What is claimed is:
1. A method for gas assisted energy beam engraving of a target object, comprising:
a. placing at least a portion of the target object in a controlled gaseous environment;
b. energizing a source of an energy beam with at least six amperes of current, the energy beam further comprising a beam size sufficient to engrave a mark in the target object to predetermined dimensions;
c. irradiating a desired portion of the target object with the energy beam while the desired portion of the target object being irradiated is present in the controlled gaseous environment; and
d. causing relative movement between the energy beam and the target object during the irradiating, the movement occurring in a predetermined manner to create a desired engraved pattern in the target object.
2. The method of claim 1, wherein the gaseous environment comprises reducing gases, inert gases, oxidizing gases, or combinations thereof.
3. The method of claim 1, wherein the energy beam is a laser beam.
4. The method of claim 1, wherein the target object comprises ceramics, stainless steel, aluminum, and titanium.
5. A method for gas assisted laser engraving of a target object, comprising:
a. placing at least a portion of the target object into a controlled gaseous environment;
b. irradiating at least a portion of the target object in the controlled gaseous environment with a laser beam from a laser, the laser drawing a current level of at least six amperes, the laser beam further comprising sufficient power and beam dimensions to engrave a desired mark in the target object;
c. simultaneously with the irradiating, covering a surface of the target object which is being irradiated with a predetermined cover gas; and
d. simultaneously with the irradiating, causing relative movement between the laser beam and the target object, at a preselected speed to create a desired pattern.
6. The method of claim 5, wherein the cover gas in step (c) is ejected from a nozzle in communication with a source of cover gas and the controlled gaseous environment.
7. The method of claim 6, further comprising moving the nozzle such that the cover gas is directed at the surface of the target object being irradiated.
8. The method of claim 5, wherein the cover gas is heavier than the gas composition which makes up the earth’s atmosphere.
9. The method of claim 5, wherein step (d) further comprises moving a laser which emits the laser beam.
10. The method of claim 5, wherein step (d) further comprises moving the target object.
11. The method of claim 5, wherein the cover gas comprises gases selected from the group of gasses consisting of argon, nitrogen, oxygen, carbon dioxide, or combinations thereof.
12. The method of claim 5, wherein the target object comprises metallic objects and ceramic objects.
13. A system for engraving, comprising:
a. a controlled gaseous environment;
b. a target holder disposed within the controlled gaseous environment, the target holder having sufficient size to support a target object to be engraved in the controlled gaseous environment;
c. an energy beam device drawing at least six amperes, the energy beam device able to generate an energy beam of predetermined dimensions that can be made to irradiate a predetermined portion of the target object;
d. a source of cover gas; and
e. an outlet for the cover gas in communication with the controlled gaseous environment and the source of cover gas.
14. The system of claim 13 further comprising a controllable energy beam moving device coupled to the energy beam device, the energy beam moving device having a controllable incremental step movement of an approximately 60 micron step size.
15. The system of claim 13 further comprising a controllable target object moving device.
16. The system of claim 13 wherein the energy beam device is a laser.
17. The system of claim 13 wherein the outlet for the cover gas further comprises a nozzle in communication with the source of cover gas, the nozzle further capable of being directed at regions of the target object being irradiated.
18. The system of claim 13, wherein the cover gas comprises gases selected from the group consisting of argon, nitrogen, oxygen, carbon dioxide, and combinations thereof.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method comprising:
receiving by a data-processing system:
(a) first data based on a geometric characterization of one or more organs at risk proximate to a target volume of a patient P, wherein the geometric characterization associates each of a plurality of distances from the target volume with a respective percentage of the volume of the one or more organs at risk;
(b) second data comprising a size of the target volume and the respective sizes and shapes of the one or more organs at risk; and
generating, by said data-processing system using a predictive model, one or more radiation treatment planning parameters for said patient P based on the first data and the second data.
2. The method of claim 1, wherein said one or more radiation treatment planning parameters are represented by at least one of a dose distribution and a dose volume histogram.
3. The method of claim 1 further comprising receiving by a data processing system third data based on a geometric characterization of said target volume with respect to said one or more organs at risk.
4. The method of claim 1 wherein the first data and the second data are derived via a feature selection algorithm that is applied to a larger set of data that represent geometric characterizations of one or more organs at risk proximate to a target volume.
5. The method of claim 4 wherein said feature selection algorithm comprises a principal component analysis, and wherein said principal component analysis is based on an N-by-N covariance matrix, and wherein N is a positive integer greater than one.
6. The method of claim 5 wherein the first data and the second data are based on the M largest eigenvalues of said covariance matrix, and wherein M is a positive integer between 1 and N inclusive.
7. The method of claim 1 and further wherein said predictive model has been trained on a set of input-output pairs, and wherein the output of each input-output pair is based on a dose distribution and a dose volume histogram for a respective patient, and wherein the input of each input-output pair comprises one or more data that are based on the geometric characterization for said respective patient.
8. A method comprising training by a data-processing system a predictive model on a plurality of input-output mappings, wherein the output of each input-output mapping is based on a dose distribution and a dose volume histogram for a respective patient, and wherein the input of each input-output mapping comprises one or more data that are based on a geometric characterization of one or more organs at risk proximate to a target volume of the respective patient, wherein said geometric characterization associates each of a plurality of distances from said target volume with a respective percentage of the volume of said one or more organs at risk.
9. The method of claim 8 further comprising generating a computer-executable program based on the trained predictive model.
10. The method of claim 8 wherein said one or more data also comprises the size of one or more partial volumes.
11. The method of claim 8 wherein said geometric characterization associates a three-dimensional point in said target and one or more organs at risk with a set of values that represent the unique relationship between the point and the target and the organs at risk.
12. The method of claim 8 wherein the output of each input-output mapping is derived via a feature selection algorithm that is applied to dose volume histograms for a plurality of patients.
13. The method of claim 12 wherein said feature selection algorithm comprises a principal component analysis, and wherein said principal component analysis is based on an Z by Z covariance matrix, and wherein Z is the plurality of points from a dose volume histogram for a respective patient, and wherein Z is a positive integer greater than one.
14. The method of claim 13 wherein the output of an input-output mapping comprises the Q largest eigenvalues of said covariance matrix, and wherein Q is a positive integer between 1 and Z inclusive.
15. A method comprising storing in a database, by a data-processing system:
(a) one or more planning parameters of a first radiation treatment plan, wherein said first radiation treatment plan is for a first patient and is generated by one of:
(i) an expert human planner, and
(ii) a data-processing system using one or both of multi-objective optimization and pareto front search;
(b) a first geometric characterization of a first non-empty set of organs at risk proximate to a first target volume of said first patient, wherein the first geometric characterization is a function of volume for the organs at risk;
(c) one or more planning parameters of a second radiation treatment plan, wherein said second radiation treatment plan is for a second patient and is generated by one of:
(i) an expert human planner, and
(ii) a data-processing system using one or both of multi-objective optimization and pareto front search; and
(d) a second geometric characterization of a second non-empty set of organs at risk proximate to a second target volume of said second patient, wherein the second geometric characterization is a function of volume for the organs at risk.
16. The method of claim 15 further comprising:
generating by said data-processing system:
(i) a first input-output mapping based on said first geometric characterization and on said one or more planning parameters of said first radiation treatment plan, and
(ii) a second input-output mapping based on said second geometric characterization and on said one or more planning parameters of said second radiation treatment plan; and
training by said data-processing system a predictive model on said first input-output mapping and said second input-output mapping.
17. A method comprising:
receiving by a data-processing system one or more data, wherein at least one of the data is based on a geometric characterization of one or more organs at risk proximate to a target volume of a patient P, and wherein the geometric characterization associates each of a plurality of distances from said target volume with a respective percentage of the volume of said one or more organs at risk; and
generating by the data-processing system one or more radiation treatment planning parameters for the patient P based on the one or more data.
18. A method comprising:
receiving first data and second data at a data-processing system, wherein:
(a) the first data is based on a geometric characterization of one or more organs at risk proximate to a target volume of a patient P; and
(b) the second data is based on the size of the target volume and the respective sizes and shapes of the one or more organs at risk; and
generating by the data-processing system one or more radiation treatment planning parameters for the patient P based on the first data and the second data.