Knowledge-based spatial dose metrics and methods to generate beam orientations in radiotherapy
A system for estimating a dose from a radiation therapy plan includes a memory that stores machine-readable instructions and a processor communicatively coupled to the memory, the processor operable to execute the instructions to subdivide a representation of a volume of interest into voxels. The processor also determines distances between a planned radiation field origin and each respective voxel. The processor further computes geometry-based expected (GED) metrics based on the distances, a plan parameter, and a field strength parameter. The processor sums the metrics to yield an estimated dose received by the volume of interest from the planned radiation field.
1. A method for estimating a dose from a radiation therapy plan, the method comprising:
generating a plurality of normal vectors that emanate from a plurality of points on a surface of a representation of a target volume and extend to a body surface;
generating a dose fall-off curve along each of the normal vectors based on the radiation therapy plan, to produce a plurality of dose fall-off curves;
selecting a subset of the plurality of normal vectors that traverse an organ at risk;
determining a mean dose fall-off curve for the organ at risk based on the subset;
determining a dose-volume histogram for the organ at risk based on the mean dose fall-off curve and a mean distance between the target volume and the organ at risk; and
determining preferred beam orientations based on the dose fall-off curve along each of the normal vectors.
2. The method of claim 1 , wherein the generating the dose fall-off curve along each of the normal vectors comprises accessing stored patient results associated with historical therapy plans.
3. The method of claim 1 , further comprising:
determining a dose gradient corresponding to each of the normal vectors based on the dose fall-off curve along each of the normal vectors;
projecting the dose gradient corresponding to each of the normal vectors along a plane that is orthogonal to the normal vector; and
generating a preference matrix on the surface of a sphere based on the projected dose gradients, the preference matrix including a thermal surface map based on intersections of the projected dose gradients;
wherein the determining the preferred beam orientations is based on the preference matrix.
4. The method of claim 1 , further comprising:
determining a dose gradient corresponding to each of the normal vectors based on the dose fall-off curve along each of the normal vectors;
defining a boundary around the target volume based on the dose gradients; and
determining a sensitivity of a beam orientation with respect to a target position error.
5. The method of claim 4 , wherein the boundary is defined based on a distance margin at each location of the normal vectors, wherein the distance margin is indicated by a ratio of a maximum acceptable dose uncertainty to the dose gradient along a respective normal vector of the normal vectors.
6. The method of claim 1 , further comprising determining a three-dimensional dose for the organ at risk using the mean dose fall-off curve.
7. The method of claim 1 , further comprising:
determining first derivatives of the dose fall-off curves;
determining a second derivative of the mean dose fall-off curve; and
identifying a dose gradient that achieves the radiation therapy plan using the first derivatives and the second derivative.
8. A system for estimating a dose from a radiation therapy plan, the system comprising:
a memory that stores machine-readable instructions; and
a processor communicatively coupled to the memory, wherein the processor is operable to execute the machine-readable instructions to
generate a plurality of normal vectors that emanate from a plurality of points on a surface of a representation of a target volume and extend to a body surface,
quantify a dose fall-off curve along each of the normal vectors based on the radiation therapy plan, to produce a plurality of dose fall-off curves,
group a subset of the plurality of normal vectors that traverse an organ at risk,
determine a mean dose fall-off curve for the organ at risk based on the subset,
derive a dose-volume histogram for the organ at risk based on the mean dose fall-off curve and a mean distance between the target volume and the organ at risk, and
determine preferred beam orientations based on the dose fall-off curve along each of the normal vectors.
9. The system of claim 8 , wherein the processor is further operable to execute the machine-readable instructions to access stored patient results associated with historical therapy plans.
10. The system of claim 8 , wherein the processor is further operable to execute the machine-readable instructions to:
compute a dose gradient corresponding to each of the normal vectors based on the dose fall-off curve along each of the normal vectors;
project the dose gradient corresponding to each of the normal vectors along a plane that is orthogonal to the normal vector; and
generate a preference matrix on the surface of a sphere based on the projected dose gradients, the preference matrix including a thermal surface map based on intersections of the projected dose gradients;
wherein the preferred beam orientations are determined based on the preference matrix.
11. The system of claim 8 , wherein the processor is further operable to execute the machine-readable instructions to:
compute a dose gradient corresponding to each of the normal vectors based on the dose fall-off curve along each of the normal vectors;
construct a boundary around the target volume based on the dose gradients; and
evaluate a sensitivity of a beam orientation with respect to a target position error.
12. The system of claim 11 , wherein the processor is further operable to execute the machine-readable instructions to:
construct the boundary based on a distance margin at each location of the normal vectors, and
wherein the distance margin is indicated by a ratio of a maximum acceptable dose uncertainty to the dose gradient along a respective normal vector of the normal vectors.
13. The system of claim 8 , wherein the processor is further operable to execute the machine-readable instructions to determine a three-dimensional dose for the organ at risk using the mean dose fall-off curve.
14. The system of claim 8 , wherein the processor is further operable to execute the machine-readable instructions to:
compute first derivatives of the dose fall-off curves;
compute a second derivative of the mean dose fall-off curve; and
use the first derivatives and the second derivative to identify a dose gradient needed to achieve the radiation therapy plan.
15. A non-transitory computer-readable medium having computer-readable program code embodied thereon that, when executed by a processor, causes the processor to perform a method comprising:
generating a plurality of normal vectors that emanate from a plurality of points on a surface of a representation of a target volume and extend to a body surface;
generating a dose fall-off curve along each of the normal vectors based on a radiation therapy plan, to produce a plurality of dose fall-off curves;
selecting a subset of the plurality of normal vectors that traverse an organ at risk;
determining a mean dose fall-off curve for the organ at risk based on the subset;
determining a dose-volume histogram for the organ at risk based on the mean dose fall-off curve and a mean distance between the target volume and the organ at risk; and
determining preferred beam orientations based on the dose fall-off curve along each of the normal vectors.
16. The non-transitory computer-readable medium of claim 15 , wherein the generating the dose fall-off curve along each of the normal vectors comprises accessing stored patient results associated with historical therapy plans.
17. The non-transitory computer-readable medium of claim 15 , wherein the method further comprises:
determining a dose gradient corresponding to each of the normal vectors based on the dose fall-off curve along each of the normal vectors;
projecting the dose gradient corresponding to each of the normal vectors along a plane that is orthogonal to the normal vector; and
generating a preference matrix on the surface of a sphere based on the projected dose gradients, the preference matrix including a thermal surface map based on intersections of the projected dose gradients;
wherein the determining the preferred beam orientations is based on the preference matrix.
18. The non-transitory computer-readable medium of claim 15 , wherein the method further comprises:
determining a dose gradient corresponding to each of the normal vectors based on the dose fall-off curve along each of the normal vectors;
defining a boundary around the target volume based on the dose gradients; and
determining a sensitivity of a beam orientation with respect to a target position error;
wherein the boundary is defined based on a distance margin at each location of the normal vectors, wherein the distance margin is indicated by a ratio of a maximum acceptable dose uncertainty to the dose gradient along a respective normal vector of the normal vectors.
19. The non-transitory computer-readable medium of claim 15 , wherein the method further comprises determining a three-dimensional dose for the organ at risk using the mean dose fall-off curve.
20. The non-transitory computer-readable medium of claim 15 , wherein the method further comprises:
determining first derivatives of the dose fall-off curves;
determining a second derivative of the mean dose fall-off curve; and
identifying a dose gradient that achieves the radiation therapy plan using the first derivatives and the second derivative.