Radiation treatment plan optimization method and device

Abstract

The invention relates to a radiation treatment plan optimization method and device. A control circuit accesses patient information of a patient and optimizes a radiation treatment plan of the patient in accordance with both the patient information and a dose distribution objective function, where a corresponding available solution space is limited in accordance with at least one three-dimensional conformal solution.

Description

Technical Field

[0001] These teachings generally relate to treating a planned target volume of a patient with energy according to an energy-based treatment plan, and more particularly to optimizing the 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 does not inherently distinguish between unwanted material and adjacent tissues, organs, etc., which are desirable or even critical for the continued survival of the patient. As a result, energy such as radiation is typically applied in a carefully administered manner to at least attempt to confine the energy to a given target volume. So-called radiation treatment plans typically function in the above-described manner.

[0003] A radiation treatment plan typically includes specified values ​​for each of the various treatment platform parameters during each of a plurality of sequential fields. A treatment plan for a radiation treatment session is typically generated automatically by 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 actually a singular optimal solution. Such optimization typically includes automatically adjusting one or more physical treatment parameters (typically while complying with one or more corresponding constraints on these aspects) and mathematically calculating possible corresponding treatment outcomes (such as dose levels) to identify a given set of treatment parameters that represents a good compromise between a desired treatment outcome and avoiding undesirable side effects.

[0004] However, the prior art in the above aspects does not fully meet all the needs of all application scenarios. Summary of the invention

[0005] In one aspect, a method is provided, the method comprising, by a control circuit: accessing patient information of a patient; optimizing a radiation treatment plan for the patient based on the patient information and a dose distribution objective function, wherein a corresponding available solution space is restricted based on at least one three-dimensional conformal solution.

[0006] In another aspect, an apparatus is provided that includes a control circuit configured to: access patient information of a patient; and optimize a radiation treatment plan for the patient based on the patient information and a dose distribution objective function, wherein a corresponding available solution space is restricted based on at least one three-dimensional conformal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] 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, wherein:

[0008] Figure 1 including block diagrams as configured in accordance with various embodiments of these teachings;

[0009] Figure 2 including flow charts as configured according to various embodiments of these teachings;

[0010] Figure 3 includes schematic representations of various embodiments as configured in accordance with these teachings;

[0011] Figure 4 includes schematic representations of various embodiments as configured in accordance with these teachings; and

[0012] Figure 5 Included are schematic representations of various embodiments as configured in accordance with these teachings.

[0013] The elements in the drawings are shown for simplicity and clarity, and do not have to be drawn to scale. For example, the size and / or relative positioning of some elements in the drawings may be exaggerated relative to other elements to help improve the understanding of the various embodiments of this teaching. In addition, common but well-known elements that are useful or necessary in commercially feasible embodiments are usually not depicted, so as to facilitate the less obstructed viewing of these various 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 specificity about the order is not actually required. The terms and expressions used herein have the common technical meanings of such terms and expressions as given by the technicians in the above-mentioned technical field, unless different specific meanings are set forth in addition herein. Unless otherwise specifically indicated, the word "or" when used in this article should be interpreted as having a disjunctive structure rather than a conjunctive structure. DETAILED DESCRIPTION

[0014] In general, according to these various embodiments, control circuitry accesses patient information of a patient and optimizes a radiation treatment plan for the patient based on both the patient information and a dose distribution objective function, wherein a corresponding available solution space is constrained based on at least one three-dimensional conformal solution. (In general, the solution space refers to the set of all possible points of the optimization problem that satisfy the constraints of the problem, and thus comprises an initial set of candidate solutions to the problem).

[0015] By one approach, the corresponding available solution space can be constrained from at least one three-dimensional conformal solution by at least partially constraining the available solution space to three-dimensional conformal solutions. By one approach, the corresponding available solution space can be constrained from at least one three-dimensional conformal solution by at least partially constraining the available solution space to three-dimensional conformal partial solutions. By yet another approach, the corresponding available solution space can be constrained from at least one three-dimensional conformal solution by at least partially constraining the available solution space to conformal multi-leaf collimator solutions.

[0016] By one approach, optimizing the radiation treatment plan based on a dose distribution objective function may include optimizing the radiation treatment plan based at least in part on an inverse treatment plan.

[0017] By one approach, the patient information may include information about multiple treatment targets in the patient. In this case, the radiation treatment plan may include a multi-target arc field radiation treatment plan, and by one approach, the optimization of the treatment target projections may be calculated separately for each treatment target and for each different gantry orientation.

[0018] By one approach, at least some optimization iterations may include selecting a subset of treatment targets of the plurality of treatment targets corresponding to the selected control points, wherein the subset of treatment targets represents treatment targets to be irradiated with the selected control points. If desired, the multi-leaf collimator leaf positions may be optimized for each treatment target projection in a separate optimization step. By one approach, possible multi-leaf collimator leaf position values ​​may be constrained based on default multi-leaf collimator leaf positions.

[0019] So configured, these teachings can be particularly useful when applied to multi-target arc field treatment plans, where there may be multiple small targets that are treated using multiple arc fields with different incidence angles. In such an application scenario, each arc can be represented as a sequence of control points, and during optimization, those sequences can be subdivided into smaller arc sectors for easier optimization.

[0020] These and other benefits may become more apparent upon a thorough review and study of the following detailed description. Referring now to the drawings, and in particular to the Figure 1 , an illustrative device 100 that is compatible with many of these teachings will first be presented.

[0021] 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) conductive paths (such as paths including conductive metals such as copper or silver) that transmit power in an orderly manner, which path(s) will typically also include corresponding electrical components (including both passive (such as resistors and capacitors) and active (such as any of a variety of semiconductor-based devices) as appropriate) to allow the circuit to implement the control aspects of these teachings.

[0022] 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 customized by design for a specific purpose rather than an integrated circuit intended for general use), 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 options for such a structure are well known and understood in the art and do not require further description here. The control circuitry 101 is configured (e.g., by using corresponding programming as will be familiar to those skilled in the art) to perform one or more of the steps, actions, and / or functions described herein. (It will be recognized that, as desired, a "control circuit" may physically include multiple discrete hardware platforms. Accordingly, it will be understood that references to a "control circuit" may refer to a single hardware platform or multiple hardware platforms.)

[0023] 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 fully remote to the control circuit 101 (where, for example, the memory 102 is physically located in another facility, city, or even country than the control circuit 101).

[0024] In addition to information as described herein (such as optimization information for a particular patient and information about a particular radiation treatment platform), the memory 102 may also be used, for example, to non-transitorily store computer instructions that, when executed by the control circuit 101, cause the control circuit 101 to behave as described herein. (As used herein, such references to "non-transitory" will be understood to refer to the non-transitory state of the stored content (and thus exclude when the stored content merely constitutes a signal or wave), rather than to 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)).

[0025] 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 transducer, a printer, etc.) to facilitate receiving information and / or instructions from a user and / or providing information to a user.

[0026] If desired, the control circuit 101 may also be operably coupled to a network interface (not shown). The control circuit 101 configured in this manner may communicate with other elements (both other elements within the device 100 and other elements outside 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 particular detailed description here.

[0027] By one approach, some or all of any desired patient-related imaging information may be obtained by a computed tomography device 106 and / or other imaging device 107 as is known in the art.

[0028] In this illustrative example, the control circuit 101 is configured to ultimately output an optimized energy-based treatment plan (such as, for example, the optimized radiation treatment plan 113). The energy-based treatment plan typically includes specified values ​​for each of the various treatment platform parameters during each of the plurality of sequential exposure fields. In this case, the energy-based treatment plan is generated by an optimization process, examples of which are further provided herein.

[0029] By one approach, the control circuit 101 may be operably coupled to an energy-based treatment platform 114 configured to deliver therapeutic energy 112 to a corresponding patient 104 according to an optimized energy-based treatment plan 113, the corresponding patient 104 having at least one treatment volume 105 and also having one or more organs at risk (at least one of the organs at risk). Figure 1 108 to the Nth organ at risk 109). These teachings are generally applicable to use with any of a variety of energy-based treatment platforms / devices. In a typical application scenario, the energy-based treatment platform 114 will include an energy source, such as a radiation source 115 of ionizing radiation 116.

[0030] By one approach, the radiation source 115 can be selectively moved via the gantry along an arcuate pathway (where the pathway at least to some extent encompasses the patient itself during the administration of the treatment). The arcuate pathway 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 pathway 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 pathway.

[0031] As an illustrative example, radiation source 115 may include, for example, a radio frequency (RF) based linear particle accelerator (linac based) x-ray source. 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.

[0032] As desired, a typical energy-based treatment platform 114 may also include: one or more support devices 110 (such as a couch) to support the patient 104 during the treatment session, one or more patient fixation devices 111, a gantry or other movable mechanism that allows selective movement of the radiation source 115, and one or more energy shaping devices (for example, beam shaping devices 117 such as jaws, multi-leaf collimators, etc.) that provide selective energy shaping and / or energy modulation.

[0033] In a typical application scenario, it is contemplated herein that the patient support device 110 may be selectively controlled by the control circuit 101 to move in any direction (i.e., any X, Y, or Z direction) during an energy-based treatment session. Because the aforementioned elements and systems are well known in the art, further detailed description of these aspects is not provided herein unless otherwise relevant to the description.

[0034] Reference now Figure 2, a process 200 will be described that can be performed, for example, in conjunction with the above-described application scenario (and more specifically via the above-described control circuit 101). In general, the process 200 is used to facilitate generating an optimized radiation treatment plan 113, thereby facilitating treating a specific patient with therapeutic radiation using a specific radiation treatment platform according to the optimized radiation treatment plan.

[0035] At block 201, the process 200 provides for the control circuit 101 to access patient information of a patient (e.g., from the aforementioned memory 102). The patient information may include, for example, information about treatment targets and / or one or more organs at risk (including, but not limited to, patient images). These teachings will accommodate the patient information including information 202 about multiple treatment targets in the patient, and wherein the radiation treatment plan includes a multi-target arc field radiation treatment plan. The foregoing includes situations where the patient presents with multiple relatively small treatment targets.

[0036] At block 203 , the control circuit 101 then optimizes the radiation treatment plan for the patient according to the aforementioned patient information and the dose distribution objective function, wherein the corresponding available solution space is restricted according to at least one three-dimensional conformal solution.

[0037] These teachings will accommodate various methods to so limit the available solution space. As one example, the corresponding available solution space is limited by at least partially limiting the available solution space to three-dimensional conformal solutions. As another example, the corresponding available solution space is limited according to at least one three-dimensional conformal solution by at least partially limiting the available solution space to three-dimensional conformal partial solutions. And as yet another example, the corresponding available solution space is limited according to at least one three-dimensional conformal solution by at least partially limiting the available solution space to conformal multi-leaf collimator solutions. It will be understood that these embodiments are intended for illustrative purposes and are not intended to imply any particular limitations in these regards.

[0038] By one useful approach, optimizing the radiation treatment plan based on a dose distribution objective function as described above may include optimizing the radiation treatment plan at least in part based on inverse treatment planning. In inverse treatment planning, one starts with a desired dose distribution in a patient (e.g., within a treatment target and / or an organ at risk), and then calculates an optimal beam configuration to achieve that distribution.

[0039] As noted above, the patient information may include information about a plurality of treatment targets in the patient, and wherein the radiation treatment plan includes a multi-target arc field radiation treatment plan. In this case, an optimization of the corresponding treatment target projection may be calculated separately for each treatment target and for each different gantry orientation corresponding to a treatment plan arc.

[0040] Also in this case (and in lieu of, or in combination with, the foregoing separate calculations), at least some of the optimization iterations may include selecting a subset of treatment targets from a plurality of treatment targets that correspond to selected control points, wherein the subset of treatment targets represents treatment targets to be irradiated with the selected control points. In this case, the multi-leaf collimator leaf positions may be optimized for each treatment target projection in a separate optimization step. (Multi-leaf collimators are known in the art and include a plurality of individual portions (referred to as "leaves") formed of a high atomic number material (such as tungsten) that may be independently moved into and out of the path of a radiation therapy beam to selectively block (and thereby shape) the beam. Typically, the leaves of a multi-leaf collimator are organized in pairs, the paired leaves are aligned co-linearly relative to each other, and the paired leaves may be selectively moved toward and away from each other. A typical multi-leaf collimator has many such leaf pairs, typically more than twenty, fifty, or even one hundred such pairs.) If desired, possible multi-leaf collimator leaf position values ​​may be constrained based on default multi-leaf collimator leaf positions.

[0041] At optional block 204, these teachings are adapted to then administer therapeutic radiation to the patient according to the resulting optimized radiation treatment plan using, for example, the aforementioned radiation treatment platform 114.

[0042] So configured, these teachings can provide for combining three-dimensional conformal planning, algorithmic treatment strategy optimization, and general inverse planning for radiation treatment sequence determination to obtain efficient and robust plans for situations where target dose conformality and short delivery times are more important than the level of plan quality that can be obtained, for example, by using beam modulation via multi-leaf collimator leaves. In these aspects, these teachings will accommodate optimizing radiation treatment plans using inverse treatment planning methods (and thus using objective functions such as dose distribution) while restricting the available solution space to three-dimensional conformal solutions (or, by one approach, solutions that are at least close to three-dimensional conformal partial solutions, where, for example, "conformal" can mean that the solution in the leaf position space of selected control points consists of leaf positions that remain near the selected target projection boundary). (By possibly different approaches, a "near conformal" solution may refer to active leaf positions that are near the target projection (where "active" refers to the leaf that is the active portion of the actual delivered dose, relative to, for example, a closed leaf pair where the closed position is less important in these respects) in all (or at least most) of the control points (at least most of the relevant time). This vicinity may refer to active leaves that are no more than some predetermined specified distance (such as, for example, 3 mm (measured from the beam view projection to the isocenter distance)) from some portion of the target projection, or that are within some predetermined percentage distance (such as, for example, 0%, 10% or 20%) of the width of the target projection.

[0043] In the above aspects, if desired, conformality can refer to adjacent leaves acting as smooth boundaries and not modulating individually back and forth. One possible metric to be applied in the above aspects is the surface area of ​​the projection being covered by the leaves at any given control point. In this case, these teachings will adapt to specify that one metric of "closeness" is that the leaf opening at a control point discloses at least 70%, 80%, 90%, or 100% (or any specific percentage within this range) of the selected target projection.

[0044] Further details consistent with these teachings will now be presented. It will be understood that the specific details of these examples are intended for illustrative purposes and are not intended to imply any particular limitations regarding these teachings.

[0045] By one approach, these teachings can be applied in stereotactic radiosurgery (SRS) planning methods, where the objective function typically benefits from high dose gradients (i.e., dose falloff) outside the target boundaries, and where the solution space is restricted to a conformal multi-leaf collimator aperture (or, if desired, a nearly conformal aperture). In this context, "conformal aperture" means an aperture that conforms to the target projection on the multi-leaf collimator plane. By one approach, and in the case of multiple targets, the target projection can be a projection of a subset of the targets.

[0046] By one approach, these teachings can be viewed as including an inverse planning optimization (such as volumetric modulated arc therapy (VMAT)) in that a user-defined cost function can be minimized by iteratively searching through possible leaf sequences (and associated dosing setting weights). In addition, however, these teachings can provide different ways of searching the leaf position space. Some illustrative examples in these regards will now be presented.

[0047] During the initial phase of the optimization process, target projections are calculated separately for each target and for each different gantry orientation. By one approach, default multi-leaf collimator leaf positions can be determined separately for each target. (If desired, in the general case, these target projections can be allowed to overlap).

[0048] The optimization iteration process may include the step of selecting a subset of targets for selected control points. In this example, the subset represents one or more targets to be irradiated with those control points. Various strategies may be used for how to select a set of control points. By one approach, an iteration may focus on only a subset of control points. By another approach, for a certain set of continuous control points, it may be specified that the subsets of targets are the same. By one approach, for each subset of targets, specified constraints may be followed. For example, it may be specified that the subsets need to be selected so that any two of the targets in the subset do not share a pair of leaves that would be required for conformal treatment of the two targets. If desired, this step may also include consideration of other discrete optimization parameters, such as, but not limited to, beam energy.

[0049] If desired, the above steps may include: when selecting a subset of targets, and in addition to the cost function evaluation, also using geometric rules to constrain the valid subset. Other possible examples of geometric rules include, but are not limited to:

[0050] Avoid selecting target pairs that are too close to each other (using a chosen threshold distance) in order to more aggressively avoid so-called dose bridging;

[0051] Avoid selecting targets that are too deep within the patient (i.e., where the radiation must travel more than a predetermined threshold distance through normal tissue before reaching the target);

[0052] Taking into account the time it takes to move a leaf from one target to another, essentially enforcing that if two target apertures do share a leaf pair, there needs to be at least some time period between the closing of the first target aperture and the opening of the second target aperture; and / or

[0053] The bias is established to provide incident rays at a greater angular spacing for each target.

[0054] In another optimization step, the multi-leaf collimator leaf positions can be optimized individually for each target projection. In this step, the possible leaf position values ​​can be limited to the vicinity of the default multi-leaf collimator leaf positions (e.g., a specified deviation from the default position can be allowed). An example of a "nearby" threshold that may be useful is any value between, for example, 0.5 mm and 5.0 mm. Instead of the foregoing or in combination with it, a relative measure X% of the target projection width at a particular control point can be adopted, such as, for example, 0%, 10% or 20% (or any value within this range). Moreover, in order to conform (e.g., by keeping adjacent leaves close to each other to produce a smooth aperture), this relative measure can be used as a constraint for a group of adjacent leaves. As an example, if the selected leaves are allowed to find solutions near the default position, those solutions can be limited to leaf positions that allow the same relative movement from their individual default positions. In other words, the leaves will need to move as a group. By one approach, this step can also adapt to parameters shared by all leaves in the control point, such as metering setting weights, jaw positions, or collimator angles.

[0055] If desired, and with reference to the foregoing subset selection step and the separate optimization step of the multileaf collimator leaf positions for each target projection separately, these teachings will accommodate not necessarily performing each at equal cadence. These teachings will accommodate, for example, the leaf position optimization step being run multiple times for each subset selection step (or, by another approach, not being run at all in some iterations).

[0056] Still referring to the aforementioned subset selection step and the separate optimization step of the multi-leaf collimator leaf positions for each target projection individually, by one approach, these two activities can utilize different optimization strategies. The former step can benefit from a relatively small set of possible choices of discrete optimization parameters, while the latter step can use, for example, a gradient-based optimization method in which the cost function gradient is projected through dose space and fluence space to a request to move a leaf inward or outward. In this application scenario, the restricted location of acceptable leaf positions can facilitate the projection of the gradient into leaf position space.

[0057] Figure 3 A schematic representation of the progress of the optimization when viewed from the beam viewing angle of a certain control point (gantry angle) is presented. Columns 301-303 correspond to different potential paths that the optimizer can take. Box 304 represents the multi-leaf collimator plane in a given corresponding control point, where the leaves are moved in the direction of the stripes (in this illustrative example, the direction is a 45 degree angle). The dashed circle 305 represents the three target structure projections, and the actual multi-leaf collimator aperture (i.e., leaf opening) is represented as a solid line.

[0058] Figure 3Particularly presented is the initialization of the optimizer before the first actual iteration. In this example, the multi-leaf collimator aperture (depicted as a solid circle) at this stage is only defined for book-keeping, meaning that if some leaves are active in more than one target projection profile, there is no problem.

[0059] Figure 4 Depicted is the target selection stage of the optimization iteration, where only a subset of all target projections is employed. According to these teachings, geometric constraints (in this illustrative example, only one leaf pair can contribute to one target projection) limit the possible choices in each case. The selected subset(s) in each of the possible paths here is represented by reference numeral 401.

[0060] Figure 5 Illustrated is that the leaf aperture optimization described herein allows deviation from pure conformal leaf positions (as shown by reference numeral 501 in some cases).

[0061] It will be recognized that these teachings allow the combination of the benefits of three-dimensional conformal methods (such as low monitor units in addition to rapid delivery, and an intuitively understandable relationship between field geometry and dose distribution) with some of the benefits of inverse planning (such as the ability to state the desired quality of the plan via a cost function and to achieve automatic optimal leaf positions). It will also be recognized that these teachings are particularly suitable for multi-target SRS (or stereotactic body radiation therapy (SBRT)) treatments, where target dose uniformity generally does not play a critical role (and thus excessive leaf modulation deep inside the target aperture is not required).

[0062] Those skilled in the art will recognize that various modifications, changes, and combinations can be made to the above embodiments without departing from the scope of the present invention, and such modifications, changes, and combinations will be considered within the scope of the inventive concept.

Claims

1. A method comprising: By the control circuit: access to a patient's patient information; A radiation treatment plan for the patient is optimized according to the patient information and a dose distribution objective function, wherein a corresponding available solution space is restricted according to at least one three-dimensional conformal solution. 2 . The method of claim 1 , wherein the corresponding available solution space is restricted according to at least one three-dimensional conformal solution by at least partially restricting the available solution space to three-dimensional conformal solutions.

3. The method of claim 1, wherein the corresponding available solution space is restricted according to at least one three-dimensional conformal solution by at least partially restricting the available solution space to three-dimensional conformal partial solutions.

4. The method of claim 1, wherein the corresponding available solution space is restricted according to at least one three-dimensional conformal solution by at least partially restricting the available solution space to conformal multi-leaf collimator solutions.

5. The method of claim 1 , wherein optimizing the radiation treatment plan according to the dose distribution objective function comprises: The radiation treatment plan is optimized based at least in part on an inverse treatment plan.

6. The method of claim 1, wherein the patient information comprises information regarding a plurality of treatment targets in the patient, and wherein the radiation treatment plan comprises a multi-target arc field radiation treatment plan. 7 . The method according to claim 6 , wherein the optimization of the treatment target projection is calculated separately for each treatment target and for each different gantry orientation.

8. The method of claim 6, wherein at least some optimization iterations comprise selecting a subset of treatment targets of the plurality of treatment targets corresponding to selected control points, wherein the subset of treatment targets represents treatment targets to be irradiated with the selected control points.

9. The method of claim 8, wherein the multi-leaf collimator leaf positions are optimized for each treatment target projection in a separate optimization step.

10. The method of claim 9, wherein possible multi-leaf collimator leaf position values ​​are constrained according to default multi-leaf collimator leaf positions.

11. An apparatus comprising: A control circuit configured to: access to a patient's patient information; as well as A radiation treatment plan for the patient is optimized according to the patient information and a dose distribution objective function, wherein a corresponding available solution space is restricted according to at least one three-dimensional conformal solution.

12. The apparatus of claim 11, wherein the control circuit is configured to restrict the corresponding available solution space according to at least one three-dimensional conformal solution by at least partially restricting the available solution space to three-dimensional conformal solutions.

13. The apparatus of claim 11, wherein the control circuit is configured to restrict the corresponding available solution space according to at least one three-dimensional conformal solution by at least partially restricting the available solution space to three-dimensional conformal partial solutions.

14. The apparatus of claim 11, wherein the control circuit is configured to restrict the corresponding available solution space according to at least one three-dimensional conformal solution by at least partially restricting the available solution space to conformal multi-leaf collimator solutions.

15. The apparatus of claim 11, wherein the control circuit is configured to optimize the radiation treatment plan according to the dose distribution objective function by optimizing the radiation treatment plan at least in part according to an inverse treatment plan.

16. The apparatus of claim 11, wherein the patient information comprises information regarding a plurality of treatment targets in the patient, and wherein the radiation treatment plan comprises a multi-target arc field radiation treatment plan. 17 . The apparatus of claim 16 , wherein the control circuit is configured to optimize the treatment target projection by calculating the treatment target projection separately for each treatment target and for each different gantry direction.

18. The apparatus of claim 16, wherein at least some optimization iterations comprise selecting a subset of treatment targets of the plurality of treatment targets corresponding to selected control points, wherein the subset of treatment targets represents treatment targets to be irradiated with the selected control points.

19. The apparatus of claim 18, wherein the control circuit is configured to optimize the multi-leaf collimator leaf positions for each treatment target projection in a separate optimization step.

20. The apparatus of claim 19, wherein possible multi-leaf collimator leaf position values ​​are constrained according to default multi-leaf collimator leaf positions.

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