Siemens Healthcare Gmbh

Siemens Healthineers Ag

Teodora Chitiboi

Puneet Sharma

Lucian Mihai Itu

Andrei Bogdan Gheorghita

Saikiran Rapaka

Michaela Schmidt

Jens Wetzl

Christian Geppert

A value indicative of an ejection fraction, EF, of a cardiac chamber of a heart is based on a temporal sequence of cardiac magnetic resonance, CMR, images of the cardiac chamber. A neural network system has an input layer configured to receive the temporal sequence of a stack of slices of the CMR images along an axis of the heart. The temporal sequence is one or multiple consecutive cardiac cycles of the heart. The neural network system has an output layer configured to output the value indicative of the EF based on the temporal sequence. The neural network system has interconnections between the input layer and the output layer and is trained with a plurality of datasets. Each of the datasets comprises an instance temporal sequence of the stack of slices of the CMR images along the axis over one or multiple consecutive cardiac cycles for the input layer and an associated instance value indicative of the EF for the output layer.

Technique for quantifying a cardiac function from CMR images

Systems and methods for predicting a location for acquiring a target view of an anatomical object of interest in an input image are provided. An input image of an anatomical object of interest of a patient is received. An output image is generated using a machine learning based network. The output image depicts a projection of a 3D image plane for acquiring a target view of the anatomical object of interest identified on the input image. The output image is output.

Automatic, dynamic, and adaptive slice planning for cardiac MRI acquisition

Systems and methods for generating a synthesized contrast enhanced medical image are provided. An input medical image is received. A synthesized contrast enhanced medical image is generated based on the input medical image using a trained machine learning based generator network. The synthesized contrast enhanced medical image includes one or more synthesized contrast enhanced regions of pathological tissue. The synthesized contrast enhanced medical image is output.

Synthesis of contrast enhanced medical images

The application relates to a computer implemented method for determining a basal and an apex plane in a set of Magnetic Resonance, MR, image slices of a heart, wherein the set of MR image slices comprises short axis views of the heart obtained over the heartbeat. The set of MR image slices is applied to a multitask deep learning artificial intelligence Model which is configured to identify a basal plane slice and an apex plane slice on the applied set of image slices, wherein the multitask deep learning artificial intelligence model is further configured to determine at least one further parameter of cardiac anatomy or of a cardiac function. A first output of the multitask deep learning artificial intelligence Model is determined as the apex plane slice and a second output as the basal plane slice. At least one further output of the multitask deep learning artificial intelligence Model is determined as the at least one further parameter of the cardiac anatomy or of the cardiac function.

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Jersey City, NJ, United States of America

Teodora Chitiboi