Patent Description:
Charged particle therapy employs beams of energized protons, carbon ions, or other charged particles. Currently, one of the most common types of particle therapy is proton therapy. Proton therapy, also referred to as proton beam therapy, is a medical procedure that uses a beam of protons to irradiate diseased tissue. One of the advantages of proton therapy in comparison to the conventional photon radiotherapy such as, X-ray or gamma ray, for example, for the treatment of cancer, is the reduced integral dose to the patient. Integral dose can refer to a total amount of energy experienced by the patient during radiative kinds of treatments. Proton therapy can help to minimize damage to tissues and structures while focusing a preferred dose upon the target tissue.

Proton therapy may provide superior tumor coverage and deliver a lower integral dose to a patient's body compared to conventional radiotherapy. As compared to traditional passive-scattering proton therapy, spot-scanning proton therapy techniques may provide superior target coverage by scanning the target spot-by-spot and layer-by-layer similar to three-dimensional printing techniques. But current spot-scanning proton therapy beam delivery techniques may be limited in performance and may only be capable of delivering limited proton beams in one treatment fraction (i.e., normally one treatment fraction only consists of <NUM>-<NUM> treatment fields). An exemplary proton beam treatment system comprising a rotating beam delivery device configured to continuously scan a target volume is described in publication <CIT>.

A system for delivering a charged particle beam at a target is disclosed. In implementations, a particle beam is delivered from an output device at a plurality of control points and the system is configured to deliver a substantially continuous particle beam about the plurality of control points.

This description describes implementations and methods of particle spot-scanning therapy to deliver a particle beam in a substantially continuous manner. In implementations, a particle beam is delivered in a substantially continuous manner in connection with one or both of a gantry, a couch, or other arrangements whereby an impact angle of the particle beam is altered. Throughout the description hereof, the term proton is used as an example of a charged particle. It is to be appreciated that the invention hereof should not be so limited to a proton and the inventors recognize that the principles are applicable to all charged particles and the terms are generally interchangeable in the context of this disclosure.

The inventors hereof have identified inefficiencies associated with conventional spot-scanning proton therapy (e.g., Intensity Modulated Proton Therapy (IMTP) and the like. Namely, conventional techniques may not maximize an effectively continuous proton delivery. For example, and perhaps among other things, such conventional systems may effectively cease proton delivery when changing between impact angles (defined below) with reference to a desired target (defined below) (a proton machine will typically first adjust the impact angle and then deliver the proton beam and thereby limit treatment efficiency. ) In addition, stopping and starting of the proton delivery can impact the calibration of the proton delivery system as some systems experience a vibration when switching between delivery and non-delivery.

With reference now to the Figures, <FIG> illustrates an implementation of a proton delivery system <NUM> for delivering a substantially continuous proton treatment to a patient using one or more arc trajectories or non-iso centric movement with couch and gantry at same time. As illustrated, system <NUM> includes a particle accelerator <NUM> that delivers one or more proton beams <NUM>, via a beam line <NUM>, to output <NUM>. Particle accelerator <NUM> accelerates charged particles and arranges charged particles in well-defined beams before extracting proton beams <NUM> via beam line <NUM>. Examples of particle accelerators include colliders, cyclotrons, synchrotrons, laser proton accelerators, and the like.

In some implementations, accelerator <NUM> may be positioned remote from output <NUM> such that accelerator <NUM> may be centrally located and selectively connectable to multiple outputs <NUM>.

With continued reference to <FIG>, beam <NUM> is output <NUM> towards a desired target of a body <NUM> having an isocenter or multiple continuous moving isocenters <NUM> located on a treatment station <NUM>. Beam is directed at the desired target with respect to an impact angle (IA) relative to the current isocenter <NUM>, which will be described in additional detail below. For ease of disclosure, body <NUM> may be referenced herein as a tumor and treatment station <NUM> will be referenced as a couch or a table, yet it is to be appreciated that other targets and treatment stations are contemplated and the disclosure should not be limited to the examples.

Impact angle as used in this disclosure is the angle in which body <NUM> experiences beam <NUM> and is shown as the angle between a plane that extends through the isocenter <NUM> of body <NUM> (parallel with treatment station <NUM>) and beam <NUM>. For ease of disclosure, each change in impact angle IA is the result of movement of one or both of (i) adjustment of output <NUM> about a gantry <NUM> among a plurality of control points <NUM> (as shown in the Figures) with respect to body <NUM>, (ii) adjustment of body <NUM> at a plurality of control points <NUM> with respect to output <NUM>, via movement of the treatment station <NUM> or the like, (iii) movement of output <NUM> and movement of body <NUM>, each with respect to one another independently or simultaneously, or (iv) other suitable means.

In an implementation, magnets (not shown) may be provided in or about output <NUM> and/or along the beam line <NUM> that have adjustable currents to selectively adjust beam <NUM>. In an implementation, a degrader or energy selection system (such as a wedge or the like) in the beamline may be provided to offer a selectively adjustable proton energy; for example, different energies may be more desirable based on an appropriate treatment. A range shifter (sometimes referred to as a bolus)(not shown) may be used (e.g., in the gantry nozzle) to attenuate the proton energy. A proton multi-leaf collimation system may be used to sharp the proton spot lateral penumbra during the delivery and rotation of the gantry, couch, or the like. Such an attenuation and selection may be utilized to alter the energy of the proton beam and achieve desired depths of treatment. For purpose of this disclosure, different depths within body <NUM> will be referenced as energy layers. Multiple energy layers can be used in the system <NUM> described herein to effectively treat a three-dimensional tumor <NUM>.

In some examples, range shifter may be used to degrade, or broaden, beam <NUM>. During a session, range shifter may move continuously during the gantry rotation with respect to isocenter <NUM>. Range shifter may be used to optimize an air gap between the range shifter and the patient's skin to ensure the proton beam <NUM> reaches a designated position <NUM> that has a pre-defined size and is generally associated with tumor <NUM>.

In some arrangements, system <NUM> may be deployed in conjunction with an a configuration system, such as, for example, an imaging system. In implementations, the configuration system may be a a Cone-Beam Computed Tomography (CBCT), a Fluoroscope, stereotactic imaging system, surface matching camera system, or other similar devices that can monitor the patient during the proton beam delivery.

In an implementation, system <NUM> delivers a substantially continuous beam of protons <NUM> during a session and throughout any changes to one or both of the impact angle (IA) - whether such impact angle is altered via changing control points via (i) movement of output <NUM> along a gantry G, (ii) movement of couch; or (iii) a combination thereof. For ease of disclosure, the remainder of this disclosure will disclose embodiments where impact angle is altered by movement of output <NUM> about control points <NUM> along a gantry G but it is equally contemplated that IA may be otherwise altered and the scope of this disclosure should not be limited to the disclosed embodiments.

As described in more detail below, system <NUM> may be generally configured to minimize a number of energy layers for one, some, or all of the beams that may be directed toward one or more control points. To achieve such minimization, in an implementation, system <NUM> may undertake one or more of the following steps (i) filters lower weighted energy layers from the session, (ii) filters lower weighted proton spots, and (iii) re-arranges the remaining energy layers and/or proton spots between two or more consecutive control points which thereby maintains the same robust plan quality and is formatted to yield a substantially continuous step-and-shoot proton arc delivery.

A session will now be described. In an implementation, output <NUM> emits beam <NUM> towards a desired location of tumor <NUM> of patient <NUM> at a number of impact angles IA; accordingly, control points <NUM>. As an example, output <NUM> moves about a track <NUM> in a manner that facilitates the proton beam <NUM> to reach target <NUM> about different impact angles IA and at a number of associated control points <NUM>. For example, system <NUM> may be configured to provide an optimized session in accordance with a cancer treatment session.

In implementations, system <NUM> provides session algorithms and platforms (i.e., executing on the data processing hardware <NUM>) to deliver an optimized session to patient <NUM> via output <NUM>. In an implementation, system <NUM> maximizes one or more sessions through: (a) determining an optimized number of energy layers per control point (e.g., <NUM>-<NUM> energy layers), (b) determining an optimum number of control points (and/or impact angles IA), and (c) performing spot beam weighting and positioning at each control point <NUM> (and/or imaging angle). In other words, system <NUM> provides a session that optimizes the number, and position, of control points <NUM> (and/or impact angles) and identifies the weight and position of each beam reaching a desired location about tumor <NUM>.

In an implementation, system <NUM> may include one or more substantially continuous scanning modes, such as, but not limited to a step-and-shoot spot scanning mode, a continuous arc delivery mode, or other delivery modes. In a step-and-shoot spot scanning mode, system <NUM> switches energy layers of beam <NUM> by selecting different energy layers (e.g., by using degrader and adjusting magnets as described above) to direct beam <NUM> to the desired positions about target <NUM> among various impact angles (IA) and while the imaging angle (IA) is adjusting (e.g., while gantry is rotating between control points <NUM>).

In implementations, system <NUM> delivers one or more beams <NUM> at each control point <NUM> (and/or impact angle (IA)) and, in some embodiments, each proton beam <NUM> may be associated with an energy layer. In an implementation, each energy layer may be different and, in other implementations some or all of the energy layers may be common.

In some implementations, a step-and-shoot approach may save time by switching among one or more energy layers while adjusting impact angles IA. In other words, system <NUM> can change the one or more beams <NUM> between one or more energy layers during the IA adjustment, thereby resulting in a reduced overall session time. For example, considering a gantry rotation speed of three degrees per second, during the one second it may take gantry to rotate to a next control point three degrees away, system <NUM> may change the one or more energy layers in which beam <NUM> is directed.

As earlier described, in some examples, at each control point, beam <NUM> may include <NUM>-<NUM> energy layers. In an exemplary session, each control point <NUM> may include a beam <NUM> having one energy layer. In alternative sessions, it may be desired to reach one or more energy layers for one or more control points <NUM> - in which case, system <NUM> changes energy layers without gantry rotation at such control points <NUM>. As a result, system <NUM> may provide multiple beams <NUM>, each having a different energy layers. Both scenarios result in full or partial tumor <NUM> (three-dimensional) coverage from multiple impact angles IA and/or control points <NUM> thereby providing a system that delivers a full and robust tumor coverage dose through one or more arc trajectories.

In a continuous arc delivery mode, instead of delivering the proton beam <NUM> at each static control point <NUM>, system <NUM> continuously delivers the proton beam <NUM> while changing the impact angle IA or control points <NUM>. So instead of utilizing discrete control points <NUM> or impact angles IA that are described above for the step and shoot examples, in a continuous arc delivery mode, system <NUM> considers each control point <NUM> or impact angle IA as being within an angular range (e.g., -<NUM> < α < <NUM> degrees) or a position range ( e.g., -<NUM> < x < +<NUM> couch position ) which will be referred to herein as a control point sampling frequency (CPSF). It will be appreciated, therefore, that a higher control point sampling frequency indicates a smaller angle or position spread between adjacent control points. As a result, such discrete radiation delivery through step & shoot mode through different control points will be a close approximation to the continuous radiation delivery with such control point range (e.g., from -<NUM> degree to <NUM> degree partial arc and/or -<NUM> to +<NUM> couch movements).

In implementations, and as described in the examples that follow, control point sampling frequency (CPSF) may be introduced to effectively minimize the dosimetric difference witnessed upon body <NUM> as compared with the previously described step-and-shoot delivery. For example, the desired control point sampling frequency of the continuous session may suggest that four degrees between each effective control point (e.g. from <NUM> to <NUM> degrees) is substantially dosimetrically equal to a step and shoot session delivered at <NUM> degrees.

In an implementation, there is almost no dosimetric difference between statically delivering the proton beam <NUM> at one degree (step-and-shoot mode) and dynamically delivering the proton beam from <NUM> to <NUM> degree (continuous arc delivery mode). But in the latter continuous case, delivering proton beam <NUM> continuously during the gantry rotation having a CPSF of <NUM> degree - additional time may be saved and gantry inertia, vibrations during stop and start or other mechanical issues can be avoided.

In some implementations, system <NUM> may be configured to determine an optimized number of control points <NUM> and re-sampling the control points <NUM> to achieve a desired control point sampling frequency CPSF. In addition, system <NUM> may be configured to filter energy layers associated with each control point <NUM> such that the energy layers are weighted and those having low monitor units (MU) are removed. In some implementations, system <NUM> may also be configured to organize and allocated energy layers to nearby control points <NUM> instead of, prior to, or after filtering the energy layers.

To improve the calculation and optimization speed, system <NUM> may employ a progressive dose grid sampling method, which may be defined by the unit of energy deposition in the Computer Tomography (CT) set or patient body. For example, one (<NUM>) cubic center size cube consists of <NUM> dose grids with <NUM> x <NUM> x <NUM> size or <NUM> dose grid with <NUM> x <NUM> x <NUM> size. An implementation of such a progressive dose grid sampling method may utilize a a coarse dose grid size, and then progressively reduce the dose grid size during the optimization.

System <NUM> includes memory hardware <NUM> in communication with the data processing hardware <NUM>. The memory hardware <NUM> stores instructions that when executed on the data processing hardware <NUM> cause the data processing hardware <NUM> to perform operations, such as the method described with respect to <FIG>, the method described with respect to <FIG>, or the method described with respect to <FIG>.

<FIG> describes an example arrangement of operations for a method <NUM> of operating system <NUM>. At block <NUM>, system <NUM> pre-defines a proton arc range (i.e., an initial angle αi and a stop angle αs) associated with gantry opening <NUM> and/or the rotation of table <NUM> or couch/table translation movement (i.e., an initial position x<NUM> and a stop position xs). In some examples, a user defines the proton arc range. For example, system <NUM> sets an initial angle αi or position of control point <NUM> (i.e., gantry location) of proton output <NUM> emitting beam <NUM>. In some examples, system <NUM> sets the initial angle αi at <NUM> degrees and an initial stop angle αs at <NUM> degrees. In some examples, if the gantry is capable of rotating at <NUM> degrees, then the initial angle αi is set at zero degrees and the initial stop angle αs is set at <NUM> degrees. Other values of the initial angles αi and stop angles αs are possible as well. In some implementations, since table/couch <NUM> is capable of translational movement, system <NUM> also sets a table/couch initial angle and position for the table/couch <NUM> with respect to proton output <NUM>. In this case, control point <NUM> is defined as table translation and rotational movement, for example every one centimeter, every two centimeters, or other every one degree, every two degree as well. Therefore, in some implementations, system <NUM> defines an initial angle αi for output <NUM> and/or an initial angle for the table/couch <NUM> resulting in the rotation of one or both of the proton output <NUM> and the table/couch <NUM>. As a result, system <NUM> considers the rotation of one or both of the output <NUM> and the table/couch <NUM> with respect to one another to generate the proton arc range.

At block <NUM>, system <NUM> determines a coarse control point sampling frequency CPSF as shown in <FIG>. In other words, system <NUM> identifies a number of gantry locations or control points <NUM>. As shown in <FIG>, system <NUM> identifies eight control points <NUM> within the gantry's <NUM>-degrees of freedom. Other number of coarse control points may be used as well.

Referring back to <FIG>, at block <NUM> system <NUM> determines an optimization treatment plan for patient <NUM>. The optimization treatment plan determines a beam dose plan used by the beam <NUM> to irradiate body <NUM> as well as spare nearby tissue. In some examples, system <NUM> considers the anatomy of patient <NUM>, and determines a beam energy (i.e., energy layer), a beam spot position, and a number of protons to be delivered in each beam <NUM> to patient <NUM>. In addition, system <NUM> optimizes a dose distribution in patient <NUM> (for example, robustness optimization by considering daily treatment setup and proton range uncertainties; radiobiology effect (RBE) optimization by considering radiation biology effect of the proton beam), which allows a robust dose distribution or biological effective dose to body <NUM> as well as spare the healthy tissue and organs under these uncertainties. In some implementations, system <NUM> determines the effects of potential changes to body <NUM>, for example, and adjusts the treatment plan accordingly, which may be referred to as treatment plan adaptation. Some changes may include, the patient gaining or losing weight, the tumor changing size, or other considerations. By using robust optimization, system <NUM> is capable of providing optimal robust target coverage while sparing healthy tissue.

At block <NUM>, system <NUM> may first (A) optionally optimize the sampling frequency of the control points <NUM> (e.g. iteratively increasing control points numbers at block <NUM>) , the energy layer(s) and proton spots associated with each control point <NUM> to optimize the delivery efficiency for delivering proton beam <NUM>, resulting in an optimized treatment plan. In other words, system <NUM> uses a random iterative process that selects the optimized energy layers and spot position and weightings of beam <NUM> for the treatment.

Following the optional control point, energy layer optimization and spot delivery sequence optimization described about in block <NUM> or, instead skipping the optional optimization about (A), system <NUM> may be formatted to either:.

In an implementation, at block <NUM> system <NUM> generates an optimized plan for patient <NUM>. As described, the determined plan may be tailored to accommodate a specific patient <NUM>, and may be adjustable based on variables of patient <NUM> (e.g., the patient's daily treatment setup, proton range uncertainties, tumor motion, weight, the size of the tumor <NUM>, other patient related measurements, and the like. ) In an implementation, system <NUM> includes a processor that is additionally programmed to generate, and identify, one or more alternative plans that may alternatively account for different plan parameters, plan qualities, delivery efficiencies, clinician defined variables, and the like. ) In an implementation, a user is able to pick one from the one or more number of plans. In an implementation, a database may be provided that includes hundreds of plans which could be based on the objective value of each plan, each individual objective function or delivery time for different machines (fix gantry, full gantry, synchrotron or cyclotron machine) or parameters (energy layer numbers, spot numbers or MU). An example graphical user interface is provided at <FIG> that illustrates a number of such plans in which the clinician could select.

<FIG> and <FIG> describe a more detailed example arrangement of operations than <FIG> for a method <NUM> of operating system <NUM>. At block <NUM>, similar to block <NUM> of <FIG>, system <NUM> defines a proton arc range (i.e., the initial angle αi and stop angle α s of output <NUM> within the rotation of gantry and/or couch <NUM> (see <FIG>)). For example, system <NUM> sets an initial angle αi as the initial control point <NUM> (i.e., gantry location) of output <NUM> emitting beam <NUM>. In some implementations, system <NUM> also sets a table initial angle for the table <NUM>. Therefore, in some implementations, system <NUM> defines an initial angle αi for output <NUM> and/or a table initial angle for table <NUM>, such that beam <NUM> from output <NUM> is capable of reaching tumor <NUM> at the desired impact angle IA. The operating system could deliver multi-iso center particle beam therapy or non-coplanar multi-isocenter particle beam with couch/table and gantry movements.

At block <NUM>, similar to block <NUM> of <FIG>, system <NUM> determines a coarse control point sampling, as shown in <FIG>, between the identified initial angle αi and the identified initial stop angle α s. In other words, system <NUM> identifies a set of gantry and/or couch locations or control points <NUM> between the identified initial angle αi and the identified initial stop angle α s. As shown in <FIG>, system <NUM> identifies eight control points <NUM> within the gantry's <NUM>-degrees. In this example, the initial angle αi is at zero degrees and the initial stop angle α s is at <NUM> degrees. Other numbers of sampling control points <NUM> between the initial angle αi and the initial stop angle α s are possible as well.

Referring back to <FIG> and <FIG>, at block <NUM>, 330A system <NUM> determines an optimization treatment plan for patient <NUM> that determines a beam dose plan used by beam <NUM> at the identified control points <NUM> (identified at block <NUM>) to irradiate tumor <NUM> similar to the optimization treatment plan described above with respect to block <NUM> of <FIG>. In some examples, data processing hardware <NUM> executes the optimization of the treatment plan based on information stored on the memory hardware in communication with the data processing hardware <NUM>. The optimization may include one or more optimization techniques or methods, such as but not limited to, robust optimization, four or five-dimensional (time and geometry change or frequency dimension) robust optimization, adaptive optimization, and radiation biological effect (RBE) optimization. The optimization treatment plan includes identifying an energy layer associated with a beam, a spot position, and a number of protons to be delivered in each beam <NUM> originating from output <NUM> at the identified control points <NUM> (identified at block <NUM>). In addition, system <NUM> determines the optimization treatment plan for patient <NUM> at the identified control points <NUM> by considering the anatomy of the patient <NUM>. In addition, system <NUM> optimizes a dose distribution in patient <NUM> (for example, by considering daily treatment setup and proton range uncertainties), which allows a robust dose distribution to the tumor as well as spare the healthy tissue and organs under these uncertainties. In some implementations, system <NUM> determines the effects of potential changes to tumor <NUM>, for example, and adjusts the treatment plan accordingly, which may be referred to as treatment plan adaptation. Some changes may include the patient gaining or losing weight, the tumor changing size, or other considerations. By using robust optimization, system <NUM> is capable of providing optimal robust target coverage while sparing healthy tissue.

At block <NUM>, system <NUM> optionally randomly selects between an energy filtration method at block <NUM>, 342A and a control point re-sampling, energy layer re-distribution method and spot delivery sequence re-distribution at block <NUM>. At optional block 342A, system <NUM> filters the energy layers of beam <NUM>. In other words, system <NUM> removes low-weighted energy layers associated with one beam or the total beams associated with the treatment plan. System <NUM> may define a cut off MU weighting threshold for one or both of the energy layers orthe spot numbers, so that the energy layers or spot numbers fail to meet the cutoff threshold will not be further considered at later steps of the method. For example, system <NUM> identifies the lowest <NUM>% of MU weighting energy layers associated with all control points <NUM> and removes the identified lowest <NUM>% of energy layers associated with all the control points <NUM>. Other cutoff percentages may be used as well. In other examples, the MU weighting threshold for the energy layer may be associated with beams <NUM> outputted at each control point.

As previously discussed, at block <NUM>, system <NUM> may either (i) randomly select between the energy filtration method at block 342A and the control point re-sampling, energy layer re-distribution and spot delivery sequence re-organization method at block <NUM> (as illustrated in <FIG>) or (ii) allow the practitioner to identify a pre-defined control point sampling frequency (CPSF) and, based on the desired CPSF, pre-defines energy layers and perform sorting based on the particular treatment (as illustrated in <FIG>).

If system <NUM> selects the control point re-sampling, energy layer re-distribution and spot delivery sequence method at block <NUM>, then system <NUM> filters one or both of the energy layers or the spots associated with beams <NUM> of the treatment plan.

In an implementation, system <NUM> re-samples the control points <NUM>, or more specifically increases the number of control points as shown in <FIG> illustrates a method that system <NUM> uses to split a control point <NUM> into first and second control points <NUM> (1a and 1b), while <FIG> shows a method used by system <NUM> to add a control point <NUM> (e.g., adding control point <NUM>). While certain splitting methods are disclosed, other splitting methods may be employed and the disclosure should not be so limited.

<FIG> illustrates an implementation of a first control point <NUM>, <NUM> split into two new control points 1a, 1b, <NUM>, each having a position different from the position of the first control point <NUM>, <NUM>, for example, adjacent to the first control point <NUM>, <NUM>, such as, on either side of the first control point <NUM>, <NUM>. In some examples, the first control point <NUM>, <NUM> may be split into more than two control points <NUM>, e.g., three or more. Additionally, referring to <FIG>, the energy layer(s) (EL) associated with a control point <NUM> are re-distributed and re-organized. For example, the first control point <NUM>, <NUM> is capable of emitting beams <NUM>, where each beam has an energy layer EL from the energy layers EL1-ELn. Each energy layer EL1-ELn is optimized to deliver a robust proton treatment therapy to the patient <NUM> and ensure a robust tumor coverage as well as sparing organs that are not cancerous. In some examples, the energy layers EL1-ELn are arranged in ascending/decending order where the first energy layer EL1 associated with a first beam <NUM> has less energy than the last energy layer ELn associated with a different beam <NUM>. In other words, the different beam <NUM> having the last energy layer ELn (highest energy layer) reaches the furthest distance within the tumor <NUM>. The first control point <NUM>, <NUM> is split between a first new control point 1a, <NUM> and a second new control point 1b, <NUM>. As shown, system <NUM> splits the energy layers EL1-ELn of the first control point <NUM>, <NUM> by consecutively giving each one of the first and second new control points 1a, 1b, <NUM> energy layers EL1-ELn of the first control point <NUM>, <NUM>. Therefore, once all the energy layers EL1-ELn of the first control point <NUM>, <NUM> are split between the first and second new control points 1a, 1b, <NUM>, then the first new control point 1a, <NUM> has a number of energy layers NEL(1a) calculated according to: <MAT> <MAT> where N is the total number of energy layers EL of the control point <NUM>, <NUM> prior to being split. In addition, the second new control point 1b, <NUM> has a number of energy layers NEL(1b) calculated according to: <MAT> <MAT>.

In some implementations, an MU associated with a beam at the first control point beam <NUM>, <NUM> for a specific energy layer i may be determined by: <MAT> where i is an energy layer EL, and N is the total number of energy layers.

After splitting the first control point <NUM>, <NUM>, each of the first and second new control points <NUM> has a beam energy calculated based on the following equations when N is even: <MAT> <MAT> The beam energy for the first and second new control point <NUM> may be calculated based on the following equations when N is odd: <MAT> <MAT> where N is the total number of the energy layer.

In an implementation, system <NUM> employs a spot number (weighting) mechanism in addition to, or separately from, the energy layer filtration as described above. The spot number or weighting reduction mechanism may be utilized to filter, or otherwise remove, the MU spots or lines sequentially designated as being below a certain threshold. It is to be appreciated that this filtration may occur simultaneously, or randomly, during the optimizations. In exemplary implementations, the threshold may be determined as a bottom ten percent (<NUM>%) after energy layer filtration, integrated with energy layer filtration, or independent of energy layer filtration.

In an implementation, system <NUM> may be designed to undertake energy layer re-connection to reduce the number of energy layers and the associated switching time. For example, and among others, system <NUM> adjusts the energy layer from a first beam impact angle (IA) to the same energy level when an adjacent impact angle (IA) has (i) an energy difference that is below a threshold level, and (ii) a comparable MU weighting. For example, consider 115MeV and 10MU when the first impact angle (IA) is <NUM> degrees, and 110MeV and 5MU when the adjacent impact angle (IA) is <NUM> degree. In this instance, the energy layers of 110MeV may be adjusted to 115MeV so the system reduced one (<NUM>) energy layer switching time during the proton beam delivery.

<FIG> illustrate another example of splitting the energy layers <NUM> associated with a control point <NUM>, <NUM>, which may include for each energy layer (EL), dividing the MU associated with that energy layer EL between a first and second new control point 1a, 1b, <NUM> based on a threshold MU (e.g., a fraction of the MU associated with the original control point <NUM>, <NUM>) associated with each one of the first and second new control points 1a, 1b, <NUM>. For example, an energy layer EL<NUM>-ELn of a first control point <NUM> has a first MU value. The MU value may be split between the first new control point 1a, <NUM> and the second new control point 1b, <NUM>, where each of the first and second new control points 1a, 1b, <NUM> is associated with a fraction fa, fb of the MU value associated with the energy level EL<NUM>-ELn. The summation of the fractions fa, fb equals to one (fa + fb = <NUM>). In other words, the first new control point 1a, <NUM> may have a first fraction fa of the energy layer EL<NUM>-ELn and the second new control point 1b may have a second fraction fb of the energy layer EL<NUM>-ELn. For example, the energy level EL<NUM>-ELn may have an MU value of <NUM> MU. After splitting the energy level EL<NUM>-ELn into the first new and second new energy levels 1a, 1b, <NUM>, then the EL<NUM>-ELn energy level EL<NUM>-ELn may having a first fraction fa being half the MU value of the MU of the energy layer EL<NUM>-ELn, while the second new control point 1b, <NUM> has an energy layer having the remaining half of the MU value of the MU of the energy level EL<NUM>-ELn. As such, the total number of energy layers of the first and second new control points 1a, 1b, <NUM> are doubled; however, the total MU of the first and second new control points 1a, 1b, <NUM> is equal to the MU associated with the old control point <NUM>, <NUM>. Therefore, if the energy level EL<NUM>-ELn has an total MU of <NUM> MU, then the first control point 1a, <NUM> may have an MU of <NUM> MU and the second control point 1b, <NUM> has an MU of 60MU. If the energy level EL<NUM>-ELn has an MU of 120MU, then the first control point 1a, <NUM> may have an MU of 40MU (where fa is <NUM>/<NUM>) and the second control point 1b, <NUM> has an MU of 80MU (where fa is <NUM>/<NUM>). In another implementation, the system may use a combination of both the energy split such as re-distribution together with employing a split of the MU weighting of each energy mechanism.

Referring back to <FIG> and <FIG>, in some implementations, a second control point is added in addition to an original first control point, where the first control point <NUM>, <NUM> remains in the same location and the second control point <NUM>, <NUM> has an adjacent location to the first control point <NUM>, <NUM>. In some examples, more than one control point <NUM> is added to the first control point <NUM>, <NUM>, e.g., a third or more control points may be added. Referring to <FIG>, a second control point <NUM>, <NUM> is added in addition to the first control point <NUM>, <NUM>. <FIG> illustrate the energy layer EL<NUM>-ELn re-organization and re-distribution process. <FIG> illustrates an original first control point <NUM>, <NUM> that includes energy layers EL<NUM>-ELn. In this case, system <NUM> adds a second control point <NUM>, <NUM>, which consecutively takes every other energy layer EL<NUM>-ELn from the first control point <NUM>, <NUM>, which results in a first new control point <NUM>, <NUM> shown in <FIG>, and the second control point <NUM>, <NUM> shown in <FIG>. As a result, the new first control point <NUM>, <NUM> (<FIG>) has less energy layers EL<NUM>-ELn than the original control point shown in <FIG>. In addition, the first new control point has a number of energy layers calculated based on equation <NUM>, while the second new control point <NUM>, <NUM> has a number of energy layers calculated based on equation <NUM>.

In some implementations, an MU associated with the first control point beam <NUM>, <NUM> for a specific energy layer i may be determined by equation <NUM> above. In addition, the new first control point 1a, <NUM> and the added control point <NUM>, <NUM> have a beam energy determined by the following equations when N is even: <MAT> <MAT> if N is odd: <MAT> <MAT> where N is the total number of the energy layer.

As described in <FIG>, <FIG>, <FIG>, and <FIG> the energy layers EL1-ELn of a first control point <NUM> are split (<FIG>, <FIG>) or reduced (<FIG> and <FIG>), or its associated MU values are split (<FIG>, <FIG>) in a consecutive manner, more specifically splitting each energy layer EL to one of the new consecutive points. However, the energy layers EL associated with the first control point <NUM>, <NUM> may be split in other ways, such as, but not limited to, the MU associated with each energy layer EL of the control point, a total value of MUs per control point, a total number of energy layers EL associated with each control point <NUM>, or any other method.

It is emphasized that any method to re-organize and re-distribute the energy layers EL may be used, that the number of energy layers may not be maintained, i.e., one or more additional energy layers could be added as a re-sampling mechanism in block <NUM>. Similarly, in some examples, each energy level within a control point may be split differently than another energy level within the same control point. In some examples, energy layer will go through a sorting process that higher energy layer moving to control point <NUM> and lower energy layers moving to control point <NUM>.

<FIG> and <FIG> illustrate an exemplary implementation of a method to enhance a spot delivery sequence to thereby undertake a re-organization and re-distribution of a first control point (e.g., having a gantry angle <NUM>°). As illustrated in each of <FIG> and <FIG> one or more control points <NUM> may be divided into two or more control points (e.g., gantry angles of <NUM>° and <NUM>° or couch position of x = <NUM> and <NUM>) wherein the resultant, divided control points each have a position or gantry angle that is different from the position of the control point from which the division occurred, <NUM>. In an implementation, for example, the divided control points may be positioned adjacent to the first control point, <NUM>, such as, on either side of the first control point, <NUM>. In another example, the first control point, <NUM> may be split into more than two control points <NUM>, e.g., three or more. In addition to the energy layer re-distribution and re-organization such as <FIG>, each control point, <NUM>, might contain multiple energy layers; such that each energy layer contains a layer of spots and further wherein each spot has a position in an X, Y direction (as referenced from the beam eye view).

An example of a sequence re-organization and re-distribution will now be described. In an implementation, the a control point, <NUM> is capable of emitting beams <NUM>, wherein at least one of the emitting beams has energy layer(s) EL. In the described example, each energy layer EL<NUM>-ELn may be directed to one or more spots which may be, in a preferred form, optimized to deliver a robust proton treatment therapy to the patient <NUM> (e.g., to help ensure robust tumor coverage, spare organs that are not cancerous, and the like). It is to be appreciated that the control point splitting can be used in a variety of environments, including line scanning sequence particle therapy machines as shown in <FIG> and spiral scanning sequence particle therapy machines as shown in <FIG>. In each of <FIG> and <FIG>, the spots of the specific energy layer illustrated with respect to the first control point, <NUM> are divided into first and second new control points based on the machine delivery sequence. Accordingly, the radiation dose delivered from control point that was divided is approximately equal to the aggregate radiation dose that is delivered by first and second control points. The position or gantry angle of the first and second control points is so close relative to first and second control point such that the proton beam delivered through the continuous arc delivery approximately equals the proton beam had it been delivered at the position of the static control point from which first and second control points were derived. Accordingly, the result of the described re-distribution and re-organization from a primary control point divided into two or more sub-control point is interpolation of the energy and spot delivery sequence for a continuous and dynamic particle arc treatment. For clarity, the division of the control points may include, energy layers, spots, or a combination of energy layer and spot delivery sequence re-organization and re-distribution. And, for greater clarity, the foregoing re-organization and re-distribution technique may be incorporated at one or both of the treatment plan system to optimize the plan and in the hardware (e.g., by the gantry, beamline, cyclotron, or the like) in each cse to deliver an efficient and effective particle arc therapy.

In an implementation, blocks 342A and <NUM> may be implemented randomly for example, implementing block 342A one or more times than implementing block <NUM> one or more times, or implementing block <NUM> one or more times than implementing block 342A one or more times. The two blocks 342A and <NUM> are interchangeable and their interchangeability does not affect the treatment plan. However, the interchangeability of the two blocks 342A and <NUM> may affect the calculation time/speed for determining the treatment plan. For example, when system <NUM> executes block 342A first, the system <NUM> filters or removes low-weighted energy layers in the plan, which results in less energy layers and spots compared to when system <NUM> re-samples the control points <NUM> at block <NUM> first. More energy layers and spots take more time to calculate and optimize. Therefore, when system <NUM> executes block <NUM> before block 342A, it might take the system <NUM> longer to find a plan than when the system <NUM> executes block 342A before <NUM>. For example, assuming there are eight control points each having <NUM> energy layers and <NUM> spots, then if system <NUM> executes block 342A first, the result will remain eight control points <NUM> with <NUM> energy layers and <NUM> spots, which is less energy layers and spots than the original plan. Then system <NUM> executes block <NUM> and re-samples the control points <NUM>, where each control point has less energy layers than the original control points prior to filtration. However, if system <NUM> re-samples (block <NUM>) the control points <NUM> prior to filtration (block 342A, then system <NUM> has to perform calculations on a larger number of energy layers and spots, which increases the time to determine an optimization treatment plan.

In an alternative system, block <NUM> of <FIG> may be obviated and replaced with a user pre-defined treatment plan identified by a user (See <FIG>). For example, a practitioner may identify a pre-defined control point sampling frequency (CPSF) and, based on the desired CPSF, system <NUM> may process this information to pre-define energy layers and performing sorting of the control points to identify a plan.

At block <NUM> (similar to block <NUM>), system <NUM> determines an optimized treatment plan for patient <NUM> (e.g., a robust optimization or other types of optimizations described with respect to block <NUM>). The optimization plan determines a beam dose plan for the beam <NUM> to irradiate the tumor <NUM>. This optimization plan is based on the filtered energy layers of block 342A or the random control point re-sampling and energy layer re-organization and re-distribution at block <NUM>. Therefore, the robust optimization at block <NUM> is different than the robust optimization of block <NUM>, because each is based on the sample of control points <NUM> having different energy layers, e.g., the optimization at block <NUM> is implemented on the control points <NUM> having the identified energy layers, while the robust optimization at block <NUM> is implemented on the control points <NUM> having the filtered energy layers or resampled and reorganized energy layers or filtered spots.

As depicted and in some implementations, at block <NUM>, system <NUM> determines if the current plan quality is acceptable. Several methods may be used to determine if the plan quality is acceptable. For example, system <NUM> may determine if a current plan has reached target coverage or if an objective value is reached. For example, system <NUM> may consider a good quality plan to include a specific number of control points <NUM> within the arc rotation. Therefore, an acceptable plan quality may be identified when a plan has reached a threshold number of control points <NUM>. In other examples, a good plan quality may be identified when the plan has reached a specific proton beam delivery time that a user has defined.

In some implementations, plan quality may be assigned an objective value based on one more factors associated with a plan and a plan may be identified as a quality plan provided that the objective value is at or above an identified threshold object value. For example, system <NUM> may determines if an objective value associated with the treatment plan has increased, e.g., by <NUM>% from a previous objective plan and identify whether this increase is acceptable.

In some examples, the previous objective value is an average of one or more individual objective values. In an implementation, the objective value may be a measurement of time for the cancer treatment plan to be completed. The objective value may be other values as well. If the objective value has not increased by a threshold value (e.g., <NUM>%), then system <NUM> repeats blocks <NUM>-<NUM> until the objective value has increased by the threshold value. For example, the objective value may be a measurement of time for the cancer treatment plan to be completed. The objective value may be other values as well. If the objective value has not increased by a threshold value (e.g., <NUM>%), then system <NUM> repeats blocks <NUM>-<NUM> until the objective value has increased by the threshold value. The objective value may be determined based on an objective function, also referred to as an optimization function and cost value, shown in the below equation: <MAT> Where wtarget is a weight value associated with the target (i.e., tumor), penalties value, or an importance factor, and Ftarget is the difference between the current value vs. the goal that system <NUM> is aiming to reach, costlets, or indicators. wRisk1 is a weight value associated with the tissue or organs that are adjacent to the tumor; and FRisk1 is the difference between the current dose would be delivered to the specific organs vs the goal that system <NUM> is aiming to spare for this specific organs.

In some examples, Ftarget may be written as: FTarget = (Dtarget - D<NUM>)<NUM> where Dtarget is the goal of prescription dose to the target and D<NUM> is the current dose to the target. The bigger difference between the current value and objectives, the higher the cost value is, which also means the system need to further optimize the treatment plan to reach an optimized treatment plan.

In step-and-shoot mode, system <NUM> determines if the time for the gantry or couch rotation or translational movement, i.e., the rotation of output <NUM> with respect to table <NUM> is greater than the time to switch energy layers, then system <NUM> keeps at least one energy layer per control point <NUM>, e.g., (<NUM>-<NUM> energy layers per control point <NUM>). For example, if it takes three seconds for gantry to move between two consecutive control points <NUM>, and energy layer switching time is less than <NUM> seconds, then system <NUM> keeps at least one energy layer per control point <NUM>. In an implementation of the continuous delivery mode, system <NUM> may optionally retain the control point resampling until it reaches a desired arc sampling frequency or process as set out in a pre-defined manner (see, e. g, block <NUM> in <FIG>).

As previously discussed, higher control point sampling frequency indicates a smaller angle difference between the adjacent control points. In this situation, delivery of a beam <NUM> simultaneously with the gantry/couch rotation is a close approximation to delivering a beam <NUM> at a static control point angle. Desired arc sampling frequency means that there is enough control points within an arc so that there is almost no dosimetric difference between static step-and-shoot delivery and continuous delivery mode. Reaching a desired arc sampling frequency means that to achieve enough sampling control point so there is minimum dosimetric difference between static step-and-shoot deliveries and continuous proton beam arc delivery.

In a system <NUM> that utilizes an iterative optimization approach based on the random control point re-sampling, energy layer, spot delivery sequence re-organization, re-distribution, and energy layer filtration and spot number reduction. During the random iterative optimization process, each step may be arranged to generate a plan with an objective value. And once the objective value has exceeded a pre-defined threshold value, system <NUM> may reject the previous step and restarts the random process again. In some implementations, the optimization process includes, but is not limited to, radiobiology (RBE) optimization, physical dose optimization, and the like. For example, as illustrated in <FIG>, after system <NUM> filters the energy layers at block 342A or re-samples the control points <NUM> and re-organizes and re-distributes the energy layers at block <NUM>, 350A, if the objective value is higher than <NUM>% of the previous plan, the current filtered or re-sampled new control points will be rejected and the system <NUM> starts a new random search procedure based on the previous plan. If the objective value is lower than the previous plan, system <NUM> accepts the new filtered or re-sampled control points <NUM> and continues the random search based on the current plan.

At block <NUM>, an implementation of a system <NUM> may determine if a treatment plan has reached a user defined quality based on user preference, such as, e.g., a specific time, tumor coverage, or other measurable variables. If system <NUM> determines that the treatment plan has not reached the user defined quality, then system <NUM> reiterates block <NUM>, described above by selecting a random method between the energy layer filtration at block 342A or the control point re-sampling and energy layer re-distribution at block <NUM>. System <NUM> repeats this process until system <NUM> determines that the treatment plan reached is according to the user defined plan quality. Once, system <NUM> determines that the treatment plan reached is according to the user defined plan quality, system <NUM> can begin treatment of the tumor <NUM> according to the plan. System <NUM> randomly repeats blocks 342A and <NUM> as long as the treatment plan has not reached a user defined quality to increase or split the original coarse sampling control points (shown in <FIG>) into new and finite control points without causing unacceptable plan and dose calculation time, resulting in a step-and-shoot or a continuous delivery arc plan with desired control point <NUM> sampling frequency. Therefore, system <NUM> seeks to create enough sampling control points or a sampling rate for a continuous arc delivery. This results in a significantly reduced calculation time. For example, system <NUM> may deliver a beam <NUM> having at least one energy layer (e.g., <NUM>-<NUM> energy layers each outputted at as a separate beam) at each control point <NUM>, where system <NUM>, after executing blocks 342A and <NUM> determines that the SPArc includes <NUM> degrees of full rotation about the patient, with control points at every two degrees. In other words, system <NUM> delivers a beam <NUM> to the patient <NUM> at every two degrees or continuously delivers the beam during the gantry/couch rotation, delivering the most efficient treatment plan.

Referring to <FIG>, in some implementations, system <NUM> performs additional optional improvements to the treatment plan of <FIG>. At block <NUM>, system <NUM> performs random energy layer re-sampling on the previously reached treatment plan at block <NUM>. For example, system <NUM> randomly adds additional energy layers to the treatment plan at random control points <NUM> (i.e., existing control points <NUM>). System <NUM> may add an additional <NUM>% energy layer to further optimize the treatment plan.

At block 342B, system <NUM> performs energy layer filtration similar to the energy layer filtration performed in block 342A. Thereafter, system <NUM> may perform an optimization step at block <NUM> that is similar to the optimization referenced at blocks <NUM> and <NUM>. At block <NUM>, system <NUM> undertakes to determine if the treatment plan quality has improved compared to the last plan quality. If the system <NUM> identifies that the treatment plan quality has improved, then system <NUM> determines that the treatment plan quality may be further improved and performs block 342B-<NUM> until system <NUM> determines that the plan quality can no longer be improved. When system <NUM> determines that the treatment plan quality may not be improved, system <NUM> determines that it is the desired treatment plan for the patient <NUM>.

In some examples, the desired treatment plan may be based on user pre-defined factors. Referring to <FIG>, at block <NUM>, system <NUM> delivers an optimized and efficient cancer treatment plan with continuous beam delivery, based on one of the user preferences. For example, some clinicians prefer best plan quality, so they will choose the lowest objective value plan. Some clinicians prefer a faster delivery plan, so they might choose a plan with the shortest delivery time while compromise the plan quality. Or some clinicians will choose a moderated plan with both good plan quality as well as medium delivery time.

Traditional proton systems extract each energy layer one by one through an energy selection method. However, system <NUM> includes a proton system that will be able to extract multi-energy layers at same time. In this case, system <NUM> delivers a proton beam <NUM> having multi-energy layers at a control point <NUM> in a step-and-shoot or continuously without costing additional energy layer switch time. In energy re-distribution mechanism <NUM>, system <NUM> use the methods described in <FIG> to re-distribute the energy layer to the new control points <NUM>.

<FIG> is a schematic view of an example computing device <NUM> that may be used to implement the systems and methods described in this document.

The treatment planning and delivery mechanism includes computational based optimal beam angle (step and shoot) and optimal arc trajectory (continues arc) searching software platform and delivery framework to improve overall treatment plan quality and delivery efficiency. The optimal beam angle and trajectory-searching algorithm utilizes the entire solid angle search space for treatment dose optimization to further increase the therapeutic ratio. Optimal arc trajectories are generated and selected based on global optimization of the spot positions, spot weighting, and beam angles. The most efficient arc trajectories are selected for treatment delivery.

The computing device <NUM> includes a processor <NUM>, <NUM>, memory <NUM>, a storage device <NUM>, <NUM>, a high-speed interface/controller <NUM> connecting to the memory <NUM> and high-speed expansion ports <NUM>, and a low speed interface/controller <NUM> connecting to low speed bus <NUM> and storage device <NUM>.

In some implementations, the low-speed controller <NUM> is coupled to the storage device <NUM> and low-speed expansion port <NUM>. The low-speed expansion port <NUM>, which may include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet), may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device, such as a switch or router, e.g., through a network adapter.

For example, it may be implemented as a standard server 800a or multiple times in a group of such servers 800a, as a laptop computer 800b, or as part of a rack server system 800c.

Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), FPGAs (field-programmable gate arrays), computer hardware, firmware, software, and/or combinations thereof.

These computer programs (also known as programs, software, software applications or code) include machine instructions for this programmable processor and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal.

Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Moreover, subject matter described in this specification can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, data processing apparatus. The computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter affecting a machine-readable propagated signal, or a combination of one or more of them. The terms "data processing apparatus", "computing device" and "computing processor" encompass all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. A propagated signal is an artificially generated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus.

A computer program (also known as an application, program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit), or an ASIC specially designed to withstand the high radiation environment of space (known as "radiation hardened", or "rad-hard").

Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio player, a Global Positioning System (GPS) receiver, to name just a few.

One or more aspects of the disclosure can be implemented in a computing system that includes a backend component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a frontend component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such backend, middleware, or frontend components.

In some implementations, a server transmits data (e.g., an HTML page) to a client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device).

In certain circumstances, multi-tasking and parallel processing may be advantageous.

Claim 1:
A system for delivering a charged particle beam at a target in a patient, the system comprising an output device and data processing hardware, wherein the output device is configured to deliver the charged particle beam at a plurality of control points with respect to the target, wherein the output device is adapted to deliver a substantially continuous charged particle beam about the plurality of control points; wherein the system is configured to, via the data processing hardware:
identify a pre-selected control point sampling frequency, the control point sampling frequency indicating an angle or position spread between adjacent control points;
using such control point sampling frequency, derive the control points;
pre-define energy layers based on one or both of the control points and the control point sampling frequency, wherein each energy layer defines a depth within the target associated with a beam energy which is configured to reach the depth within the target; and
sort the energy layers between the control points.