Radiotherapy planning optimization method and apparatus

Abstract

The present disclosure relates to radiation therapy plan optimization methods and apparatus. To facilitate optimizing a radiation therapy plan for a particular patient using a particular radiation therapy machine having a multi-leaf collimator, a control circuit can obtain a treatment duration parameter, and then optimize the radiation therapy plan using a cost function having a term dependent on the treatment duration parameter, thereby providing an optimized radiation therapy plan having an optimized delivery time.

Description

Technical Field

[0001] These teachings generally involve treating the patient's planned target area with energy according to an energy-based treatment plan, and more specifically involve optimizing the energy-based treatment plan. Background Technology

[0002] The use of energy to treat medical conditions encompasses known areas of existing technological research. For example, radiation therapy is a crucial component of many treatment programs aimed at reducing or eliminating harmful tumors. Unfortunately, the energy applied cannot distinguish between harmful substances and adjacent tissues, organs, or parts that are needed or even essential for the patient's continued survival. Therefore, energy, such as radiation, is typically applied cautiously, with an attempt to at least confine the energy to a given target area. So-called radiation therapy programs generally serve this purpose.

[0003] Radiation therapy plans typically include specified values ​​for each of the various treatment machine parameters in each of multiple sequence fields. Treatment plans for radiation therapy courses are usually generated automatically through a process known as optimization. As used herein, "optimization" will be understood as an improvement on candidate treatment plans, without necessarily ensuring that the optimized result is actually the only optimal solution. Such optimization typically involves automatically adjusting one or more physical therapy parameters (usually while observing one or more corresponding limitations in these aspects) and mathematically calculating possible corresponding treatment outcomes (such as dose levels) to identify a set of given treatment parameters that represent a good balance between the desired treatment outcome and the avoidance of adverse side effects.

[0004] Typical radiotherapy plans are created using optimization algorithms that attempt to minimize a cost function that includes one or more user-defined optimization objectives. These objectives are typically dose targets, designed to maximize target dose coverage and homogeneity while minimizing the dose to organs at risk. Summary of the Invention

[0005] One aspect of this disclosure provides a method for optimizing a radiotherapy plan for a specific patient, the radiotherapy plan using a specific radiotherapy machine with a multi-leaf collimator, the method comprising: acquiring a treatment duration parameter via control circuitry; and optimizing the radiotherapy plan using a cost function having terms dependent on the treatment duration parameter, thereby providing an optimized radiotherapy plan with optimized delivery time.

[0006] Another aspect of this disclosure provides an apparatus for optimizing a radiotherapy plan for a specific patient, the radiotherapy plan using a specific radiotherapy machine with a multi-leaf collimator, the apparatus comprising: control circuitry configured to: acquire a treatment duration parameter; and optimize the radiotherapy plan using a cost function having terms dependent on the treatment duration parameter, thereby providing an optimized radiotherapy plan with optimized delivery time.

[0007] Another aspect of this disclosure provides a non-transitory computer-readable medium for optimizing a radiotherapy plan for a specific patient, the radiotherapy plan using a specific radiotherapy machine with a multi-leaf collimator, the non-transitory computer-readable medium including instructions stored thereon, the instructions performing the following steps when executed on a processor: obtaining a treatment duration parameter; optimizing the radiotherapy plan using a cost function having terms dependent on the treatment duration parameter, thereby providing an optimized radiotherapy plan with optimized delivery time. Attached Figure Description

[0008] By providing the optimized methods and apparatus for radiotherapy described in the following detailed description, various needs are at least partially met, especially when studied in conjunction with the accompanying drawings, wherein:

[0009] Figure 1 Includes block diagrams of various embodiments configured according to these teachings;

[0010] Figure 2 Includes flowcharts illustrating configurations based on various embodiments of these teachings;

[0011] Figure 3 This includes diagrams configured according to various embodiments of these teachings;

[0012] Figure 4 This includes diagrams configured according to various embodiments of these teachings; and

[0013] Figure 5 This includes diagrams configured according to various embodiments of these teachings.

[0014] The elements in the figures are illustrated for simplicity and clarity and are not necessarily drawn to scale. For example, the size and / or relative positioning of some elements in the figures may be exaggerated relative to others to aid in understanding the various embodiments of this teaching. Furthermore, common but easily understood elements (those that are useful or necessary in commercially viable embodiments) are generally not depicted to facilitate an unhindered understanding of these various embodiments of this teaching. Certain actions and / or steps may be described or depicted in a particular order of occurrence, but those skilled in the art will understand that such particularity regarding the order is not actually necessary. The terms and expressions used herein have their ordinary technical meaning as attributed to such terms and expressions by those skilled in the art, unless otherwise described herein with a different specific meaning. The word "or" as used herein should be understood as having a parallel structure rather than a subordinate structure, unless otherwise expressly stated. Detailed Implementation

[0015] Generally, these various embodiments can help optimize radiation therapy plans for specific patients using a particular radiotherapy machine with a multi-leaf collimator. In these respects, the control circuitry can acquire treatment duration parameters and then use a cost function with terms dependent on the treatment duration parameters to optimize the radiotherapy plan, thereby providing an optimized radiotherapy plan with optimized delivery time.

[0016] One method allows the treatment duration parameter to correspond to a time-based physiological limit for a specific patient. As an illustrative example of these aspects, this time-based physiological limit could correspond to the duration for which at least the target portion of a specific patient can remain substantially immobile. Another method allows the treatment duration parameter to include a user-selected treatment duration parameter.

[0017] One approach to optimizing a radiotherapy plan may include using a soft maximum as a smooth approximation of the maximum function to improve the numerical stability of the optimization. Alternatively, or in combination with the above approach, optimizing a radiotherapy plan may include representing radiotherapy machine axes in a cost function, where these axes (such as, but not limited to, predetermined radiation source locations) do not need to be optimized.

[0018] In one approach, the cost function described above may also include a dose measurement component that needs to be optimized. Furthermore, in another approach, either alternative to or in combination with the above approach, the cost function may include a monitoring unit delivery time component.

[0019] By an alternative approach, these teachings will take into account the use of radiation therapy machines and optimized radiation therapy plans to deliver therapeutic radiation to specific patients.

[0020] In one method, these teachings may include a non-transitory computer-readable medium having a computer program that, when executed by a computer (such as a processor), performs any or more of the steps, actions, and / or functions described herein, including: causing the computer to perform: obtaining a treatment duration parameter and then using a cost function to optimize a radiotherapy plan, the cost function having terms that depend on the treatment duration parameter, thereby providing an optimized radiotherapy plan with optimized delivery time.

[0021] The various benefits become apparent upon a thorough examination and study of the following detailed description. Refer now to the accompanying drawings, and especially to... Figure 1 First, an illustrative device 100 will be presented that conforms to many of the teachings in these teachings.

[0022] In this particular example, the enabling device 100 includes a control circuit 101. As a “circuit”, the control circuit 101 therefore includes a structure comprising at least one (and typically many) conductive paths (such as paths composed of conductive metals such as copper or silver) that deliver power in an ordered manner, and which typically also include corresponding electrical components (including not only passive components such as resistors and capacitors but also active components such as any of a variety of semiconductor-based devices) to allow the circuit to implement the control aspects of these teachings.

[0023] Such control circuitry 101 may include fixed-purpose hard-wired hardware machines (including, but not limited to, application-specific integrated circuits (ASICs) (circuits customized for a specific purpose rather than intended for general use), field-programmable gate arrays (FPGAs), etc.), or may include partially or fully programmable hardware machines (including, but not limited to, microcontrollers, microprocessors, etc.). These architectural options for such structures are well known and understood in the art and require no further description herein. Control circuitry 101 is configured (e.g., by using appropriate programming known to those skilled in the art) to perform one or more of the steps, actions, and / or functions described herein.

[0024] It will be understood that the control unit 101 may include a single integrated platform, or it may include multiple such circuits that work together with each other.

[0025] Control circuitry 101 is operatively coupled to memory 102. Memory 102 may be an integral part of control circuitry 101, or may be physically separated ( wholly or partially) from control circuitry 101 as needed. Memory 102 may also be local relative to control circuitry 101 (where, for example, both share a common circuit board, chassis, power supply, and / or enclosure), or may be partially or entirely remote relative to control circuitry 101 (where, for example, memory 102 is actually located in another facility, metropolitan area, or even another country compared to control circuitry 101). Like control circuitry 101, memory 102 may comprise a single structure, or may comprise multiple memory machines that collectively constitute the “memory” of device 100.

[0026] In addition to information (such as patient-specific optimization information and information about specific radiotherapy machines as described herein), the memory 102 can 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, this reference to "non-transitory" will be understood to refer to a non-transitory state with respect to the stored content (thus excluding the case where the stored content merely constitutes a signal or wave), rather than to the volatility of the storage medium itself, and therefore includes not only non-volatile memory (such as read-only memory (ROM)) but also volatile memory (such as dynamic random access memory (DRAM))).

[0027] Alternatively, the control circuitry 101 may also be operatively coupled to the user interface 103. The user interface 103 may include any of a variety of user input mechanisms (such as, but not limited to, keyboards and keypads, cursor control devices, touch-sensitive displays, voice recognition interfaces, gesture recognition interfaces, etc.) and / or user output mechanisms (such as, but not limited to, visual displays, audio converters, printers, etc.) to receive information and / or instructions from the user and / or provide information to the user.

[0028] If needed, the control circuitry 101 can also be operatively coupled to a network interface (not shown). With this configuration, the control circuitry 101 can communicate with other components (both within and outside the device 100) via the network interface. Network interfaces (including not only wireless devices but also non-wireless devices) are well known in the art and require no particular detail herein.

[0029] By means of a method, computed tomography apparatus 106 and / or other imaging apparatus 107 known in the art can provide some or all of any desired patient-related imaging information.

[0030] In this illustrative example, control circuitry 101 is configured to ultimately output an optimized energy-based treatment plan (such as, for example, an optimized radiotherapy plan 113). Such an energy-based treatment plan typically includes specified values ​​for each of a variety of treatment machine parameters in each of a plurality of sequence fields. In this case, the energy-based treatment plan is generated through an optimization process, an example of which is also provided herein.

[0031] In one method, control circuitry 101 can be operatively coupled to an energy-based therapeutic machine 114, which is configured to deliver therapeutic energy 112 to a site having at least one therapeutic target area 105 and one or more organs at risk, according to an optimized energy-based therapeutic plan 113. Figure 1 The corresponding patient 104 (represented by organs at risk 1 through N, 108 and 109). These teachings are generally applicable to use with any of a variety of energy-based therapeutic machines / devices. In a typical application setting, the energy-based therapeutic machine 114 will include an energy source, such as a radiation source 115 of ionizing radiation 116.

[0032] In one method, the radiation source 115 can be selectively moved along an arcuate pathway by a sling (wherein, during treatment, the pathway at least partially encompasses the patient). The arcuate pathway may comprise a complete or near-complete circle, as needed. In one method, control circuitry 101 controls the movement of the radiation source 115 along the arcuate pathway, and thus can control when the radiation source 115 begins to move, stops moving, accelerates, decelerates, and / or the speed at which the radiation source 115 travels along the arcuate pathway.

[0033] As an illustrative example, radiation source 115 may include, for example, a linac-based X-ray source based on a radio frequency (RF) linear particle accelerator. A linac is a particle accelerator that greatly increases the kinetic energy of charged subatomic particles or ions by subjecting charged particles to a series of oscillating potentials along a straight beamline. This ion accelerator can be used to generate ionizing radiation (e.g., X-rays) 116 as well as high-energy electrons.

[0034] A typical energy-based therapy machine 114 may also include: one or more support devices 110 (such as an examination table) for supporting the patient 104 during the treatment session; one or more patient fixation devices 111; a sling or other movable mechanism for allowing selective movement of the radiation source 115; and one or more energy shaping devices (such as beam shaping devices 117, such as jaw plates, multi-leaf collimators, etc.) for providing selective energy shaping and / or energy modulation as needed.

[0035] In a typical application setting, this document assumes that the patient support device 110 can be selectively controlled via control circuitry 101 to move in any direction (i.e., any X, Y, or Z direction) during an energy-based therapeutic session. Because the aforementioned components and systems are well known in the art, detailed descriptions of these aspects are not provided herein unless otherwise described.

[0036] Now refer to Figure 2 The following describes a process 200 that can be executed, for example, in conjunction with the application settings described above (more specifically, via the control circuit 101 described above). Generally, this process 200 is used to help generate an optimized radiotherapy plan 113, thereby facilitating the treatment of a specific patient with therapeutic radiation using a specific radiotherapy machine according to the optimized radiotherapy plan.

[0037] In block 201, control circuitry 101 acquires a treatment duration parameter (e.g., by retrieving the parameter from the aforementioned memory (102)). In one method, the treatment duration parameter corresponds to a time-based physiological limit for a particular patient. For example, a time-based physiological limit could correspond to the duration for which at least a target portion of a particular patient can remain substantially still. An illustrative example of these aspects is the duration for which a particular patient can stably hold their breath, thereby preventing respiratory-related movements of their body.

[0038] In one method, and as shown in optional box 202, the treatment duration parameter may include a user-selected treatment duration parameter. This selection can be entered, for example, through the user interface 103 described above.

[0039] One method is to empirically determine the value of this parameter by having a specific patient hold their breath a given number of times and timing the duration of each such effort. The average duration can be used as a treatment duration parameter, or the shortest duration of these various efforts can be used as a treatment duration parameter, or some other method may be used as needed.

[0040] In block 203, control circuitry 101 uses a cost function to optimize a radiotherapy plan. This cost function has terms that depend on a treatment duration parameter to provide an optimized radiotherapy plan with optimized delivery time. A cost function (also referred to in some contexts as an objective function or loss function) is a mathematical expression used in optimization algorithms to evaluate how well a particular solution or set of parameters achieves the desired outcome. The cost function takes the algorithm's input parameters and calculates the numerical cost associated with those parameters, effectively quantifying the error or gap from the optimal solution. In optimization problems, the objective is to find a set of parameters that minimizes or maximizes the cost function, depending on the specific problem at hand. Minimization is more common, where the objective is to find the parameters that produce the lowest possible cost, thus indicating the best or most efficient solution that meets defined criteria.

[0041] As an illustrative example, the cost function could have terms related to time-based physiological limits for a particular patient, such as the duration for which a particular patient can stably hold their breath. One approach is that this optimization can assume only a single such breath-holding action. Another approach is that this optimization can assume multiple breath-holding actions, separated by time intervals (and perhaps performed at control points).

[0042] One approach is to include dosimetric components to be optimized (such as the required dose to the patient’s target area and / or the maximum dose limit for non-target organs / tissues).

[0043] Alternatively, or in combination with the methods described above, the cost function may optionally include a monitoring unit delivery time component. A monitoring unit (MU) is a standardized measure of the dose output from a radiotherapy machine (such as a linear accelerator) and can be used to calibrate the delivery of a prescribed radiation dose to a patient's target assembly. The monitoring unit is directly related to the amount of charge generated by ionizing a reference volume of air under specific conditions, thus providing a quantifiable relationship between machine settings and the actual dose delivered. During treatment planning, the number of monitoring units can be calculated to ensure accurate delivery of the intended dose to the patient, taking into account factors such as machine calibration, beam energy, treatment geometry, and patient-specific characteristics.

[0044] One approach to optimizing radiotherapy planning may include using soft maximization as a smooth approximation of a maximization function to improve the numerical stability of the optimization process. The applicant has determined that using a hard maximization function (which strictly enforces an explicit cutoff at a certain dose level) during optimization can lead to numerical instability. This instability can result in anomalous or non-convergent behavior because the optimization algorithm may struggle to find a solution that perfectly satisfies the hard constraints. Soft maximization, on the other hand, can be introduced as a smooth approximation of the maximization function. By penalizing doses exceeding a certain threshold rather than completely prohibiting them, soft maximization allows for a more flexible approach. This penalty can be incorporated into the objective function as a term that gradually increases as the dose exceeds the desired limit. The applicant has determined that the use of soft maximization can help smooth the optimization curve, making it easier for the algorithm to find a stable and optimal solution.

[0045] In addition to or in combination with the methods described above, these teachings will take into account that the cost function also includes a component of the unit delivery time.

[0046] With this configuration, the optimization process can be optimized at least in part with ensuring adequate dose to the target area (and appropriate protection of organs at risk if necessary), although the patient's respiratory behavior needs to be taken into account.

[0047] As shown in optional box 204, these teachings will also take into account the administration of therapeutic radiation to a specific patient using a radiation therapy machine and the optimized radiation therapy plan described above. Administration of radiation according to the plan may include, for at least a portion of the treatment, turning off the radiation beam when the specific patient is breathing, and allowing the radiation beam to turn on when the specific patient is not breathing.

[0048] Further details consistent with these teachings will now be presented. It will be understood that the specific details in these examples are intended for illustrative purposes and are not intended to suggest any particular limitations regarding these teachings.

[0049] In time-critical treatments, the ability to control treatment delivery timing can be beneficial. Examples of such treatments include breath-holding lung therapy and palliative care. In breath-holding lung therapy, the planner typically considers the patient's ability to hold their breath and attempts to plan the treatment in a way that allows for accurate delivery during the breath-holding period. If treatment delivery is interrupted in the high-dose-rate portion of the delivery trajectory (e.g., when respiratory-based movement of the patient is detected), the accuracy of the delivered dose can be affected. In palliative care, the patient may be in considerable distress, which can limit their ability to remain still during treatment.

[0050] In many existing methods, delivery time is indirectly controlled by limiting the number of monitoring units and the target of those units. However, this method can only control one of many axes that can affect the final delivery time. Furthermore, while the number of monitoring units indicates the delivery time, the actual time required to deliver the monitoring units depends on how the monitoring units are distributed along the delivery trajectory and various machine parameters (such as maximum monitoring units / radian, maximum dose rate, etc.).

[0051] The following illustrative examples illustrate specific implementations that use delivery time as the optimization objective to address situations such as those described above. When delivery time is passed as the objective to the treatment plan optimizer, a term related to treatment time is added to the cost function used for leaf sorting. In one method, this term can be any function of treatment time. C time ( t ):R→R.

[0052] First, this example will handle the time component of the cost function.

[0053] The cost function in blade sorting can be as follows: , Total cost C tot Including dosage measurement components C dos and treatment time related items C time . X It includes each control point i Each axis at the location j of x ij The total position vector, F(X) It is based on the energy density at the axial position, and t(X) This is the total delivery time. The total delivery time can be approximated as the sum of the intervals between each control point. The interval time is actually the maximum value of the time for each axis. ,in t ji It is the axis j In interval i The minimum time spent within. For the axis j This time can be calculated as ,in (control points) i Central axis j (the change in quantity), and It is the axis j The maximum speed. Similarly, with exponential... intervali The slowest axis within is defined as .

[0054] Typically, an efficient optimization process requires calculating the gradient of the cost function near a known solution. For the cost function described above, the gradient (or any degree of freedom for the optimization) with respect to a given blade position can be calculated as follows:

[0055] The gradient of the treatment time term can be expressed as: The first term can be calculated based on the advanced utility of a certain treatment time, and the latter part can consider how the position of a certain axis in a certain control point affects the total time.

[0056] From a certain perspective, only the slowest axis will affect the total time. in δ jj' It is the symbol for the Kronecker delta.

[0057] Next, this example will use soft maximum values ​​to handle the gradient of the time component.

[0058] To improve the numerical stability of the optimizer, in this example, the formula... In this context, `max` is replaced with a soft-maximum function or a normalized exponential function. The soft-maximum function can be viewed as a generalization of the standard maximum function. Considering that it is represented as... The generalized weighted average can be obtained by choosing the classical maximum function. .

[0059] For softmax, you can Replace with :

[0060] These teachings will consider other methods for replacing max with a softer function, if needed. One approach, for example, could utilize ln( .

[0061] The gradient can then be formatted as: in , and .

[0062] Note that for each i , β It can be (and if) x i 's The sizes are different, so they should be different (i.e., they can be...). β Replace with β i ).

[0063] With this configuration, applying the soft maximum method can impose higher costs on the axes that cause the most significant reduction in machine speed.

[0064] By one approach, these teachings can be viewed as categorizing all degrees of freedom of the treatment machine into three classes: (1) unoptimized machine axes that were defined before optimization (e.g., gantry angles are typically set for each control point at the start of optimization or before the optimizer is invoked, and these gantry angles do not necessarily change during optimization); (2) freely optimized parameters as described above (note that their motion is not directly constrained, but only by treatment time constraints); and (3) strictly optimized parameters, where the optimizer is allowed to change the parameters so that they do not reduce treatment speed (regardless of total treatment time). Note that only the first two classes specified above will appear in the time cost function term, as the variables in the third class do not affect treatment time.

[0065] As a concrete example, consider a scenario where the blade position is considered to be handled according to the third category specified above, and the hanger position should be handled according to the first category specified above. The spacing of the control points along the hanger axis can be set so that hanger movement always requires time. t g This time is the time required to move the gantry at full speed between two consecutive control points.

[0066] Then, the only optimizable variable corresponding to the second category mentioned above is the control point interval. mu i The monitoring units delivered within the unit. Therefore, there are only two values ​​in the softmax calculation. In this example, these teachings will focus on the time cost gradient of the monitoring unit. mu i The maximum velocity within is the maximum dose rate. d rate .

[0067] Figure 3 The chart 300 shown presents the time cost as

[0068] Now consider using soft maximum and 0.1 An example of gradient over seconds. Specifically, and refer to... Figure 4 As shown in Figure 400, the soft-maximum time cost term can be applied to actual large-segment lung cases, where... 0.134 Seconds. First, in this example, lung cases are optimized without a delivery time target. In this chart 400, x The axis corresponds to the control point index, and y The axes represent time in seconds. Of particular note is line 401, which shows the time required to deliver a monitoring unit within that control point interval, and line 402, which corresponds to the hanger time. (Line 403 corresponds to the collimator time, and line 404 corresponds to the blade time.)

[0069] Note that in this example, the total delivery time is 19.4 seconds.

[0070] In the following example, the lung case was instead optimized based on these teachings with a delivery time target of 14 seconds (to account for, for example, patients who cannot stably hold their breath for more than 15 seconds). Figure 5 The corresponding optimization results are shown. The monitoring unit timeline 501, hanger timeline 502, collimator timeline 503, and blade timeline 504 are compared with the corresponding results in the previous example (e.g., ...). Figure 4 (As shown) for comparison and contrast, and in particular note, the total delivery time is now 14.2 seconds, a significant reduction compared to the 19.4 seconds achieved in the previous example.

[0071] These instructions allow users / planners to explicitly define the required delivery time (or the required upper limit for delivery time), where the time estimate is based on all axes of the treatment machine, if necessary.

[0072] Another aspect of these teachings is provided by the subject matter of the following clauses (where it will be understood that any of these clauses may be combined with any one or more of the other clauses as needed).

[0073] Clause 1. A method for optimizing a radiotherapy plan for a specific patient using a specific radiotherapy machine with a multi-leaf collimator, the method comprising: via control circuitry: acquiring a treatment duration parameter; and optimizing the radiotherapy plan using a cost function having terms dependent on the treatment duration parameter, thereby providing an optimized radiotherapy plan with optimized delivery time.

[0074] Clause 2. The method according to Clause 1, wherein the treatment duration parameter includes a treatment duration parameter selected by the user.

[0075] Clause 3. The method according to Clause 1, wherein the treatment duration parameter corresponds to a time-based physiological limit for a particular patient.

[0076] Clause 4. The method according to Clause 3, wherein the time-based physiological limit corresponds to the duration during which at least the target portion of a particular patient can remain substantially immobile.

[0077] Clause 5. The method according to Clause 1, wherein the cost function further includes a dose measurement component to be optimized.

[0078] Clause 6. The method according to Clause 1, wherein optimizing the radiotherapy plan includes using a soft maximum as a smooth approximation of the maximum function to improve the numerical stability of the optimization.

[0079] Clause 7. The method described in Clause 1, wherein optimizing the radiotherapy plan includes representing the radiotherapy machine axes in the cost function, which do not require optimization.

[0080] Clause 8. The method described in Clause 7, wherein the axis of the radiotherapy machine includes the predetermined location of the radiation source.

[0081] Clause 9. The method described in Clause 1, wherein the cost function includes a monitoring unit delivery time component.

[0082] Clause 10. The method described in Clause 1 further includes: administering therapeutic radiation to a specific patient using a radiation therapy machine and an optimized radiation therapy plan.

[0083] Clause 11. An apparatus for optimizing a radiotherapy plan for a specific patient using a specific radiotherapy machine with a multi-leaf collimator, the apparatus comprising: control circuitry configured to: acquire a treatment duration parameter; and optimize the radiotherapy plan using a cost function having terms dependent on the treatment duration parameter, thereby providing an optimized radiotherapy plan with optimized delivery time.

[0084] Clause 12. The device according to Clause 11, wherein the treatment duration parameter includes a treatment duration parameter selected by the user.

[0085] Clause 13. The device according to Clause 11, wherein the treatment duration parameter corresponds to a time-based physiological limit for a particular patient.

[0086] Clause 14. The device according to Clause 13, wherein the time-based physiological limit corresponds to the duration for which at least the target portion of a particular patient can remain substantially immobile.

[0087] Clause 15. The apparatus according to Clause 11, wherein the cost function further includes a dose measurement component to be optimized.

[0088] Clause 16. The apparatus according to Clause 11, wherein the control circuitry is configured to improve the numerical stability of the optimized radiotherapy plan by using a soft maximum value as a smooth approximation of the maximum value function.

[0089] Clause 17. The apparatus of Clause 11, wherein the control circuitry is configured to optimize a radiotherapy plan by representing radiotherapy machine axes in a cost function, the radiotherapy machine axes of which do not require optimization.

[0090] Clause 18. The apparatus of Clause 17, wherein the axis of the radiotherapy machine includes a predetermined location of the radiation source.

[0091] Clause 19. The apparatus according to Clause 11, wherein the cost function includes a monitoring unit delivery time component.

[0092] Clause 20. A non-transitory computer-readable medium for optimizing a radiotherapy plan for a specific patient using a specific radiotherapy machine with a multi-leaf collimator, the non-transitory computer-readable medium including instructions stored thereon that, when executed on a processor, perform the following steps: obtaining a treatment duration parameter; optimizing the radiotherapy plan using a cost function having terms that depend on the treatment duration parameter, thereby providing an optimized radiotherapy plan with optimized delivery time.

[0093] Those skilled in the art will recognize that various modifications, alterations, and combinations can be made with respect to the above embodiments without departing from the scope of the invention. As an example of these aspects, these teachings can be used in conjunction with mechanisms for detecting a patient's actual respiratory behavior during treatment, so that the execution of a radiotherapy plan can be modified in real time to accommodate this respiratory behavior. Therefore, such modifications, alterations, and combinations should be considered within the scope of the inventive concept.

Claims

1. A method for optimizing a radiotherapy plan for a specific patient, the radiotherapy plan using a specific radiotherapy machine with a multi-leaf collimator, the method comprising: By controlling the circuit: Obtain the treatment duration parameter; The radiotherapy plan is optimized using a cost function that has terms that depend on the treatment duration parameter, thereby providing an optimized radiotherapy plan with optimized delivery time.

2. The method according to claim 1, wherein the treatment duration parameter includes a treatment duration parameter selected by the user.

3. The method of claim 1, wherein the treatment duration parameter corresponds to a time-based physiological limit for the particular patient.

4. The method of claim 3, wherein the time-based physiological limit corresponds to the duration for which at least the target portion of the particular patient can remain substantially immobile.

5. The method of claim 1, wherein the cost function further includes a dose measurement component to be optimized.

6. The method of claim 1, wherein optimizing the radiotherapy plan includes using a soft maximum as a smooth approximation of the maximum function to improve the numerical stability of the optimization.

7. The method of claim 1, wherein optimizing the radiotherapy plan includes representing the radiotherapy machine axis in the cost function, the radiotherapy machine axis not requiring optimization.

8. The method of claim 7, wherein the axis of the radiotherapy machine includes a predetermined radiation source location.

9. The method of claim 1, wherein the cost function includes a monitoring unit delivery time component.

10. The method according to claim 1, further comprising: The specific patient is given therapeutic radiation using the aforementioned radiation therapy machine and the optimized radiation therapy plan.

11. An apparatus for optimizing a radiotherapy plan for a specific patient, the radiotherapy plan using a specific radiotherapy machine having a multi-leaf collimator, the apparatus comprising: The control circuit is configured as follows: Obtain the treatment duration parameter; The radiotherapy plan is optimized using a cost function that has terms that depend on the treatment duration parameter, thereby providing an optimized radiotherapy plan with optimized delivery time.

12. The apparatus of claim 11, wherein the treatment duration parameter includes a user-selected treatment duration parameter.

13. The apparatus of claim 11, wherein the treatment duration parameter corresponds to a time-based physiological limit for the particular patient.

14. The device of claim 13, wherein the time-based physiological limit corresponds to the duration for which at least a target portion of the particular patient can remain substantially immobile.

15. The apparatus of claim 11, wherein the cost function further includes a dose measurement component to be optimized.

16. The apparatus of claim 11, wherein the control circuit is configured to optimize the radiotherapy plan by using a soft maximum value as a smooth approximation of the maximum value function, thereby improving the numerical stability of the optimization.

17. The apparatus of claim 11, wherein the control circuitry is configured to optimize the radiotherapy plan by representing the radiotherapy machine axis in the cost function, wherein the radiotherapy machine axis does not need to be optimized.

18. The apparatus of claim 17, wherein the axis of the radiotherapy machine includes a predetermined radiation source location.

19. The apparatus of claim 11, wherein the cost function includes a monitoring unit delivery time component.

20. A non-transitory computer-readable medium for optimizing a radiotherapy plan for a specific patient, the radiotherapy plan using a specific radiotherapy machine with a multi-leaf collimator, the non-transitory computer-readable medium comprising instructions stored thereon, the instructions performing the following steps when executed on a processor: Obtain the treatment duration parameter; The radiotherapy plan is optimized using a cost function that has terms that depend on the treatment duration parameter, thereby providing an optimized radiotherapy plan with optimized delivery time.

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