ADMINISTRATION OF THERAPEUTIC RADIATION USING DEEP LEARNING MODELS TO GENERATE LEAF SEQUENCES

A memory has stored therein a fluence map that corresponds to a particular patient and a deep learning model. The deep learning model is trained to deduce a leaf sequence for a multi-leaf collimator from a fluence map. The deep learning model comprises a neural network model that was trained, at lea...

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Bibliographische Detailangaben
Hauptverfasser:	Kuusela, Esa, Basiri, Shahab
Format:	Patent
Sprache:	eng
Schlagworte:	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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Beschreibung
Zusammenfassung:	A memory has stored therein a fluence map that corresponds to a particular patient and a deep learning model. The deep learning model is trained to deduce a leaf sequence for a multi-leaf collimator from a fluence map. The deep learning model comprises a neural network model that was trained, at least in part, via a reinforcement learning method. A control circuit accesses the memory and is configured to iteratively optimize a radiation treatment plan to administer the therapeutic radiation to the patient by, at least in part, generating a leaf sequence as a function of the deep learning model and the fluence map by employing a plurality of agents to each separately use the deep learning model to each generate a leaf sequence for only a single leaf pair of the multi-leaf collimator.