Method and apparatus to facilitate generating a leaf sequence for a multi-leaf collimator

A memory has a fluence map that corresponds to a particular patient stored therein. This memory also has at least one deep learning model stored therein trained to deduce a leaf sequence for a multi-leaf collimator from a fluence map. A control circuit operably coupled to that memory iteratively opt...

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Bibliographische Detailangaben
Hauptverfasser:	Kuusela, Esa, Peltola, Jarkko Y, Rusanen, Marko T, Basiri, Shahab
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING CONTROL OR REGULATING SYSTEMS IN GENERAL CONTROLLING COUNTING ELECTROTHERAPY FUNCTIONAL ELEMENTS OF SUCH SYSTEMS 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 MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS PHYSICS RADIATION THERAPY REGULATING ULTRASOUND THERAPY
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Beschreibung
Zusammenfassung:	A memory has a fluence map that corresponds to a particular patient stored therein. This memory also has at least one deep learning model stored therein trained to deduce a leaf sequence for a multi-leaf collimator from a fluence map. A control circuit operably coupled to that memory iteratively optimizes a radiation treatment plan to administer therapeutic radiation to that patient by, at least in part, generating a leaf sequence as a function of the at least one deep learning model and the fluence map that corresponds to the patient.