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 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.
1. An apparatus to facilitate administering therapeutic radiation to a patient, the apparatus comprising:
a memory having stored therein:
a fluence map corresponding to the patient;
a plurality of deep learning models that are each trained to deduce a leaf sequence for a multi-leaf collimator from a fluence map;
a control circuit operably coupled to the memory and configured to:
select at least a particular one of the deep learning models;
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 at least a particular one of the deep learning models and the fluence map that corresponds to the patient.
2. The apparatus of claim 1 wherein at least one of the plurality of the deep learning models outputs relatively higher monitor unit solutions.
3. The apparatus of claim 1 wherein at least one of the plurality of the deep learning models outputs relatively lower monitor unit solutions.
4. The apparatus of claim 1 wherein a first one of the plurality of the deep learning models outputs relatively higher monitor unit solutions and a second one of the plurality of the deep learning models outputs relatively lower monitor unit solutions.
5. The apparatus of claim 1 further comprising:
a radiation treatment platform that includes the multi-leaf collimator and that is configured to provide the therapeutic radiation to the patient as a function of the radiation treatment plan.
6. An apparatus to facilitate administering therapeutic radiation to a patient, the apparatus comprising:
a memory having stored therein:
a fluence map corresponding to the patient;
at least one deep learning model trained to deduce a leaf sequence for a multi-leaf collimator from a fluence map, wherein the at least one deep learning model comprises a neural network model that was trained using a training corpus that includes fluence maps for each of a plurality of corresponding field/control points;
a control circuit operably coupled to the memory and 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 at least one deep learning model and the fluence map that corresponds to the patient.
7. The apparatus of claim 6 wherein the neural network model was trained, at least in part, via a supervised learning method.
8. The apparatus of claim 6 wherein the neural network model was trained, at least in part, via a reinforcement learning method.
9. A method to facilitate administering therapeutic radiation to a patient, the method comprising:
accessing a memory having stored therein:
a fluence map corresponding to the patient; and
a plurality of deep learning models that are each trained to deduce a leaf sequence a multi-leaf collimator from a fluence map; and
via a control circuit operably coupled to the memory:
selecting at least a particular one of the deep learning models
iteratively optimizing 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 at least a particular one of the deep learning models and the fluence map that corresponds to the patient.
10. The method of claim 9 wherein at least one of the plurality of the deep learning models outputs relatively higher monitor unit solutions.
11. The method of claim 9 wherein at least one of the plurality of the deep learning models outputs relatively lower monitor unit solutions.
12. The method of claim 9 wherein a first one of the plurality of the deep learning models outputs relatively higher monitor unit solutions and a second one of the plurality of the deep learning models outputs relatively lower monitor unit solutions.
13. The method of claim 9 further comprising:
providing the therapeutic radiation to the patient as a function of the radiation treatment plan.
14. A method to facilitate administering therapeutic radiation to a patient, the method comprising:
accessing a memory having stored therein:
a fluence map corresponding to the patient; and
at least one deep learning model trained to deduce a leaf sequence a multi-leaf collimator from a fluence map wherein the at least one deep learning model comprises a neural network model that was trained using a training corpus that includes fluence maps for each of a plurality of corresponding field/control points; and
via a control circuit operably coupled to the memory:
iteratively optimizing 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 at least one deep learning model and the fluence map that corresponds to the patient.
15. The method of claim 14 wherein the neural network model was trained, at least in part, via a supervised learning method.
16. The method of claim 14 wherein the neural network model was trained, at least in part, via a reinforcement learning method.