Two-step beam geometry optimization and beam entry angles without isocenter

An automated method for obtaining beam geometry (BG) parameters for treatment planning without explicit determination of an isocenter of a radiation beam delivery system delivering the treatment plan to a patient, the method comprising:

• determining a reference point within the target;
• obtaining a predicted beam entry angle relative to the reference point by executing a machine learning model trained to predict, based on patient geometry, the beam entry angle relative to the reference point;
• displaying the predicted beam entry angle superimposed on the patient geometry for evaluation by a user;
• allowing the user to accept the predicted beam entry angle or to interactively modify the predicted beam entry angle until an acceptable beam entry angle is obtained;
• and
• allowing the user to accept the reference point as the isocenter or manually enter an isocenter,
• wherein the accepted beam entry angle is chosen as the beam geometry (BG) parameter when the reference point is accepted as the isocenter, and the beam geometry (BG) parameter is calculated from the accepted beam entry angle when the manually entered isocenter is in close proximity to the reference point.

A system for developing a treatment plan for the delivery of a prescribed radiation dose to a treatment volume within a patient, comprising:

• a processor;
• and
• a memory coupled to the processor, the memory including instructions that when executed by the processor cause the processor to:
• receive information related to the prescribed radiation dose, the treatment volume, and a plurality of parameters associated with the radiation beam delivery system, the plurality of parameters including a beam geometry (BG) parameter;
• develop a treatment plan optimization model based on the received information, the treatment plan optimization model being configured to find an optimal dose distribution within the treatment volume;
• and
• generate an optimal treatment plan based on the treatment plan optimization model,
• wherein the generating of the optimal treatment plan includes:
• a first step configured to limit a search range for the beam geometry (BG) parameter;
• and
• a second step configured to optimize the treatment parameters,
• wherein the beam geometry (BG) parameter is optimized based on the limited search field obtained in the first step and without explicit determination of an isocenter of a system used for the radiation dose delivery.

A system for obtaining beam geometry (BG) parameters for treatment planning without explicit determination of an isocenter of a radiation beam delivery system delivering the treatment plan to a patient, the system comprising:

• a processor;
• and
• a memory coupled to the processor, the memory including instructions that when executed by the processor cause the processor to:
• determine a reference point within the target;
• obtain a predicted beam entry angle relative to the reference point by executing a machine learning model trained to predict, based on patient geometry, the beam entry angle relative to the reference point;
• display the predicted beam entry angle superimposed on the patient geometry for evaluation by a user;
• allow the user to accept the predicted beam entry angle or to interactively modify the predicted beam entry angle until an acceptable beam entry angle is obtained;
• and
• allow the user to accept the reference point as the isocenter or manually enter an isocenter,
• wherein the accepted beam entry angle is chosen as the beam geometry (BG) parameter when the reference point is accepted as the isocenter, and the beam geometry (BG) parameter is calculated from the accepted beam entry angle when the manually entered isocenter is in close proximity to the reference point,
• the processor being further configured to:
• shift the patient geometry relative to the determined isocenter;
• and
• further adjust the gantry angle based on the shift.