Radiation processing plan optimization method and device

Abstract

The embodiment of the invention relates to a radiation processing plan optimization method and device. During the radiation treatment plan optimization cycle, the control circuitry may perform a dosimetric optimization iteration to produce a dosimetric-based plan result and then perform a non-dosimetric optimization iteration to produce a non-dosimetric-based plan result. The control circuit may then evaluate the dosimetry-based plan result and the non-dosimetry-based plan result to produce a convergence evaluation result. Convergence evaluation results may then be considered when determining whether to end a continuous radiation treatment plan optimization cycle.

Description

Technical Field

[0001] These teachings relate generally to treating a planned target volume of a patient with energy according to an energy-based treatment plan, and more particularly to optimizing an energy-based treatment plan. Background Art

[0002] The use of energy to treat medical conditions comprises a known area of ​​prior art effort. For example, radiation therapy comprises an important component of many treatment plans for reducing or eliminating unwanted tumors. Unfortunately, the energy applied cannot inherently distinguish between unwanted material and adjacent tissues, organs, etc., which are necessary or even critical for the continued survival of the patient. As a result, energy such as radiation is often applied in a carefully managed manner to at least attempt to confine the energy to a given target volume. So-called radiation treatment plans generally function in the aforementioned manner.

[0003] A radiation treatment plan typically includes specified values ​​for each of the various treatment platform parameters during each of a plurality of consecutive fields. Treatment plans for a radiation treatment course are typically generated partially or fully automatically through a so-called optimization process. As used herein, "optimization" will be understood to refer to improving a candidate treatment plan without necessarily ensuring that the result of the optimization is in fact a single optimal solution. Such optimization typically includes automatically incrementally adjusting one or more physical treatment parameters (typically while observing one or more corresponding limits of these aspects) and mathematically calculating the possible corresponding treatment results (such as dose levels) to identify a given set of treatment parameters that represents a good compromise between the desired therapeutic result and avoiding undesirable side effects.

[0004] Direct aperture optimization is an example of a radiation treatment plan optimization method that typically aims to achieve rapid treatment delivery by directly modeling the corresponding multi-leaf collimator segment shapes and weights. Unfortunately, direct aperture optimization can present a challenging optimization problem, at least in part, because such an approach can include combining the computationally demanding task of dose assessment with the essentially combinatorial optimization of leaf sequences (or other non-trivial coupled degree-of-freedom considerations corresponding to the behavior of the treatment platform during radiation treatment administration). Summary of the invention

[0005] Embodiments of the present disclosure provide a method and apparatus for facilitating optimization of a radiation treatment plan for a specific patient, so as to solve one or more problems existing in the prior art.

[0006] An embodiment of the present disclosure provides a method for facilitating optimization of a radiation treatment plan for a specific patient, the method comprising: during a radiation treatment plan optimization cycle, a control circuit: performs dosimetric optimization iterations to generate dosimetric-based plan results; performs non-dosimetric optimization iterations to generate non-dosimetric-based plan results; evaluates the dosimetric-based plan results and the non-dosimetric-based plan results to generate convergence evaluation results; and determines whether to end a continuous radiation treatment plan optimization cycle based at least in part on the convergence evaluation results.

[0007] Another embodiment of the present disclosure provides an apparatus for facilitating optimization of a radiation treatment plan for a specific patient, the apparatus comprising a control circuit, wherein the control circuit is configured to: perform dosimetric optimization iterations to generate dosimetric-based plan results; perform non-dosimetric optimization iterations to generate non-dosimetric-based plan results; evaluate dosimetric-based plan results and non-dosimetric-based plan results to generate convergence evaluation results during a radiation treatment plan optimization cycle; and determine whether to terminate a continuous radiation treatment plan optimization cycle based at least in part on the convergence evaluation results.

[0008] According to an embodiment of the present disclosure, when it is determined to end a continuous radiation treatment plan optimization cycle, an optimized radiation treatment plan is adaptively output. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The above needs are at least partially met by providing a radiation treatment plan optimization method and apparatus as described in the following detailed description, particularly when studied in conjunction with the accompanying drawings, in which:

[0010] Figure 1 Included are block diagrams of various embodiment configurations according to these teachings;

[0011] Figure 2 including flow charts configured according to various embodiments of these teachings;

[0012] Figure 3 including flow charts configured according to various embodiments of these teachings;

[0013] Figure 4 including flow charts configured according to various embodiments of these teachings;

[0014] Figure 5 including flow charts configured according to various embodiments of these teachings;

[0015] Figure 6 including flowcharts configured according to various embodiments of these teachings; and

[0016] Figure 7 Pseudo code is presented for various embodiment configurations according to these teachings.

[0017] The elements in the figure are illustrated for simplicity and clarity, and are not necessarily drawn to scale. For example, the size and / or relative position of some elements in the figure can be exaggerated relative to other elements to help improve the understanding of various embodiments of the present invention. In addition, common but well-known elements that are useful or necessary in commercially feasible embodiments are usually not depicted, so as to facilitate a clearer understanding of these different embodiments of this teaching. Certain actions and / or steps can be described or depicted in a specific order of occurrence, and those skilled in the art will understand that this type of specificity about the order is not actually required. The terms and expressions used herein have the common technical meanings consistent with the terms and expressions given by the technicians in the above-mentioned technical field, unless different specific meanings are given in addition herein. Unless otherwise specified, the word "or (or)" used herein should be interpreted as having a disjunctive structure rather than a conjunction structure. DETAILED DESCRIPTION

[0018] In general, according to these various embodiments, during a radiation treatment plan optimization cycle, the control circuit performs a dosimetric optimization iteration to produce a dosimetric-based plan result, and then performs a non-dosimetric optimization iteration to produce a non-dosimetric-based plan result. The control circuit then evaluates the dosimetric-based plan result and the non-dosimetric-based plan result to produce a convergence evaluation result. The convergence evaluation result can then be considered when determining whether to end the continuous radiation treatment plan optimization cycle.

[0019] By one approach, the dosimetric optimization iterations include iterative operations for exploring fluence settings. By another approach, in lieu of or in combination with the aforementioned approach, the non-dosimetric optimization iterations include iterative operations for exploring at least one control point setting.

[0020] By one approach, during one or more subsequent radiation treatment plan optimization cycles, these teachings provide for performing subsequent dosimetry optimization iterations to produce subsequent dosimetry-based plan results, but not performing non-dosimetry optimization iterations to produce subsequent non-dosimetry-based plan results. The control circuitry may then evaluate the subsequent dosimetry-based plan results relative to the non-dosimetry-based plan results (as previously determined) to produce subsequent convergence assessment results. The control circuitry may then determine whether to terminate the consecutive radiation treatment plan optimization cycles based at least in part on the subsequent convergence assessment results.

[0021] By one approach, evaluating dosimetry-based planning results and non-dosimetry-based planning results to produce a convergence assessment result includes, at least in part, applying an optimization penalty based on the convergence assessment result, the convergence assessment result representing a deviation between the dosimetry-based planning results and the non-dosimetry-based planning results.

[0022] By one approach, performing a dosimetry optimization iteration to produce a dosimetry-based planning result can include, at least in part, performing the dosimetry optimization iteration based, at least in part, on a previous interim result determined during a non-dosimetry optimization iteration.

[0023] In practice, these teachings will support a highly flexible and practical application setting. By one approach, for example, upon determining the end of a continuous radiation treatment plan optimization cycle, these teachings will adapt and then output an optimized radiation treatment plan. Therapeutic radiation can then be administered to a particular patient based at least in part on the optimized radiation treatment plan.

[0024] These and other benefits may become more apparent upon a thorough review and study of the following detailed description. Figure 1 , an illustrative device 100 compatible with many of these teachings will first be presented.

[0025] In this particular example, enabling device 100 includes control circuit 101. As a "circuit," control circuit 101 thus includes a structure that includes at least one (and typically many) electrically conductive paths (such as paths composed of a conductive metal such as copper or silver) that transmit power in an orderly manner, and the path(s) typically also include corresponding electrical components (both passive (such as resistors and capacitors) and active (such as any of a variety of semiconductor-based devices), as appropriate) to permit the circuit to implement the control aspects of these teachings.

[0026] Such control circuitry 101 may include a fixed-purpose hardwired hardware platform (including, but not limited to, an application-specific integrated circuit (ASIC) (which is an integrated circuit customized by design for a specific purpose rather than for general purpose), a field programmable gate array (FPGA), etc.), or may include a partially or fully programmable hardware platform (including, but not limited to, a microcontroller, a microprocessor, etc.). These architectural choices for such structures are well known and understood in the art and do not require further description here. The control circuitry 101 is configured to perform one or more of the steps, actions, and / or functions described herein (e.g., by using corresponding programming that will be well understood by those skilled in the art).

[0027] The control circuit 101 is operably coupled to a memory 102. The memory 102 may be integrated into the control circuit 101, or may be physically separate (in whole or in part) from the control circuit 101 as desired. The memory 102 may also be local to the control circuit 101 (where, for example, the two share a common circuit board, chassis, power supply, and / or housing), or may be partially or completely remote to the control circuit 101 (where, for example, the memory 102 is physically located in another facility, metropolitan area, or even country than the control circuit 101).

[0028] In addition to information such as optimization information for a particular patient and information about a particular radiation treatment platform as described herein, the memory 102 may also be used, for example, to non-transitory store computer instructions that, when executed by the control circuit 101, cause the control circuit 101 to operate as described herein. (As used herein, references to "non-transitory" will be understood to refer to the non-transitory state of the stored content (thus excluding the case where the stored content merely constitutes a signal or wave), rather than the volatility of the storage medium itself, and thus include both non-volatile memory (such as read-only memory (ROM)) and volatile memory (such as dynamic random access memory (DRAM)).

[0029] By an optional approach, the control circuit 101 is also operatively coupled to a user interface 103. The user interface 103 may include any of a variety of user input mechanisms (such as but not limited to a keyboard and keypad, a cursor control device, a touch-sensitive display, a voice recognition interface, a gesture recognition interface, etc.) and / or user output mechanisms (such as but not limited to a visual display, an audio converter, a printer, etc.) to receive information and / or instructions from a user and / or provide information to a user.

[0030] If desired, the control circuit 101 may also be operatively coupled to a network interface (not shown). So configured, the control circuit 101 may communicate with other components (internal and external to the device 100) via the network interface. Network interfaces, including both wireless and non-wireless platforms, are well known in the art and do not require specific elaboration herein.

[0031] By one approach, a computed tomography device 106 and / or other imaging device 107 known in the art may acquire some or all of any desired patient-related imaging information.

[0032] In this illustrative example, the control circuit 101 is configured to ultimately output an optimized energy-based treatment plan (e.g., such as the optimized radiation treatment plan 113). Such an energy-based treatment plan typically includes specified values ​​for each of various processing platform parameters during each of a plurality of consecutive exposure fields. In this case, the energy-based treatment plan is generated by an optimization process, examples of which are provided elsewhere herein.

[0033] By one approach, the control circuit 101 is operably coupled to an energy-based treatment platform 114 configured to deliver energy to a volume having at least one treatment volume 105 and one or more organs-at-risk (OAR) according to an optimized energy-based treatment plan 113. Figure 1 The invention provides a method for delivering therapeutic energy 112 to a corresponding patient 104 (represented by a first organ at risk 108 to an Nth organ at risk 109). These teachings are generally applicable to any of a variety of energy-based treatment platforms / devices. In a typical application setting, the energy-based treatment platform 114 will include an energy source, such as a radiation source 115 of ionizing radiation 116.

[0034] By one approach, the radiation source 115 can be selectively moved along an arcuate path via a gantry (wherein the path at least to some extent surrounds the patient during treatment administration). The arcuate path can include a complete or nearly complete circle as desired. By one approach, the control circuit 101 controls the movement of the radiation source 115 along the arcuate path and can accordingly control when the radiation source 115 starts moving, stops moving, speeds up, slows down, and / or the speed at which the radiation source 115 travels along the arcuate path.

[0035] As an illustrative example, radiation source 115 may include, for example, an x-ray source based on a radio frequency (RF) linear particle accelerator (linac) based. A linac is a particle accelerator that greatly increases the kinetic energy of charged subatomic particles or ions by subjecting the charged particles to a series of oscillating electric potentials along a linear beamline, which can be used to generate ionizing radiation (e.g., x-rays) 116 and high energy electrons.

[0036] A typical energy-based treatment platform 114 may also include: one or more support devices 110 (such as a treatment couch) for supporting the patient 104 during a treatment session; one or more patient fixation devices 111; a gantry or other movable mechanism for allowing selective movement of the radiation source 115; and one or more energy shaping devices (e.g., beam shaping devices 117 such as jaws, multi-leaf collimators, etc.) to provide selective energy shaping and / or energy modulation as needed.

[0037] In a typical application setting, it is assumed herein that during an energy-based treatment session, the patient support device 110 can be selectively controlled to move in any direction (i.e., any X, Y, or Z direction) by the control circuit 101. Because the aforementioned elements and systems are well known in the art, a detailed description of these aspects will not be provided herein except where relevant to describe other aspects.

[0038] Reference now Figure 2 , a process 200 that may be implemented in conjunction with the above-described application arrangement (and more particularly via the above-described control circuit 101) will be described. In general, the process 200 may be used to facilitate generating an optimized radiation treatment plan 113 to facilitate treating a specific patient with therapeutic radiation using a specific radiation treatment platform 114 in accordance with the optimized radiation treatment plan 113.

[0039] The process 200 will be understood to occur during a single radiation treatment plan optimization cycle, represented by reference numeral 201 .

[0040] At block 202, the control circuit performs a dosimetry optimization iteration to produce a dosimetry-based planning result. These teachings will accommodate any of a variety of methods of these aspects. By one approach, the dosimetry optimization iteration includes an iterative operation for exploring fluence settings. Of course, fluence represents the radiation fluence integrated over time and includes the basic metric in dosimetry (i.e., the measurement and calculation of absorbed dose of ionizing radiation in matter and tissue). By one approach, if desired, the dosimetry optimization iteration may at least in part include performing a dosimetry optimization iteration based at least in part on an intermediate result determined during a previous non-dosimetry optimization iteration. (By "previous," the foregoing may refer to a previous non-dosimetry optimization iteration performed during the current optimization cycle or during a previous optimization cycle for the current optimization exercise.)

[0041] At block 203, the control circuit performs a non-dosimetric optimization iteration to produce a non-dosimetric based planning result. These teachings will accommodate any of a variety of methods of these aspects. By one approach, the non-dosimetric optimization iteration includes an iterative operation for exploring at least one control point setting. A control point refers to a specific location or angle at which a radiation beam is turned on or off during treatment. Control points are typically used to help shape the radiation beam and optimize the dose distribution to target the tumor while sparing surrounding healthy tissue.

[0042] At block 204, the control circuit 101 then evaluates the dosimetry-based plan results and the non-dosimetry-based plan results to generate a convergence assessment result. By one approach, the evaluation may include, at least in part, applying an optimization penalty based on the convergence assessment result, the convergence assessment result representing a deviation between the dosimetry-based plan results and the non-dosimetry-based plan results. For example, the greater the deviation, the greater the corresponding optimization penalty.

[0043] At decision block 205, the control circuit 101 then determines whether to end the continuous radiation treatment plan optimization cycle based at least in part on the above-mentioned convergence evaluation result 206. When the control circuit 101 determines to continue an additional optimization cycle at reference numeral 207, these teachings will adapt to various methods. As an example, the control circuit 101 can repeat the above-mentioned steps 202-205 using at least one iteration variable. As another example, the control circuit 101 can use the following reference numeral 207 to determine whether to end the continuous radiation treatment plan optimization cycle. Figure 3 Process 300 is additionally described.

[0044] When it is determined that the continuous radiation treatment plan optimization cycle is terminated, the control circuit 101 may output a corresponding optimized radiation treatment plan 113 at an optional block 208. At an optional block 209, therapeutic radiation may then be administered to the particular patient 104 using the above-described treatment platform 114 based at least in part on the optimized radiation treatment plan 113.

[0045] Reference now Figure 3 , the above process 300 will be described. As indicated by reference numeral 301, the process 300 occurs in a subsequent radiation treatment plan optimization cycle (i.e., in the process Figure 2 During a radiation treatment plan optimization cycle following the cycle in the above process 200).

[0046] At block 302 , the control circuit 101 performs subsequent dosimetry optimization iterations to generate subsequent dosimetry-based planning results.

[0047] Block 303 indicates that, unlike the above process 200, the control circuit 101 does not perform subsequent non-dosimetry optimization iterations for generating subsequent non-dosimetry-based planning results. Instead, the control circuit 101 will reuse previously generated non-dosimetry-based planning results as described below.

[0048] At block 304, the control circuit 101 then evaluates subsequent dosimetry-based planning results as well as non-dosimetry-based planning results (i.e., previous non-dosimetry-based planning results, which may be from the immediately previous iteration cycle, the previous cycle, or even potentially further back in the process) to produce a subsequent convergence assessment result.

[0049] At decision blocks 305 and 306, the control circuit 101 then determines whether to end the continuous radiation treatment plan optimization cycle based at least in part on the subsequent convergence evaluation result 307 described above. As previously described, these teachings will accommodate various methods when the control circuit 101 determines to continue with an additional optimization cycle at reference numeral 308. As an example, the control circuit 101 may repeat the aforementioned steps 202-205 or steps 302-306 using at least one iteration variable.

[0050] When it is determined that the continuous radiation treatment plan optimization cycle has ended, again as previously described, at optional block 208, the control circuit 101 may output a corresponding optimized radiation treatment plan 113, and at optional block 209, therapeutic radiation may then be administered to the specific patient 104 using the above-mentioned treatment platform 114 at least in part based on the optimized radiation treatment plan 113.

[0051] These teachings are both practical and flexible in practice, and will accommodate a variety of supplementary and / or alternative features. Additional details in this regard will now be provided, with the understanding that the scope of these teachings should not be considered to be limited or restricted by these details.

[0052] Applicants have observed that direct aperture optimization is a challenging optimization problem, at least in part because of the computationally demanding task of dose assessment combined with the essentially combinatorial optimization of leaf sequences (or other non-trivial coupled degree-of-freedom features that determine the behavior of the treatment platform during radiation treatment administration). Applicants have also observed that, while gradient-based optimization methods are powerful when applied to fluence-based optimization, computing the gradients back into control point space can be difficult and often impractical.

[0053] The direct aperture optimization type of radiation therapy plan creation is an optimization problem whose task is to solve

[0054] s opt =argmin s ∈ S′ c(s) (1)

[0055] where s opt is the optimal sequence of control points that minimizes the cost function c(s) and belongs to the set of feasible solutions S′. In practice, the cost function can be expressed in the following form

[0056] c(s)=c tot (c f (D(F(s))),c s (s)) (2)

[0057] The majority of the computational effort in cost function evaluation is related to the control point sequence only through the nested functions F and D (which represent the fluence calculation and the fluence-based dose calculation).

[0058] For the purpose of this description, c f represents the dosimetric cost (i.e., only the part of the dose calculation that depends on the control point sequence), and c s represents non-dosimetric cost. c tot represents the function that combines these two cost terms, and may comprise, for example, a weighted or unweighted sum.

[0059] Applicants believe that finding an optimal solution to Equation 2 above may be challenging because, at least in part, different parts of the chain of nested functions are very different in their computational and mathematical properties. Applicants observe that while the dose function D(f) tends to be a linear, smooth, differentiable function of the continuous fluence pixel vector values, the fluence calculation function F(s) is a non-convex function of the sequence of control points. These differences make many prior art optimization strategies impractical. For example, gradient-based methods can be efficient for finding good solutions in fluence space, but it can be difficult to guarantee that there is a feasible leaf sequence that can provide such a fluence map. Correspondingly, standard gradient-free combinatorial optimization schemes may not be able to efficiently evaluate dosimetric costs.

[0060] The present teaching will provide a method whereby sequences of control points can be individually optimized to an optimal fluence map, but the deviations between these solutions are penalized:

[0061] (f opt ,s opt ) = argmin f；s∈S′ c f (D(f))+c s (s)+w p ‖fF(s)‖. (3) When w p When the value of s in the above formula increases, opt Can be expressed as

[0062] s opt ≈argmin s∈S′ (c f (D(F(s)))+c S (s)), (4)

[0063] This means that the solution found in the mutual optimization is close to the solution of the original equation 1 above. The terms of equations 3 and 4 are as follows: f represents the fluence map and s represents the control point sequence (or leaf sequence), w p is a scalar weight factor that determines the strength of the penalty, ‖·‖ is some suitable norm (such as L2) measuring the difference between the two fluences, and c f and cs Represents dosimetric cost terms and non-dosimetric cost terms (the former depend on control points via dose calculation, and the latter do not require dose assessment).

[0064] These teachings will accommodate changing optimization logic by not requiring that every intermediate state of the optimization process is actually represented by a feasible leaf sequence. One can find the optimal solution in the fluence space and then try to find a leaf sequence that will actually produce the same fluence. As the penalty strength w_p gradually increases, the current candidate fluence will get closer and closer to the actual fluence of the candidate leaf sequence.

[0065] Figure 4 Specific examples of these teachings according to the foregoing description are presented. Specifically, Figure 4 An optimization loop 400 is depicted.

[0066] The loop 400 begins by performing a fluence update at block 401 . Figure 5 A method of this aspect is presented. In this illustrative example, the fluence update 401 begins at block 501, where a dose calculation is performed, followed by a dosimetric cost assessment at block 502. Then, at block 503, the update determines a penalty cost contribution (which may be borne at least in part based on a previously determined fluence calculation, as described below). At block 504, the gradient fluence is calculated, and at block 505, the penalty term contribution is added (wherein this calculation may also take into account the above-mentioned fluence calculation). The fluence update can then end by determining any changes to the candidate solution at block 506.

[0067] Back to Figure 4 At block 402, a leaf sequence update is performed. Figure 6 , the update may begin at block 601, where a non-dosimetric cost assessment is performed, followed by a fluence calculation at block 602. At block 603, a penalty cost contribution is incurred (and may be informed by the fluence information from the fluence update 401), and then, at block 604, one or more new candidate solutions are determined. The illustrative update process ends at block 605, where a particular candidate solution is selected.

[0068] Back to Figure 4 , at block 403, the penalty strength is cyclically updated, and then at block 404 a decision is made as to whether the solution has sufficiently converged. When it is not yet established, the foregoing process can be repeated. When it is established, the process can be terminated.

[0069] Figure 7 Pseudo-code 700 is presented for implementing a method consistent with the foregoing teachings.

[0070] So configured, it will be appreciated that, at least to a large extent, the optimization in the fluence space and the optimization in the control point space are decoupled. This helps to support greater flexibility in organizing the main optimization method. For example, although the optimization is done in the fluence space, the solution space search can utilize efficient gradient descent methods without having to return to a solution that also finds a feasible leaf sequence at each iteration. This enables an assessment of the dosimetric quality that can be approximately achieved (e.g., if a set of clinical objectives are used to define the plan quality) without the need to find a leaf sequence that can actually generate a candidate fluence for each intermediate state.

[0071] In typical prior art methods, the search space in fluence optimization can potentially include solutions that violate the laws of physics (e.g., by including negative fluence values), or solutions that are physically impossible (e.g., by including very large positive values). Therefore, finding a feasible leaf sequence in each iteration is already important to ensure that the optimizer remains in a feasible fluence space. It should be understood that the present teachings will be implemented by f Embedding such constraints in tf.sup.i to accommodate a constrained search in the effective fluence space helps avoid unnecessary determination of feasible leaf sequences in each iteration.

[0072] It should be understood that the present teachings do not require that control points be defined from the start of the optimization. Instead, the fluence optimization may be performed based on multiple computational directions only. The optimal number of control points may then be defined based on the complexity of the leaf mutations required to achieve the optimal fluence map. Such resource allocation may be part of the leaf sorting optimization.

[0073] It should also be noted that these teachings will support iterations of leaf sequence optimization and iterations of fluence optimization being performed at different intervals. For example, these teachings will support starting the optimizer to first focus on finding a converged candidate fluence with w_p set to zero, and only then begin leaf sorting, thereby avoiding the situation where time is spent searching candidate leaf sequences for suboptimal dosimetry solutions. By one approach, the penalty term (weighted by w_p) can be replaced by a fluence shape penalty that is simply a function of the fluence map and is intended to penalize fluences that are generally difficult to efficiently reproduce with the intended beam shaping / modulation elements (such as a multi-leaf collimator).

[0074] Those skilled in the art will appreciate that various modifications, changes and combinations may be made to the above-described embodiments without departing from the scope of the present invention, and such modifications, changes and combinations should be considered to be within the scope of the inventive concept.

Claims

1. A method for facilitating optimization of a radiation treatment plan for a specific patient, the method comprising: During the radiation treatment plan optimization cycle, the control circuit: Perform dosimetry optimization iterations to produce dosimetry-based planning results; Perform non-dosimetric optimization iterations to produce non-dosimetric based planning results; evaluating the dosimetry-based planning results and the non-dosimetry-based planning results to generate a convergence evaluation result; A determination is made whether to terminate a continuous radiation treatment plan optimization cycle based at least in part on the convergence assessment result.

2. The method according to claim 1, wherein the dosimetry optimization iteration comprises: Iterative operation for exploring fluence settings.

3. The method of claim 1, wherein the non-dosimetric optimization iteration comprises: An iterative operation used to explore at least one control point setting.

4. The method according to claim 1, further comprising: While optimizing the radiation treatment plan for the specific patient, during a subsequent radiation treatment plan optimization cycle: Conduct subsequent dosimetry optimization iterations to produce subsequent dosimetry-based planning results; No non-dosimetry optimization iterations are performed to produce subsequent non-dosimetry-based planning results; evaluating the subsequent dosimetry-based planning results and the non-dosimetry-based planning results to generate subsequent convergence evaluation results; A determination is made whether to terminate a continuous radiation treatment plan optimization cycle based at least in part on the subsequent convergence evaluation result.

5. The method of claim 1 , wherein evaluating the dosimetry-based planning results and the non-dosimetry-based planning results to generate a convergence assessment result comprises at least in part: An optimization penalty is applied based on a convergence assessment result representing a deviation between the dosimetry-based planning result and the non-dosimetry-based planning result.

6. The method of claim 1 , wherein performing the dosimetry optimization iterations to generate dosimetry-based planning results comprises at least in part: The dosimetry optimization iterations are performed based at least in part on intermediate results determined during non-dosimetry optimization iterations.

7. The method according to claim 1, further comprising: When it is determined that the continuous radiation treatment plan optimization cycle is terminated, the optimized radiation treatment plan is output.

8. The method according to claim 7, further comprising: Therapeutic radiation is administered to the specific patient based at least in part on the optimized radiation treatment plan.

9. An apparatus for facilitating optimization of a radiation treatment plan for a specific patient, the apparatus comprising: A control circuit configured to, during a radiation treatment plan optimization cycle: Perform dosimetry optimization iterations to produce dosimetry-based planning results; Perform non-dosimetric optimization iterations to produce non-dosimetric based planning results; evaluating the dosimetry-based planning results and the non-dosimetry-based planning results to generate a convergence evaluation result; A determination is made whether to terminate a continuous radiation treatment plan optimization cycle based at least in part on the convergence assessment result.

10. The apparatus of claim 9, wherein the dosimetry optimization iterations comprise: Iterative operation for exploring fluence settings.

11. The apparatus of claim 9, wherein the non-dosimetric optimization iterations comprise: An iterative operation used to explore at least one control point setting.

12. The apparatus of claim 9, wherein the control circuit is further configured to, while optimizing the radiation treatment plan for the specific patient, during a subsequent radiation treatment plan optimization cycle: Conduct subsequent dosimetry optimization iterations to produce subsequent dosimetry-based planning results; No non-dosimetry optimization iterations are performed to produce subsequent non-dosimetry-based planning results; evaluating the subsequent dosimetry-based planning results and the non-dosimetry-based planning results to generate subsequent convergence evaluation results; A determination is made whether to terminate a continuous radiation treatment plan optimization cycle based at least in part on the subsequent convergence evaluation result.

13. The apparatus of claim 9, wherein the control circuit is configured to evaluate the dosimetry-based planning results and the non-dosimetry-based planning results to generate a convergence assessment result, at least in part by applying an optimization penalty according to a convergence assessment result representing a deviation between the dosimetry-based planning results and the non-dosimetry-based planning results.

14. The apparatus of claim 9, wherein the control circuit is configured to perform the dosimetry optimization iterations to produce dosimetry-based planning results at least in part by performing the dosimetry optimization iterations based at least in part on intermediate results determined during non-dosimetry optimization iterations.

15. The apparatus of claim 9, wherein the control circuit is further configured to: When it is determined that the continuous radiation treatment plan optimization cycle is terminated, the optimized radiation treatment plan is output.

16. The apparatus of claim 15, wherein the control circuit is further configured to: Therapeutic radiation is administered to the specific patient based at least in part on the optimized radiation treatment plan.

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