The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 31, 2024

Filed:

Nov. 19, 2019
Applicant:

Koninklijke Philips N.v., Eindhoven, NL;

Inventors:

Evan Schwab, Cambridge, MA (US);

Arne Ewald, Hamburg, DE;

Assignee:

Koninklijke Philips N.V., Eindhoven, NL;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01R 33/56 (2006.01); G01R 33/563 (2006.01); G06N 3/02 (2006.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G01R 33/5608 (2013.01); G01R 33/56341 (2013.01); G06N 3/02 (2013.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01);
Abstract

The invention provides for a medical imaging system (). The medical imaging system comprises a memory () for storing machine executable instructions (). The memory further contains an implementation of a trained convolutional neural network (). The trained convolutional neural network comprises more than one spherical convolutional neural network portions ('). The trained convolutional neural network is configured for receiving diffusion magnetic resonance imaging data (). The diffusion magnetic resonance imaging data comprises a spherical diffusion portion (′). The more than one spherical convolutional neural network portions are configured for receiving the spherical diffusion portion. The trained convolutional neural network comprises an output layer () configured for generating a neural network output () in response to inputting the diffusion magnetic resonance imaging data into the trained convolutional neural network. The medical imaging system further comprises a processor () for controlling the machine executable instructions. Execution of the machine executable instructions causes the processor to: receive () the diffusion magnetic resonance imaging data; and generate () the neural network output by inputting the diffusion magnetic resonance imaging data into the trained convolutional neural network.


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