Methods for quality assurance of radiation delivery

Abstract

A method and system for generating MV detector images for evaluating the quality of radiation delivery according to a radiation therapy treatment plan is described herein. The MV detector images are generated from MV detector measurements of a small number of multi-leaf collimator (MLC) leaf openings. Radiation may be delivered to a phantom placed within the field of view of the MV detector as part of a non-therapeutic application, for example during a QA session. The simulated MV detector images may be generated by acquiring MV detector imaging data of a single MLC leaf opening for each MLC leaf, acquiring MV detector imaging data of a double MLC leaf opening for each MLC leaf pair, segmenting the treatment plan fluence map to obtain the pattern of MLC openings, and combining the MV detector imaging data of the single MLC openings and the double MLC leaf openings according to the pattern of MLC openings.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No. 63 / 294,047, filed December 27, 2021, the disclosure of which is incorporated by reference in its entirety herein. [Background technology]

[0002] Radiation therapy treatment planning aims to deliver a prescribed dose of radiation to one or more tumors while limiting irradiation of healthy tissues. Treatment planning uses CT and / or PET images and clinician-defined tumor contour plans to account for each patient's unique anatomy and their cancer pathology, and generates a radiation fluence map that results in a cumulative therapeutic dose to the tumor while not exceeding irradiation safety thresholds for radiation-sensitive structures such as organs at risk (OARs). The radiation fluence map identifies the radiation beamlets (e.g., size, shape, intensity, etc.) for each radiation firing location and / or patient platform location that result in the desired dose distribution. The treatment plan fluence map is then translated (i.e., segmented) into radiation therapy machine instructions that, when executed accurately with the patient positioned at the specified location, will deliver the prescribed dose to the tumor. The radiation therapy machine instructions may include, but are not limited to, the configuration of any radiation beam shaping components (e.g., leaf positions of a multi-leaf collimator), the gantry angle (firing position), the linear position of the patient, and the number of beamlet pulses or monitoring units emitted by the therapeutic radiation source (e.g., a linear accelerator or LINAC) for each firing position.

[0003] After the treatment plan fluence map is generated, it is evaluated to ensure that it results in delivery of the prescribed dose. One way to evaluate whether the plan fluence map delivers the desired dose distribution (i.e., quality assurance or QA of the treatment plan) is to perform a simulation using a radiation therapy system model, a patient model, and / or a beam model to calculate an approximation of the delivered dose distribution. The simulated delivered dose distribution is compared to the desired dose distribution, and the clinician can evaluate whether the difference is acceptable. Another way to evaluate whether the plan fluence map delivers the prescribed dose is to actually deliver the plan fluence map using segmented machine instructions without a patient in the treatment area. The delivered radiation fluence can be measured by a fluence measuring device located in the treatment area. A radiation therapy system using a LINAC may also include an MV detector located on the opposite side of the LINAC. In these radiation therapy systems, the delivered radiation fluence can be measured by the MV detector. Measurements of delivered radiation fluence (from one or more of the fluence measuring device and the MV detector) may be used to reconstruct the delivered dose distribution, which is evaluated against the desired dose distribution. The results of the treatment planning and delivery QA may be used to verify whether the treatment plan delivers the prescribed dose and / or whether the radiation therapy system has the ability to accurately emit radiation according to the planned fluence map. Such treatment planning and radiation delivery QA sessions are important to ensure patient safety. It is therefore desirable to have improved methods for evaluating patient treatment plans and radiation delivery (i.e., in quality assurance or QA sessions). Summary of the Invention [Means for solving the problem]

[0004] A method for evaluating the quality of radiation delivery according to a treatment plan fluence map is disclosed herein. The method may be used for therapeutic or non-therapeutic applications. One variation for evaluating the quality of radiation delivery includes using acquired MV detector imaging data of different multi-leaf collimator (MLC) configurations to generate simulated MV detector images of emitted radiation according to the treatment plan fluence map, delivering radiation according to the treatment plan fluence map while acquiring MV detector imaging data of the delivered radiation, and comparing the acquired MV detector imaging data with the simulated MV detector image to identify any differences. Radiation may be delivered to a phantom placed within the field of view of the MV detector as part of a non-therapeutic application, for example during a QA session. The simulated MV detector images may be generated by acquiring MV detector imaging data of a single MLC leaf opening for each MLC leaf, acquiring MV detector imaging data of a double MLC leaf opening for each MLC leaf pair, segmenting the treatment plan fluence map to obtain a pattern of MLC openings, and combining the MV detector imaging data of the single MLC opening and the double MLC leaf opening according to the pattern of the MLC openings. This method facilitates the generation of accurate simulated MV detector images that encompass the complexity of patient-specific treatment plan fluence maps while using a small set of empirical MV detector data. Reducing the amount of data collected may help speed up QA sessions. Reducing the amount of time the radiation therapy system is used for QA purposes may help increase the length of time the radiation therapy system is used to treat patients.

[0005] One variation of a method for generating a radiation detector image corresponding to a treatment plan fluence map multi-leaf aperture pattern (which may be used in non-therapeutic applications) may include: acquiring imaging data of a single-leaf aperture using a radiation source and a radiation detector for each leaf of a multi-leaf collimator (MLC); acquiring imaging data of a double-leaf aperture using a radiation source and a radiation detector for each leaf of the MLC; segmenting the treatment plan fluence map into the MLC aperture pattern; and generating a radiation detector image corresponding to the treatment plan fluence map by combining the acquired imaging data of the single-leaf and double-leaf apertures according to the MLC aperture pattern. The method may further include generating a graphical representation including the generated radiation detector image and outputting the graphical representation to a display device. Optionally, the method may include calculating a radiation dose to a phantom based on the generated radiation detector image. In some variations, the radiation detector may be an MV detector or a kV detector. The radiation source and radiation detector may be mounted on a gantry rotatable to a plurality of firing positions, and acquiring imaging data of the single leaf opening and the double leaf opening may include rotating the gantry to a first firing position to acquire imaging data of the single leaf opening and the double leaf opening at the first firing position, and rotating the gantry to a second firing position to acquire imaging data of the single leaf opening and the double leaf opening at the second firing position. The method may further include acquiring imaging data of the single leaf opening and the double leaf opening for each leaf of the MLC at the first firing position, and optionally acquiring imaging data of the single leaf opening and the double leaf opening for each leaf of the MLC at the second firing position. The pattern of MLC openings may include a plurality of MLC leaf instructions indicating a leaf position for each MLC leaf, or the pattern of MLC openings may include a plurality of single leaf openings and a plurality of double leaf openings.In some variations, combining the acquired imaging data may include summing the acquired imaging data of the single leaf aperture and the acquired imaging data of the double leaf aperture, and subtracting the imaging data of the single leaf aperture from an overlap area of ​​the summed imaging data of the double leaf aperture. The method may further include placing a phantom in the field of view of the radiation detector before acquiring the radiation detector imaging data of the single MLC leaf aperture and the dual MLC leaf aperture. Optionally, some variations may include placing a radiation fluence measuring device in the field of view of the radiation detector before acquiring the radiation detector imaging data of the single MLC leaf aperture and the dual MLC leaf aperture, or placing a radiation fluence measuring device in the field of view of the radiation detector before acquiring the radiation detector imaging data of the single MLC leaf aperture and the dual MLC leaf aperture. The acquired imaging data of the single leaf aperture, the acquired imaging data of the double leaf aperture, the pattern of the MLC apertures, and the generated radiation detector images may be stored in a processor memory of the radiation delivery system.

[0006] In another variation, the method may further include calculating a fill profile for each pair of adjacent MLC leaves by subtracting the imaging data of the two single MLC leaf openings from the imaging data of the corresponding double MLC leaf opening. Generating a radiation detector image corresponding to the treatment plan fluence map may include combining the acquired imaging data and fill profiles of the single leaf openings according to the pattern of the MLC openings. Combining the acquired imaging data and fill profiles of the single leaf openings may include summing the acquired imaging data of the single leaf openings and the fill profiles of the adjacent single leaf openings. Generating a radiation detector image may further include combining the acquired imaging data of the double leaf openings with the imaging data and fill profiles of the single leaf openings. Some variations may include generating a graphical representation including the generated radiation detector images and outputting the graphical representation to a display device. Optionally, the method may include calculating a radiation dose to the phantom based on the generated radiation detector images. In some variations, the radiation detector may be an MV detector or a kV detector. The radiation source and radiation detector may be mounted on a gantry rotatable to a plurality of firing positions, and acquiring imaging data of the single leaf opening and the double leaf opening may include rotating the gantry to a first firing position to acquire imaging data of the single leaf opening and the double leaf opening at the first firing position, and rotating the gantry to a second firing position to acquire imaging data of the single leaf opening and the double leaf opening at the second firing position. The method may further include acquiring imaging data of the single leaf opening and the double leaf opening for each leaf of the MLC at the first firing position, and optionally acquiring imaging data of the single leaf opening and the double leaf opening for each leaf of the MLC at the second firing position. The pattern of MLC openings may include a plurality of MLC leaf instructions indicating a leaf position for each MLC leaf, or the pattern of MLC openings may include a plurality of single leaf openings and a plurality of double leaf openings.Optionally, some methods may include placing a phantom within the field of view of the radiation detector prior to acquiring radiation detector imaging data of the single MLC leaf aperture and the dual MLC leaf aperture, or placing a radiation fluence measuring device within the field of view of the radiation detector prior to acquiring radiation detector imaging data of the single MLC leaf aperture and the dual MLC leaf aperture. The acquired imaging data of the single leaf aperture, the acquired imaging data of the double leaf aperture, the pattern of the MLC apertures, and the generated radiation detector images may be stored in a processor memory of the radiation delivery system.

[0007] Also disclosed herein is a method for assessing the quality of radiation delivery. The method may be used for therapeutic or non-therapeutic applications. One variation may include using imaging data of the single leaf and double leaf apertures of the MLC to generate a simulated radiation detector image corresponding to a treatment plan fluence map, using a therapeutic radiation source of the radiation therapy system to deliver radiation according to the treatment plan fluence map, acquiring radiation detector imaging data during radiation delivery to generate a radiation detector image, determining an image difference between the acquired radiation detector image and the simulated radiation detector image, and generating a graphical representation depicting the image difference between the acquired radiation detector image and the simulated radiation detector image. The method may include generating a notification if the image difference is greater than an acceptable threshold. The radiation therapy system may include a patient area, and the method may further include placing a phantom in the patient area before delivering radiation, for example, when the method is used in a non-therapeutic application. Alternatively or additionally, the radiation therapy system may include a patient area, and the method may further include placing a radiation fluence measuring device in the patient area before delivering radiation. Some variations may further include calculating a simulated dose based on the simulated radiation detector images, calculating a delivered dose based on the acquired radiation detector images, determining a dose difference between the simulated dose and the delivered dose, and generating a graphical representation depicting the dose difference. The method may include generating a notification if the dose difference is greater than an acceptable threshold. Optionally, the method may further include updating radiation delivery parameters if the difference is greater than an acceptable threshold.

[0008] Another variation of the method for generating a radiation detector image corresponding to a treatment plan fluence map may include acquiring imaging data of single-leaf and double-leaf apertures for each leaf of a multi-leaf collimator using a radiation source and a radiation detector, and combining the acquired imaging data to generate a radiation detector image that matches a pattern of MLC apertures corresponding to the treatment plan fluence map. The method may be used for non-therapeutic applications. The method may further include generating a graphical representation including the generated radiation detector image, and outputting the graphical representation to a display device. In some variations, the radiation detector may be an MV detector or a kV detector. The radiation source and radiation detector may be mounted on a gantry that is rotatable to multiple firing positions, and acquiring imaging data of the single-leaf and double-leaf apertures may include rotating the gantry to a first firing position to acquire imaging data of the single-leaf and double-leaf apertures at the first firing position, and rotating the gantry to a second firing position to acquire imaging data of the single-leaf and double-leaf apertures at the second firing position. The method may further include acquiring single leaf and double leaf aperture imaging data for each leaf of the MLC at the first firing position, and optionally acquiring single leaf and double leaf aperture imaging data for each leaf of the MLC at the second firing position. The MLC aperture pattern may include a plurality of MLC leaf instructions indicating a leaf position for each MLC leaf, or the MLC aperture pattern may include a plurality of single leaf apertures and a plurality of double leaf apertures. The acquired imaging data of the single leaf apertures, the acquired imaging data of the double leaf apertures, the MLC aperture pattern, and the generated radiation detector image are stored in a processor memory of the radiation delivery system. Combining the acquired imaging data may include summing the acquired imaging data of the single leaf apertures and the acquired imaging data of the double leaf apertures, and subtracting the imaging data of the single leaf apertures from an overlap area of ​​the summed imaging data of the double leaf apertures.Alternatively or additionally, the method may further include calculating a fill profile of each pair of adjacent MLC leaves by subtracting the imaging data of the two single MLC leaf openings from the imaging data of the corresponding double MLC leaf opening, and combining the acquired imaging data to generate the radiation detector image includes combining the acquired imaging data and fill profiles of the single leaf openings according to the pattern of the MLC openings. In some variations, combining the acquired imaging data and fill profiles of the single leaf openings may include summing the acquired imaging data of the single leaf openings and the fill profiles of adjacent single leaf openings. In some variations, combining the acquired imaging data to generate the radiation detector image may include combining the double leaf openings with the imaging data and fill profiles of the single leaf openings. Optionally, some methods may further include generating a graphical representation of the generated radiation detector image and outputting the graphical representation to a display device.

[0009] Although the examples provided herein are in the context of generating simulated MV detector images and using those simulated images for treatment plan delivery QA purposes, it should be understood that similar methods may be used to generate simulated images for other imaging modalities (e.g., kV CT, kV planar imaging, or kV radiation delivery systems) using other types of radiation detectors. The methods described herein may be used for purposes other than treatment plan delivery QA. For example, small animal treatment systems may use kV radiation sources with MLC. Similarly, specialized radiation therapy systems for skin or eye treatment may also use low kV energy radiation sources coupled with MLC, and the systems and methods disclosed herein may be used with such treatment systems. [Brief description of the drawings]

[0010] [Figure 1A] FIG. 1 is a functional block diagram of a variant of the radiation therapy system. [Figure 1B] 1 is a schematic representation of one variation of a radiation therapy system including a rotatable gantry, a LINAC, a beam shaping assembly, and an MV detector. [Figure 1C] 1 is a schematic representation of one variation of a radiation therapy system including a rotatable gantry, a LINAC, a beam shaping assembly, and an MV detector. [Diagram 2] 13 is a flowchart representation of one variation of a method for generating an MV detector image using acquired MV detector imaging data of different MLC configurations. [Figure 3A] FIG. 3 is a conceptual diagram of MV detector imaging data of a single leaf aperture and a double leaf aperture, and an example of how they can be combined to simulate an MV detector image corresponding to a desired pattern of MLC apertures according to the method depicted in FIG. [Figure 3B] FIG. 3 is a conceptual diagram of MV detector imaging data of a single leaf aperture and a double leaf aperture, and an example of how they can be combined to simulate an MV detector image corresponding to a desired pattern of MLC apertures according to the method depicted in FIG. [Figure 3C] FIG. 3 is a conceptual diagram of MV detector imaging data of a single leaf aperture and a double leaf aperture, and an example of how they can be combined to simulate an MV detector image corresponding to a desired pattern of MLC apertures according to the method depicted in FIG. [Figure 3D] FIG. 3 is a conceptual diagram of MV detector imaging data of a single leaf aperture and a double leaf aperture, and an example of how they can be combined to simulate an MV detector image corresponding to a desired pattern of MLC apertures according to the method depicted in FIG. [Figure 4] 13 is a flowchart representation of another variation of a method for generating an MV detector image using acquired MV detector imaging data of different MLC configurations. [Figure 5A]FIG. 5 is a conceptual diagram of MV detector imaging data and fill profiles for a single leaf aperture and a double leaf aperture, and an example of how these can be combined to simulate an MV detector image corresponding to a desired pattern of MLC apertures according to the method depicted in FIG. [Figure 5B] FIG. 5 is a conceptual diagram of MV detector imaging data and fill profiles for a single leaf aperture and a double leaf aperture, and an example of how these can be combined to simulate an MV detector image corresponding to a desired pattern of MLC apertures according to the method depicted in FIG. [Figure 5C] FIG. 5 is a conceptual diagram of MV detector imaging data and fill profiles for a single leaf aperture and a double leaf aperture, and an example of how these can be combined to simulate an MV detector image corresponding to a desired pattern of MLC apertures according to the method depicted in FIG. [Figure 5D] FIG. 5 is a conceptual diagram of MV detector imaging data and fill profiles for a single leaf aperture and a double leaf aperture, and an example of how these can be combined to simulate an MV detector image corresponding to a desired pattern of MLC apertures according to the method depicted in FIG. [Figure 5E] FIG. 5 is a conceptual diagram of MV detector imaging data and fill profiles for a single leaf aperture and a double leaf aperture, and an example of how these can be combined to simulate an MV detector image corresponding to a desired pattern of MLC apertures according to the method depicted in FIG. [Figure 6] 1 is a flowchart representation of one variation of a method for assessing the quality of radiation delivery according to a patient-specific treatment plan. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Disclosed herein is a method for generating MV detector images that are specific to a patient's treatment plan fluence map and using the generated MV detector images to evaluate the ability of a radiation therapy system to deliver the planned fluence map. The MV detector images simulate images and / or imaging data that would have been acquired on an MV detector if radiation was precisely emitted by a therapeutic radiation source (e.g., LINAC) and shaped by a multi-leaf collimator (MLC) according to the treatment plan fluence map. The MV detector images may be generated using MV detector imaging data acquired with the radiation therapy system. In some variations, the acquired MV detector imaging data may be a limited number of MLC configurations, e.g., single leaf opening and / or double leaf opening for each leaf of the MLC. This limited set of imaging data may be combined as described herein to generate an MV detector image that corresponds to the patient's treatment plan fluence map. Limiting the amount of MV detector imaging data acquired with a radiation therapy system can help reduce the amount of time the radiation therapy system is used for QA purposes and increase the availability of the radiation therapy system to treat patients.

[0012] MV detector images generated using the acquired MV detector imaging data may be used to assess how well a radiation therapy system is delivering radiation in accordance with a planned fluence map. For example, a method for assessing the quality of radiation delivery of a radiation therapy system may include delivering or emitting radiation in accordance with a treatment plan fluence map, acquiring MV detector imaging data during radiation delivery, and comparing the acquired MV detector images to generated MV detector images that simulate the delivery of the treatment plan fluence map.

[0013] The methods described herein may be used to generate MV detector images for any radiotherapy system equipped with an MV detector, and are not limited to the specific radiotherapy system described and depicted below. For example, the methods for acquiring MV detector imaging data and generating MV detector images may be used with any radiotherapy system equipped with a therapeutic radiation source that emits high-energy X-rays and an MV detector located directly opposite (e.g., opposite) the therapeutic radiation source. The therapeutic radiation source and the MV detector may be mounted on a circular gantry, or a C-arm gantry, or a robotic arm, which may include one or more motion systems configured to position the therapeutic radiation source at various firing positions around the patient area. Alternatively, or additionally, the methods described herein may be used to generate any X-ray detector image (e.g., kV CT image) based on a limited set of experimentally acquired detector imaging data (e.g., kV CT imaging data).

[0014] Radiation Therapy Systems FIG. 1A depicts a functional block diagram of a variation of a radiation therapy system that may be used with one or more of the methods described herein. The radiation therapy system (100) includes one or more therapeutic radiation sources (102) and a patient platform (104). The therapeutic radiation source may include an X-ray source, an electron source, a proton source, and / or a neutron source. For example, the therapeutic radiation source (102) may include a linear accelerator (LINAC), a Cobalt-60 source, and / or an X-ray machine. The therapeutic radiation source may be movable around the patient platform such that a radiation beam may be directed to a patient on the patient platform from multiple firing positions and / or angles. In some variations, the radiation therapy system may include one or more beam shaping elements and / or assemblies (106) that may be located in a beam path of the therapeutic radiation source. For example, the radiation therapy system may include a LINAC (102) and a beam shaping assembly (106) disposed in a path of the radiation beam. The beam shaping assembly may include one or more movable jaws and one or more collimators. At least one of the collimators may be an MLC with multiple individually controlled leaves. The MLC may be a 1-D MLC (i.e., a binary MLC where each leaf is either open or closed). The linac and beam shaping assembly may be mounted on a gantry or a movable support frame with a motion system configured to adjust the position of the linac and beam shaping assembly. In some variations, the linac and beam shaping assembly may be mounted on a support structure that includes one or more robotic arms, C-arms, gimbals, and the like. The patient platform (104) may also be movable. The radiation therapy system (100) may include one or more imaging systems (108) of one or more imaging modalities. For example, the one or more imaging systems (108) may include a kV CT imaging system, a PET imaging system, a MV X-ray imaging system, and / or an MR imaging system. The imaging system (108) may be coplanar with the treatment plane of the therapeutic radiation source, and in other variations, the imaging system (108) may not be coplanar with the treatment plane.For example, the imaging plane of a PET imaging system and / or an MR imaging system and / or an MV X-ray imaging system may be coplanar with the treatment plane, while a kV CT imaging system may have an imaging plane that does not coincide with the treatment plane. In some variations, the MV X-ray imaging system may include an MV detector mounted on a gantry and located opposite the LINAC and beam shaping assembly. FIG. 1B is a schematic representation of one variation of a radiation therapy system including a rotatable gantry (105), a LINAC (102) mounted on the gantry (105), a beam shaping assembly (106), and an MV detector (109). The MV detector is directly opposite (i.e., opposite) the LINAC and beam shaping assembly. The gantry (105) may be continuously rotatable in one direction (as represented by the arrow) or may be rotatable in both clockwise and counterclockwise directions, if desired. The beam shaping assembly (106) may include an MLC, such as any MLC described herein. Rotating the gantry (105) may move the LINAC (102) to various firing positions (e.g., firing angles) around the treatment area (111). As the gantry rotates to move the LINAC to a new firing position, as shown in FIG. 1C, the beam shaping assembly and MV detector also move in a corresponding manner. While the radiation therapy system depicted herein includes a circular gantry, it should be understood that the radiation therapy system may instead include one or more robotic arms, C-arms, and gimbals.

[0015] The radiation emitted by the LINAC and shaped by the MLC (and / or in combination with any component of the beam shaping assembly) may be measured by the MV detector. The imaging data or measurements acquired by the MV detector may be used to detect the size and shape of the MLC aperture cumulatively formed by the leaf positions. For example, the MV detector imaging data may be used to determine whether the MLC leaves of a binary MLC are open or closed, to determine the shape of the MLC aperture, and / or to calculate the radiation fluence passing through the MLC aperture. When the treatment plan fluence map is segmented into machine instructions, the machine instructions may include MLC leaf instructions that specify the position of each leaf for each firing position of the therapeutic radiation source across one or more patient platform locations. These machine instructions may be used in quality assurance (QA) procedures to evaluate the performance of the radiation therapy system. The configuration of the MLC measured by the MV detector (i.e., the shape of the MLC aperture as a result of each position of the MLC leaves) may be compared to the MLC leaf instructions derived from the treatment plan fluence map. The MV detector imaging data can be used, for example, to assess the accuracy of actuation of the MLC leaves as the MLC steps through each pattern or configuration specified by machine instructions segmented from the treatment plan fluence map.

[0016] The radiation therapy system (100) may comprise a controller (110) in communication with the therapeutic radiation source (102), the beam shaping element or assembly (106), the patient platform (104), one or more image sensors (108) (e.g., one or more imaging systems), and one or more fluence measurement devices (101). The controller 110 may comprise one or more processors and one or more machine-readable memories in communication with the one or more processors, which may be configured to execute or implement any of the methods described herein. The controller may record and store in the machine-readable memory (e.g., during a treatment session and / or a quality assurance session) information generated during delivery of radiation, including beam energy, monitoring unit, MLC and patient platform data (e.g., patient platform position coordinates), images taken during delivery, etc. The one or more machine-readable memories may store instructions that cause the processor to execute modules, processes, and / or functions associated with the system, such as one or more treatment plans, system calibration procedures, system quality assurance (QA) procedures, calculation of a radiation fluence map based on the treatment plan and / or clinical goals, segmentation of the fluence map into radiation therapy system instructions (e.g., which may direct the operation of the gantry, therapeutic radiation source, beam shaping assembly, patient platform, and / or any other components of the radiation therapy system), and image and / or data processing associated with the treatment plan and / or radiation delivery. In some variations, the memory may store treatment plan data (e.g., treatment plan emission filters, fluence maps, planning images, treatment session PET pre-scan images, and / or initial CT, MRI, and / or X-ray images). In some variations, the controller may be configured to compare data acquired during radiation delivery (e.g., acquired MV detector imaging data) with treatment plan data (e.g., expected MV detector imaging data) to evaluate how closely the actual radiation delivery matched the planned radiation delivery (e.g., a "record and verify" system). The controller of the radiation therapy system may be connected to other systems by wired or wireless communication channels.For example, the radiation therapy system controller may communicate wired or wirelessly with the radiation therapy treatment planning system controller such that fluence maps, emission filters, initial and / or planning images (e.g., CT images, MRI images, PET images, 4-D CT images), patient data, and other clinically relevant information may be transferred from the radiation therapy treatment planning system to the radiation therapy system. The delivered radiation fluence, any dose calculations, and any clinically relevant information and / or data acquired during QA and / or treatment sessions may be transferred from the radiation therapy system to the radiation therapy treatment planning system. This information may be used by the radiation therapy treatment planning system to adapt the treatment plan and / or adjust the delivery of radiation for successive treatment sessions. Additional description of radiation therapy systems is provided in U.S. Patent No. 10,695,586, filed November 15, 2017, which is incorporated herein by reference in its entirety.

[0017] Quality Assurance (QA) Methods After the treatment plan fluence map is generated, it may be evaluated to provide the desired (e.g., prescribed) dose distribution and to ensure that the radiation therapy system can deliver the planned fluence map precisely and accurately. One variation of a method for evaluating whether the radiation therapy system can deliver the planned fluence map may include emitting radiation according to the planned fluence map using the LINAC and MLC of the radiation therapy system, measuring the emitted radiation by acquiring imaging data on the MV detector, generating an MV detector image from the acquired imaging data, and comparing the generated MV detector image to an expected MV detector image. The expected MV detector image may represent the MV detector imaging data that would be acquired if the radiation therapy system could successfully execute the machine instructions from the segmented treatment plan fluence map. In some variations, the expected MV detector image may be generated from a simulation using a radiation beam model and a radiation therapy system component model, for example, an MLC model that simulates how radiation interacts with the MLC leaves and how the tongue and groove (TNG) effect affects radiation fluence and / or radiation scattering from the MLC leaves. Alternatively, or additionally, the predicted MV detector image may be generated by combining empirical MV detector imaging data according to machine instructions derived from segmenting the treatment plan fluence map. For a binary MLC, this may involve acquiring MV detector data for each MLC configuration, e.g., for each leaf in the MLC, for the MLC opening of any number of leaves, single leaf opening, double leaf opening, triple leaf opening, etc. For example, in a variation in which the radiation therapy system has a 64-leaf binary MLC, MV detector imaging data may be acquired for single leaf openings (for leaves #1-64), double leaf openings (for leaves #1-63), triple leaf openings (for leaves #1-62), etc., including up to 63 leaf openings (for leaves #1 and #2) and 64 leaf openings (for leaf #1).The acquired MV detector imaging data may then be combined together to form an MV detector image that corresponds to any pattern of MLC openings, including a pattern of MLC openings that matches a treatment plan fluence map developed for a particular patient.

[0018] However, this method of acquiring MV detector imaging data may be time-consuming and occupy the processing bank and radiation therapy system longer than desired for non-therapeutic purposes. As an example, for a 64-leaf binary MLC, for each firing position (e.g., firing angle around a circular gantry), the MLC needs to step through a total of 2080 MLC leaf patterns while firing radiation from the LINAC and acquiring MV detector data for each MLC leaf pattern. The number of MLC leaf patterns for which MV detectors need to be acquired may increase for MLCs with more leaves so that any arbitrary pattern of MLC apertures can be generated.

[0019] The methods described herein may include using this limited set of MV detector imaging data to acquire MV detector imaging data for a limited number of MLC patterns or configurations to generate expected MV detector images (which may also be referred to as simulated MV detector images) for radiation delivery QA. In one variation, the method may include acquiring MV detector imaging data for only single leaf and double leaf openings. This may help reduce the time that the radiation therapy system is used for QA sessions (i.e., non-therapeutic purposes). As an example, for a 64-leaf binary MLC, the method described herein may include acquiring MV detector imaging data for single leaf openings for 64 leaves and acquiring MV detector imaging data for double leaf openings for 63 leaves. To acquire this MV detector imaging data, the MLC needs to emit radiation from the LINAC and step through a total of 127 MLC leaf patterns while acquiring MV detector data for each MLC leaf pattern. These 127 MV detector images of these MLC leaf patterns may be combined (e.g., summed and / or subtracted) to produce an MV detector image of any MLC leaf pattern, such as an MLC leaf pattern that corresponds to a patient's treatment plan fluence map. This expected MV detector image may then be used to assess the quality of the actual radiation delivery of the treatment plan fluence map during a QA session (i.e., in the absence of the patient).

[0020] In one variation, an MV detector image of any MLC leaf pattern can be generated from single and double leaf openings by summing the MV detector imaging data of the double leaf openings that overlap each other (i.e., the overlapping areas for a particular MLC leaf) and subtracting the MV detector imaging data of the single leaf opening to remove the overlap. In another variation, an MV detector image of any MLC leaf pattern can be generated from single and double leaf openings by calculating a fill profile between each leaf of the MLC (e.g., using the acquired MV detector imaging data of the single leaf openings and the double leaf openings) and then summing the MV detector imaging data of the single leaf openings with the corresponding fill profiles. These methods can help compensate for tongue and groove artifacts that can result from summing single leaf openings.

[0021] 2 is a flowchart representation of one variation of a method for generating a predicted MV detector image (also referred to as a simulated MV detector image) that can be used to evaluate radiation delivery of a patient-specific treatment plan fluence map. The method (200) can include acquiring MV detector imaging data of a single MLC leaf opening for all leaves of the MLC using a radiation source and an MV detector (202), acquiring MV detector imaging data of a double MLC leaf opening for all leaves of the MLC using a radiation source and an MV detector (204), segmenting the treatment plan fluence map into a pattern of MLC openings (206), and generating an MV detector image corresponding to the treatment plan fluence map by combining the acquired MV detector imaging data of the single leaf openings and the double leaf openings according to the pattern of the MLC openings (208). In some variations, steps (201-204) may be performed on a radiation therapy system including the LINAC, the MLC, the MV detector, and a radiation therapy system controller (e.g., the radiation therapy system described above), while steps (206-208) may be performed using the radiation therapy system controller and / or a separate controller (e.g., a controller of a treatment planning or treatment QA system). Alternatively or additionally, the radiation therapy system may include one or more controllers, at least one of which may be in communication with the LINAC, the MLC, and the MV detector, and at least one of the other controllers may be configured to perform one or more of the steps of the method (200). For example, a first radiation therapy system controller may be configured to perform steps (201-204), while a second radiation therapy system controller may be configured to perform steps (206-208). The generated MV detector images (also referred to as expected MV detector images or simulated MV detector images) may be used to evaluate the delivery of the treatment plan. FIG. 3A depicts an example of MV detector imaging data of a single MLC leaf aperture for a binary MLC with 64 leaves.During acquisition of MV detector imaging data for a single leaf opening (202), the MLC opens one leaf at a time, from leaf 1 to leaf 64, emits radiation from the LINAC, and records imaging data for the single leaf opening. FIG. 3B depicts an example of MV detector imaging data for a dual MLC leaf opening for a binary MLC with 64 leaves. During acquisition of MV detector imaging data for a dual leaf opening (204), the MLC opens two adjacent leaves at a time, from leaf 1 to leaf 63 (because the dual leaf opening in leaf 64 includes leaf 63), emits radiation from the LINAC, and records imaging data for the dual leaf opening. Optionally, for a radiotherapy system with a rotatable gantry or arm configured to move the LINAC to multiple firing positions, a different set of single leaf and dual leaf openings may be acquired for each firing position. As an example, for a radiotherapy system including 50 firing positions, the method (200) may include repeating steps (202-204) 50 times. That is, the gantry may move (e.g., rotate) the LINAC to a first firing position, and MV detector imaging data may be acquired for the single-leaf and double-leaf openings as described above, then the LINAC may be LINAC to a second firing position, and MV detector imaging data may be acquired for the single-leaf and double-leaf openings at this second firing position, etc. The MV detector imaging data acquired in steps (202-204) may be stored in a processor memory of the radiation therapy system and / or treatment planning system. In some variations, it is anticipated that the radiation emanating from the LINAC will have a consistent profile, and only one set of single-leaf and double-leaf openings may be acquired regardless of the number of LINAC firing positions.

[0022] The fluence map may include a set of radiation beamlets and beamlet intensities for one or more LINAC firing positions that deliver a desired dose. Segmenting the plan fluence map may include converting the set of beamlets and intensities into MLC leaf instructions and / or LINAC firing instructions for each firing position of the LINAC. The segmented fluence map may indicate, for example, which MLC leaves are opened (and closed) at a given firing position. In some variations, the pattern of MLC leaves includes multiple MLC leaf instructions indicating the leaf position for each MLC leaf. The pattern of MLC leaves shapes the radiation passing from the LINAC to the MV detector. The MV detector imaging data resulting from firing radiation through a particular pattern of MLC leaves may be generated by summing the appropriate MV detector imaging data for the double leaf opening and subtracting the overlap area using the MV detector imaging data for the single leaf opening for the leaves in the overlap area.

[0023] FIG. 3C illustrates an example of a method for combining MV detector imaging data acquired for single leaf openings and double leaf openings to simulate an MV detector image corresponding to a desired pattern of MLC openings. In this example, the desired MLC opening pattern is three leaf openings across leaves 1, 2, and 3. One method for determining an MV detector image corresponding to this MLC pattern is to sum the imaging data for the double leaf opening for leaf 1 and the double leaf opening for leaf 2. Because both of these two double leaf openings include leaf 2, there is an overlap area for leaf 2. This is illustrated in FIG. 3D, where the arrows point to the overlap area on leaf 2 where the imaging data for leaf 2 shows a peak signal because it was included twice. The method may include removing this overlap area by subtracting the imaging data for the single leaf opening of leaf 2, resulting in an image corresponding to the desired MLC pattern. More generally, determining an MV detector image for an x-leaf opening (x is from 3 to the total number of leaves of the MLC) for the i-th leaf (i is the leaf number from 1 to the total number of leaves) may be done by summing the MV detector images of the double leaf openings for leaf no.

number

number

[0024] Some variations of the method (200) may include determining (207) one or more sets of single-leaf and double-leaf openings that create a pattern of MLC openings. For example, the method (200) may include determining a first set of double-leaf openings that may be summed together and a second set of single-leaf openings that may be subtracted from the sum of the double-leaf openings. The first and second sets may include all of the double-leaf and single-leaf openings for all of the MLC configurations that comprise the segmented fluence map for the entire treatment. For example, the first and second sets may include all of the double-leaf and single-leaf openings for the MLC configurations at each firing position for multiple gantry rotations (in the case of a radiation therapy system with a rotatable gantry), for multiple beam stations (i.e., multiple individual patient platform positions where the platform is parked while radiation is delivered), and / or for multiple shuttle passes through the treatment beam (i.e., moving the patient platform through the treatment beam multiple times, where each instance through the treatment beam is one pass). Alternatively, or in addition, determining 207 one or more sets of single-leaf and double-leaf apertures may include generating a set of single-leaf apertures and a set of double-leaf apertures for each shuttle pass. Expected MV detector images may be generated for each shuttle pass, and during treatment delivery QA, the quality of radiation delivery may be evaluated on a pass-by-pass basis.

[0025] In some variations, the method (200) may include placing (201) one or more phantoms and / or fluence measurement devices in the field of view of the MV detector prior to acquiring (202, 204) MV detector imaging data of the single-leaf and double-leaf apertures. The phantom may include regions having radiation attenuation and / or absorption properties that mimic patient tissue and / or tumor tissue. These MV detector imaging data of the single-leaf and double-leaf apertures in the presence of the phantom may be used in the same manner as imaging data of the single-leaf and double-leaf apertures in the absence of the phantom. The MV detector data in the presence of the phantom may be used to generate an expected MV detector image when the treatment plan fluence map is delivered to the phantom (which may mimic or model anatomical and / or physiological aspects of the patient and / or tumor). Alternatively or additionally, the phantom may include a fluence measurement device. Examples of fluence measuring devices may include, but are not limited to, diode arrays, thin film transistors (TLTs), thermoluminescent dosimeters (TLDs), membranes, and / or any radiation photon detection device. Measurements acquired by the fluence measuring device during a radiation delivery QA session may be compared to the MV detector imaging data, and the comparison may be used to calculate a calibration factor that relates the MV detector imaging data to the measurements from the fluence measuring device. The calibration factor may be used to perform absolute dose calculations during a radiation delivery session, e.g., a treatment session, along with a CT scan of the patient. In some variations, the method may include calculating a radiation dose to a phantom using the acquired MV detector data and the generated radiation detector image based on the known geometric and material properties of the phantom. After the dose delivered to the phantom is determined, the delivered dose may be evaluated using an index such as a gamma evaluation (e.g., a gamma index analysis). For example, gamma index values ​​for various points on a test dose distribution (dose distribution delivered to a phantom) and a reference dose distribution (expected or planned dose distribution, i.e., dose according to a treatment plan) may be calculated and compared to a gamma index threshold to determine whether the dose difference is acceptable.In some variations, gamma evaluation may include determining whether the delivered dose distribution meets dose difference (DD) and / or distance agreement (DTA) criteria (e.g., 3% / 3mm) for multiple points on the distribution.

[0026] 4 is a flowchart representation of another variation of a method for generating a predicted MV detector image that can be used to evaluate radiation delivery of a patient-specific treatment plan fluence map. The method (400) may include acquiring MV detector imaging data of single MLC leaf openings for all leaves of the MLC using a radiation source and an MV detector (402), acquiring MV detector imaging data of double MLC leaf openings for all leaves of the MLC using a radiation source and an MV detector (404), calculating a fill profile for each pair of adjacent MLC leaves by subtracting the imaging data of the two single MLC leaf openings from the imaging data of the corresponding double MLC leaf opening (406), segmenting the treatment plan fluence map into a pattern of MLC openings (408), and generating an MV detector image corresponding to the treatment plan fluence map by combining the acquired MV detector imaging data and the fill profile of the single leaf openings according to the pattern of the MLC openings (410). The generated MV detector image (also referred to as expected MV detector image or simulated MV detector image) may be used to evaluate the delivery of the treatment plan. In some variations, steps (401-404) may be performed on a radiation therapy system including the LINAC, the MLC, the MV detector, and a radiation therapy system controller (e.g., the radiation therapy system described above), while steps (406-410) may be performed using the radiation therapy system controller and / or a separate controller (e.g., a controller of a treatment planning or treatment QA system). Alternatively or additionally, the radiation therapy system may include one or more controllers, at least one of which may be in communication with the LINAC, the MLC, and the MV detector, and at least one of the other controllers may be configured to perform one or more of the steps of the method (400). For example, a first radiation therapy system controller may be configured to perform steps (401-404), while a second radiation therapy system controller may be configured to perform steps (406-410).Figure 5A illustrates an example of MV detector imaging data of a single MLC leaf opening for a binary MLC having 64 leaves, and Figure 5B illustrates an example of MV detector imaging data of a dual MLC leaf opening for a binary MLC having 64 leaves. Acquisition of MV detector imaging data (402, 404) for the single leaf opening and dual leaf opening may be similar to that described above with reference to Figures 4A and 4B.

[0027] FIG. 5C illustrates the filling profile for each pair of adjacent MLC leaves, from leaves 1 and 2 to leaves 63 and 64. FIG. 5D and FIG. 5E illustrate how the filling profile of two adjacent leaves (in this example, leaves 1 and 2) may be calculated. The MV detector imaging data for the single leaf opening for leaf 1 and the single leaf opening for leaf 2 may be summed together to obtain (CALC) leaves 1 and 2. Due to the leading and trailing edges of the imaging data profiles for the individual leaves, the sum of the two single leaf openings has an area where the imaging signal is reduced, which is not accurate compared to the corresponding acquired (404) MV detector double leaf imaging data when leaves 1 and 2 are open. The filling profile is the imaging signal that compensates for the reduced imaging signal when the single leaf MV detector imaging data of the two adjacent leaves are summed. The filling profile for leaves 1 and 2 may be calculated by subtracting the summed single leaf imaging data of leaves 1 and 2 from the double leaf imaging data of leaves 1 and 2, as conceptually illustrated in FIG. 5E. MV detector imaging data for the double leaf opening for leaves 1 and 2 may then be calculated by summing the single leaf opening imaging data for leaf 1, the single leaf opening imaging data for leaf 2, and the fill profiles for leaves 1 and 2. MV detector imaging data for any desired MLC pattern may be produced using the single leaf opening imaging data for all of the leaves and the fill profiles for all of the adjacent leaves. For example, to generate an MV detector image for an MLC pattern in which leaves 4, 5, and 6 are open, the method may include summing the single leaf opening imaging data for leaf 4, the single leaf opening imaging data for leaf 5, the single leaf opening imaging data for leaf 6, and two fill profiles (i.e., the fill profiles for leaves 4 and 5 and the fill profiles for leaves 5 and 6).

[0028] Optionally, for a radiation therapy system with a rotatable gantry or arm configured to move the LINAC to multiple firing positions, a different set of single-leaf and double-leaf apertures may be acquired for each firing position. As an example, for a radiation therapy system including 50 firing positions, the method (400) may include repeating steps (402-404) 50 times. That is, the gantry may move (e.g., rotate) the LINAC to a first firing position, and MV detector imaging data may be acquired for the single-leaf and double-leaf apertures as described above, then the LINAC may be moved to a second firing position, and MV detector imaging data may be acquired for the single-leaf and double-leaf apertures at this second firing position, etc. The MV detector imaging data acquired in steps (402-406) may be stored in a processor memory of the radiation therapy system and / or treatment planning system. Segmenting (408) the treatment plan fluence map may be similar to the segmentation method described above in FIG. 2 (e.g., step 208).

[0029] Some variations of the method (400) may include determining (409) one or more sets of single-leaf aperture imaging data and fill profiles that create a pattern of MLC apertures. For example, the method (400) may include determining a first set of single-leaf apertures and a second set of fill profiles that may be summed together. The first and second sets may include all of the single-leaf apertures and fill profiles for all of the MLC configurations that comprise the segmented fluence map for the entire treatment. For example, the first and second sets may include all of the single-leaf apertures and fill profiles for the MLC configurations at each firing position for multiple gantry rotations (in the case of a radiation therapy system with a rotatable gantry), for multiple beam stations (i.e., multiple individual patient platform positions where the platform is parked while radiation is delivered), and / or for multiple shuttle passes through the treatment beam (i.e., moving the patient platform through the treatment beam multiple times, where each instance through the treatment beam is one pass). Alternatively, or additionally, determining 409 one or more sets of single leaf openings and fill profiles may include generating a set of single leaf openings and a set of fill profiles for each shuttle pass. Expected MV detector images may be generated for each shuttle pass, and during treatment delivery QA, the quality of radiation delivery may be evaluated on a pass-by-pass basis.

[0030] In some variations, the method (400) may include placing (401) one or more phantoms and / or fluence measurement devices in the field of view of the MV detector prior to acquiring (402, 404) MV detector imaging data of the single-leaf and double-leaf apertures. The phantom may include regions having radiation attenuation and / or absorption properties that mimic patient tissue and / or tumor tissue. These MV detector imaging data of the single-leaf and double-leaf apertures in the presence of the phantom may be used in the same manner as imaging data of the single-leaf and double-leaf apertures in the absence of the phantom. The MV detector data in the presence of the phantom may be used to generate an expected MV detector image when the treatment plan fluence map is delivered to the phantom (which may mimic or model anatomical and / or physiological aspects of the patient and / or tumor). Alternatively or additionally, the phantom may include a fluence measurement device. Examples of fluence measuring devices may include, but are not limited to, diode arrays, thin film transistors (TLTs), thermoluminescent dosimeters (TLDs), membranes, and / or any radiation photon detection device. Measurements acquired by the fluence measuring device during a radiation delivery QA session may be compared to the MV detector imaging data, and the comparison may be used to calculate a calibration factor that relates the MV detector imaging data and the measurements from the fluence measuring device. The calibration factor may be used to perform absolute dose calculations during a radiation delivery session, e.g., a treatment session. In some variations, the method may include calculating a radiation dose to a phantom using the acquired MV detector data and the generated radiation detector image based on known geometric and material properties of the phantom. After the dose delivered to the phantom is determined, the delivered dose may be evaluated using an index such as a gamma evaluation (e.g., a gamma index analysis). For example, gamma index values ​​for various points on a test dose distribution (dose distribution delivered to a phantom) and a reference dose distribution (expected or planned dose distribution, i.e., dose according to a treatment plan) may be calculated and compared to a gamma index threshold to determine whether the dose difference is acceptable.In some variations, gamma evaluation may include determining whether the delivered dose distribution meets dose difference (DD) and / or distance agreement (DTA) criteria (e.g., 3% / 3mm) for multiple points on the distribution.

[0031] After MV detector images are generated using imaging data for single leaf, double leaf, and / or fill profiles by any of the methods described herein (e.g., the methods described and depicted in Figures 2-4), a graphical representation including the generated MV detector images may be generated and output to a display device. The generated MV detector images may be compared to acquired MV detector images to assess whether the radiation therapy system accurately delivered radiation according to the treatment plan fluence map.

[0032] One variation of a method for assessing the quality of radiation delivery is depicted in FIG. 6. The method (600) may include using imaging data of the single leaf and double leaf apertures of the MLC (e.g., using the methods (200) or (400)) to generate a simulated MV detector image corresponding to the treatment plan fluence map (602), using a therapeutic radiation source of the radiation therapy system to deliver radiation according to the treatment plan fluence map (604), acquiring MV detector imaging data during radiation delivery to generate an MV detector image (606), determining an image difference between the acquired MV detector image and the simulated MV detector image (608), and generating a graphical representation of the image difference between the acquired MV detector image and the simulated MV detector image (610). The simulated MV detector image may be used as an expected MV detector image against which a test or QA MV detector image may be compared to determine whether the radiation therapy system is functioning adequately and accurately enough to deliver the treatment plan radiation fluence map. The simulated MV detector image may be generated using the radiation therapy system controller and / or a separate controller (e.g., a controller of a treatment planning or treatment QA system). Alternatively or additionally, the radiation therapy system may include one or more controllers, at least one of which may be configured to generate the simulated MV detector image. In some variations, if the difference between the acquired MV detector image and the simulated / expected MV detector image (and / or the delivered and expected dose distributions, further described below) is greater than a predetermined acceptable threshold, the method (600) may further include generating (612) a notification. The notification may be incorporated within the graphical representation, output to a display device, and / or may be an audible notification. In some variations, the graphical representation may also include a visual indication that the difference between the acquired MV detector image and the simulated / expected MV detector image is within an acceptable range (e.g., less than a difference threshold) and thus the radiation delivery has "passed" the QA session.If the difference is greater than an acceptable threshold, the method (600) may include updating (614) the treatment plan fluence map and / or radiation emission parameters. For example, if the delivered radiation as shown in the acquired MV detector image appears different from the expected MV detector image around the edges, the performance of the MLC leaves (e.g., leaves 1-10 and leaves 54-64) on both ends of the MLC may be evaluated. If the end leaves are not performed with sufficient accuracy, the treatment plan fluence map may be re-segmented such that other MLC leaves (e.g., the central MLC leaves) are used more frequently than the end leaves. Alternatively, the system may be checked to ensure that all of the leaves are operating according to predefined specifications.

[0033] Alternatively or additionally, the acquired MV detector images and the simulated / expected MV detector images may be used to generate corresponding dose distribution maps, i.e., delivered and expected dose distributions. The delivered dose distribution may be generated based on the acquired MV detector images, and the expected dose distribution may be generated based on the simulated MV detector images. For example, the expected dose distribution may be generated using a controller (e.g., a radiation therapy system controller and / or a treatment planning or QA controller) by loading the simulated MV detector images, loading a treatment plan fluence map (e.g., a radiation beamlet sequence), and generating an expected dose distribution, and the method (600) may include determining (609) a dose difference between the delivered and expected dose distributions. The generated dose distributions may be overlaid with an anatomical image of the patient and included in a graphical representation output to a display device. The delivered and expected dose distributions may help the clinician determine whether to adjust any radiation delivery and / or treatment plan parameters. The delivered dose distribution may be evaluated using an index such as gamma evaluation (e.g., gamma index analysis) using the expected dose distribution as a reference dose. For example, gamma index values ​​for various points on the test dose distribution (delivered dose distribution) and the reference dose distribution (expected dose distribution) may be calculated and compared to a passing gamma index threshold to determine whether the dose difference is acceptable. In some variations, the gamma evaluation may include determining whether the delivered dose distribution meets dose difference (DD) and / or distance to agreement (DTA) criteria (e.g., 3% / 3 mm) for multiple points on the distribution. The determined (609) dose difference may optionally be depicted graphically and / or textually on the generated (610) graphical representation.

[0034] Optionally, in some variations, the method (600) may include placing (603) one or more phantoms and / or fluence measuring devices within the field of view of the MV detector prior to delivering (604) the radiation. As previously described, the phantom may have geometric and / or material properties that mimic the properties of a patient such that the acquired MV detector imaging data may better represent radiation scattering and attenuation due to the patient during a treatment session. In this variation, the simulated or expected MV detector images used for the comparison may be generated using single-leaf and dual-leaf MV detector imaging data acquired in the presence of a phantom (e.g., a similar or identical phantom). The fluence measuring devices, which may be used alone or in combination with the phantom, may record measurements of radiation received at the treatment area of ​​the radiation therapy system, and these measurements may be calibrated with the acquired MV detector measurements. The resulting calibration coefficients may be used during the QA session and / or treatment session to calculate the radiation dose delivered to the treatment area based on the MV detector measurements. In some variations, steps (603-606) may be performed on a radiation therapy system including the LINAC, the MLC, the MV detector, and a radiation therapy system controller (e.g., a radiation therapy system as described above), while steps (608-614) may be performed using the radiation therapy system controller and / or a separate controller (e.g., a controller of a treatment planning or treatment QA system). Alternatively or additionally, the radiation therapy system may include one or more controllers, at least one of which may be in communication with the LINAC, the MLC, and the MV detector, and at least one of the other controllers may be configured to perform one or more of the steps of the method (600). For example, a first radiation therapy system controller may be configured to perform steps (603-606), while a second radiation therapy system controller may be configured to perform steps (608-614).

[0035] Although various inventive variations have been described and illustrated herein, those skilled in the art can 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 such variation and / or modification is deemed to be within the scope of the inventive embodiments / variations described herein. More generally, those skilled in the art will readily appreciate that all parameters, dimensions, materials, and configurations described herein are intended to be exemplary, and that the actual parameters, dimensions, materials, and / or configurations will depend on the particular application in which the teachings of the present 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 variations described above are presented merely by way of example, and it should be understood that, within the scope of the appended claims and their equivalents, variations of the invention 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

1. 1. A radiation therapy system comprising a radiation source, a multi-leaf collimator (MLC), a radiation detector, and one or more controllers, at least one of which is in communication with each of the radiation source, the MLC, and an imaging system, the controller, or a plurality of the controllers, being interposed therebetween; acquiring imaging data for a single leaf aperture using the radiation source and the radiation detector for each leaf of a multi-leaf collimator (MLC); acquiring imaging data of a double leaf opening for each leaf of the MLC using the radiation source and the radiation detector; Segmenting the treatment planning fluence map into a pattern of MLC openings; generating a radiation detector image corresponding to the treatment planning fluence map by combining the acquired imaging data of single-leaf and double-leaf apertures according to the MLC aperture pattern.

2. 10. The system of claim 1, wherein the system further comprises a display device, and the controller is further configured to generate a graphical representation including the generated radiation detector image and output the graphical representation to the display device.

3. The system of claim 1 , wherein the one or more controllers are further configured to calculate a radiation dose to a phantom based on the generated radiation detector image.

4. The system of claim 1 , wherein the radiation detector is an MV detector.

5. 2. The system of claim 1, wherein the radiation source and radiation detectors are mounted on a gantry that is rotatable to a plurality of launch positions, and a first controller of the one or more controllers is configured to acquire imaging data of the single-leaf opening and the double-leaf opening by rotating the gantry to a first launch position and using the radiation source and the radiation detector to acquire imaging data of the single-leaf opening and the double-leaf opening at the first launch position, and rotating the gantry to a second launch position and using the radiation source and the radiation detector to acquire imaging data of the single-leaf opening and the double-leaf opening at the second launch position.

6. 6. The system of claim 5, wherein the first controller is further configured to acquire single leaf and double leaf opening imaging data for each leaf of the MLC at the first launch position.

7. 7. The system of claim 6, wherein the first controller is further configured to acquire single leaf and double leaf opening imaging data for each leaf of the MLC at the second launch position.

8. The system of claim 1 , wherein the pattern of MLC openings includes a plurality of MLC leaf instructions indicating a leaf position for each MLC leaf.

9. The system of claim 1 , wherein the pattern of MLC openings comprises a plurality of single leaf openings and a plurality of double leaf openings.

10. 2. The system of claim 1, wherein combining the acquired imaging data comprises summing the acquired imaging data of the single leaf opening and the acquired imaging data of the double leaf opening, and subtracting the imaging data of the single leaf opening from an overlapping area of ​​the summed imaging data of the double leaf opening.

11. 2. The system of claim 1, wherein the system further comprises a processor memory, and wherein the acquired imaging data of the single leaf aperture, the acquired imaging data of the double leaf aperture, the MLC aperture pattern, and the generated radiation detector image are stored in the processor memory.

12. 2. The system of claim 1, wherein the one or more controllers are further configured to calculate a fill profile for each pair of adjacent MLC leaves by subtracting imaging data of two single MLC leaf openings from imaging data of a corresponding double MLC leaf opening, and wherein generating the radiation detector image corresponding to the treatment planning fluence map includes combining the acquired imaging data and fill profiles of the single leaf openings according to a pattern of the MLC openings.

13. 13. The system of claim 12, wherein combining the acquired imaging data of the single leaf opening with the fill profile comprises summing the acquired imaging data of the single leaf opening with the fill profile of an adjacent single leaf opening.

14. 13. The system of claim 12, wherein generating the radiation detector image further comprises combining acquired imaging data of a double leaf opening with imaging data and a fill profile of the single leaf opening.

15. 10. The system of claim 1, wherein the system further comprises a display device, and the controller is further configured to generate a graphical representation of the generated radiation detector image and output the graphical representation to the display device.

16. The system of claim 1 , wherein a second controller of the one or more controllers is further configured to calculate a radiation dose to a phantom based on the generated radiation detector image.

17. The system of claim 1 , wherein the radiation detector is an MV detector.

18. 2. The system of claim 1, wherein the radiation source and radiation detector are mounted on a gantry that is rotatable to a plurality of launch positions, and the one or more controllers are configured to acquire imaging data for the single-leaf opening and the double-leaf opening by rotating the gantry to a first launch position to acquire imaging data for the single-leaf opening and the double-leaf opening at the first launch position and rotating the gantry to a second launch position to acquire imaging data for the single-leaf opening and the double-leaf opening at the second launch position.

19. 20. The system of claim 18, wherein the one or more controllers are further configured to acquire single leaf and double leaf opening imaging data for each leaf of the MLC at the first launch position.

20. 20. The system of claim 19, wherein the one or more controllers are further configured to acquire single leaf and double leaf opening imaging data for each leaf of the MLC at the second launch position.

21. The system of claim 1 , wherein the pattern of MLC openings includes a plurality of MLC leaf instructions indicating a leaf position for each leaf of the MLC.

22. The system of claim 1 , wherein the pattern of MLC openings comprises a plurality of single leaf openings and a plurality of double leaf openings.

23. 2. The system of claim 1, wherein the one or more controllers comprise a processor memory, and wherein the acquired imaging data of the single leaf aperture, the acquired imaging data of the double leaf aperture, the MLC aperture pattern, and the generated radiation detector image are stored in the processor memory.

24. 2. The system of claim 1, wherein the one or more controllers comprise: a first controller in communication with each of the radiation source, the MLC, and the imaging system; and a second controller configured to perform the steps of acquiring imaging data of a single-leaf opening using the radiation source and the radiation detector for each leaf of the MLC, and acquiring imaging data of a double-leaf opening using the radiation source and the radiation detector for each leaf of the MLC; and wherein the second controller is configured to perform the steps of generating a radiation detector image corresponding to the treatment planning fluence map by segmenting a treatment planning fluence map into a pattern of MLC openings and combining the acquired imaging data of the single-leaf and double-leaf openings according to the pattern of MLC openings.

Images