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  Machine learning based processing of magnetic resonance data, including an uncertainty quantification

Zaiss, M., Glang, F., Prokudin, S., & Scheffler, K.(2022). Machine learning based processing of magnetic resonance data, including an uncertainty quantification.

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 Creators:
Zaiss, M, Author           
Glang, F1, Author           
Prokudin, S.2, Author           
Scheffler, K1, Author           
Affiliations:
1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              
2Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497642              

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 Abstract: A method of processing magnetic resonance data of a sample under investigation includes the steps ofprovision of the MR data being collected with an MRI scanner apparatus, and machine learning based data analysis of the MR data by supplying the MR data to an artificial neural network being trained with predetermined training data, wherein at least one image parameter of the sample and additionally at least one uncertainty quantification measure representing a pre diction error ofthe at least one image parameter are provided by output elements of the neural network. Furthermore, a magnetic resonance imaging (MRI) scanner apparatus being adapted for employing the method ofprocessing MR data is described.

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 Dates: 2022-06-09
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: Patent Nr.: US 2022/0179026 A1
 Degree: -

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