System and method for learning models of radiotherapy treatment plans to predict radiotherapy dose distributions

The present disclosure relates to systems and methods for developing radiotherapy treatment plans through the use of machine learning approaches and neural network components. A neural network is trained using one or more three-dimensional medical images, one or more three-dimensional anatomy maps,...

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Bibliographic Details
Main Author Hibbard, Lyndon Stanley
Format Patent
LanguageEnglish
Published 26.12.2023
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTROTHERAPY
HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA
HUMAN NECESSITIES
HYGIENE
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
MAGNETOTHERAPY
MEDICAL OR VETERINARY SCIENCE
PHYSICS
RADIATION THERAPY
ULTRASOUND THERAPY
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Summary:The present disclosure relates to systems and methods for developing radiotherapy treatment plans through the use of machine learning approaches and neural network components. A neural network is trained using one or more three-dimensional medical images, one or more three-dimensional anatomy maps, and one or more dose distributions to predict a fluence map or a dose map. During training the neural network receives a predicted dose distribution determined by the neural network that is compared to an expected dose distribution. Iteratively the comparison is performed until a predetermined threshold is achieved. The trained neural network is then utilized to provide a three-dimensional dose distribution.
Bibliography:Application Number: US201716330662