METHODS AND SYSTEMS FOR REDUCING QUANTITATIVE MAGNETIC RESONANCE IMAGING HETEROGENEITY FOR MACHINE LEARNING BASED CLINICAL DECISION SYSTEMS

Various methods and systems are provided for reducing parametric heterogeneity in quantitative magnetic resonance (qMR) images, to increase robustness of in-field machine learning model inferences. In one example, a method for reducing qMR image heterogeneity includes, receiving a first qMR image, a...

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Bibliographic Details
Main Authors Shanbhag, Dattesh, Mongia, Aanchal, Mullick, Rakesh, Chatterjee, Sudhanya
Format Patent
LanguageEnglish
Published 19.09.2024
Subjects
CALCULATING
COMPUTING
COUNTING
DIAGNOSIS
HUMAN NECESSITIES
HYGIENE
IDENTIFICATION
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
MEDICAL OR VETERINARY SCIENCE
PHYSICS
SURGERY
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Summary:Various methods and systems are provided for reducing parametric heterogeneity in quantitative magnetic resonance (qMR) images, to increase robustness of in-field machine learning model inferences. In one example, a method for reducing qMR image heterogeneity includes, receiving a first qMR image, acquired using a first value of an acquisition parameter, determining a target value of the acquisition parameter based on a training dataset of a machine learning model, generating a synthetic qMR image, wherein the synthetic qMR image simulates a qMR image acquired using the target value of the acquisition parameter, by mapping the first qMR image to the synthetic qMR image using an analytical model, and feeding the synthetic qMR image to the machine learning model.
Bibliography:Application Number: US202318182998