Patent Description:
To treat regions within the body of the subject, however, the radiation must typically penetrate healthy tissue in order to irradiate the internal treatment volume and destroy pathological cells therein. In conventional radiation therapy, large volumes of healthy tissue can thus be exposed to harmful doses of radiation, resulting in prolonged recovery periods for the patient. Radiotherapy treatment plans are often constructed to achieve the desired on-site exposure whilst keeping the exposure of healthy cells to a minimum.

Many methods work by directing radiation at a tumor from a number of directions, either simultaneously from multiple sources or multiple exposures from a single source. The intensity of radiation emanating from each source is therefore less than would be required to destroy cells, but where the radiation beams from the multiple sources converge, the intensity of radiation is sufficient to deliver a therapeutic dose.

The point of intersection of the multiple radiation beams is herein referred to as the "target point". The radiation field surrounding a target point is herein referred to as the "target volume", the size of which can be varied by varying the size of the intersecting beams.

Radiation treatment typically takes place over one or a course of several sessions during which a delivered radiation dose is broken into a plurality of portal fields. For each field, a LINAC gantry is rotated to different angular positions, spreading out the dose delivered to healthy tissue. At the same time, the beam remains pointed towards the target anatomy, which may be placed in the isocenter of the beam by positioning the patient.

<CIT> describes a method for assessing machine trajectories for collision avoidance. A three-dimensional model of a patient based at least in part on data received from a three-dimensional imaging device is generated. The three-dimensional model of the patient is aligned with a coordinate system of a three-dimensional model of a radiation treatment machine. It is then determined whether a collision between the patient and the radiation treatment machine will occur at each one of a series of control points of the radiation treatment plan.

Such radiation therapy is rationally delivered with the radiation source revolving around the patient superior/inferior axis. The source trajectory is referred to as coplanar geometry. Coplanar source trajectories are simpler to plan and deliver.

Although adding beams from non-coplanar trajectories can improve the dosimetry and reduce normal organ doses from radiotherapy, such treatment methods are not easily achievable due to the difficulties in plan optimization, collision avoidance and the creation of an efficient beam path so that a non-coplanar plan can be delivered within the time allowed by clinical work flow.

The object of the present invention is solved by the subject-matter of appended independent claim <NUM>, wherein further embodiments are incorporated in the dependent claims.

In various embodiments a sophisticated system to address technical difficulties associated with non-coplanar treatment so the radiation dosimetry can be significantly, safely and efficiently improved is provided herein.

The term "subject" and "patient" are used interchangeably to refer to a mammal from which a biological sample is obtained to determine sensitivity to ionizing and/or non-ionizing radiation. Subjects can include humans and non-human mammals (e.g., a non-human primate, canine, equine, feline, porcine, bovine, lagomorph, and the like).

The planning target volume or "PTV" in a radiation treatment refers to the volume of tissue that is to be treated with radiation. The planning target volume (PTV) is created by adding a region of tissue to the clinical target volume or "CTV" that compensates for the errors and uncertainties that occur in treating a patient with radiation.

Non-coplanar radiotherapy using modern medical linear accelerators has been proposed, tested and implemented by many investigators. The first major problem in non-coplanar treatment is collision. The collision between the gantry, couch and patient has been a persistent problem in external beam radiotherapy, more so in non-coplanar treatments. One way to avoid the risk is a dry run with the patient on the couch and the therapist moves the gantry and couch cautiously to test delivery path. The method obviously consumes precious treatment room time and can result in plan revision if a collision is detected. Therefore, most departments also adopt a policy minimizing non-coplanar beam angles that are collision prone. Since both methods are undesired in automated non-coplanar plans involving a large number of beams, pre-planning collision modeling is generally a prerequisite. In one computerized prediction method (see, e.g., <NPL>) a simplified 3D surface of the machine is used and combined with experimental measurements of potential collision points. The patient is modeled as a rectangular box fixed to the couch. This method was later adopted and modified to improve visualization (see, e.g., <NPL>; <NPL>; <NPL>; <NPL>), incorporate patient specific external contours from the CT (<NPL>) and develop an analytical collision model that is, however, computationally inexpensive (<NPL>).

Another approach involved digitizing the surface of individual moveable components on external beam therapy machines using and generating an augmented reality environment for virtual operation and collision detection (see, e.g., <NPL>). However even using such methods, the individual patient individual are not easily integrated in the collision model and used to guide beam optimization. Additionally it is believed there has not been research on navigation through the non-coplanar beams, which requires complex choreography between patient couch and gantry. When a large number of non-coplanar beams are needed, manual navigation has typically been inefficient and ultimately impractical.

The complexity of the problem is illustrated by consideration of an illustrative, but non-limiting, schematic of a treatment room <NUM> as shown in <FIG> (see <CIT>). Radiation treatment room <NUM> includes linear accelerator (linac) <NUM>, optionally an imaging system <NUM> (comprising, e.g., x-ray tube <NUM>, first support <NUM>, imaging device <NUM>, and second support <NUM>), and patient table <NUM>. The elements of radiation treatment room <NUM> may be used to deliver treatment radiation to a patient according to a radiation treatment plan. Linac <NUM> generates and emits the treatment radiation, and is primarily composed of treatment head <NUM> and gantry <NUM>. Treatment head <NUM> includes a beam-emitting device (not shown) for emitting a radiation beam that can be used during calibration, verification, and/or treatment. The radiation beam may comprise electron, photon or any other type of radiation. Also typically included within treatment head <NUM> is a beam-shielding device or collimator (e.g., MLC, not shown) for shaping the beam and for shielding sensitive surfaces from the beam. Optionally accessory tray <NUM> is mounted on treatment head <NUM> and may be configured to receive and securely hold attachments used during the course of treatment planning and treatment. These attachments may include reticles, wedges, or the like for further defining field sizes and intensities. Treatment head <NUM> is fastened to a projection of gantry <NUM>. Gantry <NUM> is rotatable around gantry axis <NUM> before, during and after radiation treatment. Although clockwise rotation is indicated by arrow <NUM>, gantry <NUM> may also or alternatively rotate counter-clockwise according to some embodiments. Rotation of gantry <NUM> serves to rotate treatment head <NUM> around axis <NUM>. Patient table <NUM> is rotatable about axis <NUM> and translatable along axes <NUM> and <NUM> before, during and/or after treatment. During radiation treatment, treatment radiation is delivered from linac <NUM> to the beam-emitting device of treatment head <NUM> and is emitted therefrom as a beam. Given the many degrees of freedom offered, inter alia, by rotation of the gantry head, vertical and/or horizontal movements of the table, and varying configurations of a multileaf collimator (MLC), numerous beams at different distances, orientations, shapes and intensities can be provided. Selection and optimization of a manageable subset of such beams, particularly in the utilization of non-coplanar beams, has provided a previously intractable problem.

The method sand devices described herein solve this problem and provide efficient and effective treatment.

In various embodiments the approach described herein proceeds by:.

More particularly, in the approach described herein, the subject (patient) surface is measured (e.g., using a 3D optical camera) and then integrated into a model fo the treatment machine (e.g., the couch and gantry model) which is used to calculate a beam geometry solution space that guides the beam orientation optimization. Modeling the solution space has two advantages. First, the beams selected by the optimization algorithm are deliverable by the particular machine to that particular subject. Second, the methods can automatically expand the solution space to a non-isocentric surface that maximally utilizes the non-coplanar solution space for superior radiation dosimetry.

It is believed that there has not previously been a method, other than manual trial and error, to determine the order of beams and the path to navigate (the radiation machine and/or patient couch) from one beam of the selected treatment set to another. This posed a significant problem in treatments utilizing a large number of non-coplanar beams. For the first time a mathematical solution is presented herein that automatically determines the beam order and efficient path (machine/couch path) connecting these beams. The method significantly reduces treatment time, improves radiation dosimetry and safety, and reduces patient discomfort and undesired intrafractional motion.

More particularly, in various embodiments, the patient surface is digitized, e.g., using a 3D optical camera (Artec MH) and fit onto a model (e.g., a CAD model) of the treatment machine. An exhaustive search of all couch and gantry combinations is performed to determine the minimal distances between the radiation source and the patient. A cocoon is generated from the search and a beam orientation optimization is performed on the surface to determine the beam angles. A level set method as described herein is used to calculate the shortest path traversing the beams. The path is optimized to avoid collision and, optionally, to reduce travel time.

The method can be used in all external beam radiotherapy treatments. The methods and device described herein invention solve practical limitations associated with non-coplanar radiotherapy so the dosimetric gains can be realized without major modification to current practice and increased cost to either patients, manufacturers or the hospitals.

The surface of the subject/patient is mapped, using a scanner to generate a three dimensional model. Three-dimensional scanning can be accomplished using a variety of technologies that include inter alia, contact scanners that probe the subject through physical contact e.g. a CMM (coordinate measuring machine)) and non-contact active scanners.

Non-contact active scanners emit some kind of radiation or light and detect its reflection or radiation passing through object in order to probe an object or environment. Possible types of emissions used include light, ultrasound or x-ray. Such active scanners typically utilize either time-of-flight measurements or triangulation measurements.

Typical time-of-flight scanners (e.g., Microsoft Kinect2) utilize laser light to probe the subject. At the heart of this type of scanner is a time-of-flight laser range finder. The laser range finder finds the distance of a surface by timing the round-trip time of a pulse of light. A laser is used to emit a pulse of light and the amount of time before the reflected light is seen by a detector is measured. Since the speed of light c is known, the round-trip time determines the travel distance of the light, which is twice the distance between the scanner and the surface. If t is the round-trip time, then distance is equal to ct/<NUM> and the accuracy of a time-of-flight 3D laser scanner depends on the precision of the time measurement. The laser range finder typically only detects the distance of one point in its direction of view. Thus, the scanner scans its entire field of view one point at a time by changing the range finder's direction of view to scan different points. The view direction of the laser range finder can be changed either by rotating the range finder itself, or by using a system of rotating mirrors. The latter method is commonly used because mirrors are much lighter and can thus be rotated much faster and with greater accuracy. Typical time-of-flight 3D laser scanners can measure the distance of <NUM>,<NUM>~<NUM>,<NUM> points every second. Numerous time-of-flight 3D laser scanners are commercially available (see, e.g., Microsoft KINECT2®, FARO FOCUS3D®, NEXTENGINE®, and the like).

Triangulation based 3D laser scanners are also active scanners that can use laser light to probe the environment. With respect to time-of-flight 3D laser scanner the triangulation laser shines a laser on the subject and exploits a camera to look for the location of the laser dot. Depending on how far away the laser strikes a surface, the laser dot appears at different places in the camera's field of view. This technique is called triangulation because the laser dot, the camera and the laser emitter form a triangle. The length of one side of the triangle, e.g., the distance between the camera and the laser emitter is known. The angle of the laser emitter corner is also known. The angle of the camera corner can be determined by detecting the location of the laser dot in the camera's field of view. These three pieces of information fully determine the shape and size of the triangle and give the location of the laser dot corner of the triangle. In most cases a laser stripe, instead of a single laser dot, is swept across the object to speed up the acquisition process.

Structured light scanners also use trigonometric triangulation, but instead of looking at laser light, these systems project a series of linear patterns onto an object. Then, by examining the edges of each line in the pattern, they calculate the distance from the scanner to the object's surface. Essentially, instead of the camera seeing a laser line, it sees the edge of the projected pattern, and calculates the distance similarly. Various triangulation-based 3D laser scanners are commercially available (see, e.g., Microsoft Kinect1®, David Laserscanner SLS-<NUM>®, REAL3D™ scanner, 3D Underworld Open Source scanner, Artec EVA™, and the like).

Other suitable scanning technologies include laser phase-shift systems. Laser phase-shift systems are another type of time-of- flight 3D scanner technology, and conceptually work similarly to pulse-based systems. However, in addition to pulsing the laser, these systems also modulate the power of the laser beam, and the scanner compares the phase of the laser being sent out and then returned to the sensor.

Still other scanning technologies include conoscope holographic scanners. These scanners measure distances by using the polarization properties of a converging light cone that reflect from an object. An anisotropic crystal is used to split a light a ray that into two components that share the same path but have orthogonal polarizations. The crystal's anisotropic structure forces each of the polarized light rays to propagate at a different velocity, thus creating a phase difference between them. This phase difference enables the formation of an interference pattern that varies with the distance from the object under measurement. In classical holography, a hologram is created by recording an interference pattern formed between an object beam and a reference beam using a coherent light source. The two beams propagate at the same velocity (same refractive index), but follow different geometric paths. This means that when overlapped, the phase difference between the two beams depends only on the geometric path difference. This phase difference is responsible for the creation of a measurable interference pattern that can later be used to reconstruct the original light field. In conoscopic holography, however, a light beam that traverses an optically anisotropic crystal is split into two beams that share the same geometric path but have orthogonal polarization modes. The refractive indices of these two beams generally differ from each other. Therefore, after the two beams exit the crystal an interference pattern is generated. The features of this pattern depend on the distance from the light's source. Since both beams propagate through the same geometric path, conoscopic holography is highly stable in comparison to interferometry-based measurement techniques. Moreover, it is also possible to perform measurements using incoherent light.

In one illustrative, but non-limiting embodiment, the subject (patient) 3D surface is acquired (mapped) at the time of computerized tomography (CT)-simulation using a 3D surface imaging camera array. One illustrative, but non-limiting array consisted of <NUM> MicroSoft Kinect2 Cameras using the time-of-fly technology. Cameras can be mounted on the CT room ceiling above the CT couch. The cameras can provide a combined view of the patient anterior and lateral surfaces. To increase the field of view in the superior/inferior direction and limit occlusions, the couch can be longitudinally translated during the optical scanning procedure, providing a 3D optical equivalent to a topogram. In various embodiments, 3D measurements accuracy is about <NUM> or better, or about <NUM> or better, or about <NUM> or better with such a measurement geometry and scan times are typically less than about <NUM> minutes, or less than about <NUM> minutes or less than about <NUM> minute. In certain embodiments the 3D measurement corrects for subject involuntary subject movement (e.g., breathing).

An example of a scanned human is shown in <FIG>. An accurate model of the treatment device can be generated, e.g., using a 3D scanner. In certain instances, an accurate treatment machine model can be provided by the manufacturer. The CAD model represented in <FIG>, was based on a CAD model of the linac and couch provided by the vendor and cross-validated using 3D camera measurements. The patient model and device model are fused according to the tumor location, using methods known to those of skill in the art.

<FIG> shows an example of a virtual reality surface (VRS) that was generated. In this instance, the VRS employs both variable source-to-tumor distances and a <NUM> added safety margin, termed the collision gap buffer (e.g., to accommodate uncertainties due to patient setup variations, and system modeling, including isocenter accuracies, couch and gantry positioning accuracies, and camera measurement accuracy). It will be appreciated, however, that it is possible to generate a VRS using fixed source-to-tumor distances and/or other added safety margins (collision gap buffers). In certain instances the added safety margin, can be about <NUM>, or about <NUM>, or about <NUM>, or about <NUM>, or about <NUM>, or about <NUM>, or about <NUM>, or about <NUM>, or about <NUM>, or about <NUM>.

Beam angles that could not be utilized because the couch could not be moved far enough to get out of the way of the gantry, or the gantry would collide with the pedestal, are excluded from the VRS. In this illustrative, but non-limiting example, approximately <NUM>% of the 4π solid angle remains available.

<FIG> shows the remaining VRS if an isocentric geometry is employed (VRSi). VRSi is much more constraining than the proposed VRS non-isocentric calculation method for sites outside the cranium and upper head-and-neck. The collision gap buffer are computed using estimated geometric surface measurement uncertainties, patient setup variations, and system modeling, including isocenter accuracies, couch and gantry positioning accuracies and camera measurement accuracy.

In various embodiments the treatment plan optimization process selects the most effective beams from all possible beam directions. The angular resolution of the treatment plan can vary from about <NUM>° up to about <NUM>°, or from about <NUM>° up to about <NUM>°, or from about <NUM>° up to about <NUM>°. In certain instances the angular resolution is about <NUM>°, or about <NUM>°, or about <NUM>°, or about <NUM>°, or about <NUM>°, or about <NUM>°, or about <NUM>°, or about <NUM>°, or about <NUM>°, or about <NUM>°.

In the example presented herein, an angular resolution of ~ <NUM>° was selected which results in <NUM>,<NUM> uniformly distributed beams, termed the beam candidate pool. The algorithm presented herein handles finer beam angle resolution without significantly increasing computational time if meaningful gains are obtained. Patient specific VRSs are obtained and used as described above. Each beam is subdivided into individually calculated beamlets with square cross-sectional lengths corresponding to the multileaf collimator (MLC) leaf width (e.g., <NUM> at <NUM> SAD). The dose per fluence is calculated and stored in a database for use during optimization.

A Direct Aperture Optimization (DAO) algorithm is employed for intensity modulation and leaf sequencing that is also based on the idea of column generation and pricing. DAO combines fluence map optimization and leaf sequencing into a single step. It can easily take MLC deliverability constraints (such as interdigitation constraints) into account, as well as dosimetric effects such as transmission and the tongue-and-groove effect and efficiency measures such as beam-on-time.

In one illustrative, but non-limiting, approach, Dbk denotes the dose delivered to a volume from aperture a ∈ Kb in beam b ∈ B and F(z) the objective function associated with dose distribution z. The optimization problem is then formulated as follows (Equation <NUM>): <MAT> where Kb is the set of deliverable apertures at angle b, B' represents selected beam orientation sets, z is the 3D dose distribution, q is the 3D dose constraint. Instead of directly solving the large combinatorial model presented above, which would be computationally intractable, a column generation algorithm is used to determine the contents of B' while explicitly taking into account the treatment plan quality. The optimization starts from an empty solution set and for each iteration, beams from the remainder of the candidate beam pool B\B' are individually added to the selected beam set, and the direct aperture optimization problem is subsequently solved. The beam that contributes most to the plan optimization objective function is kept and all other beams are returned to the candidate beam pool. The iterative process continues until the desired number of beams is reached or the objective function plateaued.

To select a new beam, solving the aperture optimization problem with all potential beam candidates and choosing one beam that had the lowest objective function value would have been possible, but the computation time would have been clinically impractical. Instead, the benefit of adding a beam is predicted rather than explicitly computed. The price, i.e., the instantaneous change in the objective value of the optimal solution per unit of the constraint of solving the direct aperture optimization model with selected B' beams is used to predict the value of the new beam. This is known as the Karush-Kuhn-Tucker (KKT)-conditions for optimality. The beam orientation and aperture optimization problem is performed interleaved using CPLEX (Academic Research Edition <NUM>). As a baseline, the objective function F(z) is defined based on a linear approximation of equivalent uniform dose (EUD) (see, e.g., <NPL>) (Equation <NUM>): <MAT> where Gs, Gr, Gr<NUM>, <MAT>, and <MAT> are objective functions for organs-at-risk (OARs), PTVs, dose gradient as defined by the ratio between the <NUM>% isodose volume and PTV, and the volume of a specific organ receiving greater than d<NUM>, d<NUM>,. hs is used to adjust the relative weighting of average and maximum dose for serial or parallel organs. αm ≥ <NUM> for OARs, αm ≤ <NUM> for PTV, hs ≤ <NUM>, hr ≤ <NUM>, respectively. The weights among multi objectives αm's are fine-tuned to reach individual planning objectives. A shell-shaped structure is added as isotropic expansion of PTV to apply the dose gradient constraint. The assignment of a voxel that that lie within multiple OARs is given to the OAR with greatest optimization priority, which is manually determined.

The number of beams is determined based on the incremental gains in dose conformality (R<NUM>), which decreases as the number of optimized non-coplanar angles increases. Since there is not a clear plateau, we use a minimal number of beams to reach the optimization goal. Based on our preliminary study, the goal can be reached for all patients using fewer than <NUM> beams.

Because of the intractable problem size if using an unconstrained number of initial apertures, we limit the initial set of apertures per beam, denoted by K̂b ∈ Kb. At each iteration, we solve a restricted version of Equation (<NUM>) using only the apertures within K̂b. Given the corresponding solution, an optimization subproblem is solved that either (i) identifies one or more promising apertures that improve the current solution when added to K̂ or (ii) concludes that no such aperture exists and therefore the current solution is optimal.

<FIG>, panels A-C, shows the planning results of two lung cancer patients in comparison to their clinical plans. We found that in comparison to <NUM> field IMRT and VMAT plans utilizing two full arcs, the two non-4π plans (<FIG>, panels A and B) were clinically equivalent to each other but the 4π plans provided significantly steeper dose gradients (<FIG>, panel C), reducing R50 by an average of <NUM>%. Additionally, the equivalent uniform dose (EUD) of heart, esophagus, trachea, bronchus and spinal cord were reduced by <NUM>%, <NUM>%, <NUM>%, <NUM>%, and <NUM>% (p≤<NUM>), respectively. Lung V<NUM>, V<NUM>, and V<NUM> were reduced by <NUM>%, <NUM>% and <NUM>% (p≤<NUM>), respectively. These large dosimetric improvements would have enabled a dose escalation for all of the patients from the clinically delivered <NUM> Gy to a minimum of <NUM> Gy (range <NUM> Gy-105Gy) without increasing organ at risk (OAR) doses relative to the clinical treatment plans.

In certain embodiments, of the methods described herein treatment positions are optimized such that the gantry is often positioned close to the patient, couch, or pedestal, so the path between beams require continuous and explicit collision avoidance. This distinguishes the current problem from conventional node navigation schemes (e.g., in CYBERKNIFE® system) where line segment between pairs of nodes are designed to be clear of collision and the physical distance defines the association cost for the corresponding travelling salesman problem. The variable source-to-tumor distance gives rise to a continuous path optimization problem on the VRS that is generally neither Euclidean nor globally convex. To this end, an optimization problem is solved with a cost objective that incorporates feasibility considerations such as clearance and mechanical travelling range, acceleration limits to manage patient position stability, as well as efficiency considerations including total couch movement, gantry traveling distance, and total delivery time. In various embodiments the level set method as applied to robotic navigation in constrained spaces is utilized.

In order to optimize a smooth transition path that traverses all beams, we the planned beams are reparameterized with their associated source-to-tumor distances, and the virtual reality surface (VRS) with respect to the couch translation, rotation, and gantry angle. Nodes on the VRS generated from the treatment plans can be used to represent the planned beams as yq, q=<NUM>, <NUM>,. , Q and define the collision zone due to mechanical restriction and/or collision geometry as C ⊂ <IMG>. The goal is to seek a path y(s) ⊂ <IMG>, s ∈ (<NUM>,<NUM>) that meets the following three requirements:.

Optimizing y may be defined by the user by minimizing y, or by minimizing specific motions such as couch vertical due to maximum speed constraints or to assure patient comfort and stability.

To meet the three path requirements, an optimization framework is formalized by quantifying the requirements as either constraints or penalties. The first two requirements are constraints, the first stating that path intersects the beams and the second that the path does not intersect the collision space (Equation <NUM>): <MAT>.

To optimize the path length, we penalty function E is developed that considers the variation of the trajectory along each direction (Equation <NUM>): <MAT> where the penalty function is computed for machine degree of freedom i and interim path γi(s). λi is a penalty function that weighs the relative importance of linear accelerator degree of freedom i in the path optimization process. Ei penalizes the total amount of variation along degree of freedom i, discouraging long or cursive paths. Given the previous definition, the optimal path γ is determined by (Equation <NUM>): <MAT>.

This formulation allows us to set λi to zero for motions that have no impact on delivery accuracy or efficiency, as may be in the case of collimator rotation. On the other hand, λi is can be set to be large to penalize less comfortable motion types, such as couch rotation.

In various embodiments the treatment plan instruction file comprising, inter alia, a treatment beam set, a trajectory for the treatment device including, for example, gantry orientations, table orientations, trajectories of gantry and table between such orientations, and optionally apertures, is written to a computer readable medium. In certain embodiments the treatment plan instruction file contains one or more of the following: machine gantry and couch positions, multileaf collimator positions, beam intensities, and imager positions at a given time or plan delivery point. In typical embodiments, the file includes inter alia all delivery points describing machine and/or couch travel path(s) and timing (e.g., timing of travel paths and/or beam times) that are needed for a complete treatment.

Illustrative, but non-limiting computer readable media, include, but are not limited to magnetic media (e.g., hard, or "floppy" drives, optical media (e.g., CD, DVD), solid state drives, programmable array logic (PAL) chip(s), static RAM, and the like. In certain embodiments, the output is to local media and/or to remote media (e.g., a server, a cloud server, an internet site, and the like).

In certain embodiments, particularly where the device is a linac, the data file may be an xml file, although other file formats are contemplated.

In various embodiments, the methods described herein are performed using a treatment planning system.

<FIG> schematically illustrates components that may comprises one non-limiting embodiment of a treatment planning system. As illustrated in <FIG>, the treatment planning system <NUM> includes an input component (e.g., unit) <NUM>, a display component (e.g., unit) <NUM>, a memory <NUM>, a computer processor (e.g., a specialized or a general purpose computer processing unit) (CPU), <NUM>, and a communication component <NUM>. The CPU unit <NUM> is connected to the input component <NUM>, display component <NUM>, memory (storage unit) <NUM>, and, optionally, communication unit <NUM>. In certain embodiments the treatment planning system <NUM> is connected to a storage device <NUM> (e.g., a hard drive or solid state drive), or data server directly e.g., via a dedicated line), or via a network. Where the storage device <NUM> (e.g., data server) is on a network, the network can be a local network or a wide area network, e.g., the data server can be accessed through the internet or other wide area network). In embodiments, where the storage device is local, it can be a drive connected directly to bus <NUM>. In certain embodiments, the storage device/data server stores patient medical records. In certain embodiments, communication unit <NUM> of the treatment planning system <NUM> is connected to the storage device <NUM> via the network and exchanges data with the storage device.

In certain embodiments the patient to be treated has had computed tomography images obtained at treatment planning time or beforehand using a CT apparatus <NUM>. Treatment planning information and CT data/images acquired by the CT apparatus <NUM> (CT data) is stored on the storage device <NUM>. The CT data is typically three-dimensional data made of CT values recorded per small region called a voxel. The treatment planning system <NUM> can use the CT data in preparing the treatment plan.

In certain embodiments the patient to be treated has had 3D surface maps generated from the patient in treatment position which can be obtained at treatment planning time or beforehand using a 3D scanner <NUM>. Similarly 3D models of the treatment device can be scanned in or can be provided from a source (e.g., from the treatment device manufacturer). In various embodiments 3D patient and/or machine surface maps can be stored on the storage device <NUM> for use in treatment planning using the methods described herein.

When a healthcare professional (e.g., physician) acting as the operator inputs patient information (e.g., a patient ID or identifying information) through the input unit <NUM>, the treatment planning system <NUM> starts to prepare treatment planning information about the patient corresponding to the patient ID (see, e.g., process in <FIG>, step <NUM>). The input unit <NUM> outputs the input patient ID to the processing unit <NUM>. Based on the patient ID, the processing unit <NUM> retrieves the 3D map of the patient surface (step <NUM>), e.g., directly from 3D scanner <NUM>, or from storage device <NUM>, or from local input, e.g., from a flash drive, and merges this surface with the machine model (step <NUM>) to generate a virtual treatment room (step <NUM>). Based on treatment parameters (e.g., dose, planning treatment volume an location, etc. ) input by the user and/or stored on storage device <NUM>, for example along with CT information stored from CT <NUM>, and/or from storage device <NUM>, all feasible beam orientations free of collision (<FIG>, step <NUM>) are determined, e.g., by processor <NUM>. Processor <NUM> then selects and optimizes beams to meet treatment planning goals (step <NUM>) and, in certain embodiments, this process may utilizes further user input provided through input unit <NUM> and/or from data storage device <NUM>. After determination of an optimized beam set, a trajectory is calculate for the selected beams that is collision free and, optionally minimizes delivery time (step <NUM>). The treatment information (e.g., beams (e.g., orientations and apertures, delivery time(s), machine and couch positions, and trajectories is formatted into one or more instruction file(s) (e.g., 714a, 714b, 714c) that can be read and executed by the treatment machine controller <NUM> and saved to tangible media (e.g., optical media, magnetic media, solid-state media, etc.) (step <NUM>).

Claim 1:
A method of generating a radiotherapy plan for treating a subject on a radiotherapy device, the method comprising:
generating (<NUM>) a virtual treatment space by fusing a subject surface model with a model of the radiotherapy device ;
determining (<NUM>), based on the virtual treatment space, all feasible radiotherapy beams with orientations that avoid collision for the radiotherapy device and subject to provide a set of radiotherapy beams;
selecting (<NUM>), from the set of radiotherapy beams, a subset of radiotherapy beams that meet treatment goals for the subject;
calculating (<NUM>) a navigation trajectory that delivers the subset of radiotherapy beams free of collision;
generating and writing (<NUM>), to a tangible medium (<NUM>), instructions executable by the radiotherapy device in accordance with the subset of radiotherapy beams and navigation trajectory.