessels using recurrent neural network
  • Hemodynamic analysis of vessels using recurrent neural network

Examples

Experimental program
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Embodiment Construction

[0142] exist figure 1 In , an embodiment of the method of predicting a hemodynamic parameter according to the first aspect of the present invention is exemplarily depicted. The method comprises the steps of: receiving 1 a vessel shape model; receiving 2 a corresponding flow distribution; and predicting 3 at least one hemodynamic parameter p k .

[0143] In the step of receiving 1 , a vessel shape model of a target vessel, here exemplarily the aorta of a human subject, is received as a first input. The vessel shape model has been extracted from an image dataset generated by a medical imaging system showing the subject's aorta (eg, a 4D MRI flow dataset generated by a 4D MRI system). The vessel shape model includes a centerline extending from the entrance to the exit of the aorta. The centerline runs through all center points along the aorta. The blood vessel shape model includes a large number of N=100 blood vessel shape points, each of which has a first coordinate in a fir...

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Abstract

The disclosure relates to hemodynamic analysis of vessels using a recurrent neural network. The present invention is related to a method of and an Artificial Intelligence (AI) system for predicting hemodynamic parameters for a target vessel, in particular of an aorta, as well as to a computer-implemented method of training an AI unit comprised by said AI system. A vessel shape model of the targetvessel and a corresponding flow profile of the target vessel are received. At least one hemodynamic parameter pk is predicted by the AI unit based on the received vessel shape model and the received flow profile. The AI unit is arranged and configured to predict at least one hemodynamic parameter pk based on a received vessel shape model and a received flow profile of the target vessel (aorta).

Description

technical field [0001] The present invention relates to a method and an artificial intelligence (AI) system for predicting hemodynamic parameters of a target vessel and to a computer-implemented method of training an AI unit comprised by said AI system. Background technique [0002] Coarctation of the aorta and aortic valve disease are the most common congenital heart defects. Treatment decision-making for these diseases is a complex process and highly dependent on the patient's condition. Treatment decisions can be significantly improved based on patient-specific hemodynamic parameters of the patient's aorta. The current clinical standard for assessing hemodynamic parameters is to perform a catheterization technique. However, the catheterization technique is an invasive procedure that poses risks to the patient (eg, radiation, cost, complications). Non-invasive methods using different computational fluid dynamics (CFD) modalities are being widely validated. These method...

Claims

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Application Information

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IPC IPC(8): G16H50/20G16H50/30G16H50/50G06N3/04G06N3/08G06F30/28G06F30/27
CPCG16H50/20G16H50/30G16H50/50G06N3/049G06N3/084G06F30/28G06F30/27G06N3/048G06N3/045A61B5/02007A61B5/02028A61B5/029
Inventor 阿诺·阿林德拉·阿迪约索
Owner SIEMENS HEALTHCARE GMBH
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