Methods for real-time image guided radiation therapy

Abstract

To provide a favorable method for real-time image guided radiation therapy.SOLUTION: Disclosed herein are systems and methods for guiding delivery of therapeutic radiation using incomplete or partial images acquired during a treatment session. A partial image does not have enough information to determine the location of a target region due to, for example, poor or low contrast and / or low SNR. The radiation fluence calculation methods described herein do not require knowledge or calculation of the target location, and yet may help to provide real-time image guided radiation therapy using arbitrarily low SNR images.SELECTED DRAWING: Figure 2B

Description

[Technical field]

[0001] (Citation of Related Application) This application claims priority to U.S. Provisional Patent Application No. 62 / 512,632, filed May 30, 2017, and U.S. Provisional Patent Application No. 62 / 537,384, filed June 26, 2017, the disclosures of both of which are incorporated herein by reference in their entireties.

[0002] (Technical field) Disclosed herein are systems and methods for guiding the delivery of therapeutic radiation using incomplete or partial images and / or image data obtained during a treatment session. [Background technology]

[0003] Image-guided radiation therapy (IGRT) uses images obtained before a treatment session to guide the application of therapeutic radiation during a treatment session. The goal of an IGRT system is to deliver a precise dose of radiation to a stationary or moving target inside a patient and avoid irradiating organs at risk. This often involves not only the generation of a treatment plan that takes into account target motion before the treatment session, but also gating or motion tracking during the treatment session (e.g., during radiation delivery). A treatment plan typically includes a fluence map or segmented fluence map that takes into account the radiation therapy machine geometry and a list of firing locations (also known as control points, which can be any location from which the therapeutic radiation source can apply radiation to the patient), from which instructions to the radiation therapy system can be generated so that the therapeutic radiation source can emit radiation that produces a dose distribution that is close to or matches the dose distribution prescribed by the treatment plan. The machine instructions may include, for example, a set of beam limiting device instructions (e.g., multi-leaf collimator or MLC instructions) indicating the beam intensity to be applied to the patient at each firing position (or control point) and / or the MLC configuration for each of those firing positions. Some radiation therapy systems may have integrated imaging systems to assist in target motion tracking during a treatment session. Some treatment plans may generate and store machine instructions directly without first generating a fluence map. However, it is generally understood that a set of machine instructions may be equivalently represented by a segmented fluence map together with the radiation therapy machine geometry and a list of firing positions or control points.

[0004] Currently, radiation therapy systems that track the motion of a target region do so by using an imaging system to obtain high SNR or high resolution images of the target region, determining the location of the centroid of the target region, shifting the treatment planning fluence map according to the back-projected shift in the location of the target centroid, and delivering the dose based on the shifted fluence map. That is, if the radiation target shifts to the left (or right), the treatment planning fluence map is shifted so that the generated beam also shifts to the left (or right). These steps may be repeated for the duration of the treatment session. Some systems may use the time-varying target centroid location to generate a motion model and predict the future location of the target region that may be used to gate radiation beam delivery. Some systems create a motion model that correlates surrogate marker motion with internal tumor motion and use the predicted tumor location (based on the surrogate marker location) to shift the fluence map.

[0005] However, these methods are limited to the acquired image x i requires high quality, high SNR complete or finished images (e.g., sufficient contrast data between the radiation target and the image background) otherwise the target centroid cannot be calculated accurately. Shifting the fluence map of the treatment plan based on inaccurate or erroneous target centroid location data may not improve the effectiveness of the treatment plan and may still result in irradiation of non-target areas. Such images acquired during a treatment session do not provide real-time target location data upon which the radiation therapy system may operate, since sufficient high quality images for centroid calculation often cannot be acquired at a sufficiently high frame rate (at least 1 or 2 images / second) due to the data acquisition requiring several minutes or imaging dose concerns (as in radiography or tomography). The inability to update or correct the delivery of radiation to account for patient and / or tumor motion in real time may result in radiation delivery to non-target areas. Summary of the Invention [Means for solving the problem]

[0006] Disclosed herein are systems and methods for guiding delivery of therapeutic radiation using incomplete or partial images and / or image data acquired during a treatment session. The partial images may not have enough information to determine the location of the target region, for example, due to poor or low contrast and / or low signal-to-noise ratio (SNR), but may be acquired quickly and frequently during a treatment session. The radiation fluence calculation method described herein does not require knowledge or calculation of the target location, but may be useful for providing real-time image-guided radiation therapy using arbitrary low SNR images. The low SNR or partial images may be acquired immediately prior to and / or during a treatment session (e.g., less than an hour, less than 30 minutes) at a relatively high frame rate (e.g., a rate similar to or greater than the average respiratory rate, at least about 2 frames / sec, at least about 4 frames / sec, etc.). The imaging data provided in the partial images, in combination with a radiation firing matrix (RFM) that may be calculated as part of a treatment plan, may be useful for guiding radiation delivery such that a predefined radiation dose is delivered to the actual real-time location of the target region over the course of a treatment session. Optionally, some methods may include storing partial images in a controller memory and generating a complete image by summing the partial images.

[0007] A variation of the method of image-guided radiation therapy using partial images may include calculating a treatment plan including a radiation firing matrix based on a tumor image data set with a sufficiently high SNR to identify the tumor location and the desired radiation dose to be delivered to the tumor. The RFM may be calculated in conjunction with the treatment plan. The treatment plan may be generated based on the desired dose constraints and / or the delineated target area. The treatment plan and the RFM may be calculated prior to the treatment session, for example, minutes, days, weeks, and / or months before the treatment session. A method for IGRT may include obtaining a partial image or low SNR image data of the tumor immediately before (e.g., minutes, seconds) and / or during the treatment session, and calculating a real-time or updated fluence map by multiplying the RFM of the treatment plan with the partial image. The fluence map may include a set of beamlets and beamlet intensities to be applied to the patient. The radiation fluence calculation may include a calculation to compensate for imperfect contrast between the tumor and background structures (e.g., filtering or masking the partial image). In some variations, masking or filtering the partial image may include limiting the field of view (FOV) of the partial image to help reduce the effect of background structures. In some variations, masking or filtering the partial image may include removing areas of the partial image that do not correspond to the location of the tumor region (with or without a margin around the tumor region). That is, a mask may be applied to the partial image to extract portions of the image that correspond to the location of the target region or radiation launch field. The methods described herein may further include calculating a segmented fluence map from the fluence map, the segmented fluence map including multi-leaf collimator (MLC) instructions and / or therapeutic radiation source firing instructions for each launch angle or position. The radiation therapy system may then deliver radiation to the patient according to the segmented fluence map for each launch angle or position.

[0008] The method for calculating the radiation fluence for delivery is to calculate a partial image of the target area xi and obtain the radiation firing matrix P and the partial image x i and calculating the radiation fluence to be delivered to the target area by multiplying the radiation firing matrix P with the partial image x. The radiation firing matrix P may be calculated based on a full image X of the target area. The full image X may be obtained during a previous imaging session. In some variations, calculating the radiation fluence may include point-wise multiplication or multiplication of the radiation firing matrix P with the partial image x. i The radiation firing matrix P may include convolving with

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[0009] Optionally, the method includes applying radiation to the target region, where applying radiation may include moving a radiation source of a radiation therapy system to a radiation beam takeoff angle α, the radiation therapy system further comprising a multi-leaf collimator having an array of leaves disposed across the radiation source; and applying a projection fluence f α into collimator leaf position commands and storing the leaf position commands in a controller memory, adjusting a position of each of the collimator leaves in accordance with the collimator leaf position commands, and emitting the radiation beam from the radiation source. The method may further include adjusting the projection fluence f until a desired fluence is applied. α The method may further include continually repeating the calculations and applying radiation to the target area after each fluence calculation. α Calculating the partial image x at the radiation beam departure angle α includes selecting a projection angle α and i Projection of x i,α Calculate the projected partial image x i,α , and storing the radiation firing matrix P α and the partial image x i,α Multiplying by (P α x i,α ) The angle-specific radiation firing matrix P α is the convolution operation

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[0010] Some methods optionally include prescan Based on the dose matrix D prescan Calculate D prescan and a dose matrix D calculated based on image X, and if the difference value exceeds a preselected threshold, generating a notification that the preselected threshold has been exceeded.

[0011] One variation of the system for radiation therapy comprises a rotatable gantry, a therapeutic radiation source mounted on the gantry, and a partial image x of the target area. i The therapeutic radiation source may include one or more imaging sensors mounted on a gantry to obtain a therapeutic radiation source and a controller in communication with the gantry, the therapeutic radiation source, and the one or more imaging sensors. The controller may be configured to calculate a radiation fluence to be delivered to the target region, where calculating the radiation fluence includes calculating a radiation firing matrix P and a partial image x i where the ray firing matrix P is calculated based on a previously acquired image X of the target area. The ray firing matrix P is

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[0012] The controller is X prescan Based on the dose matrix D prescan Calculate D prescan and a dose matrix D calculated based on image X, and if the difference value exceeds a preselected threshold, the controller may be configured to generate a notification of the preselected threshold being exceeded.

[0013] A method for evaluating treatment plan quality is also described herein. The method for evaluating treatment plan quality can include: powering on a radiation therapy system that includes a therapeutic radiation source and a controller that communicates with the therapeutic radiation source, the controller includes a processor and a memory with a treatment plan and positron emission activity data stored therein, the treatment plan includes a radiation firing matrix P and a planned fluence map, and the positron emission activity data includes line of response (LOR) data; using the controller, calculate a radiation fluence based on the radiation firing matrix P and the LOR data; using the therapeutic radiation source, emit radiation according to the calculated radiation fluence; measure the emitted radiation; calculate a fluence map based on the measured radiation; calculate a fluence difference between the calculated fluence map and the planned fluence map; and generate a notification of whether the calculated fluence difference meets or exceeds a predefined fluence difference threshold. The radiation therapy system may further include a treatment area, and the method may further include placing one or more radiographic films in the treatment area and measuring the emitted radiation by analyzing the radiographic films. The radiation therapy system may further include a radiation detector located opposite the therapeutic radiation source and in communication with the controller, and measuring the emitted radiation may include obtaining radiation data using the radiation detector and transmitting the radiation data to the controller. In some variations, the positron-emitting activity data may include diagnostic PET imaging data, for example, spatially filtered diagnostic PET imaging data. The radiation therapy system may include an array of PET detectors having a detector field of view, and the spatially filtered diagnostic PET imaging data may have a field of view corresponding to the detector field of view. Alternatively or in addition, the positron-emitting activity data may include a plurality of synthetic lines of response (LOR) generated based on diagnostic PET imaging data obtained on a PET imaging system and machine parameters of the PET imaging system.In some variations, the method may include adjusting the treatment plan if the calculated fluence difference exceeds a predefined fluence difference threshold. Adjusting the treatment plan may include updating the radiation firing matrix P. The treatment plan may include a planned dose map, and the method may further include calculating the dose map based on the measured radiation, calculating a dose difference between the calculated dose map and the planned dose map, and generating a notification of whether the calculated dose difference meets or exceeds a predefined dose difference threshold. For example, generating the notification may include calculating a match distance to fluence isodose (DTA) value and an absolute dose difference between the calculated dose map and the planned dose map for a plurality of time points, and determining whether a percentage of time points within the DTA value and within the defined absolute dose difference threshold exceeds a threshold percentage. By way of example, the threshold percentage may be about 95%, the DTA value may be about 3 mm, and the defined absolute dose difference threshold may be about 3% from the planned absolute dose.

[0014] Systems and methods for real-time segmentation of fluence maps calculated based on imaging data (e.g., partial images) acquired during a treatment session are also described herein. One variation of a radiation therapy system may include a continuously rotatable gantry, a therapeutic radiation source mounted on the gantry and movable by the gantry to multiple firing positions, a therapeutic radiation source mounted on the gantry and movable by the gantry to multiple firing positions, a multi-leaf collimator mounted on the gantry and located in the beam path of the radiation therapy source, one or more imaging sensors mounted on the gantry, and a controller in communication with the gantry, the radiation source, the multi-leaf collimator, and the one or more imaging sensors. The controller may be configured to calculate the imaging data from the one or more imaging sensors and the cumulative residual fluence map Δf cumulative and use the fluence map f calc Calculate the fluence map f calcThe radiation delivery instructions may be configured to generate radiation delivery instructions by segmenting the fluence map f into a set of radiation delivery instructions including MLC instructions and therapeutic radiation source instructions for each firing position across one or more gantry rotations. Each set of radiation delivery instructions may comprise instructions for the MLC and therapeutic radiation source for each firing position across multiple gantry rotations. The one or more imaging sensors may comprise one or more PET detectors, and / or the one or more imaging sensors comprise one or more X-ray detectors and / or one or more MRI sensors. The multi-leaf collimator may be a binary multi-leaf collimator. The MLC instructions may include leaf position instructions for each leaf in the MLC. The therapeutic radiation source instructions may include a beam pulse width, and / or a beam pulse intensity, and / or a number of beam pulses for each firing position. In some variations, the fluence map f calc Segmenting f calc into subfluence maps for each firing location and segmenting each of the subfluence maps into radiation delivery instructions. The MLC may be a binary MLC, and each subfluence map may have a probability factor. Alternatively, or in addition, the MLC may be a binary MLC, and each leaf of the MLC may have a probability factor that determines whether the leaf is in an open configuration and / or whether radiation is being emitted by the therapeutic radiation source at a selected fluence (e.g., pre-selected or calculated) level. In some variations, the cumulative residual fluence map Δf cumulative updating the delivery fluence map f based on the radiation delivery instructions delivery Calculate the delivered fluence map f delivery The fluence map f calc Increment the residual fluence map f by subtracting it from increment Calculate the incremental residual fluence map f increment The cumulative residual fluence map Δf cumulative By combining with the cumulative residual fluence map Δf cumulative This may include updating the

[0015] In some variations, the system may further include a plurality of sub-firing positions located between each of the firing positions, and the system may be configured to deliver radiation according to the radiation delivery instructions corresponding to the firing positions when the therapeutic radiation source passes a sub-firing position preceding a firing position. calc Segmenting f calc into a plurality of sub-fluence maps for each sub-firing location, and segmenting each of the sub-fluence maps into radiation delivery instructions for each sub-firing location.

[0016] One variation of a method for real-time fluence map segmentation for radiation delivery includes obtaining imaging data from one or more imaging sensors mounted on a rotatable gantry continuously during a session, and using the imaging data to generate a fluence map f calc and the cumulative residual fluence map Δf cumulative and calculating a fluence map f for one or more radiation emission positions over one or more gantry rotations during the session. calc and segmenting the radiation delivery instructions into sets of radiation delivery instructions. The radiation delivery instructions may include multi-leaf collimator (MLC) instructions and therapeutic radiation source instructions. Each set of radiation delivery instructions may include MLC instructions over multiple gantry rotations and therapeutic radiation source instructions. The MLC instructions may include leaf position instructions for each leaf in the MLC. The therapeutic radiation source instructions may include beam pulse width, and / or beam intensity, and / or number of beam pulses for each firing position. The fluence map f calc Segmenting f calcinto a plurality of subfluence maps for each therapeutic radiation source firing location and segmenting each of the subfluence maps into radiation delivery instructions. In some variations, the MLC may be a binary MLC, and each subfluence map may have a probability factor. Each leaf of the binary MLC may have a probability factor that determines whether the leaf is in an open configuration and / or whether radiation is being emitted by the therapeutic radiation source at a selected (e.g., pre-selected or calculated) fluence level. Optionally, the method may include determining a delivered fluence map f based on the radiation delivery instructions. delivery Calculate the delivered fluence map f delivery The fluence map f calc Increment the residual fluence map f by subtracting it from increment Calculate the increment residual fluence f increment The cumulative residual fluence map Δf cumulative By combining with the cumulative residual fluence map Δf cumulative By updating the cumulative residual fluence map Δf cumulative In some variations, a plurality of sub-firing positions may be located between each of the therapeutic radiation source firing positions, and the method may further include delivering radiation according to a radiation delivery command corresponding to the therapeutic radiation source firing position when the therapeutic radiation source passes a sub-firing position preceding the therapeutic radiation source firing position. calc Segmenting f calcinto a plurality of subfluence maps for each sub-firing position, and segmenting each of the subfluence maps into radiation delivery instructions for each sub-firing position. Optionally, some methods may include rotating a gantry, positioning a therapeutic radiation source mounted on the gantry at each firing position, and delivering radiation according to the radiation delivery instructions corresponding to each firing position. Obtaining imaging data and rotating the gantry may occur simultaneously. Obtaining imaging data and activating a therapeutic radiation source for radiation delivery may occur simultaneously. The one or more imaging sensors may be selected from the group consisting of a PET detector, an X-ray detector, and an MRI sensor. The present invention provides, for example, the following: (Item 1) 1. A radiation therapy system, comprising: A rotatable gantry; a therapeutic radiation source mounted on the gantry; Partial image of the target area X i one or more imaging sensors mounted on the gantry for acquiring a controller in communication with the gantry, the therapeutic radiation source, and the one or more imaging sensors; Equipped with The controller is configured to calculate a radiation fluence to be delivered to the target area, where calculating the radiation fluence comprises calculating a radiation firing matrix P and the partial image x i and wherein the radiation firing matrix P is calculated based on a previously acquired image X of the target area. (Item 2) The radiation projection matrix P is

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[0017] [Figure 1-1] 1A-1D depict a schematic diagram of one variation of a method for IGRT.

[0018] [Figure 1-2] FIG. 1E is a depiction of the radiation firing zone (RFZ) or target area and the planning target volume.

[0019] [Figure 1-3] Figure 1F depicts an example of a partial PET image, Figure 1G depicts an exemplary set of partial PET images, and Figure 1H depicts an example of a full PET image derived from summing the partial PET images of Figure 1G.

[0020] [Figure 2A] FIG. 2A depicts a block diagram of one variation of a method for delivering therapeutic radiation to a moving patient target region using partial image data acquired during a treatment session.

[0021] [Figure 2B] FIG. 2B depicts a flow chart diagram of one variation of a method for calculating radiation fluence for delivery based on partial image data acquired during a treatment session.

[0022] [Figure 2C] FIG. 2C depicts one variation of a method for treatment plan generation and verification.

[0023] [Figure 2D] FIG. 2D depicts one variation of a method for treatment plan evaluation and quality assurance.

[0024] [Figure 2E] FIG. 2E illustrates an example of a radiation firing matrix P.

[0025] [Figure 2F] FIG. 2F illustrates an example of a radiation firing matrix P.

[0026] [Figure 3A]FIG. 3A depicts one variation of a radiation therapy system that may be used with any of the methods described herein.

[0027] [Figure 3B] FIG. 3B depicts a cross-sectional view of another variation of a radiation therapy system that may be used with any of the methods described herein.

[0028] [Figure 3C] FIG. 3C depicts a schematic diagram of another variation of a radiation therapy system that may be used with any of the methods described herein.

[0029] [Figure 4] FIG. 4 illustrates an example sinogram of a radiation therapy system having 100 firing positions and a multi-leaf collimator with 64 leaves.

[0030] [Diagram 5] FIG. 5 depicts a plot conceptually illustrating a method of image-guided radiation therapy using partial images.

[0031] [Figure 6A] FIG. 6A depicts a flow chart representation of one variation of a method for delivering radiation based on a fluence map that is calculated / updated in real time during a treatment session. [Figure 6B] FIG. 6B depicts a flowchart representation of one variation of a method for real-time segmentation of fluence maps computed during a treatment session. [Figure 6C] FIG. 6C depicts a flowchart representation of one variation of a method for real-time segmentation of fluence maps computed during a treatment session. [Figure 6D] FIG. 6D depicts a flowchart representation of one variation of a method for real-time segmentation of a fluence map into sub-fluence maps during a treatment session.

[0032] [Figure 7A] FIG. 7A depicts an example plot of simulated emission / delivery of a fluence map discretized into separate fluence levels, comparing the planned fluence values, the fluence values ​​emitted after one iteration, and the fluence values ​​emitted after ten iterations. [Figure 7B] FIG. 7B depicts a graphical representation of the multi-leaf collimator leaf positions for four sub-firing positions of the firing position. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0033] Disclosed herein are systems and methods for guiding delivery of therapeutic radiation using incomplete or partial images and / or image data acquired during a treatment session. Some methods may include acquiring a partial image of a patient target area and multiplying it with a radiation firing matrix. The radiation firing matrix may be calculated based on the same data set used to calculate the treatment plan during a treatment planning session. The treatment plan may be generated in advance using patient images acquired during diagnostic imaging and / or a previous treatment session. The treatment plan may include a map or matrix that identifies one or more patient target areas (e.g., irradiation target areas such as planning target volumes or PTVs) and a desired radiation dose to be delivered to each of the one or more target areas. The radiation firing matrix (RFM) may be a matrix that specifies the transformation from a partial image to a fluence map, which may include a beamlet pattern (e.g., representing beamlets to be emitted by a therapeutic radiation source at various positions around the target area) and / or beamlet intensities to be applied to the patient during a treatment session. The RFM may be used to generate a fluence map that defines the radiation dose to be delivered to each patient target area. The fluence map may include a set of beamlets and beamlet intensities to be emitted at one or more launch angles (or launch positions) of a therapeutic radiation source (e.g., a linear accelerator or a linear accelerator and a multi-leaf collimator) to deliver a predefined radiation dose to one or more target regions. The fluence map may be converted (e.g., by segmentation) into a set of radiation therapy system machine instructions for execution by the radiation therapy system.

[0034] The treatment plan may include one or more radiation firing zones (RFZs), which may include the PTV and a margin around the PTV (as depicted in FIG. 1E). For example, the PTV may include the tumor region, and the margin around the PTV may account for PTV location estimation errors and / or movement of the PTV or tumor region, and / or possible location of the PTV or tumor region during radiation delivery, and / or geometric changes in the tumor region. The treatment plan may also identify radiation avoidance regions, such as organs at risk (OARs), where radiation exposure should be reduced or eliminated.

[0035] Also disclosed herein is a radiation therapy system configured to acquire real-time partial images during a treatment session and dynamically vary radiation delivery to a patient based on a fluence map derived by multiplying the RFM with one or more acquired partial images. One variation of the radiation therapy system includes an imaging system (such as an imaging radiation source with one or more PET detectors, and / or one or more X-ray detectors (e.g., an array of kV detectors and / or an array of MV detectors), and / or one or more MRI sensors), configured to acquire one or more partial images or imaging data in real time during a treatment session. The radiation therapy system may also include a therapeutic radiation source and one or more radiation beam shaping components disposed in a beam path of the therapeutic radiation source. The therapeutic radiation source and the beam shaping components may be mounted on a motion system (e.g., a gantry) configured to position the therapeutic radiation source relative to the patient area in real time. The beam shaping components may be configured to change their configuration in real time (e.g., change the size and / or shape of the MLC aperture) according to any real-time changes in the fluence map. The imaging system and therapeutic radiation source may be configured such that the latency between acquisition of a partial image and the corresponding radiation delivery to the patient is reduced, which in some variations may be less than about 5 minutes, e.g., less than about 1 minute, less than about 45 seconds, less than about 30 seconds, less than about 20 seconds, less than about 10 seconds, less than about 5 seconds, less than about 1 second, less than about 500 milliseconds, etc.

[0036] The RFM may be generated based on the treatment plan and multiplied with one or more partial images to calculate a fluence map. The fluence map may include a set of radiation beamlet weights (e.g., beamlet intensities) that may be segmented into a segmented fluence map, which may be used to generate a corresponding set of radiation therapy system instructions, such that the delivered dose distribution will deliver radiation to the patient in a manner that corresponds (i.e., matches or approximates) that specified in the treatment plan. Figure 4 depicts one representation of a segmented fluence map for a system that includes an MLC with 64 leaves, a therapeutic radiation source with 100 firing positions, and N slices or patient platform positions (i.e., beam stations) in the radiation therapy system. The treatment plan may include calculating an initial estimated fluence map F and an initial estimated dose distribution D based on the patient image and dose constraints. At the start of a treatment session and / or upon acquisition of one or more partial images or imaging data during a treatment session, a radiation firing matrix is ​​multiplied and / or convolved with the partial images to generate a fluence map F that can be used to direct radiation to real-time locations of the patient target area. i An example of a multiplication operation is a point-wise multiplication operation such as a diagonal matrix multiplication. An example of a convolution operation is a Toeplitz matrix operation. The fluence map F imay then be segmented, for example, into MLC instructions for radiation delivery. As previously mentioned, the shape and location of the patient target area may change between the time the treatment plan is generated and the treatment session. The shape and location of the patient and / or target area may even change during the treatment session. The imaging data acquired during the treatment session may be used to calculate a fluence map, whereby the dose delivered to the patient target area corresponds to the dose distribution defined in the treatment plan despite the shape and / or location changes of the target area and / or OAR area. The methods described herein use partial (e.g., low SNR) images to correct and / or update the fluence map. These partial images may be acquired quickly (and in some variations frequently) during treatment compared to full images. Using partial images instead of full images may allow the fluence map to be corrected or updated in real time.

[0037] (Partial and full images) A partial image may be any imaging data with linear characteristics, and the sum of multiple partial images results in a complete image. A complete image may have sufficient image data for treatment planning, target region boundary, target centroid calculation, and / or identification of anatomical structures or regions, while a partial image may not contain sufficient image data for these calculations or functions. A partial image acquired during a treatment session may have a lower SNR than the image from which a treatment plan is generated. More generally, the SNR of a partial image is less than the SNR of a complete image. FIG. 1F illustrates an example of a partial PET image, FIG. 1G illustrates a set of partial PET images, and FIG. 1H illustrates a complete PET image that is the sum of multiple partial images such as those in FIG. 1F-1G. Each of the partial images in FIG. 1F-1G was acquired within a 0.5 second time window, while a complete image is constructed from partial images acquired within a 10 minute time window. The higher the SNR of the partial images, the fewer the number of partial images required to form a complete image. That is, a partial image with a lower SNR value (e.g., about 7% or less) has less image data than a partial image with a higher SNR value (e.g., about 8% or more, about 10%), so a larger number of low SNR partial images are required to form a full image compared to a high SNR partial image. For example, a low SNR partial image may have image data that is one thousandth (1 / 1,000) of a full image (i.e., the sum of about 1,000 such partial images would result in a full image). A higher SNR partial image may have image data that is one hundredth (1 / 100) or one tenth (1 / 10) of a full image (i.e., the sum of about 100 or 10 such partial images would result in a full image, respectively). In some variations, the partial image may have an SNR that is at least about 30% less SNR than the full image. In some variations, the partial image may have an SNR that is less than about 40%, or less than about 50%, or less than about 55% of the SNR of the full image. In yet another variation, the SNR of the partial image may be about 0.18.

[0038] Alternatively or additionally, a partial image may comprise image data with a field of view (FOV) that is a subset or portion of the FOV of the full image. For example, a full image may comprise a 360 degree FOV, while a partial image may comprise a portion of that FOV (e.g., about a 30 degree FOV, about a 45 degree FOV, about a 50 degree FOV, etc.). In CT imaging, a partial image may comprise image data obtained from one or more low-dose x-rays. A partial image may comprise a single slice or a subset of a full tomographic scan (i.e., a full image includes all of the slices in a tomographic scan) or a limited set of projections. A partial image may alternatively or additionally comprise image data that is obtained within a period of time that is shorter than the period for obtaining image data for a full image. For example, if obtaining image data for a full image takes about 5 minutes, obtaining image data for a partial image may take about 1 minute or less, about 30 seconds or less, about 10 seconds or less, about 1 second or less. The partial images may be acquired in about 50% or less, about 40% or less, about 30% or less, about 20% or less, about 10% or less, about 1% or less, about 0.1% or less of the acquisition time for a full image. In some variations, the full image may comprise a diagnostic image acquired in a pre-treatment or diagnostic imaging session to identify the target region, including the size, shape, and location of the target region. In some variations, the full image may comprise tumor location and geometry data. Multiple full images of a tumor acquired over time may provide tumor motion data (e.g., tumor motion envelope). The sum of all partial images acquired immediately before and / or during a treatment session may be approximately equivalent to a full image that may be acquired before or after a treatment session.

[0039] In summary, a high SNR image can be represented as a complete image X. An imaging system operating with a radiation therapy system (e.g., a radiation therapy system having an on-board imaging system) can capture partial images x at a frame rate that is faster than the frame rate for the complete image X. i can be obtained or produced, thereby

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[0040] In the context of PET imaging, a full 3D PET image may be acquired from millions of positron annihilation emission pathways, or individual positron emission response families or LORs (i.e., response families defined by pairs of coincident photons emitted by a positron annihilation event). The LORs may be backprojected into a 3D volume. Typical LOR acquisition rates are on the order of 10,000 LORs / second, thus requiring up to hundreds of seconds to acquire sufficient data for a full PET image of acceptable quality for treatment planning (typically including data from millions of LORs), or sufficient data to identify the shape and location of a patient target region and / or to calculate a target centroid location. A partial PET image may comprise any arbitrary incomplete set of LORs. For example, some methods project the LORs acquired per second into a partial 3D volume x i A number of these partial 3D volumes x i The sum of may be a full image X (X = Σxi). A partial PET image may comprise a subset of the list-mode data. As depicted in Figures 1F-1G, such a partial PET image may not have any visually discernible cues.

[0041] In the context of MRI imaging, a full 3D MRI image is acquired from thousands of acquired pulses from an MRI imaging pulse sequence. The sequence acquires individual lines of k-space in the Fourier domain. The lines in k-space can be assembled and interpolated into a series of 2D or 3D Cartesian Fourier spaces. In 2D acquisitions, slices are individually reconstructed using an inverse 2D FFT and then assembled into a 3D stack. In 3D acquisitions, a direct 3D inverse FFT is applied and the 3D volume is reconstructed. Partial MRI images can be subsampled in k-space or by real-time 2D or 3D acquisition. Due to the linearity and noise properties of MRI, these partial images can be linearly combined to improve image quality.

[0042] In the context of CT imaging, a full 3D CT image can be obtained by taking 2D projection images (i.e., partial images) and backprojecting them into a 3D volume. Backprojection is a linear operator, so for an incomplete set of projections, or even a single projection, X = Σx i So that the partial 3D volume x i CT image may be backprojected to obtain a full CT image. Additionally, multiple low-dose (and low SNR) x-ray projection images may be summed to obtain a higher SNR image. The partial CT image may comprise one or more 2-D projection x-ray images. In some variations, the partial CT image may comprise a single x-ray projection image, as opposed to a full image, which may be a full-field tomographic CT image. Summing all of the x-ray projection images via a backprojection algorithm may result in a full CT image. In some variations, the x-ray dose may be gradually reduced as the treatment session progresses, allowing for a tradeoff between patient dose and image-guided accuracy.

[0043] Typically, IGRT methods require the acquisition of a full high SNR image to calculate the center of gravity of the target region and / or to generate a motion model used to adjust radiation delivery. Partial low SNR images may not provide sufficient data for these types of calculations. However, the method described herein adjusts radiation delivery based on a fluence map calculated using partial images acquired in real time, and emits radiation at a set of beam angles using a set of multi-leaf collimator instructions. That is, when the partial image is combined with a radiation firing matrix included in the treatment plan, an updated fluence map can be provided for radiation delivery, whereby the radiation therapy system delivers a predefined dose to the target region that has moved (and / or may continue to move). In some variations, combining the partial image with the radiation firing matrix can include multiplying the partial image with the radiation firing matrix to derive an updated (i.e., updated) fluence map. The updated fluence map may then be converted or segmented into a segmented fluence map that may be associated with a set of MLC leaf configurations and / or therapeutic radiation source firing instructions. In some variations, segmenting the updated fluence map may include calculating an MLC configuration for each firing position (e.g., firing or beam angle).

[0044] (Radiation Firing Matrix) A radiation firing matrix (RFM) may be a matrix that specifies a transformation from a partial image to a fluence map, which may include beamlet patterns and / or beamlet intensities to be applied to a patient during a treatment session. The RFM may represent a relationship between a fluence map F for radiation delivery to a patient region and an image X of that patient region. That is, the radiation firing matrix P may be any matrix such that F=P·X. The RFM may be calculated during a treatment planning session in conjunction with the calculation of the fluence map, which minimizes one or more cost functions, e.g., a cost function C(D,F) of a resulting dose distribution D and fluence F formed based on the radiation dose constraints and goals described and depicted in FIG. 2A and optional limits on F. Examples of cost functions may include, but are not limited to, a minimum dose to a target region, an average or maximum dose on an OAR, and / or fluence smoothness, total radiation output, total tissue dose, treatment time, etc. In some variations, generating the radiation firing matrix P may involve formulating an optimization problem to minimize a cost function C(D,F) and iterating through different sets of P such that the cost function C(D,F) is minimized while the following condition is satisfied:

number

[0045] The dose calculation matrix A may be calculated column-wise, for example, by ray tracing the aperture of each beamlet along a path through the RFZ or patient volume and calculating the beamlet's contribution weighted by 1 to each of the k voxels. The beamlet aperture may be an MLC aperture defined by a single MLC leaf opening (i.e., of a binary MLC or 2-D MLC). Examples of dose calculation algorithms that may be used in any of the methods described herein may include Monte Carlo simulation, collapsed cone convolution superposition, pencil beam convolution, and others.

[0046] The radiation firing matrix P may be a matrix that, when multiplied with the full image X, results in a predicted or desired delivered radiation fluence F that minimizes a cost function. The cost function may be a convex function, allowing for the use of well-known convex optimization algorithms such as gradient descent, fast gradient approximation, or interior point methods. The calculated radiation firing matrix P may represent a multiplication factor that relates the fluence F to the full image X. This relationship may be expressed as: at a particular time point during a treatment session, the fluence F of a partial image x obtained at that same time point may be multiplied by the radiation firing matrix P. i Based on the fluence f i can be used to update (e.g., f i =P x i ).

[0047] In contrast, typical treatment planning does not include the calculation of a radiation firing matrix. Treatment planning typically involves formulating an optimization problem to minimize a similar cost function C(D,F) formed from the desired constraints and objectives described herein, and iterating through different sets of F (without using P) as follows:

number

[0048] A typical treatment plan does not have any additional condition or requirement that the fluence F be related to the complete image X via a radiation firing matrix, such as F=P·X. That is, a typical treatment plan calculates the fluence F without any consideration of a multiplication factor that relates the fluence F to the complete image X.

[0049] (Fluence Map) The radiation therapy system may comprise a therapeutic radiation source, such as a linear accelerator (linear accelerator), and one or more beam-limiting components, such as jaws and / or dynamic multi-leaf collimators, both of which may be mounted on a motion system configured to move the radiation source and associated beam-limiting components around the patient area. The fluence map may comprise a set of beamlet intensities and launch positions (e.g., launch angles) that may be used by the radiation therapy system to position the radiation source and control the intensity and shape of the generated radiation beam, so that a selected / predefined dose of radiation is applied to the RFZ while limiting the amount of radiation applied to one or more organs at risk or OARs (e.g., avoidance volumes). A beamlet may be a portion of a complete radiation beam from a therapeutic radiation source, where the beamlet is defined by the multi-leaf collimator leaf opening at a particular launch position relative to the patient area. The beamlets have a fluence level, which may be determined at least in part by the size of the multi-leaf collimator leaf opening (e.g., the length and width of the collimator leaf, and / or the number of collimator leaves in an open configuration, and / or the position of one or more collimator leaves along its path of travel between a fully open configuration and a closed configuration), and / or the radiation intensity per therapeutic radiation source pulse, and / or the number of pulses fired when the therapeutic radiation source is located at a particular firing position. In some variations, the therapeutic radiation source may emit radiation pulses having discrete intensity levels (e.g., fluence levels), and the number of radiation pulses may be determined at least in part by the maximum firing rate of the therapeutic radiation source and the amount of time the therapeutic radiation source is located at the firing position. For example, in some radiation therapy systems, the therapeutic radiation source may be a linear accelerator that fires radiation pulses at two intensity values ​​(e.g., half intensity and full intensity, or 0.5 and 1). The linear accelerator may be mounted on a continuously rotating gantry, the duration that the linear accelerator is at any particular firing position and the firing rate of the linear accelerator is such that the linear accelerator can fire two pulses at each firing position.In this example, at each firing position, for each beamlet defined by a single binary multi-leaf collimator leaf, there may be five separate fluence levels: 0 (no pulse is fired), 0.5 (one pulse is fired at half intensity), 1 (one pulse is fired at full intensity or two pulses are fired at half intensity), 1.5 (the first pulse is fired at full intensity and the second pulse is fired at half intensity), and 2 (two pulses are fired at full intensity).

[0050] In some variations, the fluence map may be segmented to minimize the number of required MLC or other beam shaping component configurations required to deliver the fluence map. Based on the updated (segmented) fluence map calculated by multiplying the radiation firing matrix with one or more partial images acquired in real time, the motion system may be configured to move the therapeutic radiation source and / or components of the collimation subsystem to deliver the desired dose distribution to the patient. In some variations, the therapeutic radiation source may be mounted on a rotatable gantry, and the motion system may continuously rotate the therapeutic radiation source through each of the firing positions (e.g., firing angles around a circular gantry). The fluence map may define the beamlets emitted by the therapeutic radiation source to the RFZ or patient region as it moves to and / or through each firing position.

[0051] (method) Variations on methods for calculating radiation fluence maps using partial image data obtained immediately prior to and / or during a treatment session are described below.

[0052] Turning now to the figures, FIG. 1A depicts a diagnostic imaging session, where images of a patient and / or target region are acquired using any imaging modality. Examples of imaging modalities may include X-ray images (e.g., CT images), MRI, PET, ultrasound, etc. The acquired images may have a sufficiently high SNR so that the size, shape, and location of the anatomical features and target region, as well as the relative positioning of the target region to the patient anatomical features, may be identified. A radiation firing zone (RFZ) may be selected and delineated based on these images. By way of example, the RFZ may include a PTV (100) located at a first location (102) delineated in the images. In some variations, the RFZ may include one or more regions of interest, such as a tumor region, and / or a tumor region with a motion envelope representing an area around the tumor in which the tumor may move (e.g., a planned target volume or PTV (100)). Based on images acquired during a diagnostic imaging session in which the PTV (100) is at a first location (102), the treatment planning system may generate a treatment plan. The treatment plan may include a desired dose distribution D to be delivered to the PTV and / or RFZ identified in the images from the diagnostic imaging session. FIG. 1B depicts an example of a dose distribution D to the PTV (100) as defined in the treatment plan. For example, the treatment plan may include a dose map D with a high dose region corresponding to the first location (102), where the PTV (100) was located in the diagnostic images. In some variations, the treatment plan may also define radiation sensitive regions of the patient (e.g., organs at risk or OARs) where radiation exposure should be limited or minimized. Optionally, a radiation firing matrix P and / or a fluence map may be generated as part of the treatment plan.

[0053] During the period between the diagnostic imaging session and the treatment session, the PTV (100) may have moved from the first location (102). In some cases, the PTV (100) may continue to move during the treatment session. During radiation treatment delivery, the position of the PTV (100) may move periodically due to, for example, respiratory motion and shifting of the patient on the couch. Figure 1C depicts a situation in which the PTV (100) has moved from the first location (102) (where it was during the diagnostic imaging session) to a second location (104).

[0054] The systems and methods described herein may calculate an updated (i.e., latest) fluence map for delivery to a patient based on partial or low SNR images acquired during a radiation therapy session. These methods may include acquiring partial or low SNR images and / or image data at the beginning of a treatment session and / or during a treatment session, and multiplying a radiation firing matrix P with the partial images and / or image data to derive an updated fluence map. The updated fluence map may be segmented into a segmented fluence map, which may comprise a set of MLC configurations and radiation source positions and / or firing instructions. When radiation is delivered according to the updated (segmented) fluence map, a desired radiation dose may be delivered to a target region even if the target region shifts after treatment planning and / or before and / or during treatment. 1D conceptually depicts a delivered dose distribution when radiation is delivered according to the methods described herein, where the radiation delivery is based on an updated fluence map calculated by multiplying the radiation firing matrix P with the partial images and / or partial image data acquired during treatment. Even if the treatment plan is calculated based on the PTV (100) being located at a first location (102), radiation may be delivered to the actual location of the PTV (i.e., a second location (104)) during the treatment session.

[0055] FIG. 2A is a functional block diagram of a variation of a method for image-guided radiation therapy based on partial images, and FIG. 2B is a flow chart diagram of a method for calculating radiation fluence using partial images for image-guided radiation therapy. As depicted in FIG. 2A, a treatment plan (200) may be calculated prior to a treatment session. The treatment plan may comprise a radiation firing matrix P (200), which may be calculated, for example, based on one or more full or completed images X (202) and / or patient CT images (203). The RFZ (e.g., target region and / or PTV) and / or OAR may be identified or outlined using the full or completed images X and / or patient CT images and / or other complementary patient images in the same reference coordinate system. This information may also be used to calculate the radiation firing matrix P. Additional data regarding the RFZ and / or OARs (204), such as their size, shape, location, and degree of radiation sensitivity, maximum tolerable radiation exposure, and / or predefined radiation dose to be delivered to the irradiation target area, and / or other dose constraints (206), such as maximum and minimum doses to be delivered for each patient target area, may also be used in the calculation of the RFM. The patient CT images (203) may also be used for dose calculations, e.g., to predict the distribution of the delivered dose D0 when radiation is applied to the patient according to a radiation firing matrix P.

[0056] During a treatment session or fraction day, the radiation fluence delivered to the patient may be updated based on image data obtained immediately prior to and / or during the treatment session. Optionally, at the start of a treatment session (such as during the set-up phase of a treatment session), the patient pre-scan image X prescan can be obtained. prescan Image data from the pre-scan image X may be used to update the radiation firing matrix P (e.g., by multiplying it by a scalar coefficient) immediately prior to radiation delivery. prescan can be compared with the full image X. prescanIf the fluence map F = P X predicted during the planning stage is substantially different from the full image X (e.g., large-scale changes in target region location and / or shape), the clinician may determine that radiation treatment is not suitable for delivery to the patient, and the treatment session may be paused or stopped. In some variations, the fluence map F = P X predicted during the planning stage may be calculated using the fluence map F predicted using the pre-scan images. prescan =P·X prescan Compared to,

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[0057] Prior to the radiation delivery portion of the treatment session, radiation delivered to the patient is delivered in the form of a partial image x, as depicted in FIG. 2A. i (208) to obtain the radiation projection matrix P and the partial image x i The updated fluence map F is calculated by multiplying i The partial images can be one, two, three, or higher dimensional images. The updated fluence map F i is a partial image x i It may have dimensions independent of the dimensions of F. i can be one, two, three, or higher dimensional. iThe dimensions of may correspond to the machine parameters or configuration of the treatment system. For example, a 3D partial image may be 256 pixels by 256 pixels by 50 pixels, while a (3D) fluence map may be 100 pixels by 64 pixels by 50 pixels, corresponding to a treatment system having 100 firing positions and a one-dimensional multi-leaf collimator with 64 leaves and using 50 possible couch stop positions, or alternatively, 100 pixels by 64 pixels by 50 pixels, corresponding to a treatment system with 100 firing positions, a two-dimensional multi-leaf collimator with 64 leaf pairs, and 50 unique allowable couch (i.e., patient platform) stop positions. Due to potentially different dimensionality and size, the radiation firing matrix P is generally rectangular, and multiplication with P is linearized into a 1D vector form using column-major or row-major order. i The resulting fluence map can be linearized and converted back from a 1D vector to its proper N-dimensional form. The image-guided radiation therapy method optionally includes the MLC configuration. i To calculate the fluence map F i The radiation therapy system may then segment (212) the fluence map F (which may have dimensions corresponding to the machine configuration). The segmented fluence map may comprise a set of MLC and radiation source positioning and / or firing instructions for delivering beamlets to one or more RFZs as defined by the fluence map. The radiation therapy system may then segment (212) the fluence map F i The linear accelerator and MLC may be configured (e.g., by moving the linear accelerator and MLC to a firing position, adjusting the MLC leaf position, and optionally moving the couch) according to the calculated MLC configuration, as defined by: Optionally, at the end of or after the treatment session, the accumulated dose D i But, D treatment =ΣD i In some variations, the dose D i can be calculated based on data from a radiation detector of a radiation therapy system, such as a radiation detector (eg, an MV detector) located across from the therapeutic radiation source (eg, a linear accelerator).

[0058] The total delivered treatment session dose is calculated using linearity of matrix multiplication and partial images multiplied by the full image x treatment =Σx i This can be further analyzed using the fact that:

number

[0059] In some variations, images obtained immediately prior to treatment (e.g., in a pre-scan) can be used to estimate the dose to be delivered during the treatment session. When little or no anatomical changes occur between the time of the pre-scan and the treatment session, the delivered dose predicted in the pre-scan can be close to the actual delivered dose.

[0060] 2B summarizes one variation of a method (220) for continuously updating radiation fluence in image-guided radiation therapy using the RFM and one or more partial images. The method (220) may include generating (222) a treatment plan including a radiation firing matrix P based on one or more patient images, radiation dose constraints and goals, and patient target regions (e.g., RFZ, PTV or irradiation target region, OAR or irradiation avoidance region).

[0061] In some variations, the treatment plan and radiation firing matrix may be calculated based on patient target area data including the size, shape, location, degree of radiation sensitivity, and radiation dose constraints and goals of the RFZ and / or PTV and / or OAR. The radiation dose constraints may include, for example, a maximum allowable radiation exposure and / or a predefined radiation dose to be delivered to the irradiation target area or PTV, and / or a desired radiation dose distribution that may be determined by the controller under the guidance of a clinician and / or a user (such as a medical physicist or engineer). The radiation dose goal may include, for example, identifying and / or delineating the OAR, which should be maximally spared radiation. Generating the treatment plan may optionally include calculating a predicted dose distribution D and determining whether it meets the desired dose constraints and goals. Generating the treatment plan and radiation firing matrix P may be performed before and / or at the beginning of a treatment session.

[0062] The patient images may be complete images from which the patient target region (e.g., PTV), radiation firing zone (RFZ), and / or OARs are identified and / or delineated. For example, one of the patient images may be designated as a primary image-guided image X. Image X may be a sum of partial images, i.e., X=Σx i It could equally well be:

[0063] The method (220) calculates a ray firing matrix P for a sub-image x i The updated fluence map F to be delivered to the patient tumor region by multiplying with i One or more partial images and / or low SNR image data x may be used to calculate (226) i obtaining (224) a partial image x i can be optionally masked or filtered to include only areas within the RFZ. In some variations, the radiation firing matrix P and one or more acquired partial images x i The multiplication of a linear FIR filter f and a subimage xi , or pointwise multiplication of the partial image scaling vector s. For example, as depicted in Figures 2E and 2F, in these cases, the ray firing matrix P can be expressed as a Toeplitz matrix toep(f) or a diagonal matrix diag, respectively. The Toeplitz ray firing matrix P = toep(f) can be expressed as a Toeplitz matrix toep(f) or a diagonal matrix diag, respectively. i Multiplying by is a convolution

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[0064] Although the variations described herein may be in the context of applying radiation to a single target region, it should be understood that these systems and methods may be used to apply radiation to any number of target regions and / or any number of tumor regions or regions of interest generally. As described above, the target region may include an irradiation region, such as the PTV, and an irradiation avoidance region, such as the OAR. The radiation firing zone (RFZ) may include the PTV and a margin around the PTV.

[0065] Some variations of method (220) may include pre-processing the images used for image guidance. One variation of the method may include applying a linear pre-processing transform T to the full image X prior to computing P in step (222) and applying the same pre-processing transform T to each obtained partial image in step (224). * is the original corrected image, and the original partial image is x i * Then the images used in the treatment planning step (222) are X=T(X * ), and the partial image used in (226) is x i =T(xi * ) The linearity of the transformation ensures that the sum of the preprocessed partial images is still equal to the preprocessed full image, as shown below.

number

[0066] Some variations of method (220) may include, as a pre-processing step, using a contrast enhancement filter (CEF), e.g., a Difference of Gaussians (DoG) finite impulse response image filter, to improve image contrast in the full image used for image guidance. One variation of the method may apply a linear CEF to the full image prior to computing P, and apply the same CEF to each acquired partial image prior to fluence computation (e.g., prior to multiplying the partial image by P). X * is the original unfiltered image, and the original partial image is x i * Then the images used in the treatment planning step (222) are X=CEF(X * ), and the partial image used in (226) is x i =CEF(x i * ) The linearity of the filter ensures that the sum of the CEF filtered partial images is still equal to the full filtered image, as shown below.

number

[0067] Some variations of calculating fluence maps based on partial images acquired in real time just before and / or during a treatment session may optionally include a step for removing background anatomical structures (e.g., anatomical clutter such as bone structures) and other such noise that may be present in the partial images. Some variations may include masking out from the full and partial images only certain radiation firing zones (RFZs), e.g., target regions with additional extension, which can be done by using a pre-processing step T that includes point-wise multiplication of the images with a binary mask image that includes 0s in pixels outside the RFZ and 1s in pixels inside the RFZ. The RFZ of interest (e.g., in a fixed reference coordinate system) may include the entire range of possible tumor motions, including regular and dynamic respiratory motions, as well as irregular motions. Examples of irregular motions may include, but are not limited to, patient shifts while on the couch, couch deflections due to patient shifts and / or body weight, and setup errors. In some variations, the pre-processing step T includes a mask operation and a contrast enhancement filter. High contrast may be more easily achieved in localized areas of the image, such as near the target itself, for example within the radiation firing zone (RFZ).

[0068] In some cases, the uncorrected partial image or the partial image preprocessed with T has a resolution of Fi = P x i Since the expected delivered fluence in may be negative, negative radiation fluence may not be delivered. In the methods described herein that use one or more partial or low SNR (e.g., noise) images, the partial images may optionally be added to form a full image. Simple truncation or thresholding of negative values ​​appearing in the partial images may be achieved by using the property X=Σx iand modify the predicted dose equation D=A·P·X, possibly making it dependent on the individual partial images and / or shifting the average and / or modifying the equation in other ways. To preserve the partial image summation properties and the predicted dose equation, in some variations, "negative fluence" or "negative beamlets" may be kept carried forward to the next fluence map calculation, which may, for example, cancel future delivered fluence that would otherwise be fired. An example of such a "carry forward" algorithm is given below. 1. Initialize Co = O·F, i.e., a zero matrix of the same dimensions as the fluence. 2. The imaging system captures partial (low SNR) images. i * Get the image, x i =T(x i * ), which may include negative values. 3. Fluence F i is formed as follows:

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[0069] The same method can be applied to a maximum positive threshold: the sub-image can be truncated to a given positive threshold, and any intensity above the maximum threshold can be carried forward to the next sub-image.

[0070] (Dose variability calculation) Optionally, the expected delivered dose can be calculated based on the complete image. Due to variations in settings, imaging conditions, visceral motion, biological variations, and so forth, re-imaging the patient immediately prior to a treatment session (e.g., during a "pre-scan") may result in a complete image X that differs from the image X used in planning. prescan Therefore, the expected delivered dose D prescan may also differ from the D presented in the planning step that could have been used to secure clinician approval. To avoid requiring a new clinician approval, in some variations, the calculated / predicted dosimetric results (at treatment planning time) may include a probability distribution (e.g., as mean and standard deviation, or a full probability distribution) that represents the range of dose variability. Variability may be generated for the total delivered MU and individual DVH curves. The variability calculation is done for the full image X prescan and image X used in the plan and / or based on expected worst case expected variation in full images. This range of dose variability can be reviewed and approved by the clinician prior to the treatment session, and so long as the dose variation calculated at the time of treatment is within the approved range, the treatment session can proceed.

[0071] For example, in radiation therapy systems (e.g., biologically guided radiation therapy or BGRT systems) that are equipped with an imaging system that includes a PET detector, the calculated / predicted dosimetry results may take into account the inherent stochastic nature of PET imaging, patient biology and variability in PET uptake rates and standard uptake values ​​(SUVs), patient set-up errors, and other factors. prescan The expected difference between and X can be calculated by mapping the expected variability into image space. For example, an SUV variability of ±20% can be modeled as the image variability (X±0.2max(X)) / 1.2. The inherent noise in PET imaging using filtered backprojection reconstruction can be modeled as X+N(X), where N is a Gaussian noise function. All known variabilities can be mapped into image space and the resulting dose variability can be derived using a probabilistic model and the fact that D=A·P·X.

[0072] Method for Fluence Map Delivery with Projected Views The radiation therapy system may deliver radiation fluence per projection angle to achieve a desired dose distribution for the patient. A projection angle or view of a fluence map at a radiation beam launch angle or position may be a portion of the fluence map aligned with that particular launch angle or position. Delivery of the entire fluence map may be achieved through the sum of the separate fluence map contributions for each projection view. Given an image I, the projection of the image onto a view α may be a linear operation Rα(I). The linearity of the projection operator means that a homomorphic mapping may exist between the space of images and their projections, which may preserve the relationship between them. The definition of a partial image may be preserved in the projection space as follows:

number

[0073] This can be used to formulate radiation delivery using any of the methods described herein for a beam-by-beam (or projection view-by-projection view) delivery system that operates on partial projection images. The complete and partial images can be denoted as follows:

number

[0074] The treatment plan is a complete image X for each view α. α A separate ray firing matrix P for each view α is calculated using the corresponding projection of α where each view α may be a launch position or angle. As explained further below and depicted in FIG. 5, multiplication with the projected partial images gives a fluence map corresponding to a launch angle (or view) α. Since P is a matrix that maps the partial images to a fluence map, the fluence maps and partial images may be grouped based on their launch angle α. Thus, the matrix P may be a separate ray launch matrix P for each launch angle.α can be arranged as a block diagonal matrix from the set of α maps the projected partial images to a fluence map for the same launch angle. For example, for n launch angles (i.e., launch angle α),

number

[0075] This may correspond to a partial image guided radiotherapy method that operates separately from every projection α. i,α is acquired at a projection α, the radiation delivery device obtains a fluence map P α x i,α It may be possible to rapidly deliver a radiation dose corresponding to a given dose of radiation, which may be accomplished using any of the system radiation therapy methods described herein.

[0076] FIG. 5 depicts one variation of a method of image-guided radiation therapy using partial PET images, which can serve to improve contrast in sinogram projections by masking the partial images in image space and convolving them with the treatment plan (e.g., convolving them with the RFM of the treatment plan). The method includes acquiring a PET tomography data set over a selected time window and capturing partial PET images x i and partial PET image x i, forward projecting the masked partial PET image to a given launch location, multiplying with a radiation launch matrix, segmenting the fluence into separate MLC leaves at the particular launch location and patient platform location, and back projecting the radiation from the launch location. The equations listed in FIG. 5 depict the mathematical expression of the dose delivered at launch angle or view α (e.g., from the reference coordinate system of the radiation source located at launch angle α). The linear operator K represents the process of reconstructing the PET projection data y using filtered backprojection and masking the resulting image with a PTV mask. The linear operator R(α) represents the process of projecting the masked partial image K into view α. y The projection estimate is the RFM P (i.e., P α ) to generate the fluence delivered by the radiation therapy system. Matrix A may be a linear operator that maps fluence to dose in image space. The equation illustrated in FIG. 5 is an embodiment that may be used to map measured PET data y to dose. The operator illustrated in FIG. 5 is a linear operator on the PET measurements y and the dose D. The linearity property may allow matrix P to be optimized in the full image but delivered in the partial image.

[0077] Method for Real-Time Fluence Map Segmentation The fluence map can be delivered as a combination of beamlets fired separately, for example, at different firing positions and / or with different MLC configurations and / or therapeutic radiation source emission characteristics (e.g., pulse width, pulse strength, pulse frequency, number of pulses, etc.). Segmentation is the process by which the fluence map is converted into beamlets and / or radiation delivery instructions. The fluence map, which is calculated and / or adjusted in real time (i.e., during the treatment session) based on imaging data (e.g., partial image data) obtained during the treatment session, can be segmented into separate fluence levels and / or beamlets in real time so that the desired or planned fluence can be delivered to the current location and / or geometry of the target area. For example, the updated fluence map can be segmented into separate fluence levels and / or beamlets while the therapeutic radiation source and gantry are continuously rotating and delivering radiation to the patient. Reducing the latency between detection of target region location and / or geometry can help facilitate more precise and accurate radiation delivery to target regions, even when they move during a treatment session.

[0078] In some variations, the radiation therapy planning method includes calculating radiation fluence such that the delivered radiation meets treatment goals and objectives (e.g., organ at risk exclusion, target coverage, and minimum dose, etc.). Such a fluence map (i.e., planned fluence map) may indicate the fluence to be delivered at each firing position (or control point) in light of the geometric and / or motion limitations of the gantry and / or robotic arm on which the therapeutic radiation source is mounted, but the planned fluence map may not include specific radiation therapy system instructions (e.g., MLC leaf configuration, radiation source beam pulse parameters, etc.). When a treatment session begins, radiation delivery instructions may be generated by segmenting the planned fluence map and used to operate the radiation therapy system hardware. However, as imaging data is acquired during the session, the planned fluence map may be updated based on that data and / or replaced by a fluence map calculated based on the acquired imaging data. The radiation therapy system may then segment the updated or calculated fluence map into radiation therapy system instructions, which may enable delivery of arbitrarily shaped radiation fluence in response to real-time imaging signals (e.g., partial images or noisy imaging signals obtained during a treatment session that may reflect the real-time location of the target area).

[0079] In some variations, the radiation fluence for each firing position or control point may be updated during a treatment session, and / or a cumulative fluence map (i.e., the sum of all radiation fluences across all firing positions or control points) may be updated. Modifying radiation delivery based on real-time signals may facilitate radiation delivery to the target region despite any target motion and / or deformation. Although the examples herein describe real-time segmentation using PET imaging data, it should be understood that real-time segmentation may use imaging data from any modality, e.g., fluoroscopy, MR, ultrasound, etc. The fluence map and corresponding segmentation into radiation delivery instructions and / or beamlets may occur once per firing position, or once per several firing positions or control points, etc. Each time the fluence is updated, it may be translated into MLC configurations or apertures (i.e., apertures formed by opening and closing leaves of the MLC in a prescribed configuration) and / or other delivery commands for one or more firing positions or control points to deliver the desired fluence. FIG. 6A is a flowchart representation of a method of real-time segmentation during a treatment session. Although the examples herein are described in the context of a radiation therapy system with a continuously rotating circular gantry with firing positions at specific angles around the circumference of the gantry and a movable patient platform that translates within the bore of the gantry, it should be understood that one or more of these method steps may be applicable to a radiation therapy system of any gantry type (e.g., C-arm, robotic arm, circular gantry that moves along an arc segment, etc.) that may have any number of control points anywhere around the patient and / or radiation therapy system isocenter.

[0080] The steps depicted in method (650) may be repeated throughout a treatment session. Method (650) includes moving (652) a therapeutic radiation source to a firing position, loading (654) a fluence map corresponding to the firing position, updating (656) the fluence map (if required) based on imaging or other sensor data, and updating (657) a set of k MLC aperture shapes and / or other collimation component configurations {s j} and the corresponding dose unit {d j} (658) and determining the dose unit {d j} for j=1···k iterative delivery of aperture shapes / configurations {s j and adjusting (660) the MLC and / or other collimation components to have a .times. ...

[0081] In some variations, the therapeutic radiation source may be held stationary at the firing position as the MLC is adjusted through all k aperture shapes. The number of aperture shapes k may be a fixed or predetermined parameter, and / or may be based on the maximum fluence level of the fluence map, and / or may be determined at least in part based on the geometric shape complexity of the fluence map. In some examples, the number of aperture shapes k may be less than about 100, less than about 50, less than about 20, less than about 10, less than about 4, and / or less than about 2. In other variations, completing all of the k configurations may require k rotations of the therapeutic radiation source through all firing positions to deliver the total predefined dose, with all firing positions adjusting the MLC to only one desired configuration at each firing position in every rotation. In other variations, the therapeutic radiation source may continue to move as the MLC is adjusted through all k configurations for the k sub-firing positions located between the firing positions, as long as the total desired dose across the k configurations can be delivered before the radiation source is positioned at the next firing position. The method (650) may be repeated at each separate patient platform position (i.e., beam station, platform is stopped in place or not moving) or may be repeated as the patient platform is continually moving through the therapeutic radiation beam path.

[0082] However, as with any segmentation of a fluence map into radiation therapy system instructions, there may be discrepancies (i.e., fluence differences or deviations resulting in fluence residuals, or segmentation errors) between the planned fluence map and the radiation fluence deliverable by the system due to various factors including, but not limited to, machine geometry constraints (e.g., number, size, speed, range of motion, and / or shape of MLC leaves) and / or system capability constraints (e.g., therapeutic radiation source firing rate, pulse width, timing, and synchronization constraints). Segmentation errors may occur when discretizing a continuous fluence map into segmented fluence maps consisting of separate fluence levels. Such fluence segmentation errors may result from imperfect segmentation and imperfect dose calculations (e.g., the precise MLC leaf configuration may not be known at the time of treatment plan dose calculation). For example, the MLC leaf may be very close to the shape of the fluence to be delivered from a particular firing position, but there may be slight differences between the shape of the MLC aperture achieved by the MLC leaf configuration and the shape of the fluence intended to be delivered. Another example of a factor that may affect the segmentation error is that the fluence map for a particular firing position may not be deliverable with an integer number of MLC leaf configurations and / or may not be deliverable at segmented fluence levels. That is, the fluence deliverable at a firing position may be discrete fluence levels deliverable with a single MLC leaf configuration and / or therapeutic radiation source pulse width or number, and the calculated fluence map may specify fluence levels that cannot be discretized without a fluence remainder or residual amount. Furthermore, depending on the structure of the therapeutic radiation source, the number of segmented fluence levels may be limited, and as a result, the calculated fluence map may not be discretized into segmented fluence levels without a fluence remainder or residual.

[0083] When segmentation occurs prior to a treatment session, these segmentation errors can be mitigated by identifying the target regions affected by these fluence deviations or segmentation errors and adjusting the radiation therapy system instructions to deliver any differences in fluence throughout the treatment session. For example, the fluence deviations or segmentation errors can be compensated for by reweighting the dose levels associated with all of the MLC leaf configurations throughout the treatment plan. To mitigate dose calculation errors, the treatment planning system may perform additional dose calculations after calculating the correct MLC leaf configurations to properly model leaf edge effects. However, in treatment sessions where radiation treatment is based on imaging data acquired in real time, the cumulative fluence map deliverable by the system for the entire treatment session is not known because the fluence map from the treatment planning system is continually and / or dynamically updated based on real-time data. During a session, as radiation treatment progresses, the segmentation errors may accumulate and result in an unacceptable dose delivery profile over time. Real-time segmentation cannot use traditional methods to compensate for this and may include other methods to mitigate imperfect segmentation and dose calculation inaccuracies. Furthermore, in the course of real-time fluence map updates, the updated fluence map may include fluence values ​​that are not deliverable by the radiation source at the next firing location. This may result in residual fluence that may be carried over to the next fluence map update for another (e.g., future) firing location. For example, the residual fluence may be delivered by (repeatedly) emitting radiation from a therapeutic radiation source at the same firing location.

[0084] One method for real-time segmentation and mitigation of segmentation errors during a radiation therapy session involves the use of real-time acquired imaging data and a residual fluence map, Δf residual and use the fluence map f calcThe updated fluence map may be a fluence map calculated during treatment planning. residual is the fluence map f calc The calculated fluence map f may be an aggregate of segmentation errors and fluence deviations introduced during a treatment session prior to the calculation of f. In some variations, a fluence map for delivery at a particular firing location may be calculated by updating the fluence map corresponding to that firing location before the therapy source is moved to that firing location. calc may be segmented into radiation delivery instructions deliverable at that firing position over one or more gantry rotations. Some methods use residual fluence maps Δf throughout a treatment session. residual (e.g., once per firing position, once per gantry rotation, etc.). Each set of radiation delivery instructions can be system / machine instructions, such as MLC instructions (e.g., MLC aperture shape achieved by MLC leaf configuration, leaf position instructions for each leaf in the MLC, etc.), for each firing position across one or more gantry rotations, and therapeutic radiation source instructions (e.g., beam pulse width, number of beam pulses to be fired at each firing position, firing probability coefficient for each firing position or sub-firing position, and / or firing probability coefficient for each MLC leaf at each firing position or sub-firing position) for each firing position across one or more gantry rotations. The radiation beam pulse width and number of pulses for each firing position can be selected to approximate or match the fluence to be delivered at that firing position.

[0085] A radiation therapy system may be configured to include a therapeutic radiation source that can emit radiation at several discrete fluence levels to help mitigate segmentation errors and / or fluence deviations or residuals that may occur when a fluence map is discretized. In some variations, having an increased number of discrete fluence levels may help the radiation therapy system segmentation process generate a segmented fluence map that is closer to the updated fluence map, which may help reduce fluence residuals or residuals. For example, a radiation therapy system that includes a therapeutic radiation source and a dynamic binary multi-leaf collimator, both mounted on a high-speed rotating circular gantry (e.g., rotating at about 60 RPM to about 70 RPM), may have at least two discrete fluence levels, since each leaf may be either open or closed at a firing position. Additional discrete fluence levels may be added if the therapeutic radiation source can fire two or more pulses at each firing position and / or if the radiation pulse width can be adjusted to two or more widths. For example, a radiotherapy system with a binary multi-leaf collimator that can fire up to two pulses at each firing position with two possible pulse width values ​​for each pulse can have five separate fluence levels (0, 0.5, 1, 1.5, 2) for discretizing the fluence map. In some variations where the therapeutic radiation source can fire radiation pulses while being moved between firing positions (e.g., at sub-firing positions located between two firing positions), additional separate fluence levels can be available to discretize a fluence map with continuous fluence values ​​into a segmented fluence map with separate fluence levels. The fluence map can be segmented with more precision if the multi-leaf collimator can change its configuration at each sub-firing position.

[0086] One or more variations of the method for real-time segmentation and mitigation of segmentation errors during a radiation therapy session may include assigning a firing probability factor (i.e., a probability factor) for a particular beamlet at a particular firing position (or sub-firing position). The firing probability factor (i.e., a probability factor) defines the likelihood that a therapeutic radiation source will fire that beamlet and / or the likelihood that a therapeutic radiation source will fire a beamlet at a particular fluence level at a firing position or sub-firing position. Alternatively or in addition, a probability factor may be calculated for each MLC leaf and may define the likelihood that a binary MLC leaf is in an open configuration (or closed configuration) at a particular firing position (or sub-firing position). The firing probability factor may be derived from a fluence map calculated for that firing position (or sub-firing position). Stochastic radiation delivery may help reduce fluence residuals when the fluence value to be delivered is between discrete (e.g., deliverable) fluence levels. In variations where the calculated fluence for a firing position is between two discrete fluence levels, the fluence for delivery (i.e., the segmented fluence level) may be rounded up or down to the next fluence level depending on the firing probability factor. For example, when the fluence value falls below a threshold fluence level (e.g., below a minimum deliverable fluence level at a firing position), the firing probability factor may represent the likelihood that the fluence for delivery will be rounded up to the threshold fluence level (e.g., the likelihood that one or more MLC leaves are in an open configuration). The probability factor represents the probability of firing a beamlet at a particular fluence level (e.g., by opening or closing MLC leaves and / or adjusting linear accelerator pulse emission parameters such as pulse width and / or pulse intensity according to the probability factor) and may be calculated by dividing a fractional fluence increment (e.g., which may be the difference between the desired fluence and the deliverable fluence) by a preselected fluence level to be delivered at the sub-firing position (e.g., the minimum deliverable fluence).For example, a therapeutic radiation source such as a linear accelerator may be configured to emit radiation pulses at two intensity levels (e.g., 0.5 and 1) and emit up to two pulses at each sub-firing position. Thus, the linear accelerator may be capable of delivering and emitting four non-zero fluence levels (0.5, 1, 1.5, and 2) per sub-firing position. For a single firing position that groups several sub-firing positions (e.g., four sub-firing positions per firing position), the therapeutic source may deliver multiple non-zero fluence levels (e.g., 16 non-zero fluence levels) and vary the MLC configuration at all sub-firing positions. The variations described herein have four sub-firing positions per firing position, but in other variations, there may be any number of sub-firing positions per firing position, e.g., 2, 3, 4, 5, 6, 7, 8, 10, 12, 20, more than 20, etc.

[0087] In a radiation therapy system in which the linear accelerator is rotated multiple times continuously through the firing positions, the probability factor may determine the likelihood that the MLC leaves are in an open configuration at a particular firing position each time the MLC and linear accelerator are at that firing position. Alternatively, or in addition, the probability factor may determine the likelihood that the linear accelerator will fire a beamlet at a particular fluence level each time the MLC and linear accelerator are at that firing position. For example, if a calculated fluence map indicates that a fluence value of 1.7 should be delivered at a certain firing position, and the linear accelerator is configured to deliver fluence only at fluence levels (0, 0.5, 1, 1.5, and 2) where the minimum deliverable fluence level is 0.5, then the fluence level of 1.7 may be resolved into two fluence values, namely 1.5 (which is a deliverable fluence level) and 0.2 (which is a non-deliverable fluence value). A firing probability factor value of 0.4 may be calculated by dividing 0.2 (the difference between the desired fluence value and the deliverable fluence level) by 0.5 (e.g., the smallest deliverable unit of fluence at a sub-firing location, or the preselected fluence level to be delivered at a sub-firing location), i.e., (1.7-1.5) / 0.5=0.4. During delivery, a fluence level of 1.5 may be emitted by the linear accelerator each time the linear accelerator is positioned at (and / or passes through) a firing location, but an additional 0.5 fluence level is delivered according to the probability factor. That is, each time the linear accelerator is positioned at a firing location, there is a 40% chance that a fluence level of 2 is emitted (i.e., 1.5 with the extra 0.5 fluence level) and a 60% chance that a fluence level of 1.5 is emitted (i.e., without the extra 0.5 fluence level). Alternatively, or in addition, each time the linear accelerator is positioned at a launch position, there is a 40% chance that the MLC leaves are in an open configuration at a particular sub-launch position such that a fluence level of 2 is emitted, and a 60% chance that the MLC leaves are in a closed configuration at a particular sub-launch position such that a fluence level of 1.5 is emitted.As the linear accelerator is continually rotated around the patient, over multiple passes the fluence delivered to the patient from that firing position may converge to 1.7. In some variations, a random numerical value between 0 and 1 may be selected from an unbiased uniform random distribution and compared to a firing probability coefficient to determine whether an extra 0.5 fluence level should be delivered. In this example, if the random numerical value is less than or equal to 0.4, an extra 0.5 fluence level is emitted by the linear accelerator (i.e., the linear accelerator emits a fluence level of 2) and / or the MLC leaves are in an open configuration, and if the numerical value is greater than 0.4, an extra 0.5 fluence level is not emitted by the linear accelerator and / or the MLC is in a closed configuration. Such stochastic firing may allow the radiation therapy system to increase the accuracy and / or precision of delivery beyond the amount of the minimum deliverable dose, and may not require carrying any state information associated with the firing position. This approach may be advantageous, for example, when delivering a dose to a moving target.

[0088] FIG. 7A depicts one example of a simulation resulting from delivering fluence based on probability coefficients as described above. In this example, a therapeutic radiation source (e.g., a linear accelerator) is continuously rotated around a patient and the delivered fluence for each MLC leaf opening of a 64-leaf binary MLC is plotted. The first solid line (700) represents the planned fluence values ​​to be delivered for each leaf based on a fluence map for a certain launch position, which may be calculated using any of the methods described herein. For example, these fluence values ​​may be calculated by plotting the RFM for the corresponding launch angle against a partial image (x i ) is calculated by multiplying the fluence map (F i) when the fluence map is segmented into beamlets deliverable by the linear accelerator, the continuous fluence values ​​can be discretized into deliverable fluence levels (e.g., 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4) that can be delivered over a number of sub-firing positions corresponding to a firing position. In the example of FIG. 7A, the fluence values ​​can be delivered over four sub-firing positions, and up to two fluence levels (0.5 and 1) can be delivered per sub-firing position. The second line (702) with square indicia represents the fluence levels that can be delivered for each leaf after the linear accelerator has orbited once around the patient (e.g., one rotation, one iteration or pass of the gantry). As can be seen from the plot, when the fluence value is between fluence levels, over a single iteration, the delivered fluence is rounded up or down to the fluence level based on the calculated probability factor. In this example, the maximum planned fluence that can be delivered at a launch location across all MLC leaves (i.e., according to a fluence map calculated using any of the methods described herein) is 4, which can be decomposed into a maximum deliverable fluence level of 1 for each of the four sub-launch locations. For example, as depicted in FIG. 7A, the planned fluence value for MLC leaf number 40 is approximately 1.6. Since the fluence level per sub-launch location is 1, the probability factor can be (1.6-1) / 1=0.6. Based on a probability factor of 0.6, there is a 60% chance that the delivered fluence level for MLC leaf number 40 is 2 and a 40% chance that the delivered fluence level for MLC leaf number 40 is 1. For example, with a probability factor of 0.6, there can be a 60% chance that MLC leaf number 40 is open and a 40% chance that MLC leaf number 40 is closed. Each MLC leaf may have a different probability coefficient that is calculated based on the fluence map and may be calculated by the system controller. Referring back to line (702) in Figure 7A, in this particular single iteration, the fluence level for MLC leaf number 40 is 2.However, over the ten iterations represented by the third dotted line (704), the average emitted fluence for MLC leaf number 40 converges close to a fluence value of 1.6. That is, by emitting a fluence level of 2 for about 60% of the time and a fluence level of 1 for about 40% of the time (e.g., MLC leaf number 40 is in an open configuration about 60% of the time and in a closed configuration about 40% of the time), the average emitted fluence may eventually converge toward about 1.6. Stochastically firing at certain discrete fluence levels over multiple passes or iterations may facilitate precise delivery of the planned fluence value, which may help to offset and / or reduce any fluence differences when the planned fluence is discretized into beamlet fluence levels as part of the segmentation process.

[0089] In some variants, the fluence map f calc Optionally, segmenting f calc into a plurality of sub-fluence maps for each firing position corresponding to sub-firing positions located between the firing positions, and segmenting each of the sub-fluence maps into radiation delivery instructions for each sub-firing position. For a radiation therapy system with a binary MLC, each sub-fluence map may additionally have a firing probability coefficient, and each leaf of the MLC may be configured to open or close based on the firing probability coefficient.

[0090] One variation of a method for real-time segmentation of updated fluence maps and mitigation of segmentation errors is depicted in FIG 6B. The method (600) includes acquiring (602) imaging data during a treatment session (e.g., from an imaging sensor of any modality, such as a PET detector, an X-ray detector, an MR sensor, etc., that may be rotatable around the patient) and generating (603) the imaging data and a residual fluence map Δf residual Using and, FP m By updating the fluence map corresponding to the launch position FP m Fluence map for fcalc 604, and calculating the fluence map f calc FP m (e.g., segmenting (606) the radiation delivery instructions into a set of segmented fluence maps that specify beamlets for one or more firing positions or angles. In some variations, the fluence map f calc Calculating (604) may be performed using an existing fluence map from a treatment plan loaded onto the radiation therapy system (i.e., fluence map F) or a previously updated fluence map during an earlier phase of a treatment session (i.e., fluence map F). i or previous f calc ) may include updating the fluence map f calc may be a fluence map across the entire treatment session (i.e., across all firing locations, all patient platform locations, or beam stations, for the entire duration of the treatment session), and / or may be a fluence map across a subset of firing locations, beam stations, and / or treatment session time intervals, and / or across one or more of the following firing locations (e.g., FP m , F.P. m+1 ), which may be a fluence map for each MLC leaf for each firing location and / or sub-firing location. Segmenting (606) may also include calculating one or more probability coefficients for each MLC leaf for each firing location and / or sub-firing location, as described above. Optionally, the radiation therapy system may then include delivering radiation to the patient according to the radiation delivery instructions. For example, the method (600) may optionally include segmenting the therapeutic radiation source into firing locations FP m and / or adjusting the MLC leaf configuration to deliver radiation according to the radiation delivery instructions. In some variations, after calculating the segmented fluence map, the method (600) may further include updating a residual fluence map that may be used for future fluence map updates. The method (600) may generate a delivered fluence map f based on the radiation delivery instructions. deliveryand / or calculating a segmented fluence map (608), and calculating the delivered fluence map f delivery The fluence map f calc The residual fluence map Δf residual and updating (610) the residual fluence map Δf residual may occur before, during, and / or after the update. In some variations, the radiation delivery instructions may include radiation delivery source instructions for multiple gantry rotations (e.g., one set of instructions for each gantry rotation at the patient platform position or beam station).

[0091] In some variations, real-time fluence map segmentation and segmentation error mitigation uses partial images to generate a fluence map (e.g., by multiplying the partial image with the treatment planning radiation firing matrix) and a residual fluence map Δf residual An example of a method (620) is depicted in FIG. 6C. The method (620) may include acquiring (622) one or more partial images during a treatment session (e.g., from an imaging sensor of any modality, such as a PET detector, an X-ray detector, an MR sensor, etc., that may be rotatable around the patient) and calculating FP m The RFM from the treatment plan corresponding to the radiation firing matrix P for the firing angle α α ) by one or more partial images to obtain the launch position FP m Fluence map for f calc and the residual fluence map Δf residual and then calculating 624 the fluence map f calc Launch position FP m for (and optionally for each radiation firing position during a treatment session, or for the next firing position FP m+1 and segmenting (626) the fluence map f for the firing location FPm into a set of MLC and therapeutic radiation source instructions. calc is one or more partial images at their launch positions FPm RFM from the treatment plan corresponding to α )

[0092] Alternatively or in addition, a fluence map calc Calculating (624) may be done using an existing fluence map from the treatment plan (i.e., fluence map F) or a previously updated fluence map during an earlier phase of a previous treatment session (i.e., fluence map F i , or the previous f calc In some variations, the updated fluence map f calc may be a fluence map for the entire treatment session (i.e., across all firing locations, all patient platform locations or beam stations, for the entire duration of the treatment session) or may comprise a fluence map for a subset of firing locations, beam stations, and / or treatment session time intervals, and / or for one or more of the following firing locations (e.g., FP m , F.P. m+1 ) can be a fluence map for the fluence map f calc Segmenting may generate a set of MLC and therapeutic radiation source commands for all firing positions at a particular (i.e., current) patient platform position or beam station (e.g., the platform is stationary). In some variations where the gantry rotates multiple times at a beam station, the updated fluence map f calc The segmented fluence map may comprise separate fluence levels for one or more firing positions for each gantry rotation and / or one or more sets of MLC and therapeutic radiation source commands.

[0093] Segmentation of the fluence map (e.g., the result of steps (606) and (626) of the methods of FIGS. 6B and 6C, respectively) may generate a set of MLC instructions and therapeutic radiation source instructions. Example instructions may include firing position-based MLC leaf opening commands or data, and firing station-based linear accelerator pulse parameters. FIG. 7A is a graphical representation of an example of linear accelerator pulse parameters (e.g., fluence values ​​discretized into deliverable fluence levels over one or more gantry rotations), and FIG. 7B is a graphical representation of an example of each of the binary MLC leaf positions over four sub-firing positions corresponding to a single firing position in a single gantry rotation. Each cell or block in the plot represents a particular leaf at a particular sub-firing position, and the color represents the position of the leaf, with darker shading indicating the leaf is in a closed configuration and lighter shading indicating the leaf is in an open configuration. The sum of the fluence emitted through the binary MLC apertures of the four sub-firing positions can be close to or equal to the calculated fluence for that firing position. The profile of the binary MLC opening can correspond to the linear accelerator pulse parameters of FIG. 7A. At each pass, the binary MLC opening shape (i.e., the individual MLC leaf positions) can change according to the calculated fluence map. Various MLC leaf configurations for the four sub-firing positions can be possible while still cumulatively delivering the calculated fluence for that firing position. For example, the rows can be permuted (i.e., changing the order of the MLC leaf configurations) and / or the columns can be permuted (i.e., slightly adjusting the angle from which the fluence is emitted) while the sum of the fluence delivered across the four sub-firing positions still remains close to or equal to the calculated fluence for that firing position.

[0094] Optionally, the radiation therapy system may then deliver radiation to the patient according to the radiation delivery instructions. For example, the method (620) may optionally include aligning the therapeutic radiation source at the firing position FP mand / or adjusting the MLC leaf configuration to deliver radiation according to the radiation delivery instructions. In some variations, after calculating the segmented fluence map, the method (620) may further include updating a residual fluence map that may be used for future fluence map updates. The method (620) may calculate a delivered fluence map f based on the radiation delivery instructions. delivery and / or calculating a segmented fluence map (621), and calculating the delivered fluence map f delivery The fluence map f calc The residual fluence map Δf residual and updating (623) the residual fluence map Δf residual may occur before, during, and / or after the update. In some variations, the radiation delivery instructions may include radiation delivery source instructions for multiple gantry rotations (e.g., one set of instructions for each gantry rotation at a patient platform position or beam station). The method (620) may be repeated for multiple gantry rotations at each beam station.

[0095] In some variations, the real-time fluence map segmentation and segmentation error mitigation includes calculating the MLC and / or therapeutic radiation source instructions or commands for each firing position and / or sub-firing position. The MLC may step through a series of aperture shapes at each of the sub-firing positions while the therapeutic radiation source progresses through an angular region corresponding to the firing position, so that the sum of the fluence delivered through each of the sub-firing position aperture shapes is equal to (or close to) the fluence to be delivered at the corresponding firing position. FIG. 7B depicts an example of a series of MLC aperture shapes for four sub-firing positions of a firing position. Alternatively or in addition, the segmentation and segmentation error mitigation may include decomposing the fluence map into sub-fluence maps to be delivered at firing positions and / or locations between firing positions (i.e., sub-firing positions). For example, a fluence map for an entire treatment session may be decomposed into a series of sub-fluence maps to be delivered at a firing position and / or a series of sub-firing positions located between firing positions. Any of the segmentation methods described herein may be used to calculate the subfluence map for each sub-firing position. In some radiation therapy systems with a continuously rotating gantry, there may be 50 firing positions and four sub-firing positions for each firing position. For a particular firing position, the MLC may transition through various aperture shapes at each of the sub-firing positions preceding the firing position such that the subfluence map for that particular firing position is delivered as the therapeutic radiation source delivers radiation at the firing position. The segmentation method may generate radiation delivery instructions for each sub-firing position between two firing positions.

[0096] 6D depicts one variation of a method for segmenting 630 an updated fluence map for sub-firing locations into a set of radiation delivery instructions. The method 630 includes acquiring 632 imaging data from an imaging sensor mounted on a rotatable gantry during a treatment session, and calculating 633 a residual fluence map Δf calculated during treatment planning, the acquired imaging data, and the residual fluence map Δf residualUsing the fluence map f for the launch position FPm calc Calculate (634) and FP m-1 and FP m Radiation sub-emission positions between (e.g., FP m-0.5 , F.P. m-0.25 etc.), the fluence map f calc into a plurality of subfluence maps (636) and segmenting each subfluence map for each sub-firing location and / or firing location into a set of MLC and therapeutic radiation source instructions (638). Segmenting (638) may also include calculating one or more probability coefficients for each MLC leaf for each firing location and / or sub-firing location, as described above. The imaging data may be obtained from an imaging sensor of any modality, such as a PET detector, an X-ray detector, an MR sensor, etc. Optionally, the method (630) may include configuring (640) the MLC and therapeutic radiation source according to the instructions at each sub-firing and / or firing location during a treatment session. In some variations, the set of MLC and therapeutic radiation source instructions may include MLC and therapeutic radiation source instructions for multiple gantry rotations (e.g., one set of instructions for each gantry rotation in a beam station). The method (630) may be repeated for each patient platform position or beam station and / or may be repeated for multiple gantry rotations at each beam station. In some variations, after calculating the segmented fluence map, the method (630) may further include updating a residual fluence map that may be used for future fluence map updates. The method (630) may generate a delivered fluence map f based on the radiation delivery instructions. delivery and / or calculating a segmented fluence map (631), and a delivered fluence map f delivery The fluence map f calc The residual fluence map Δf residual and updating (633).

[0097] The real-time calculated or updated fluence map may be segmented into MLC and therapeutic radiation source commands to be executed over multiple gantry rotations while the patient is held stationary at the patient platform location (i.e., the beam station). In some variations, the updated fluence map f for a particular firing location may be calc may be delivered in one gantry rotation (e.g., using sub-firing positions, as described above) or may be delivered in two or more gantry rotations. For example, f calc may be decomposed into a set of fluence maps that can be delivered at a particular firing location over multiple gantry rotations. That is, a calculated fluence map for a particular beam station may be decomposed or divided into one or more fluence map "dose" that can be delivered over two or more gantry rotations. Applying therapeutic radiation multiple times from the same firing location may help to mitigate the segmentation error by calculating the amount and distribution of the fluence segmentation error or fluence residual and delivering the fluence residual in a subsequent gantry rotation. In one method, the segmentation error is calculated based on the updated or calculated fluence map f calc and the delivered fluence map f delivery (i.e., radiation delivery command) residual Calculate the residual fluence Δf residual on subsequent gantry rotations across the firing position (e.g., in a system with a continuously rotating gantry). Alternatively, in systems where the duration that the therapeutic radiation source is positioned at a firing position can be adjusted (i.e., not a continuously rotating gantry), the MLC and therapeutic radiation source can be adjusted to deliver multiple fluence levels as specified by multiple segmented fluence maps.

[0098] Segmentation errors may also result from leaf fluence errors. Leaf fluence errors represent the difference between a fluence map deliverable through the MLC aperture shape (e.g., may be estimated using a leaf model applied to the segmented fluence map) and a segmented fluence map deliverable through a hypothetical "ideal" MLC that does not exhibit any of the leaf artifacts described below. For example, the leaf model may represent leaf artifacts such as groove notches at the leaf edges that cause radiation beam shadows that may modify the fluence at the end of the leaf. Other sources or causes of radiation beam shadows may include leaf mountings (e.g., screws, shafts, etc.). Leaf fluence errors resulting in fluence residuals may be delivered in subsequent gantry rotations, as described above. The method described above accounts for these additional factors. delivery This can be extended to model in the calculations such that the residual fluence Δf residual may include these additional errors.

[0099] As previously mentioned, the segmented fluence map may comprise a set of radiation therapy system or machine instructions including, but not limited to, the MLC configuration at each firing position (e.g., for each gantry rotation and each beam station), the radiation pulse width, and the number of pulses at each firing position. In some variations where the radiation therapy system comprises binary MLC (bMLC), where leaves may be either open or closed, one method for mitigating segmentation errors is to use probabilistic leaf opening to estimate the residual fluence map Δf residual and the firing probability factor (i.e., the probability factor) at which the leaf moves to the open configuration is the residual fluence Δf residual In some variations, the leaves may be open during delivery, but the emitted beamlet fluence level may be determined by the firing probability factor, as described above. The updated fluence map f for the MLC calc The segmentation of the fluence map f calcinto discrete fluence levels, where the minimum fluence level may be the smallest dose unit that can be delivered. Probabilistic segmentation (i.e., segmenting a fluence map of continuous fluence values ​​into fluence maps of discrete fluence levels, where each fluence level may have a corresponding firing probability coefficient) may be used to deliver a fluence residual less than what the therapeutic radiation source could deliver. In one variation of probabilistic segmentation, the probability of leaf opening may be a fluence residual or a sub-level fluence.

[0100] When the firing position is cycled by the radiation source multiple times (e.g., in the case of a continuously rotating gantry) and radiation is applied according to firing probability coefficients calculated based on the residual fluence level, multiple random decisions may converge to the desired fluence and any remaining residual noise may be random noise.

[0101] One or more of the above methods may be combined and used for real-time segmentation of fluence maps in biologically guided radiation therapy (BGRT), where the radiation therapy system uses PET emission data to update the fluence map. A cumulative fluence map for an entire treatment session or fraction may be calculated at various time points throughout the session or fraction, and / or a fluence map for each firing position or control point may be continuously updated just before the therapeutic radiation source is moved to that firing position or control point. In addition to updating the fluence map based on PET emission data, the fluence map may also be calculated to include a residual fluence, which is a combination of segmentation error (e.g., any of the segmentation errors described above) and / or the amount of undeliverable fluence from the previous firing position or control point (e.g., any negative fluence value and / or undelivered fluence due to machine anomalies, etc.). The method may further include discretizing the updated fluence map into a map of separate fluence levels (e.g., segmented fluence levels) and segmenting the map of separate fluence levels into a set of radiation delivery instructions or system / machine instructions and parameters (e.g., firing locations / angles, bMLC configurations, pulse timing, etc.). In some variations, the method for fluence map segmentation may optionally include calculating a firing probability coefficient for one or more leaves of the bMLC, where the fluence value to be delivered by one or more leaves is between two segmented fluence levels (or below a threshold fluence level, such as a minimum separate fluence level). In addition to the bMLC aperture shape or leaf configuration, the set of radiation delivery instructions or system / machine instructions resulting from the fluence map segmentation may comprise radiation beam pulse width modulation and the number of pulses to be fired each time the radiation source is positioned at (or passes through) a firing position.In particular, for systems with a constantly rotating gantry where the time that the therapeutic radiation source is at a firing position is within a few milliseconds, the therapeutic radiation source or beam generating system may only be able to generate a small number of pulses (e.g., less than 5 pulses), thereby limiting the amount of dose modulation per MLC aperture shape or leaf configuration. Varying the pulse width may help to increase the amount of dose modulation at each firing position. Alternatively, or in addition, radiation beam pulses may be applied between firing positions at sub-firing positions (i.e., while the therapeutic radiation is moved from one firing position to the next firing position). Firing radiation beam pulses at sub-firing positions may approximate the effect of increasing the number of beam pulses at subsequent firing positions.

[0102] (Treatment Plan Normalization) Optionally, the treatment plan may be normalized with patient image data acquired during a pre-scan just before the treatment session. In this case, the imaging system acquires new image X, which is intended to be a more up-to-date version of image X used in the treatment plan. prescan This can help deliver the desired dose and corresponding therapeutic radiation to the patient despite any changes in patient data from when the treatment planning images were acquired and treatment began. prescan Normalization of the treatment plan or radiation firing matrix using may be performed such that, if any geometric or other differences in the target or other patient organs or their relative positions exist, these differences may be taken into account in the treatment plan and / or RFM. This may help to facilitate delivery of the radiation dose as planned (e.g., help to deliver a radiation dose as close to the desired outcome as possible). The normalization may be performed by applying the updated radiation firing matrix P prescan and the predicted dose during treatment planning can be calculated using the pre-scan image X prescan This is approximately equal to the dose recalculated using

number

[0103] In the method, a controller of the radiation therapy system receives a known dose calculation matrix A, a treatment plan RFM P, a treatment plan full image X, and a pre-scan image X. prescan Given P prescan may be configured to normalize any differences in target region and / or patient parameters between the simulation and the imaging system of the radiation therapy system by recalculating

[0104] P prescan One way to calculate P prescan = k P, where k is a scalar,

number

[0105] During treatment delivery, updated P prescan By using the above, any differences in scale or shape between the images can be corrected without re-creating the treatment plan.

[0106] In some variations, P prescan X prescan can be approximated by the rigid deformation w of X, i.e.

number

number

[0107] In some variations, P prescan X prescan can be approximated by a general linear function R of X, i.e.

number

number

[0108] In some variations, X prescan Dose D calculated based on prescan and the planned dose D (i.e., D prescan -D) exceeds a preselected threshold (e.g., as selected or determined by a clinician), the treatment session may be paused or stopped. The treatment plan may be reevaluated, and if any, recalculated, to reflect the patient's changes.

[0109] In some variations, the desired radiation dose for the RFZ or target region may be calculated from a point of view (POV) of the target region (e.g., irradiation target region). Calculating the desired dose from the POV of the target region may be useful for moving targets. Typically, radiation therapy tends to plan and deliver radiation in a patient-based coordinate system. In this coordinate system, the target moves inside the patient when target motion is present. In POV dose space, the target is fixed and the patient moves. In radiation therapy, the POV target dose may be clinically relevant to meet dose prescriptions and achieve treatment success for the target. Dose in the patient-based coordinate system may be useful for defining organ-at-risk (OAR) constraints.

[0110] (Treatment Plan Evaluation and QA) The treatment plan and corresponding radiation firing matrix may be evaluated prior to the treatment session to verify that radiation delivered based on the RFM and partial images acquired during the treatment session will provide a predefined dose (or within an approved tolerance) of radiation to all RFZs and / or reduce exposure of any OARs. Evaluating the quality of the treatment plan and the RFM may include loading the treatment plan (which may include a planned fluence map and / or a planned dose map) and the RFM into a radiation therapy system (e.g., a controller of the radiation therapy system), providing partial image data to the radiation therapy system, calculating radiation fluence for delivery by multiplying the RFM and the partial image data, delivering radiation according to the calculated radiation delivery fluence, measuring the delivered radiation, calculating a fluence map (and / or a dose map) based on the measured radiation, and comparing the calculated fluence map to the planned fluence map (and / or the planned dose map). If the difference between the calculated fluence (and / or dose) map and the planned fluence (and / or dose) map exceeds a predefined difference threshold, the treatment plan and RFM may be adjusted (e.g., sent back to the treatment planning system for replanning). Providing a set of positron-emitting activity data to the radiation therapy system instead of using a PET-avid phantom (i.e., a phantom with one or more positron-emitting regions) may facilitate and / or simplify and / or accelerate treatment plan evaluation. For example, during treatment plan evaluation, "playing back" the positron-emitting activity from a PET imaging session may allow treatment plan and / or RFM quality to be evaluated using a non-PET-avid phantom and / or MV X-ray detector measurement. This may also help reduce personnel exposure to radiation and may help reduce the amount of treatment system time spent for quality assurance (QA). For example, a phantom-free QA procedure may reduce the time spent on repeated radiation viewing room entry and exit and / or positioning the phantom on the treatment system patient platform.

[0111] The partial image data provided to the therapy system may comprise one or more positron emission response lines (LORs), MRI subsampling in k-space, or CT2-D projection x-ray images, as described above. In the context of positron emission-induced radiotherapy, the set of positron emission activity data may comprise PET data obtained during a diagnostic PET imaging session (e.g., data on the positron emission events themselves, a set of LORs, and / or digitized and / or filtered PET detector output signals). For example, diagnostic PET images used for treatment planning may also be used to evaluate and verify treatment plan and / or RFM quality. In some variations, a complete set of positron emission activity or LOR data from a diagnostic PET imaging session may be used to evaluate treatment plan and / or RFM quality, or a portion of the positron emission activity or LOR data may be used (e.g., a portion of the positron emission activity data corresponding to an RFZ, an OAR, and / or a field of view of the therapy system PET detector). In still other variations, simulated or synthetic positron emission activity data or LORs may be used to evaluate treatment plan and / or RFM quality. The simulated or synthetic LOR may be generated based on the geometry of a PET detector on the diagnostic PET image and / or the PET imaging system and / or radiation therapy system from which the diagnostic PET image was obtained. The simulated or synthetic LOR may be generated by a treatment planning system or a radiation therapy system. The positron emission activity data may comprise a PET detector signal resulting from detection of a positron emission event in the patient (e.g., a voltage output over time, digitized and / or filtered PET detector data) and / or may be LOR data extracted from the PET detector signal.Positron emission activity or LOR data may include, but is not limited to, the relative timing between detection of coincident photons, PET and / or detector noise including noise from the radiation source (e.g., to reflect the expected or likely SNR or noise profile during a treatment session), detector dark current, PET detector afterglow, artifacts due to patient movement (e.g., platform movement and / or patient agitation), and / or uncorrelated or "random" events such as patient movement or shift.

[0112] FIG. 2C depicts one variation of a workflow that includes evaluating the quality of the treatment plan and / or RFM before a treatment session. The method may include generating a treatment plan and RFM (250), evaluating the quality of the treatment plan and / or RFM (252), and then proceeding to the treatment session (254) if the treatment plan and / or RFM meet certain defined quality criteria. The treatment plan and RFM calculation (250) may use PET and / or CT image data (251) and / or any of the parameters (202-204, 206) depicted in FIG. 2A. The generated treatment plan may comprise an RFM P, a planned dose map D0, and a planned fluence map F0. The treatment plan and associated parameters may be transmitted to a radiation therapy system for quality assessment (252). The PET image and / or CT image (251) data may be transmitted to a radiation therapy system, and / or the positron emitting activity data may be generated based on filtering (e.g., spatially filtering) a set of PET image or LOR data acquired during an imaging session, or synthesizing a set of LORs. The positron emitting activity data may comprise, for example, a set of digitized and / or filtered PET detector outputs that mimic or simulate signals from a PET detector during a treatment session for the patient. The positron emitting activity data may include LORs from the RFZ (e.g., PTV, tumor region), and may optionally include background LORs (to simulate non-specific PET tracer uptake rates), OAR LORs, LORs resulting from various noise sources and / or non-specific tracer uptake rates, etc. The positron emitting activity (i.e., LOR) data and / or PET image data may be transmitted from the PET imaging system and / or treatment planning system to a radiation therapy system. The radiation therapy system can then generate and emit radiation in response to the positron emission activity (i.e., moving or rotating the gantry, activating the linear accelerator, adjusting the position of the leaves of a dynamic multi-leaf collimator, etc.).If the emitted radiation provides a dose and / or fluence as prescribed by the treatment plan, the treatment plan is determined to be of sufficient acceptable quality to be used in the patient treatment session (254). If not, the treatment plan and / or RFM P are adjusted by the treatment planning system and then re-evaluated on the radiation therapy system. One or more radiation measurement devices may obtain radiation emission data, and the controller may calculate a fluence and / or dose map based on the obtained emission data. Examples of measurement devices may include radiographic film, scintillators, single or array ion chambers and / or diodes, scintillator-camera based EPIDs, liquid-filled ion chamber EPIDs, dose chambers, etc. Alternatively or in addition, the emitted radiation may be measured using a phantom placed within the patient treatment area, a phantom having any desired shape, weight, or density, and any number of measurement devices as described above. In some variations, the phantom may not be PET-compatible and may comprise, for example, an array of detectors such as ARCCHECK® and MAPCHECK® (Sun Nuclear Corporation), MATRIXX, PTW Seven29 from IBA Dosimetry, and / or radiographic film, and may be located within the patient treatment area (e.g., on the patient platform and / or within the bore of a circular gantry) and / or within the beam path of the radiation source (e.g., downstream from the beam collimation system, across the MLC, etc.). In other variations, the phantom may be PET-compatible.

[0113] FIG. 2D depicts one variation of a method for evaluating the quality of a treatment plan and / or an RFM. The method (260) includes loading the treatment plan onto a radiation therapy system controller (262) and loading PET images and / or positron emitting activity data onto the controller (264). As described above, the treatment plan may comprise an RFM, a predefined or planned dose map, and / or a fluence map. The positron emitting activity data and / or PET image data may comprise simulated or synthetic LOR and / or PET detector signals recorded during a PET imaging session. The positron emitting activity (i.e., LOR) data and / or PET image data may be spatially limited to an ROI, such as an RFZ and / or an OAR. The method (260) may further include emitting radiation according to the treatment plan, the positron emitting activity data, and / or the PET image data (266). The radiation emitted by the system may be determined using any of the methods described herein. For example, the radiation therapy system may emit radiation by multiplying partial PET data from the loaded data by the RFM to obtain a calculated fluence map, segmenting the fluence map into hardware instructions and parameters (e.g., launch location / angle, MLC configuration, pulse timing, etc., using any of the methods described above), and then activating a therapeutic radiation source (e.g., a linear accelerator) and applying radiation according to the segmented fluence map. The method (260) may include measuring (268) the emitted radiation. Measuring the emitted radiation may include measuring the radiation (energy) fluence (energy per unit area, e.g., J / cm) over the duration of the session (e.g., continuously or at predetermined time points or control points). 2) and / or radiation dose (energy per unit mass, e.g., J / kg). Radiation emission data may be obtained using a combination of signals from MV detectors and / or dose monitor chambers and MLC position sensors of a radiation therapy system, and / or radiographic films or sensor arrays (e.g., diode, ion chambers or scintillation 1-D or 2-D arrays), diamond detectors, and / or 3-D dosimeters such as polymer gel dosimeters located in the radiation beam path (e.g., phantoms installed in the patient treatment area and / or attached to the emission aperture of the therapeutic radiation source and / or dynamic MLC). Radiation fluence may be calculated, for example, by determining the MLC configuration (e.g., leaf position) using MVD data in combination with the firing position and the number of radiation pulses fired at a particular firing position. For example, the position of each leaf in the MLC can be determined from the MVD imaging data. The number of radiation pulses fired may be determined based on the MVD data and / or the dose monitor chamber of the linear accelerator. In variations in which a phantom is used, the radiation dose may be measured using an ion chamber and / or calculated in combination with fluence measurements based on the phantom's geometry, density, attenuation coefficient, and / or mass. The system controller may calculate (270) a delivered radiation fluence and / or dose map and then compare (272) the calculated fluence and / or dose map to a desired fluence and / or dose map defined by the treatment plan. The delivered radiation dose or fluence map may be calculated using machine parameter data, such as radiation pulse data (e.g., pulse width, duration, magnitude, frequency, etc.), dynamic MLC configurations (e.g., leaf position and patient platform position at each firing angle), gantry position (e.g., speed, positioning relative to the patient treatment area and / or patient platform), etc.Based on a comparison of the delivered radiation dose and / or fluence map with the planned dose and / or fluence map, the controller may determine whether the treatment plan meets a prescribed quality criterion. Optionally, the clinician may provide input to the controller regarding whether the treatment plan or RFM meets a prescribed quality criterion. The quality criteria may include consistency of radiation delivery over time according to the planned dose or fluence map, and / or accuracy of radiation delivery to the RFZ, and / or avoidance of OARs. For example, the controller may calculate a match distance to isodose (DTA) value and a gamma metric, which is a combined score representing the absolute dose difference between the measured and planned dose and / or fluence map. The clinician may specify that for a plan or RFM to be of sufficient quality for treatment, radiation delivered according to the plan must deliver a dose within a prescribed tolerance to an area within a prescribed DTA value for a prescribed percentage of measurements or time points during a session. In some variations, a treatment plan of sufficient quality for treatment may deliver a dose such that a specified percentage of measurement points meet a specified level of DTA and a threshold gamma level for absolute point dose difference. For example, a clinician may specify that a pass criterion for a treatment plan is that radiation is delivered within about 3 mm DTA of one or more RFZs with less than about 3% deviation from the dose specified for each RFZ over at least 95% of the time points or measurement points. Optionally, the method (260) may include proceeding with the patient treatment or revising the treatment depending on whether the treatment plan and / or RFM meets the quality criteria. If the plan or RFM meets the quality criteria, the plan or RFM may be used for the patient treatment session. If the plan or RFM does not meet the quality criteria, the plan or RFM may be revised. The plan or RFM revision or update may be performed by the treatment system controller and / or the treatment planning system.

[0114] (Verification of treatment plan QA methods) The method for evaluating the radiation therapy treatment described above may itself be verified to confirm that replacing positron emission activity or LOR data with PET detector signals results in radiation delivery that is substantially close to that during treatment. A variation of the method for verifying the treatment plan QA method described above may include: selecting a phantom (simulating a target or tumor region in a patient) with a predefined PET affinity region; generating a treatment plan for the phantom; loading the treatment plan onto a radiation therapy system; loading the phantom onto a patient platform; detecting positron emission or LOR from the phantom; emitting radiation according to the treatment plan and emission data; measuring the emitted radiation; and calculating a first fluence map and / or a first dose map based on the measurement of the emitted radiation. The LOR emitted by the phantom may be recorded and stored in the memory of the treatment system controller. The method may then include removing the PET affinity phantom and repeating radiation delivery as described above using the recorded LOR data instead of the PET affinity phantom (similar to the treatment plan evaluation method depicted in FIG. 2D). The method may include calculating a second fluence map and / or a second dose map based on the measurements of the emitted radiation, and then comparing the first fluence map to the second fluence map (and / or the first dose map and the second dose map). If the difference between the first fluence map and the second fluence map (and / or the first dose map and the second dose map) is below a preselected threshold, the treatment plan verification method and radiation therapy system are deemed valid. If the difference is at or above a preselected threshold, a notification may be generated to alert the operator to perform additional testing of the treatment plan and / or the radiation therapy system.

[0115] (system) A radiation therapy system that may be used to provide image-guided radiation therapy may include an imaging system configured to acquire partial images at a rate that exceeds the rate at which the target region moves. For example, a tumor may move at a maximum speed of about 0.5 cm / sec, with a periodicity of about 4 seconds. A sufficiently fast imaging system may acquire a partial image within about 250 ms to 500 ms. If the imaging system acquires partial images at a frequency that is greater than twice the maximum frequency of the tumor motion (or one-half the periodicity), the partial images may include a complete sampling of the tumor motion trajectory. The imaging system of the radiation therapy system may acquire partial tomographic images x such that the motion of the target region is relatively small. i may be configured to acquire within a certain time window. Notably, any temporal blurring in partial image acquisition is directly mapped to a temporal blurring of radiation fluence and thus the dose delivered to the patient. The imaging system may include any suitable imaging modality, such as PET, CT, MRI, ultrasound, etc. In some variations, the radiation therapy system may include a motion system on which the imaging system may be mounted. Optionally, the therapeutic radiation source and one or more beam-shaping components of the radiation therapy system may be mounted on the same gantry. In some variations, the imaging system may be mounted on a circular gantry configured to rotate around the patient area at a speed of about 30 RPM or more (e.g., about 60 RPM, about 65 RPM, about 70 RPM). Alternatively or in addition, the imaging system may be capable of acquiring tomographic data without any rotation (e.g., an MRI imaging system).

[0116] A radiation therapy system that can be used to provide IGRT can include a therapeutic radiation source configured to deliver a therapeutic radiation beam in real time. That is, the therapeutic radiation source can be mounted on a motion system that rapidly moves the radiation source to each firing position around the patient area, and the beam shaping components are configured to change the beam shaping configuration in the time interval between firing positions so that the radiation beam can be applied to the target area before the target area has substantially moved. The longer the latency between partial image acquisition and radiation delivery, the higher the likelihood that the target area will have moved by the time radiation is delivered. Thus, the motion system (e.g., gantry, chassis, arm, etc.) and the beam shaping components (e.g., jaws, collimators, etc.) can be configured to move (e.g., motion system to move the radiation source to a new firing position) and change configuration (e.g., collimator or jaws to move / change the positioning of a radiopaque element) within about 10 ms or less. For example, a radiation therapy system may include an imaging system (e.g., PET, CT, MRI), a rotatable gantry, a linear accelerator mounted on the rotatable gantry, and a dynamic multi-leaf collimator mounted on the gantry and disposed in the beam path of the linear accelerator. The dynamic multi-leaf collimator may be, for example, a binary multi-leaf collimator, with each leaf being in either an open or closed configuration when located at a firing location, and transitioning between the open and closed configurations while moving between firing locations. An example of a radiation therapy system may have a gantry rotating at 60 rpm, the dynamic multi-leaf binary collimator may change configurations within 10 ms, and the synchronous therapy radiation source may fire several pulses within 10 ms. The synchronous therapy radiation source emits pulses of radiation during short periods of movement between configurations while the leaves of the binary multi-leaf collimator are stationary. The command to move the binary multi-leaf collimator may be generated with a short latency of as little as 10 ms after partial image acquisition.Another example of a radiation therapy system may have a gantry that rotates at 5 rpm, a dynamic 2D multi-leaf collimator in which each leaf, when in the firing position, continuously changes configuration, which may be in any intermediate position between its fully closed and fully open positions, and an asynchronous therapeutic radiation source that is either pulsed or continuous emitting at a rate of about 100 Hz or higher. In some variations, the leaves of the 2D dynamic multi-leaf collimator may be capable of moving at a speed that can track twice the velocity of the tumor. The speed may be based on a geometric calculation using the location of the therapeutic radiation source relative to both the 2D multi-leaf collimator and the target. The latency between the desired 2D multi-leaf collimator leaf position and the corresponding target position may be as short as 10 ms from the acquisition of a partial image.

[0117] For example, a tumor moves at a speed of about 0.5 cm / sec with a periodicity of 4 seconds, and a sufficiently fast therapeutic beam delivery system may be able to deliver radiation within about 10 ms of detecting a partial image. The radiation delivery system may be configured to: i etc., and it may be possible to deliver an appropriate fluence response (using the RFM from the treatment plan) with low latency.

number

[0118] Fluence F i However, if it is not delivered immediately after it is calculated, the actual target may have shifted from the location to be delivered. The amount of error may depend on the spatial extent of the shift. In short, a radiation therapy system for IGRT may be configured to obtain partial images (some of which may be tomographic) at a frame rate that captures the motion of the target, and may have a beam delivery system that can change the multi-leaf collimator configuration to deliver new partial doses or beamlets to the location of the moving target region in the RFZ.

[0119] (High Speed ​​Gantry System) One variation of the motion system may include a rotatable gantry. For example, the rotatable gantry may be a continuously rotating gantry configured to rotate 360 ​​degrees around the patient area. FIG. 3A depicts an example of such a gantry that may be configured to rotate at a rate of about 60 RPM. In this variation, the latency between acquisition of a partial image and delivery of beamlets according to a fluence map updated based on the acquired partial image may be about 100 ms to about 5 s.

[0120] In some variations, the radiation therapy system can rotate the therapeutic radiation source and collimator around an axis and can optionally stop at various firing angles α. The controller for the radiation therapy system can track the speed of the gantry as it rotates around the patient area. The gantry can rotate relatively slowly with a low or fixed angular velocity, or relatively quickly with a higher angular velocity, such as one rotation at approximately the frame rate of the imaging system.

[0121] Additional details and variations of radiation therapy systems including high-speed gantries are described in U.S. Application No. 15 / 814,222, filed November 15, 2017, which is incorporated by reference in its entirety herein.

[0122] (Dynamic MLC) The radiation therapy system may include beam shaping components, such as MLCs, configured to change the configuration of the leaves within a selected time interval. For example, the selected time interval may be the time it takes for the motion system to move the linear accelerator from a first firing position to a second firing position. The position of the leaves as a function of time may be determined, at least in part, by the temporal bandwidth and / or configuration transition speed of each MLC leaf. In some variations, the collimator may not complete its configuration change within the time it takes for the motion system to move the linear accelerator between firing positions, resulting in the target region experiencing a partial dose from a previous partial image.

[0123] The radiation therapy system may include a rotatable gantry that rotates the multi-leaf collimator about an axis. If the gantry is not rotating fast (e.g., slower than the frame rate of the partial image acquisition), the projections R from the partial images may be captured. α (x i ), the multi-leaf collimator delivers a dose from a given takeoff angle α for R α (x i+1 In a variation in which the gantry moves rapidly relative to the imaging system, the collimator may be a dynamic multi-leaf collimator with a leaf actuation mechanism that is in a desired configuration to deliver a dose at R α (x i ) to the new angle R α+1 (x i ) the positions of the leaves may be changed to their locations in the gantry. The new angles may be based on the angular velocity of the gantry.

[0124] The leaf positions of the dynamic MLC at each firing location may be determined, at least in part, by the acquired partial images. That is, the radiation beam or fluence shaped by the dynamic MLC may enable the delivery of a partial intensity modulated dose. The dose pattern summed over the treatment session may be close to the final intensity modulated desired target of the treatment plan. Some radiotherapy systems may include a 2D MLC, and in such systems, the MLC may not be able to faithfully represent the desired dose distribution Dα, but the MLC may be configured to calculate correction factors to be used in further firing projections, correct for incompatibility, and configure the MLC to the desired projected dose distribution. Some radiotherapy systems may include a high-speed binary MLC, which may include a leaf actuation mechanism that moves each of the leaves to a new MLC configuration or pattern for each firing location or gantry angle. This architecture may even facilitate generalized target tracking of multiple simultaneous targets. Further details of dynamic binary multi-leaf collimators that may be used in radiation therapy systems are provided in U.S. patent application Ser. No. 15 / 179,823, filed Jun. 10, 2016, which is incorporated by reference in its entirety herein.

[0125] 3A depicts one variation of a radiation therapy system that can be used for image-guided radiation therapy based on partial images. The radiation therapy system (300) may include a gantry (302) rotatable around a patient area (304), one or more PET detectors (306) mounted on the gantry, a therapeutic radiation source (308) mounted on the gantry, and a dynamic multi-leaf collimator (310) disposed in the beam path of the therapeutic radiation source. In some variations, the radiation therapy system may include a first array (306a) of PET detectors, a second array (306b) of PET detectors disposed opposite the first array, a linear accelerator (308) or a linear accelerator, and a dynamic binary multi-leaf collimator (310). The system may further comprise a controller in communication with the gantry, the PET detector, the linear accelerator, and the MLC, the controller having one or more memories that may store treatment plans, radiation firing matrices, fluence maps, system instructions / commands, and a processor configured to execute the calculations and methods described herein. The patient located in the patient area may be injected with a PET tracer that emits positrons, which may accumulate in a particular region of the patient (e.g., an irradiation target region such as a tumor region). The annihilation of the positron with a nearby electron may result in the emission of two photons that travel in opposite directions and define a line. The one or more acquired partial images or detected partial image data may comprise one or more positron annihilation emission paths (i.e., a line of response or LOR, emission path). In some variations, the PET detector may be a time-of-flight PET detector, which may help identify the location of the positron annihilation event. The previously calculated treatment plan P may be updated according to the PET imaging data or partial PET images acquired by the PET detector, and the fluence map may be updated so that the linear accelerator and MLC leaf configuration / beamlet selection take into account tumor movement. The fluence map may be updated using the imaging data or partial images as the patient is moved through the patient area (e.g., moved stepwise or otherwise through the gantry bore).Optionally, the radiation therapy system (300) may include a CT imaging system mounted on the same gantry as the therapeutic radiation source or on a separate gantry. Additional details and examples of PET-based radiation therapy systems are described in U.S. Application No. 15 / 814,222, filed November 15, 2017, which is incorporated herein by reference in its entirety.

[0126] 3B depicts another variation of a radiation therapy system that may be used for image-guided radiation therapy based on partial images. The radiation therapy system (320) may include a gantry (322) rotatable around the patient area (324), a kV imaging system (326) having a kV X-ray source (328) and a kV detector (330) mounted on the gantry, a therapeutic radiation source (332) (e.g., an MV X-ray source), and an MV detector (334) mounted on the gantry (322). The kV detector (330) may be located opposite the kV X-ray source (328), and the MV detector (334) may be located opposite the MV X-ray source (332). Optionally, the kV imaging system may include a dynamic MLC (327) above the kV X-ray source (328). The system may include a dynamic MLC (331) disposed above the MV X-ray source (332). The partial image or imaging data may comprise image data acquired by the kV detector after each kV X-ray source pulse. An example of a partial kV X-ray image may include X-ray projection image data, such as 2D projection data. The fluence map may be calculated by multiplying the treatment plan radiation firing matrix P with the kV X-ray imaging data or partial kV X-ray image acquired by the kV detector so that radiation emission by the linear accelerator and MLC leaf pattern / beamlet selection may take tumor movement into account. The fluence map may be updated using the imaging data or partial image as the patient is moved through the patient area (e.g., moved stepwise or otherwise through the gantry bore). Alternatively or additionally, the partial MV X-ray image or data acquired by the MV detector may be used to update the fluence map and / or the treatment plan. Additional details and examples of radiation therapy systems are described in PCT Application No. PCT / US2018 / 025252, filed March 29, 2018, which is incorporated by reference in its entirety into this specification.

[0127] 3C depicts another variation of a radiation therapy system (350) that may be used for image-guided radiation therapy based on a partial image. The radiation therapy system (350) may include a gantry (351) with a first pair of arms (352) rotatable around a patient area and a second pair of arms (354) rotatable around a patient area, an imaging system with a kV radiation source (356) mounted on a first arm (352a) of the first pair of arms (152) and a kV detector (358) mounted on a second arm (352b) of the first pair of arms (352), and a therapeutic radiation system with an MV radiation source (360) mounted on a first arm (354a) of the second pair of arms (354) and an MV detector (362) mounted on a second arm (354b) of the second pair of arms (354). The first and second arms of the first pair of arms (352) may be located opposite each other (e.g., on opposite sides of a patient area, facing each other, and / or about 180 degrees from each other) such that the kV radiation source (356) and the kV detector (358) are located opposite each other (e.g., the kV detector is located in the beam path of the kV radiation source). The first and second arms of the second pair of arms (354) may be located opposite each other (e.g., on opposite sides of a patient area, facing each other, and / or about 180 degrees from each other) such that the MV radiation source (360) and the MV detector (362) are located opposite each other (e.g., the MV detector is located in the beam path of the MV radiation source). The partial image or imaging data may comprise image data acquired by the kV detector after each kV x-ray source pulse. An example of a partial kV x-ray image may include x-ray projection image data, such as 2D projection data. The fluence map may be calculated by multiplying the treatment planning radiation firing matrix P with the kV x-ray imaging data or partial kV x-ray images acquired by the kV detector so that radiation emitted by the linear accelerator as well as the MLC leaf pattern / beamlet selection may account for tumor motion. The fluence map may be updated using the imaging data or partial images as the patient is moved through the patient area (e.g., moved stepwise or otherwise through the gantry bore).Alternatively, or in addition, partial MV X-ray images or data obtained by the MV detector may be used to update the fluence map and / or treatment plan.

[0128] (controller) A system (e.g., treatment planning system, radiation therapy system) that may be configured to update a treatment plan (e.g., fluence map, sinogram, and / or radiation firing matrix) based on a low SNR or partial image may include a controller in communication with an imaging system and / or a therapeutic radiation source and / or a multi-leaf collimator and / or a gantry of a radiation therapy system. The controller may include one or more processors and one or more machine-readable memories in communication with the one or more processors. The controller may be connected to the radiation therapy system and / or other systems by wired or wireless communication channels. In some variations, the controller of the treatment planning system may be located in the same or different room as the patient. For example, the controller may be coupled to a patient platform or placed on a transporter or medical cart adjacent to the patient and / or operator.

[0129] The controller may be implemented in accordance with numerous general-purpose or special-purpose computing systems or configurations. Various exemplary computing systems, environments, and / or configurations that may be suitable for use with the systems and devices disclosed herein may include, but are not limited to, software or other components embodied in or on personal computing devices, network equipment, servers or server computing devices such as routing / connectivity components, portable (e.g., handheld) or laptop devices, multiprocessor systems, microprocessor-based systems, and distributed computing networks.

[0130] Examples of portable computing devices include smartphones, personal digital assistants (PDAs), mobile phones, tablet PCs, phablets (personal computing devices larger than a smartphone but smaller than a tablet), wearable computers in the form of smart watches, portable music devices, and the like.

[0131] (Processor) In some embodiments, the processor may be any suitable processing device configured to run and / or execute a set of instructions or code, and may include one or more data processors, image processors, graphic processing units, physical processing units, digital signal processors, and / or central processing units. The processor may be, for example, a general-purpose processor, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), etc. The processor may be configured to run and / or execute an application process and / or processes and / or functions associated with other modules, systems and / or networks associated therewith. The underlying device technologies may be provided in a variety of component types, such as, for example, metal-oxide-semiconductor field-effect transistor (MOSFET) technologies such as complementary metal-oxide-semiconductor (CMOS), bipolar technologies such as emitter-coupled logic (ECL), polymer technologies (e.g., silicon-conjugated polymers and metal-conjugated polymer-metal structures), mixed analog and digital, etc.

[0132] (Memory) In some embodiments, the memory may include a database, which may be, for example, a random access memory (RAM), a memory buffer, a hard drive, an erasable programmable read only memory (EPROM), an electrically erasable read only memory (EEPROM), a read only memory (ROM), a flash memory, etc. The memory may store instructions for causing the processor to execute modules, processes, and / or functions associated with the system, such as one or more treatment plans, full or high SNR images, partial or low SNR images, calculation of a fluence map based on the treatment plan and / or clinical goals, segmentation of the fluence map into radiation therapy system instructions (which may, for example, direct the operation of the gantry, therapeutic radiation source, multi-leaf collimator, and / or any other components of the radiation therapy system and / or diagnostic or treatment planning system), and image and / or data processing associated with treatment planning and / or delivery.

[0133] Some embodiments described herein relate to computer storage products with non-transitory computer-readable media (which may also be referred to as non-transitory processor-readable media) having instructions or computer code thereon for performing various computer-implemented operations. The computer-readable media (or processor-readable media) is non-transitory in the sense that it does not itself include a transitory propagating signal (e.g., a propagating electromagnetic wave that carries information over a transmission medium such as space or a cable). The media and computer code (which may also be referred to as code or algorithms) may be designed and constructed for a specific purpose or purposes. Examples of non-transitory computer readable media include, but are not limited to, magnetic storage media such as hard disks, floppy disks, and magnetic tapes, optical storage media such as compact disks / digital video disks (CD / DVD), compact disks-read only memory (CD-ROM), and holographic devices, magneto-optical storage media such as optical disks, solid-state storage devices such as solid-state drives (SSD) and solid-state hybrid drives (SSHD), carrier signal processing modules, and hardware devices specially configured to store and execute program code such as application specific integrated circuits (ASICs), programmable logic devices (PLDs), read only memories (ROMs), and random access memory (RAM) devices. Other embodiments described herein relate to computer program products that may include, for example, the instructions and / or computer code disclosed herein.

[0134] The user interface may serve as a communication interface between an operator or clinician and the treatment planning system. The user interface may include an input device and an output device (e.g., a touch screen and a display) and may be configured to receive input and output data from one or more of the support arm, the external magnet, the sensor, the delivery device, the input device, the output device, the network, the database, and the server. Sensor data from one or more sensors may be received by the user interface and output by one or more output devices visually, audibly, and / or through tactile feedback. As another example, operator control of the input device (e.g., joystick, keyboard, touch screen) may be received by the user and then processed by a processor and memory for the user interface to output control signals to one or more of the support arm, the external magnet, the intracavity device, and the delivery device.

[0135] Some variations of treatment planning systems for generating fluence maps may include a display device that may enable an operator to view graphical and / or textual representations of the fluence map, and / or dose distribution, and / or regions of interest, and / or volumes of interest, and / or patient anatomical images, and / or patient data (e.g., physiological and / or biological), etc. In some variations, the output device may include a display device including at least one of a light emitting diode (LED), a liquid crystal display (LCD), an electroluminescent display (ELD), a plasma display panel (PDP), a thin film transistor (TFT), an organic light emitting diode (OLED), an e-paper / e-ink display, a laser display, and / or a holographic display.

[0136] (communication) In some embodiments, the treatment planning system and / or radiation therapy system may communicate with other computing devices via one or more networks, each of which may be any type of network (e.g., wired network, wireless network), for example. A wireless network may refer to any type of digital network that is not connected by any type of cable. Examples of wireless communication in a wireless network include, but are not limited to, cellular, radio, satellite, and microwave communication. However, a wireless network may connect to a wired network to interface with the Internet, other carrier voice and data networks, business networks, and personal networks. Wired networks are typically carried via copper twisted pair, coaxial cable, and / or fiber optic cable. There are many different types of wired networks, including wide area networks (WANs), metropolitan-wide networks (MANs), local area networks (LANs), Internet area networks (IANs), campus area networks (CANs), global area networks (GANs) such as the Internet, and virtual private networks (VPNs). Hereinafter, a network refers to any combination of wireless, wired, public, and private data networks that are typically interconnected through the Internet to provide a unified networking and information access system.

[0137] Cellular communications may encompass technologies such as GSM, PCS, CDMA or GPRS, W-CDMA, EDGE or CDMA2000, LTE, WiMAX, and 5G networking standards. Some wireless network deployments combine networks from multiple cellular networks or use a mix of cellular, Wi-Fi, and satellite communications. In some embodiments, the systems, apparatus, and methods described herein may include radio frequency receivers, transmitters, and / or optical (e.g., infrared) receivers and transmitters to communicate with one or more devices and / or networks.

[0138] Although various variations of the invention have been described and illustrated herein, those skilled in the art will readily envision various other means and / or structures for performing the functions and / or obtaining one or more of the results and / or advantages described herein, and each of such variations and / or modifications are deemed to be within the scope of the embodiments of the invention described herein. More generally, those skilled in the art will readily appreciate that all parameters, dimensions, materials, and configurations described herein are meant to be exemplary, and that the actual parameters, dimensions, materials, and / or configurations will depend on the specific application or applications in which the teachings of the invention are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific inventive variations described herein. Thus, the foregoing variations are presented by way of example only, and it will be understood that within the scope of the appended claims and their equivalents, the inventive variations may be practiced otherwise than as specifically described and claimed. The inventive variations of the present disclosure are directed to each individual feature, system, article, material, kit, and / or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and / or methods is included within the inventive scope of the present disclosure, if such features, systems, articles, materials, kits, and / or methods are not mutually inconsistent.

Claims

[Claim 1] A system as described in the drawings.

Images