Methods for real-time targeting and monitoring of organs at risk for radiotherapy

Abstract

This specification describes variations of methods for monitoring and / or tracking a region of interest within a patient during a radiotherapy session. For example, a method for monitoring a region of interest may include comparing an image of the target region acquired during real-time radiation delivery with a reference image of the same target region acquired before treatment delivery and determining the difference between the two images. If it is determined that the treatment image differs from the reference image, radiation delivery may be stopped to prevent radiation from being delivered to healthy tissue as a result of an anatomical shift in the target region. This specification also describes systems that may be used to implement such methods.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims priority to U.S. Provisional Patent Application No. 63 / 493,488, filed Mar. 31, 2023, the disclosure of which is incorporated herein by reference in its entirety.

[0002] The disclosure herein generally relates to systems and methods for monitoring a region of interest and / or an organ at risk in real - time during a radiation therapy session and controlling treatment delivery. The method can be used for radiation - induced high - energy photon delivery, such as biologically - guided radiation therapy.

Background Art

[0003] Radiotherapy, or radiation therapy, uses high-energy photons or other particles to treat a wide variety of diseases. For example, radiotherapy is commonly used to treat cancerous tumors. During treatment, a gantry equipped with a radiation source moves around the patient, emitting radiation beams from several locations to a region of interest within the patient. Precisely tracking the region of interest (e.g., tumor location) is a crucial factor in maximizing the radiation dose to the target area and limiting radiation exposure to the patient's healthy tissues. Furthermore, precisely tracking organs near the target area (i.e., organs at risk) that are particularly vulnerable to radiotherapy-related toxicity can be used to help provide information on how to limit radiation exposure to the patient's healthy tissues. Some radiotherapy systems use image guidance to track the region of interest and facilitate radiation delivery to that area. However, current image-guided radiotherapy systems and methods require high-quality and / or complete images of the region of interest to guide therapeutic delivery. Such images take time to acquire and reconstruct and typically do not characterize the region of interest in real time. Furthermore, current radiotherapy systems do not currently offer a way to track the region of interest in real time within the treatment zone (e.g., due to anatomical shifts) and to stop treatment when the region of interest is no longer within the treatment zone. Therefore, there is a need for new and improved real-time monitoring of the region of interest in radiotherapy that reduces the risk of radiotoxicity to patients by monitoring and responding to real-time changes in the region of interest. [Overview of the project] [Means for solving the problem]

[0004] In some variations, a method for real-time radiotherapy treatment includes generating a set of pre-scan images of a region of interest (ROI) in the patient, acquiring a set of real-time images of the ROI, comparing the set of real-time images with the set of pre-scan images, and terminating the radiotherapy treatment session if the set of real-time images deviates from the set of pre-scan images by a predetermined threshold. In some variations, the method includes generating a graphical notification or other notification of the deviation status of the set of real-time images from the set of pre-scan images. In some variations, acquiring the set of real-time images includes acquiring one or more subsets of the real-time images at a control point. In some variations, the method includes delivering radiotherapy treatment to the patient from the control point if the set of real-time images does not deviate from the set of pre-scan images by a predetermined threshold. In some variations, the method is performed by a first module (e.g., a controller or processor) in the radiotherapy treatment system, and terminating the radiotherapy treatment session includes sending a command from the first module to a second module (e.g., a controller or processor) configured to control the hardware of the radiotherapy treatment system. In some variations, the method includes generating one or more visual, auditory, or tactile notifications indicating a deviation status of a set of real-time images from a set of pre-scan images. In some variations, ending a radiotherapy treatment session includes automatically saving a record of the radiotherapy treatment delivered to the patient and generating an updated set of pre-scan images containing the ROI. In some variations, each of the pre-scan image set and the real-time image set contains a 3D image of at least a portion of the ROI. In some variations, the method includes defining a predetermined threshold based on the set of pre-scan images, the predetermined threshold containing one or more predetermined thresholds.In some variations, each set of prescan images and each set of real-time images includes one of the following: PET images, SPECT images, CT images, MRI images, X-ray images, ultrasound images, or a combination thereof. In some variations, generating a set of prescan images includes obtaining a subset of prescan images at each of several control points and combining the subsets of prescan images. In some variations, the method includes combining multiple subsets of prescan images corresponding to several control points to generate a combined prescan image of the ROI. In some variations, each of the several control points includes a beam station in a radiotherapy system, which may be a patient platform location selected for radiotherapy delivery to a portion of the ROI within the patient. In some variations, the ROI includes an inner ROI within an outer ROI. In some variations, the method includes identifying the inner ROI before real-time radiotherapy treatment and localizing the outer ROI before real-time radiotherapy treatment.

[0005] In some variations, the method includes calculating pre-scan image metrics and real-time image metrics, and comparing a set of real-time images of an ROI with a set of pre-scan images of an ROI includes comparing the pre-scan image metrics and real-time image metrics. In some variations, each of the pre-scan image metrics and real-time image metrics includes two or more image metrics.

[0006] In some variations, the set of prescan images includes a sum of multiple subsets of prescan images (e.g., images taken at each control point or beam station), and calculating the prescan image metric involves calculating the autocross-correlation between each subset of the multiple subsets of prescan images and the sum of prescan images, while calculating the real-time image metric involves calculating the local cross-correlation between the set of real-time images and the sum of prescan images.

[0007] In some variations, the real-time image metric and the pre-scan image metric each include the rate of change of maximum signal intensity. In some variations, the set of pre-scan images includes a combined pre-scan image having multiple subsets of pre-scan images, and the method includes calculating the rate of change of maximum signal intensity for multiple subsets of pre-scan images to determine the pre-scan image metric, generating a hybrid image of the ROI by replacing the corresponding subsets of pre-scan images in the combined pre-scan image with the set of real-time images, and calculating the rate of change of maximum signal intensity for the hybrid image to determine the real-time image metric. In some variations, the ROI includes an inner ROI within an outer ROI, and the maximum signal intensity includes the maximum signal intensity within the inner ROI. In some variations, the rate of change of maximum signal intensity indicates the movement of the inner ROI relative to the outer ROI.

[0008] In some variations, the real-time image metric and the pre-scan image metric each include the rate of change of the standard deviation of the image signal. In some variations, the set of pre-scan images includes a combined pre-scan image containing multiple subsets of pre-scan images, and the method includes calculating the rate of change of the standard deviation of the signal for each of the multiple subsets of pre-scan images in order to determine the pre-scan image metric, generating a hybrid image containing ROIs by replacing the corresponding subsets of pre-scan images in the combined pre-scan image with the set of real-time images, and calculating the standard deviation of the signal for the hybrid image in order to determine the real-time image metric. In some variations, the pre-scan image metric includes the maximum rate of change of the standard deviation of the signal intensity in the multiple subsets of pre-scan images. In some variations, the ROI includes an inner ROI within an outer ROI, and calculating the standard deviation of the signal includes excluding the signal in the inner ROI and calculating the standard deviation of the signal in the outer ROI. In some variations, the rate of change of the standard deviation of the signal indicates the movement of an anatomical object to the outer ROI if the set of real-time images deviates from the set of pre-scan images by a predetermined threshold.

[0009] In some variations, the ROI includes a volumetric shell containing an inner ROI within an outer ROI, and the real-time image metric and pre-scan image metric, respectively, include the average intensity of the ROI. In some variations, the method may include adjusting the signal intensity of the inner ROI, calculating the average intensity of the ROI using the adjusted signal of the inner ROI to determine the pre-scan image metric, and calculating the average intensity of the ROI against a set of real-time images to determine the real-time image metric. In some variations, adjusting the signal intensity of the inner ROI may include duplicating the signal of the inner ROI and shifting the duplicated signal to multiple locations within the outer ROI, and calculating the pre-scan image metric includes calculating the average intensity of the ROI at each of the multiple locations. In some variations, the duplicated signal of the inner ROI may be used to simulate the presence of anatomical objects within the ROI. In some variations, calculating the average intensity of the ROI against a set of real-time images includes applying weighting factors to the calculation of the average intensity of the ROI.

[0010] In some variations, a method for monitoring ROIs within a patient during a radiotherapy treatment session includes acquiring multiple subsets of prescan images containing at least a portion of the ROI at multiple corresponding beam stations, and combining multiple subsets of prescan images corresponding to multiple beam stations to generate an aggregated prescan image of the ROI. For each of the multiple beam stations, the method includes combining one or more sets of consecutive prescan images to generate a subset prescan image of the aggregated prescan image, and calculating the autocross-correlation between the subset images corresponding to the beam station and the aggregated prescan image. The method further includes acquiring a set of real-time images at a beam station, combining the set of real-time images with the remainder of one or more sets of consecutive real-time images to generate a subset real-time image of the ROI, wherein the one or more sets of consecutive real-time images correspond to one or more sets of consecutive prescan images for the beam station, calculating local cross-correlations between the subset real-time image of the ROI and the aggregated prescan image of the ROI, comparing the local cross-correlations to autocross-correlations to determine whether the local cross-correlations are within acceptable limits, generating a graphical notification to indicate whether the local cross-correlations are within acceptable limits, and optionally modifying radiotherapy to the patient if the local cross-correlations are not within acceptable limits.

[0011] In some variations, a method for monitoring ROIs within a patient during a radiotherapy treatment session includes acquiring a set of pre-scan images of at least a portion of the ROI at multiple corresponding beam stations, and combining multiple subsets of the pre-scan images corresponding to the multiple beam stations to generate an aggregated pre-scan image of the ROI. For each of the multiple beam stations, the method further includes: combining one or more sets of consecutive prescan images to generate a subset prescan image of the aggregated prescan image; using the multiple subset prescan images corresponding to the multiple beam stations to calculate the rate of change of the maximum prescan image signal relative to the aggregated prescan image of the ROI; obtaining a set of real-time images containing at least a portion of the ROI; calculating the maximum real-time image signal relative to the set of real-time images; generating a hybrid image of the ROI by replacing the corresponding set of prescan images in the aggregated prescan image with the set of real-time images; calculating the rate of change of the maximum real-time image signal relative to the hybrid image; comparing the rate of change of the maximum real-time image signal with the rate of change of the maximum prescan image signal; generating a graphical notification to indicate whether the maximum real-time image signal is within acceptable limits; and, if the maximum real-time image signal is not within acceptable limits, optionally modifying the radiotherapy treatment for the patient.

[0012] In some variations, a method for monitoring ROIs within a patient during a radiotherapy treatment session includes acquiring multiple subsets of prescan images of at least a portion of the ROI at multiple corresponding beam stations and generating an aggregated prescan image of the ROI. At each of the multiple beam stations, the method further includes calculating a prescan minimum signal activity metric, determining an overall minimum signal activity threshold, acquiring a set of real-time images of the ROI, calculating a real-time signal activity metric, and comparing the real-time signal activity metric to the overall minimum signal activity threshold. If the real-time signal activity metric is greater than the overall minimum signal activity threshold, the method includes registering the set of real-time images to a corresponding set of prescan images and calculating a normalized mutual information metric (NMI) using the registered set of real-time and corresponding prescan image sets. If the NMI metric exceeds a predetermined NMI threshold, the method includes: calculating a z-score to compare a set of real-time images with an aggregated pre-scan image; generating a graphical notification to indicate whether the z-score is within an acceptable range; and, if the z-score is not within an acceptable range, optionally modifying the radiotherapy treatment for the patient. [Brief explanation of the drawing]

[0013] [Figure 1A] An exemplary schematic diagram of a radiotherapy patient with three regions of interest monitored during a radiotherapy session is shown.

[0014] [Figure 1B] This diagram shows an exemplary schematic representation of deformation patterns in the region of interest for monitoring during radiotherapy sessions.

[0015] [Figure 2]Shows a conceptual diagram of a variant of a method for acquiring an image of a region of interest during a radiotherapy session.

[0016] [Figure 3] A and B show a conceptual diagram of a variant of a method for generating an image of a region of interest during a radiotherapy session.

[0017] [Figure 4] Shows a schematic diagram of an exemplary radiotherapy treatment system for use with the methods described herein.

[0018] [Figure 5] Shows an exemplary flowchart representation of a variant of a method for monitoring a region of interest during a radiation delivery session.

[0019] [Figure 6] Shows an exemplary flowchart representation of an example of a method for monitoring a region of interest during a radiation delivery session.

[0020] [Figure 7] Shows simulation results of processing image data to determine an imaging monitoring metric according to the exemplary methods described herein.

[0021] [Figure 8] Shows an exemplary flowchart representation of an example of a method for monitoring a region of interest during a radiation delivery session.

[0022] [Figure 9A] Shows three images of a region of interest monitored during a radiation delivery session using the exemplary methods described herein.

[0023] [Figure 9B] Shows a graphical representation of image monitoring metrics calculated during a radiation delivery session using the exemplary methods described herein.

[0024] [Figure 10] An exemplary flowchart is shown illustrating an example of a method for monitoring a region of interest during a radiation delivery session.

[0025] [Figure 11] Two graphical representations of the distribution plots used to determine the image monitoring metrics as described herein are shown. [Modes for carrying out the invention]

[0026] Various embodiments and non-limiting examples of the present invention are described herein and shown in the accompanying drawings.

[0027] This specification describes methods for real-time monitoring and / or tracking of a region of interest within a treatment zone during a radiation delivery session, which in some variations may be a radiotherapy treatment session. Generally, the methods provide various ways of determining whether the region of interest remains within the treatment zone during a treatment session and stopping the treatment if the region of interest moves outside the treatment zone, for example, due to anatomical movement, patient movement, etc. The methods described herein are also useful for real-time monitoring and / or tracking of organs at risk in relation to the treatment zone. Generally, the methods can help determine whether an organ at risk has moved into or out of the treatment zone during a radiotherapy session and stop the radiotherapy session if the organ at risk has moved into the treatment zone beyond a predetermined threshold. Thus, the methods provided herein provide a safety mechanism for stopping radiotherapy treatment to limit potential radiation exposure to healthy tissues and organs at risk. Although the methods described herein are described in relation to treatment sessions, it should be understood that these methods may also be used in relation to quality assurance or calibration sessions that are not treatment sessions, where radiation may be delivered to a phantom and / or radiation sensors on behalf of the patient. In such modified forms, the treatment zone may be referred to as the radiation zone, and the treatment zone may be referred to as the target area or area of ​​interest. If the target area moves outside the radiation zone and / or if a non-target area moves inside the radiation zone, a notification may be generated to inform the user and / or a command signal may be sent to the radiotherapy system to stop radiation delivery. The methods described herein can be used in any type of radiotherapy, e.g., image-guided radiotherapy (e.g., intensity-modulated radiotherapy (IMRT), stereotactic radiotherapy (IMRT), bio-guided radiotherapy (BgRT)).

[0028] Generally, the methods described herein include generating a set of prescan images of a region of interest within a treatment zone in a patient, obtaining a set of real-time images containing the region of interest, comparing the set of real-time images with the set of prescan images, and terminating a radiotherapy treatment session if the set of real-time images deviates from the set of prescan images by a predetermined threshold. For example, if the characteristics of the real-time images (e.g., intensity range, location of high-intensity regions, noise level, frequency, or spectral characteristics) differ from the prescan images by a predetermined threshold, radiation delivery may be terminated or paused. Various modalities can be used to generate prescan and real-time images (e.g., PET, CT, X-ray, MRI, SPECT, etc.), and various methods for generating and comparing images are described throughout this specification.

[0029] A region of interest (ROI) is a targeted area for radiation delivery and typically includes one or more tumors (i.e., targets, target volume) to be treated during a radiotherapy treatment session. As described throughout this specification, ROIs may be determined and / or localized before the delivery of radiotherapy using medical imaging (e.g., PET, CT, X-ray, MRI, SPECT, etc.). In some variants, an ROI may include more than one anatomical region. In some variants, the intended radiation dose to an ROI may be higher than the dose delivered to the surrounding area.

[0030] As described herein, real-time imaging methods of an ROI may include determining an outer ROI zone (e.g., a biological tracking zone BTZ) and an inner ROI zone (e.g., a planned target volume PTV). For example, as shown in Figure 1A, a radiotherapy patient 100 has a first ROI 102 that includes a BTZ 104 encompassing a PTV 106. As described throughout this specification, the PTV 106 may be an inner ROI zone, and the BTZ may be an outer ROI zone. The outer ROI zone may encompass the entire ROI and may be used to help define a safe zone for radiation delivery. That is, radiation may be delivered to an inner ROI zone (e.g., a moving tumor or target) located anywhere inside the outer ROI zone (e.g., BTZ). The inner ROI zone (e.g., PTV) may encompass the total volume of tumor or tissue to be treated within the ROI (treatment volume) and may also include a surrounding margin to compensate for uncertainties in planning or treatment delivery. For example, the periphery margin may include an in-vivo margin to account for tumor movement and a setup margin to account for other uncertainties. Alternatively, in some variants, the inner ROI zone may encompass the tumor (e.g., total tumor volume) without a margin for movement and / or setup uncertainties. While dose delivery may be prescribed for the inner ROI zone, radiation delivery may occur anywhere within the outer ROI zone during radiation delivery. For example, the tumor may be within a target volume with a periphery margin, and together these define the therapeutic volume (i.e., inner ROI) within the biological tracking zone (i.e., outer ROI). In conventional SBRT, the outer ROI zone may be the planned target volume (PTV), and the inner ROI zone may be the clinical target volume (CTV) encompassing the target (e.g., tumor, lesion) for radiotherapy. In some variants, the in-vivo volume (ITV) may be a margin added to the CTV to compensate for the in-vivo physiological movement and variations in size, shape, and position of the CTV during therapy. In BgRT, the outer ROI zone may be the BTZ, and the inner ROI zone may be the PTV. The BTZ can be defined by adding a margin (for example, to account for tracking errors) to the ITV.PTV can be defined by adding a margin (sometimes called a biological guidance margin) to the total tumor volume GTV. Thus, as shown in Figure 1B, PTV126 defined by BgRT may encompass ITV124, CTV122, and GTV120. The methods described herein can be used with one or more ROIs (e.g., two, three, four, or more ROIs, each possibly containing an outer ROI zone and an inner ROI zone).

[0031] Referring again to Figure 1, patient 100 has an organ of risk (OAR) 108, which can be optionally monitored or tracked using the same methods as those used to monitor or track ROIs. OARs may be anatomical objects that are particularly vulnerable to radiotoxicity and whose radiation exposure needs to be limited. In some variant forms, constraints on the treatment plan may include a maximum OAR dose, and the plan may be constrained so that the radiation exposure of the OAR does not exceed the maximum OAR dose. During a radiation delivery session, the OAR may be monitored or tracked using any of the methods described herein to help ensure that the OAR is not irradiated beyond a specified maximum OAR dose. For example, movement of patient 100 (e.g., due to breathing, peristalsis, etc.) may cause OAR 108 to enter ROI 102 (e.g., BTZ 104) during treatment, potentially exposing OAR 108 to the radiation prescribed for PTV 106. Therefore, one or more OARs may be monitored or tracked during a radiotherapy session to account for the movement of healthy tissue into the safe zone (e.g., BTZ104) for treatment delivery.

[0032] According to some variations of the method described herein, a volume or set of prescan images and a volume or set of real-time images are generated or acquired and then compared. Each volume of images contains multiple images generated or acquired to monitor ROI and / or OAR. Prescan images may be acquired during an imaging session prior to a radiotherapy delivery session (e.g., immediately before the treatment session, or at some extended point before the treatment session, or several days or weeks before the treatment session). Real-time images may be acquired during the radiotherapy delivery session. Each volume or set of images may have one or more subsets of images, and these subsets of images may be acquired at various control points (e.g., specific locations or positions on the patient platform within the radiotherapy system) and correspond to these control points. In some variations of the method described herein, the patient may be moved to each control point within a set of control points where medical imaging and / or treatment (e.g., radiation) delivery may take place. A volume of image subsets may contain one or more subsets of images corresponding to one or more control points in the radiotherapy system. In other words, each control point can define a subset or volume of image subsets of at least a portion of the ROI that can be treated at the control point. In some variations, the number of image subsets acquired at the control point may depend on the amount of image data required for reconstruction and further processing. This number of image subsets may be referred to as the index of the sampled images over a limited time (i.e., LTS index or LTS volume). Furthermore, each image within a subset, a volume of image subsets, or a set of images (e.g., a set containing more than one volume of image subsets) may provide an image of a portion of the ROI. Images can be aggregated or combined to form a larger or complete image of the ROI. As an example, Figure 2 shows several control points 202 as numbered vertical lines (1-16) intersecting an ROI containing BTZ214, PTV212, and GTV210.Here, each LTS volume 204 contains an image of the ROI, which is a combination of four subsets of images, each subset corresponding to one of the numbered control points 202. In other variations, each LTS volume may contain any number of image subsets for any number of control points, e.g., three image subsets for three control points, five image subsets for five control points, and so on. As shown, each LTS volume (e.g., LTS1(204)) is a unique volume of image subsets, since each contains the first subset of images in a combination of image subsets corresponding to a unique control point. For example, an LTS volume 204 labeled "LTS1" contains an image that is a combination of a subset of images acquired at control point 1, a subset of images acquired at control point 2, a subset of images acquired at control point 3, and a subset of images acquired at control point 4. In some variations of the method described herein, the method may include summing the acquired images to form a larger or complete image of the ROI. For example, referring again to Figure 2, each of the image subsets shown in 202 (i.e., subsets 1 to 16) may be stitched together 208 to generate a combined image 206, which may be a complete image containing the entire ROI (i.e., BTZ210 encompassing PTV212 and GTV210). In some variations, a method for forming a larger or complete image of the ROI may include: obtaining subsets of images of the ROI at multiple control points; generating multiple LTS volumes, each LTS volume containing two or more combinations of the obtained image subsets; and generating or forming a larger image of the ROI by aggregating (i.e., combining) two or more of the multiple LTS volumes.

[0033] In some variations of the methods described herein, the method may include generating a hybrid image containing imaging data acquired at different times. In some variations, the hybrid image may contain imaging data from the same region or volume of ROI and / or OAR, but the imaging data may be acquired at different times. For example, the hybrid image may be a combination of imaging data acquired at the start of a radiation delivery session (e.g., pre-scan imaging data) and imaging data acquired during radiation delivery (e.g., real-time imaging data). This hybrid image may be used to provide further image acquisition and / or radiation delivery information. For example, the hybrid image may be generated during a radiation delivery treatment session and may be a combination of all available real-time images and remaining pre-scan images or remaining real-time images from a previous treatment pass. In one variation, the method for generating a hybrid image may include generating a reference image by combining multiple LTS volumes (also referred to as LTS indices), acquiring a real-time (e.g., new) image containing multiple real-time LTS volumes, and generating a hybrid image by replacing one or more LTS volumes of the reference image with one or more corresponding real-time LTS volumes. The reference image may be a combination of imaging data acquired at multiple control points, for example, multiple subsets of images from multiple control points. A real-time LTS volume may correspond to a reference image LTS volume if the reference image LTS volume consists of imaging data from the same control points. A hybrid image may contain multiple LTS volumes, some of which are from the reference image, and others that are generated from real-time (e.g., current, new) imaging data. Figures 3A and 3B show examples of how hybrid images can be generated.Referring to Figure 3A, a reference or pre-scan image 302 containing the LTS indices of seven images (corresponding to control points P1-P7, respectively) is used during the first treatment pass of the radiotherapy session to create hybrid images 304 and 306, and finally, a combined real-time image 308 containing a combination of all real-time images acquired during the first pass. The hybrid image may be updated at each control point where the complete LTS volume of the real-time images is acquired and replaced with a reference image (pre-scan image 302) for the corresponding LTS volume of the pre-scan images. That is, as shown in Figure 3B, the hybrid image may be a combination of real-time and pre-scan images, where each LTS volume of the real-time images, such as real-time LTS volume 320, may be a combination of images (e.g., R1 and R2), and each LTS volume of the pre-scan images, such as pre-scan LTS volume 322, may be a combination of images (e.g., P3 to P7). Referring again to Figure 3A, after the first treatment pass is completed (i.e., real-time images have been acquired for all LTS volumes), the aggregated real-time image 308 can be used as a reference image for the second treatment pass (i.e., replacing the pre-scan image 302), and newer real-time images acquired during the second pass (e.g., those contained in LTS volume R1') can replace the corresponding images collected during the first treatment pass to create another hybrid image. This process can be repeated for all treatment passes of a radiotherapy session.

[0034] In some variations of the methods described herein, the methods may further include calculating metrics that represent the characteristics of an image (which may be referred to throughout this specification as image metrics or image monitoring metrics), e.g., pre-scan image metrics, real-time image metrics, or both. The metrics may be any suitable metrics that are useful for comparing images or for assisting in providing information about radiotherapy treatment sessions. For example, an image metric may be calculated to represent the state or status of an object being imaged (e.g., ROI). An image metric may be the signal intensity value of one or more pixels or voxels in an image. That is, an image is a spatial map of pixels (2D) or voxels (3D), each having a signal intensity value. Thus, calculations may be performed on the intensity or signal values ​​of pixels or voxels captured by the image. For example, the signal intensity of a pixel / voxel, or group of pixels / voxels, in an image (e.g., a real-time image) may be determined and compared with the signal intensity of a corresponding pixel / voxel, or group of pixels / voxels, in a corresponding image (e.g., a pre-scan image) to determine the similarity between the two images. In some variations, the signal intensity value may be a statistical value of the signal intensity value, such as the mean signal intensity, maximum signal intensity, minimum signal intensity, or standard deviation of the signal intensity (e.g., the standard deviation of the mean signal intensity, maximum signal intensity, or minimum signal intensity), or a rate of change of the signal intensity value (or its statistics). The rate of change of the signal intensity value may be calculated for a set of images by recording the signal intensity value for each subset of images in the set (e.g., at each control point or for each LTS index) and comparing each recorded signal intensity value (e.g., via linear modeling). As another example, since an image is a spatial map, the image metric may be the location of the signal intensity value or statistical value of the signal intensity value of one or more pixels or voxels in the image (having coordinates on the x, y, and / or z axes of the acquired or generated image). As an example, the image metric may be the location of the maximum signal intensity value of one or more pixels or voxels in the image.In some variations, image metrics can be used in calculations to determine the state or status of the imaged object. As detailed below, image metrics can be monitored independently to provide information on the real-time location status of ROIs within the patient (e.g., ROI shift) and / or real-time occlusion status (e.g., through adjacent healthy tissue). One or more image metrics may be determined before radiation delivery (e.g., prescan image metrics, which may be determined during an imaging-only session or treatment planning session, and / or during a prescan on the same day as the treatment session), and again during the radiotherapy delivery session (e.g., real-time image metrics, which may be determined while radiation is being delivered). For example, a real-time image metric value similar to a prescan image metric value may indicate that the real-time status of the ROI is similar to its status during the prescan. This may indicate, for example, that the ROI has not moved or changed since the prescan. In some variations, prescan and / or real-time image metrics can be used to calculate thresholds or acceptable ranges of values ​​to compare with real-time image metrics during the treatment delivery session.

[0035] When metrics are used and compared to one another, they attempt to approximate the movement and / or occlusion of the ROI and / or OAR. If the metrics of the real-time image are within the acceptable tolerance of the pre-scan image, the metric values ​​may exceed a predetermined lower threshold and / or fall below a predetermined upper threshold. In one variant, if the comparison between the pre-scan image metric and the real-time image metric exceeds a predetermined threshold during treatment delivery, it may indicate that the ROI is no longer in the same location as it was during the pre-scan and / or that the OAR has encroached upon the ROI. Thus, the treatment plan may be modified in real time or terminated for safety reasons.

[0036] In some variations, pre-scan and / or real-time images may undergo processing to determine pre-scan and / or real-time image metrics. Processing an image may include applying filters to the image (e.g., shift-invariant filters, enhancement filters, background noise reduction filters, convolutional filters, spatial filters, bitmasks, etc.), shifting the image (e.g., shifting at least a portion of the image relative to a reference point), extracting data from the image, categorizing the image, visualizing the image (e.g., graphically), applying weights to the image, and so on. In some variant forms, the image comparison methods described herein rely on the contrast between a high-intensity foreground region (e.g., a PTV emitting a strong signal) and a low-intensity background region (e.g., the BTZ excluding the PTV (i.e., BTZ-PTV) which does not emit a strong signal) to determine image metrics and thresholds used to determine whether (1) the foreground region (e.g., the PTV) has shifted relative to the background region (e.g., the BTZ) and / or (2) a nearby anatomical object (e.g., an OAR) has shifted into the background region.

[0037] For example, image monitoring metrics may be used to determine whether the planned therapeutic volume (PTV) has shifted out of the biological tracking zone (BTZ) (e.g., tumor displacement metric), and may include the maximum correlation location, maximum correlation intensity, and / or the percentage change in the maximum BTZ value. As another example, image metrics may be used to determine whether the OAR has shifted into the BTZ (e.g., OAR intrusion metric), and may include the change in BTZ-PTV signal intensity and / or the percentage change in the standard deviation of BTZ signal activity. In some variations, the change in BTZ-PTV signal intensity may be a change in signal intensity in an area within the BTZ but excluding the PTV area (BTZ area minus PTV area). The methods described herein may include modifying the therapeutic radiotherapy for a patient if the image metrics indicate significant tumor displacement from the ROI and / or significant OAR intrusion into the ROI (i.e., the calculated real-time image metrics exceed a given image metric threshold). For example, a radiotherapy treatment system for use with the method described herein may generate a notification indicating whether a real-time image or image metric is within a predetermined threshold and / or tolerance range determined using a prescan image (e.g., below a predetermined upper threshold and / or above a predetermined lower threshold), and if the real-time image or image metric is not within a safe range, or below and / or above a safe threshold, it may enter a locked state (e.g., beam interlock) (e.g., radiation delivery to the patient may be stopped or paused). For example, in a global motion event where the real-time image metric indicates that the target volume is no longer within the ROI (e.g., BTZ) or that the OAR has entered the ROI, the system may trigger a beam-off interlock. When an interlock is triggered, the system may automatically save a record of the delivered treatment and create a new “partial” treatment plan for the remainder of the fraction. In some variations, the treatment session may then be restarted with the new treatment plan, e.g., a new “partial” treatment plan.In some variations, one or more tumor displacement monitoring indices and / or one or more OAR penetration monitoring indices may be used simultaneously to determine whether it is safe to continue the treatment delivery session. Therefore, comparison of pre-scan and real-time images and / or their associated metrics can help guide radiation delivery so that the prescribed radiation dose is delivered to the actual real-time location of the area of ​​interest throughout the course of the treatment session, thereby helping to reduce the cumulative toxicity of radiotherapy to the patient. The methods described herein can be used with compatible radiotherapy treatment systems, which are described in more detail below.

[0038] system Any suitable radiotherapy system can be used in conjunction with the method described herein.

[0039] For example, a radiotherapy system may include an integrated imaging system for any suitable imaging modality (e.g., PET, CT, ultrasound, X-ray, MRI, SPECT, etc.). For example, the imaging system may include at least one PET detector supporting the imaging mode to deliver biologically guided radiotherapy. In some variations, the radiotherapy system may include a gantry to which the imaging system can be mounted. Therapeutic radiation sources and one or more beamforming components of the radiotherapy system may be mounted on the gantry. The beamforming components may be configured to change their configuration in real time in accordance with any real-time changes to the target position and / or treatment plan. The treatment plan may be optimized before treatment delivery and may include a radiation delivery matrix and a planning fluence map, the fluence map may include a set of beamlets and beamlet intensities applied to the patient. In some variations, the treatment plan may include multi-leaf collimator (MLC) commands and / or irradiation commands for the therapeutic radiation source for a sequence of control points. The radiotherapy system can deliver radiation to the patient according to a segmented fluence map for the corresponding control points, each control point may include multiple irradiation angles or positions. For a BgRT radiotherapy treatment session, the radiotherapy system can deliver radiation to the patient according to a real-time generated fluence map by convolving a radiation matrix (e.g., one or more irradiation filters) with imaging data acquired by a PET detector. This fluence map can be segmented in real time into radiotherapy system commands (e.g., MLC commands and / or therapeutic source irradiation commands) for delivery.

[0040] In some variations, the imaging system may be mounted on a circular or substantially circular gantry configured to rotate around the patient area at a speed of approximately 1 RPM or greater (e.g., approximately 10 RPM, 40 RPM, 60 RPM, 70 RPM, etc.). A BgRT radiotherapy system including one or more arrays of PET detectors (e.g., two opposing PET arcs) may be configured to rotate at approximately 60 RPM or greater. Additionally or alternatively, the imaging system may be capable of acquiring tomographic data without rotation. In some variations, the imaging system may be configured to continuously acquire real-time images of at least a portion of the ROI throughout the entire treatment delivery session. These real-time image subsets or combined image subsets may be rapidly processed (e.g., within milliseconds) by a controller into machine commands to control the radiotherapy beam. For example, in BgRT delivery, real-time image subsets may be filtered and convolved with a radiation matrix (e.g., irradiation filters) to generate a delivery fluence map, which is then segmented in real time into radiotherapy system commands to control the treatment beam.

[0041] Figure 4 shows one variant of a radiotherapy system that may be used for real-time monitoring of ROI during a radiotherapy session. The radiotherapy system 400 may include a gantry 402 that can rotate around a patient area 404, a patient platform 412 that can move within the patient area, one or more PET detectors 406 (e.g., PET-CT, PET-MRI) mounted on the gantry, a therapeutic radiation source 408 mounted on the gantry, and a dynamic multi-leaf collimator 410 positioned in the beam path of the therapeutic radiation source. In some variants, the radiotherapy system may include a first array of PET detectors 406a and a second array of PET detectors 406b positioned opposite the first array, a linear accelerator 408 (i.e., LINAC), and a dynamic binary multi-leaf collimator 410. In some variants, the PET detectors 406 may have sufficient timing resolution and / or detector sensitivity to acquire time-of-flight (TOF) PET data. A patient positioned within patient area 404 may be injected with a PET tracer that emits positrons, which may accumulate in specific areas of the patient (e.g., radiation target areas such as tumor areas). Positron annihilation with a nearby electron results in the emission of two photons moving in opposite directions, which can define a line. One or more acquired prescan or real-time images or detected image data may contain one or more positron annihilation emission paths (i.e., response lines or LORs, emission paths). In some variants, the PET detector may be a time-of-flight PET detector, which may help pinpoint the location of positron annihilation events. In some variants, the PET detector may support image reconstruction modes to support PET-guided radiotherapy. For example, real-time image reconstruction may be used for PET-guided radiotherapy. In some variants, a previously calculated treatment plan may be updated according to real-time images and / or image data acquired by the PET detector, and the fluence map may be updated to account for the movement of the target volume within the ROI (e.g., BTZ) in the selection of LINAC and MLC leaf configurations / beamlets.The fluence map may be updated using real-time images or image data as the patient moves through the patient area (e.g., translationally or otherwise through the gantry bore).

[0042] The patient may be moved to each of the control point sequences for imaging and / or treatment delivery. For example, the patient platform 412 may be a platform configured to move relative to the gantry 402. For example, the platform may include translational motion capabilities along the x, y, and z axes, and / or rotational motion capabilities along pitch, yaw, and roll rotations. In some variations, the platform 412 may translate along the y-axis of the treatment plane and pause at one or more discrete control points 414 (i.e., beam stations) where images of the ROI may be acquired and / or radiotherapy may be delivered. The one or more control points 414 may be spaced apart by distances between 0.5 mm and 5.0 mm, such as between approximately 1 mm and 4 mm, approximately 1.5 mm and 3 mm, or approximately 2 mm and 2.5 mm. For example, each control point in the sequence may be spaced about 2.1 mm apart from adjacent control points. Radiation may be delivered to the patient at each control point using single-pass or multi-pass motion of platform 412, where the term “pass” refers to the movement of platform 212 to each of the control points 414 during therapeutic delivery, and the ROI (e.g., PTV) passes through the therapeutic plane (e.g., radiation field or beamlet emitted by LINAC) once. In some variations, multi-pass motion may include two, three, four, or more passes. For example, a therapeutic session may include four passes, each involving two forward and backward movements of the platform. In some variations, therapeutic delivery from the radiotherapy system may be delivered across a sequence of control points 414, and the delivery may further be delivered across one or more irradiation positions or angles (platform angle subgroups) of LINAC at each of the control points.

[0043] Optionally, the radiotherapy system 400 may include a CT and / or X-ray imaging system mounted on the same gantry as the therapeutic radiation source, or mounted on a separate gantry. For example, a kVCT imaging system may be mounted on the gantry to acquire 3D CT fan-beam images for localizing and aligning the patient for therapeutic delivery. In some variations, the imaging plane of the kVCT imaging system may be separate from the LINAC treatment plane, while in other variations, the kVCT imaging plane may be coplanar with the LINAC treatment plane. It should be understood that other imaging modalities (e.g., MRI, SPECT, etc.) may be used (or integrated) with a similar radiotherapy system, either additionally or as an alternative, to implement the methods described in more detail below.

[0044] The methods described herein may be performed by a radiotherapy system via software, hardware, or a combination thereof. For example, the system may include a first software node for controlling and delivering radiotherapy delivery commands to a second software node, which then implements the radiotherapy delivery commands by controlling delivery hardware (e.g., MLC410 in Figure 4). The delivery commands sent to the software node to control the delivery hardware may, for example, cause the radiotherapy system to lock and permanently or temporarily terminate the treatment. As another example, a combination of software and hardware can be used to create and update a radiotherapy treatment plan for a patient, taking into account the location of real-time ROIs or deviations of obstacles from pre-scan ROIs. Hardware modules may include, for example, a general-purpose processor (or microprocessor or microcontroller), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), etc., along with corresponding machine-readable memory components. Software modules (which run on hardware) can be represented in a wide variety of software languages ​​(e.g., computer code), including MATLAB®, C, C++, Java®, Python, Ruby, Visual Basic®, and / or other object-oriented, procedural, or other programming languages ​​and development tools. Examples of computer code include, but are not limited to, files containing microcode or microinstructions, such as those created by compilers, machine instructions, code used to create web services, and higher-level instructions executed by a computer using an interpreter. Additional examples of computer code include, but are not limited to, control signals, encryption code, and compression code.

[0045] Furthermore, the radiotherapy system may include a controller communicatively coupled to one or more of the gantry, PET detector, LINAC, and / or MLC, the controller having memory capable of storing treatment plans, fluence maps, and system instructions / commands, etc. The controller may also include a processor for performing the calculations and methods described herein, and may be configured to receive and store data. The processor may be any suitable processing device configured to invoke and / or execute a set of instructions or codes stored in a machine-readable memory component, and may include one or more data processors, image processors, graphics processing units, physics calculation units, digital signal processors, and / or central processing units. In some variations, the controller may include more than one processor. In some variations, the controller may include a display capable of supporting a user interface, which may enable medical professionals to view, modify, or otherwise interact with and / or control the collected radiotherapy treatment data (e.g., treatment plans, prescans, and / or real-time images). In some variations, the user interface may display recommendations for modifications to the therapy. The user interface may be displayed on a suitable computing device capable of interfaceing with one or more medical databases (e.g., EMR) stored in one or more machine-readable memory components. In some variations, the controller may provide therapeutic information through the user interface (e.g., display, audio feedback, visual feedback, etc.).

[0046] method The methods described herein may be used in real-time radiotherapy or any radiotherapy session including radiotherapy sessions in which radiation is emitted on behalf of the patient to a phantom and / or radiation detector (e.g., quality assurance or calibration radiotherapy sessions). In some variations, the methods can be used to deliver a prescribed dose of radiation to an ROI within the patient. Figure 5 is a flowchart representation of one variation of the method for monitoring a region of interest during a radiotherapy session, e.g., a radiotherapy session. While Figure 5 shows that each step of Method 500 is performed once, it should be understood that Method 500 may include optional steps (i.e., steps 502 and / or 512) and / or additional steps, and may be a continuous process with a feedback loop between steps (e.g., a feedback loop may exist between steps 510 and 508).

[0047] In one variant, method 500 can be used to deliver the prescribed radiation to the patient when a real-time determination is made that the treatment delivery is safe, and to trigger a radiation beam interlock when a real-time determination is made that the treatment delivery may not be safe. Any of the various imaging comparison methods described throughout can be used. Method 500 may include performing an optional imaging-only session 502 in which a reference image of the ROI in the patient may be generated or acquired. The reference image may be acquired from the patient in the treatment position using an imaging system of the radiotherapy system (e.g., a PET imaging system of a BgRT radiotherapy system) and / or a diagnostic imaging system (e.g., a diagnostic PET-CT and / or PET-MRI imaging system). The reference image may be used to generate a treatment plan 504 which may include instructions to the radiotherapy system for delivering the prescribed radiation dose to the patient. Once the treatment plan is generated, the radiotherapy session may be initiated, and before the delivery of radiation to the patient, a prescan image of the patient's ROI is acquired 506 and used to calculate metric values ​​and thresholds for the prescan image. Subsequently, a treatment delivery session may be initiated, and one or more selected image metrics may be monitored (e.g., against a predetermined threshold or range) 508 and / or used in calculations to conclude whether treatment delivery can be safely continued according to a real-time treatment plan. In some variations, monitoring image metrics 508 may include acquiring real-time images during radiation delivery and calculating the value(s) of the image metrics(s) based on the real-time images. In some variations, method 500 may include delivering therapeutic radiation to the ROI(s) simultaneously with or sequentially monitoring the image metrics(s) 306 510. Conclusions regarding whether real-time treatment delivery is safe may be made by the user in conjunction with information provided by the radiotherapy system, which may determine the real-time status of the ROI based on one or more image metrics.For example, notifications may optionally be generated to indicate a significant change in the real-time image or real-time image metric of the ROI against the prescan image of the ROI, the prescan image metric, or the prescan metric threshold 512. If such a change exceeds an acceptable range or threshold, treatment delivery may not be safe for the patient. Therefore, a radiation beam interlock may optionally be triggered 514, thereby permanently or temporarily terminating treatment delivery. Variations of steps 502-514 are described in further detail below.

[0048] Optionally, session 502 will be conducted for imaging only. An imaging-only session may be performed prior to the radiotherapy treatment session (including steps 506-510). During the imaging-only session, a representative reference image of the patient containing any ROI (e.g., a PET volumetric 3D image) may be acquired. Images and simulation image data (e.g., CT simulation image data) from the imaging-only session may be provided as input to a treatment planning system to generate a treatment plan. In some variations, the simulation image data may include defined and / or localized contours (e.g., BTZ, PTV, OAR). In some variations of the method, the imaging-only session may be omitted, and existing patient images (e.g., diagnostic PET images, MR images, ultrasound, and / or CT images) may be used for treatment planning.

[0049] To initiate an imaging-only session, a localized image (e.g., a CT image) may be acquired and registered to a defined and / or localized contour (e.g., BTZ, PTV, OAR) and a simulated image (e.g., a CT image). Image registration may be used to verify that the ROI is in the expected location (e.g., tumor volume is within the PTV) and to facilitate the mapping of subsequently acquired image data (e.g., a PET image) to the contour in the simulated image reference frame. Images may be acquired across multiple control points using, for example, one or more passes on the patient platform. The number of control points in the passes may depend on the size of the ROI. That is, as described with respect to Figure 4, the control points may be separated by a fixed distance (e.g., about 2.1 mm), thereby increasing the number of control points required to image the ROI and deliver treatment from there as the size of the ROI increases.

[0050] To acquire images, the patient may be placed on a platform (e.g., patient platform 412 in Figure 4). In some variations, the patient may receive an injection containing a radioactive tracer (e.g., fluorodeoxyglucose (FDG)) and / or any associated PET tracer for PET-guided radiotherapy (e.g., BgRT). The patient may then be moved by the platform to one or more control points from which PET images of the ROI can be acquired. For example, PET detectors 406a,b shown in Figure 4 can be used to detect PET radiation from a treatment volume incorporating the radioactive tracer. At each control point, an image volume may be acquired. That is, each control point may define a subset or volume of image subsets of at least a portion of the ROI being treated at the corresponding control point. In some variations, the number of image subsets acquired at a control point may depend on the amount of image data required for reconstruction and further processing. This number of image subsets may be referred to as the LTS index or LTS volume of the sampled images over a limited time. In some variations, an LTS volume at a given control point may include a combination of subsets of images from more than one control point for reconstruction and processing (e.g., evaluating image metrics). For example, an LTS volume may include a subset of images corresponding to a given control point, and at least a portion of subsets of images corresponding to adjacent control points. In some variations, an LTS volume may include a combination of several subsets of images acquired at several corresponding control points (e.g., a combination of subsets of images corresponding to 4, 8, 10, or 12 control points). For example, an LTS volume may include a subset of images acquired at a given control point, plus subsets of images acquired at two adjacent control points.As another example, and as also shown in Figure 2, an LTS volume (labeled LTS1, LTS2, and LTS3) may contain four subsets of images corresponding to four control points, the four subsets including an image subset for the current control point and further subsets of images from three other control points (e.g., three preceding control points, three succeeding control points, or a combination thereof). Furthermore, an LTS volume may contain a rolling subset of control points from which images are acquired such that the index collects a unique image volume for each control point. In some variations, the set of pre-scan images may contain a combination of LTS volumes (e.g., a combination of the LTS volume for the current control point plus one or more LTS volumes corresponding to other control points). In some variations, an LTS volume may contain overlapping images that can be averaged (e.g., the signal intensity for each pixel or voxel in an overlapping region may be averaged during image reconstruction). For example, an LTS volume for a given control point may include a subset of images for that control point, a subset of images for a preceding control point, and LTS volumes for two preceding adjacent control points. That is, in these variations, a subset of images for a preceding control point may overlap with a unique LTS volume for that preceding control point. Any overlapping portion of the images within the LTS volume can be averaged for image reconstruction. Similarly, overlapping portions of combined images within a complete aggregated image of the entire ROI (e.g., overlapping portions of images in multiple LTS volumes used to generate an aggregated prescan image) can be averaged (e.g., the signal intensity for each pixel or voxel within an overlapping region can be averaged during image reconstruction).

[0051] Reconstructed images acquired during an imaging-only session may be used to develop a treatment plan for the patient. In some variations, image reconstruction may involve using a computer to reconstruct a three-dimensional image showing the distribution of radiotracer concentrations within the patient. In some variations, reconstructed images acquired at each control point within a set of control points may be stitched together to yield an aggregated or complete image representing the patient's ROI. As further described throughout, images (e.g., aggregated images, subsets of images, volumes of image subsets) and / or image data collected during imaging-only sessions and / or pre-scan acquisition sessions may be used to calculate or define thresholds or tolerances for image monitoring metrics (e.g., maximum correlation location, maximum correlation value, rate of change of BTZ maximum value (e.g., intensity value), intensity change of BTZ-PTV shell, rate of change of BTZ activity standard deviation, etc.). Real-time images and / or image metrics may then be compared to these thresholds or tolerances to help determine whether it is safe to continue treatment or whether treatment should be permanently or temporarily terminated.

[0052] 504 generates a treatment plan Once images and / or image data are output from an imaging-only session, a treatment plan for the patient may be generated and / or optimized. In some variations, the number of control points and treatment paths relative to the planned treatment volume may be prescribed depending on the size of the treatment volume and / or the number of target regions. In some variations, the treatment plan may define a number of image subsets relative to the LTS volume to determine the amount of image data required to calculate the image metrics and thresholds described herein. For example, the treatment plan may define a number of image subsets relative to the LTS volume to determine the minimum amount of image data required to calculate the image metrics and thresholds. In some variations, the prescribed radiation dose may be divided into several smaller doses (i.e., fractions) over more than one radiotherapy session (e.g., several radiotherapy sessions over a period of several days). Thus, the methods described herein may be repeated for each fraction and / or until the total dose of radiation is delivered to the patient.

[0053] In some variations, the treatment plan may include dose distributions relative to ROIs, such as dose maps relative to PTV. In some variations, the treatment plan may also define the patient's radiosensitive area (OAR) where radiation exposure should be limited or minimized. Optionally, for BgRT, one or more irradiation filters (e.g., irradiation matrices) and / or fluence maps may be generated as part of the treatment plan. For example, a fluence map may be defined by irradiation filters that act on (e.g., convolve with) the matrix of the target volume PET projection, and the data used to generate the irradiation matrix (e.g., irradiation filters) may be calculated from imaging data obtained during a PET imaging-only session. Since the tumor PET projection is a fixed quantity, a cost function can be used to find the optimal irradiation filter to achieve the prescribed dose or fluence distribution. One or more simulated CT images, contours defined by the simulated images (e.g., BTZ, PTV, OAR), PET planning images, and dose targets may be inputs for optimizing the irradiation matrix using a cost function optimization process. While not wishing to be limited by theory, the principle of superposition allows irradiation filters (e.g., radiation irradiation matrices) to be applied to real-time LTS images acquired during a treatment session to generate limited fluences, where the sum of the limited fluences is the intended total fluence. This principle underpins the “real-time” nature of the monitoring method described herein, because it makes it possible to modify the delivery of radiotherapy beamlets in response to rapidly collected packets of real-time imaging data (e.g., PET radiation). Alternatively, for image-guided radiotherapy (e.g., intensity-modulated radiotherapy (IMRT), stereotactic radiotherapy (SBRT)), the cost function optimization process may involve iterating through different fluence maps to achieve the desired prescribed dose while minimizing (or optimizing) the cost function. Creating a treatment plan may involve segmenting the resulting fluence map into radiotherapy system commands, such as irradiation commands for MLCs and / or therapeutic radiation sources for each control point.

[0054] To calculate image metric values ​​and thresholds, pre-scan images are acquired (506). Prescan images of the patient's ROI may be acquired 506 or generated after the creation of the treatment plan 504 and after the delivery of therapeutic radiation 510. In some variations, prescan images may be acquired or generated immediately before the start of the patient's treatment delivery session (e.g., 5 hours, 1 hour, 30 minutes, 10 minutes, etc.). In some variations, prescan images may be acquired or generated on the day of the treatment delivery session after the patient has been injected with a PET tracer. That is, acquiring prescan images 506 may be the first step in a radiotherapy treatment session. For example, on the day of treatment, method 500 may include acquiring or generating prescan images of the patient in the treatment position (e.g., supine on a patient platform). For example, for a BgRT treatment session, the patient may be injected with a PET tracer, positioned in the BgRT radiotherapy system in the treatment position, and a PET prescan may be performed to acquire PET imaging data. In some variations, performing a prescan imaging session may involve (1) selecting one or more image metrics to be used to help determine the safety of real-time delivery of radiation to the patient, and / or (2) using the prescan images to calculate thresholds and / or acceptable ranges of values ​​for one or more image metrics to indicate a level of therapeutic safety. The image metrics described in more detail below may include maximum correlation location, maximum correlation, BTZ maximum value change rate, BTZ-PTV shell intensity change, BTZ activity standard deviation change rate, or a combination thereof.

[0055] In other words, pre-scan imaging data can be analyzed and / or processed to determine pre-scan image metrics for comparing real-time images with pre-scan images to guide radiotherapy treatment sessions. Processing pre-scan images may include one or more of the following: applying filters to the images (e.g., shift-invariant filters, enhancement filters, background noise reduction filters, convolutional filters, spatial filters, bitmasks, etc.), shifting the images (e.g., shifting at least a portion of the image relative to a reference point), extracting pre-scan image data from the images, categorizing the images, visualizing the images (e.g., graphically via histograms), applying weights to the images, and registering the images. For example, an enhancement filter may be applied to the pre-scan and / or real-time images to produce signal-enhanced pre-scan and / or real-time images. As another example, a mask filter can be applied to the pre-scan images to mask specific areas of an ROI (e.g., a BTZ mask can be used to hide the PTV and analyze only the BTZ-PTV shell region). In some variations, a bitmask can be used to filter the acquired PET imaging data to remove PET imaging data from areas outside the ROI.

[0056] Image metrics (e.g., pre-scan image metrics or real-time image metrics) may be image characteristics or values, and may be any suitable metric useful for comparing images or for assisting in providing information about radiotherapy treatment sessions. In particular, image metrics may be signal intensity values ​​of one or more pixels or voxels in an image. For example, the signal intensity of a pixel or group of pixels in an image (e.g., a real-time image) may be determined and compared with the signal intensity of a corresponding pixel or group of pixels in a corresponding image (e.g., a pre-scan image) to determine the similarity between the two images. In some variations, the signal intensity value may be a statistical value of the signal intensity value, such as the mean signal intensity, maximum signal intensity, minimum signal intensity, or standard deviation of the signal intensity (e.g., the standard deviation of the mean signal intensity, maximum signal intensity, or minimum signal intensity), or a rate of change of the signal intensity value (or its statistics). The rate of change of the signal intensity value may be calculated for a set of images by recording the signal intensity value for each subset of images in the set (e.g., at each control point or for each LTS volume) and comparing each recorded signal intensity value (e.g., via linear modeling). As another example, an image metric could be the location of the signal intensity value or statistical value of signal intensity values ​​for one or more pixels or voxels in an image (along the x, y, and / or z axes of the acquired or generated image). For example, an image metric could be the location of the maximum signal intensity value of one or more pixels or voxels in an image. In some variations, image metrics can be used in calculations to determine the state or status of the object being imaged. As described in detail below, image metrics can be monitored independently to provide information on the real-time location status of ROIs within a patient (e.g., ROI shift) and / or real-time occlusion status (e.g., through adjacent healthy tissue).

[0057] In some variations of Method 500, acceptable tolerances may be determined with respect to pre-scan image metrics(s) to represent acceptable movement and / or cutoff of ROIs and / or OARs. For example, acceptable movement of a PTV relative to a BTZ might be movement of a PTV within a BTZ such that the majority of the PTV is still contained within the BTZ (e.g., approximately 50% to 100% of the PTV). Similarly, an example of acceptable movement of an OAR relative to a BTZ might be movement of the OAR, resulting in the BTZ containing only a small portion of the OAR's volume (e.g., approximately 0% to 50% of the OAR), in other words, less than approximately 50% of the OAR is within the BTZ. The acceptable tolerances may be a range of acceptable image metric values ​​(e.g., signal intensity values ​​and / or signal intensity location values) for comparison with real-time image metrics, or one of an upper or lower threshold for acceptable image metric values. In some variations, pre-scan image metrics (or multiple metrics) calculated for each control point within a set of control points can be compared to define an overall acceptable tolerance for image metric values ​​and compared with all real-time images acquired during treatment. For example, the acceptable range for image metric values ​​may be used for image comparisons at each control point and may be a range of values ​​between the overall maximum signal intensity value (e.g., the maximum signal intensity in a group of pixels / voxels or pixels / voxels across all image subsets or combined image subsets in the pre-scan image) and the overall minimum signal intensity (e.g., the minimum signal intensity in a group of pixels / voxels or pixels / voxels across all image subsets or combined image subsets in the pre-scan image). In this example, at each control point within a set of control points visited during a treatment delivery session, each calculated real-time image metric may be compared to the same range of acceptable image metric values ​​for the same set of control points determined during pre-scan acquisition.In some variants, the overall pre-scan image metric for comparison with all real-time images may use only the aggregated pre-scan images (i.e., the image metric may be determined by analyzing the aggregated pre-scan images without comparing the pre-scan image metric for each control point).

[0058] In some variations, pre-scan image metrics (or multiple metrics) calculated for the images corresponding to each control point can be used to define local acceptable tolerances for image metric values ​​(e.g., values ​​determined using a subset of images or a volume of image subsets for the LTS index relative to the control point) and compared with real-time images corresponding to the control point during treatment. As another example, the acceptable range for image metric values ​​may be unique for each control point, for example, a range of values ​​between the local maximum signal intensity value and the local minimum signal intensity value. In this example, the real-time image metric can be compared with a unique range of acceptable image metric values ​​corresponding to a given control point. In some variations, if the comparison of the real-time image metric value with the acceptable tolerance for the pre-scan image metric value reveals that the real-time image metric value is not within the acceptable tolerance (i.e., the value is above or below the acceptable tolerance), this may indicate that the ROI is not in the same location as it was during the pre-scan, and / or that the OAR has entered the ROI.

[0059] In some variations, defining tolerances and / or thresholds for image metrics may involve performing calibration or simulation procedures for procedures in which prescan images (e.g., aggregated prescan images, images within the LTS volume) may be manipulated to simulate events where the target volume has moved significantly outside the BTZ and / or where the OAR has moved significantly inside the BTZ. For example, prescan and real-time images of an ROI may radiate more signal than the background region (e.g., BTZ-PTV) and depict a foreground region (e.g., PTV) that is highly contrasted with the background region (e.g., BTZ-PTV). Therefore, a real-time image of an ROI with lower signal intensity than the corresponding prescan image of the ROI may indicate that at least a portion of the PTV has shifted outside the BTZ. Irradiating a PTV that is not in the expected location may be unsafe as it may increase the risk of radiotoxicity to the patient. Similarly, compared to an unblocked ROI, an image of a blocked ROI (e.g., an OAR or non-target anatomical object entering the ROI) may have a higher signal value due to the additional signal contributed by the blocking object. If a real-time image of an ROI shows more signal than the corresponding pre-scan image of the ROI, it is possible that at least a portion of the OAR or anatomical object has shifted into the BTZ. If the BTZ ("safe zone" for irradiation) is blocked by healthy tissue, it may not be safe to deliver radiation to the PTV, as this increases the risk of radiotoxicity to the patient. In both cases, radiation delivery should be delayed until the PTV enters the BTZ. Therefore, a simulation procedure using pre-scan images may represent either a PTV displacement event or an OAR intrusion event, providing real-time information for the treatment session so that the radiation dose can be stopped or delayed as needed. A PTV displacement event may indicate that a percentage of the PTV's volume or linear dimension has moved outside the BTZ.The PTV displacement threshold may represent approximately 10% tumor displacement from the BTZ, 30% tumor displacement from the BTZ, 50% tumor displacement from the BTZ, 70% tumor displacement from the BTZ, or 90% tumor displacement from the BTZ. Similarly, an OAR invasion event may indicate that a percentage of the acquired OAR volume or linear dimension has moved into the BTZ. The OAR invasion threshold may represent approximately 10% OAR invasion into the BTZ, 30% OAR invasion into the BTZ, 50% OAR invasion into the BTZ, 70% OAR invasion into the BTZ, or 90% OAR invasion into the BTZ.

[0060] The PTV deviation calibration procedure may include shifting the prescan image to reduce the overlap between the PTV and BTZ (e.g., to about 5% to about 95%, such as about 20% to about 75%, about 30% to about 65%, or about 40% to about 60%). For example, the prescan image may be shifted so that there is at least 50% overlap between the PTV and BTZ. An image metric or threshold may be calculated using the shifted prescan image and used as a threshold during treatment delivery (i.e., a beam interlock may be triggered if the signal intensity of the real-time image is below a predetermined threshold).

[0061] The OAR penetration simulation procedure may include simulating signal characteristics that can represent the OAR into the BTZ of a prescan image, and re-evaluating the image with the simulated signal characteristics. For example, the OAR penetration simulation may include increasing the signal intensity in the BTZ of the prescan image and re-evaluating the image with the increased signal intensity. Image metrics or thresholds may be calculated based on this simulation and used during treatment delivery (e.g., a beam interlock may be triggered if the signal in the real-time image is above a predetermined threshold). In some variations, processing the prescan image of the ROI may include using the PTV signal to mimic the OAR. That is, the total PTV signal (e.g., the sum of the signal intensity of each pixel or voxel in the PTV) may be adjusted (i.e., amplified or duplicated) relative to the BTZ to simulate at least a portion of the OAR in the BTZ. For example, in some variations, the simulated OAR in the BTZ may be assumed to be the same size and intensity as the acquired target volume. Therefore, the target volume signal can be replicated, with at least a portion of it overlapping with the BTZ by a certain amount (for example, half of the replicated signal may overlap with the BTZ), and the OAR penetration threshold can be determined.

[0062] 508 to monitor image metrics (multiple metrics possible) Method 500 may include monitoring one or more image metrics during a radiotherapy delivery session 508. In some variations, monitoring selected image metrics over the course of a radiotherapy session 508 may include determining real-time image metrics to compare with thresholds defined by prescan metrics or their use (further calculation). Sequentially acquired real-time images of the ROI may be analyzed and / or processed to determine one or more real-time image metrics, and one or more real-time image metrics may be compared with one or more prescan image metric values ​​and / or metric thresholds calculated based on prescan imaging data. In some variations, a volume of real-time images in an LTS index may be analyzed and / or processed to determine one or more image metrics.

[0063] As described below, determining a real-time image metric can be analogous to determining a corresponding pre-scan image metric. A real-time image metric may be calculated for each control point within a set of control points. In some variations, a real-time image metric may be compared to a corresponding pre-scan image metric for the same control point (e.g., a threshold and / or tolerance for the metric value is determined for each control point based on pre-scan imaging data). In some variations, a real-time image metric may be compared to a pre-scan image metric that is the same for all control points (i.e., a threshold and / or tolerance for the metric value is determined for use at all control points).

[0064] In some variations, real-time images may undergo processing to determine real-time image metrics. Processing real-time images may include one or more of the following: applying filters to the image (e.g., shift-invariant filters, enhancement filters, convolutional filters, spatial filters, bitmasks, etc.); shifting the image (e.g., shifting at least a portion of the image relative to a reference point); extracting real-time image data from the image; categorizing the image; visualizing the image (e.g., graphically via a histogram); applying weights to the image; registering the image; and so on. For example, an acquired image may contain a foreground signal (e.g., a target volume signal) and a background signal (e.g., a BTZ signal), and a mask may be applied to the image so that only the background signal is considered in further processing (e.g., a BTZ mask). As another example, an enhancement convolutional filter, such as a Gaussian filter, may be applied to the acquired pre-scan image and / or real-time image to smooth the image and / or reduce image noise. As yet another example, in some variations, corresponding pre-scan and real-time images may be registered so that one or more common features of the images (e.g., BTZ, PTV, OAR(s)) can be compared. Generally, image registration is the process of finding spatial transformations so that corresponding points in different images arrive at the same spatial and anatomical locations. The images may be obtained by different instruments or different imaging modalities. In some variations, normalized cross-information between images may be measured, and / or the overlap percentage between images may be calculated as part of the image registration process. In some variations, non-rigid image registration may be performed using a combination of normalized cross-information and spatial information to account for spatial information in the image neighborhood and / or to increase the weight of spatial information within the image.For example, adaptive Gaussian filtering may be introduced into a local image structure tensor and used to extract spatial information from an image while normalized mutual information is distributed to each pixel or voxel, and discrete normalized mutual information may be multiplied by a weighting term to obtain a new measure (of normalized mutual information). Registered images with a maximized normalized mutual information metric and / or a high overlap percentage may be determined to be similar. Thus, by registering prescan images and real-time images and comparing the registered images, an indication of similarity between the prescan images and real-time images may be provided. A high degree of similarity may support the conclusion that the prescribed radiation dose is safe to deliver to the patient. In general, registering prescan images (e.g., a volume of prescan image subsets corresponding to control points, or prescan images with aggregated ROIs) and real-time images (e.g., a volume of real-time image subsets corresponding to control points) may involve designating one image, which may also be called a fixed image, as a reference image, and applying geometric transformations or local displacements to the other image to align them with the reference image. In some variants, the reference image may be a pre-scan image, and the real-time image may be aligned with the pre-scan image.

[0065] In some variations, pre-scan images and / or real-time images acquired during a previous treatment pass can be used in conjunction with real-time images acquired during the current treatment pass to determine real-time image metrics. For example, in some variations, real-time images acquired to provide treatment information at a given control point can replace the corresponding reference images (e.g., pre-scan images, real-time images acquired from preceding treatment passes) within a combined reference image to generate a hybrid image. The hybrid image may be used to calculate real-time image metrics and may be dynamically updated as treatment progresses. For example, as shown in Figure 3A, at a first control point in a sequence of control points, the first LTS index of the real-time image (e.g., R1 in hybrid image 304, or R1' in hybrid image 310) may replace the first LTS index of the reference image within the combined reference image (e.g., P1 in combined pre-scan image 302 may be replaced by R1, R1 in combined real-time image 308 may be replaced by R1', etc.). Next, at a second control point in the control point sequence, the second LTS index of the real-time image (e.g., R2 in hybrid image 306) may replace the second LTS index of the corresponding reference image in the hybrid image (e.g., P2 in prescan image 302 and hybrid image 304). As shown by the aggregated real-time image 308 in Figure 3A, after the first treatment pass, all prescan images may be replaced with the corresponding real-time images. For subsequent treatment passes, newly acquired real-time images may replace the corresponding real-time images acquired during the preceding treatment pass.

[0066] The following describes exemplary variations of image metrics and thresholds that can be calculated for pre-scan and real-time imaging data, where real-time image metric values(s) can be compared with corresponding pre-scan image metric values(s) during a radiotherapy delivery session to determine in real time whether there was a significant deviation in the location and / or blockade of the ROI. In some variations, image metrics can be determined or calculated without using pre-scan image metrics.

[0067] Maximum correlation position A real-time image that is highly correlated with the corresponding pre-scan image may indicate that the real-time ROI is similar to the ROI captured by the pre-scan image. For example, a high level of correlation between the real-time image and the pre-scan image may indicate that the ROI in the real-time image may be in the same location and / or orientation and / or have the same shape as the ROI in the pre-scan image. The maximum correlation metric may represent the amount of overlap between the cross-correlated images, depending on the degree to which the intensity of the real-time image correlates with the signal intensity of the pre-scan image. This may indicate, for example, that the real-time location of an ROI is similar to the location of the ROI during the pre-scan. Thus, the maximum correlation location metric can be used to monitor any movement or shift (in the x, y, or z axis) of the inner ROI relative to the outer ROI (e.g., movement or displacement of the PTV from the BTZ). The maximum correlation location metric may be the location (defined by one or more coordinates of the x, y, and z axes) where the maximum signal intensity resulting from the cross-correlation calculated between the two images occurs. At a given control point, a set of pre-scan images can be autocorrelated with the aggregated pre-scan images of the ROIs (i.e., the signals can be cross-correlated with themselves) and evaluated to determine the pre-scan maximum correlation position. The autocorrelation may represent a zero-shift or low-shift event of the PTV relative to the BTZ, because the resulting maximum signal intensity occurs with zero lag or displacement in the coordinate system. In some variations, a set of shifted pre-scan images corresponding to a given control point can be autocorrelated with the aggregated pre-scan images of the ROIs. The shifted pre-scan images may be processed so that, for example, only about 50% of the PTV overlaps with the BTZ (in one or more of the x, y, and z axes). Thus, the shifted pre-scan maximum correlation position can be determined using the shifted pre-scan autocorrelation, which may represent a large-shift event of the PTV relative to the BTZ, because the resulting maximum signal intensity occurs with a lag greater than zero in the coordinate system.This can simulate the possible values ​​(multiple) of the maximum correlated position metric when the PTV moves relative to the BTZ.

[0068] To determine the real-time maximum correlation location, local cross-correlation can be calculated between a set of real-time images acquired at control points and a pre-scan image aggregated with ROIs. Shifts (in the x, y, or z axes) of the real-time images from the corresponding location index relative to the control points may be identified. Shifts exceeding a predetermined threshold (e.g., a threshold determined based on pre-scan imaging data) may indicate a significant deviation of the target volume out of the BTZ. Notifications may be generated to provide the user with information about the target volume deviation.

[0069] Maximum correlation strength Additionally, or alternatively, the maximum correlation intensity metric can be used to monitor the deviation of the target volume from the ROI. The maximum correlation intensity metric can be used to monitor any movement or shift (in the x, y, or z axes) of the inner ROI relative to the outer ROI (e.g., deviation of PTV from the BTZ). The maximum correlation intensity metric may be the maximum signal intensity (among the signal intensities of multiple pixels or voxels being analyzed) of the cross-correlation between two images. At a given control point, a set of pre-scan images may be autocorrelated with the aggregated pre-scan images of the ROI (i.e., the signals may be cross-correlated with themselves) and evaluated to determine the pre-scan maximum correlation intensity. The autocorrelation may represent zero-shift or low-shift events of PTV relative to the BTZ because the maximum signal intensity value resulting from the correlation will be higher (e.g., compared to the maximum signal intensity resulting from a shifted pre-scan image that is cross-correlated with the aggregated pre-scan image). In some variations, a set of shifted pre-scan images corresponding to a given control point can autocorrelate with the sum of the pre-scan images of the ROI. The shifted pre-scan images may be processed so that, for example, only about 50% of the PTV overlaps with the BTZ (in one or more of the x, y, and z axes). Thus, the shifted pre-scan maximum correlation position can be determined using the shifted pre-scan autocorrelation, which may represent a high-shift event of the PTV relative to the BTZ because the maximum signal intensity value resulting from the correlation will be lower (compared to the maximum signal intensity resulting from the autocorrelation).

[0070] To determine the real-time maximum correlation intensity metric, local cross-correlation can be calculated between the volume of the real-time image acquired at the control point and the aggregated pre-scan image of the ROI. The maximum signal intensity value of the local cross-correlation is then determined and can be used to evaluate the change in maximum signal intensity relative to the pre-scan image data. In some variations, weighting coefficients may be applied to the maximum signal intensity of the local cross-correlation to reduce the detection sensitivity for treatment sessions using low-intensity imaging sessions and enhance the detection sensitivity for high-intensity imaging sessions. Changes exceeding a predetermined threshold (e.g., a threshold determined based on pre-scan imaging data) may indicate a significant deviation of the target volume out of the ROI. Notifications may be generated to provide the user with information about the target volume deviation.

[0071] BTZ maximum value change rate Certain imaging modalities (e.g., PET) may exhibit a distribution where the PTV accounts for the majority or virtually all of the signal activity within the BTZ. Therefore, if a portion of the PTV leaves the BTZ, the peak value of signal activity within the BTZ may decrease. Thus, monitoring the rate of change of the maximum imaging signal activity within the ROI as a function of control points can provide information about any displacement from the outer ROI to the inner ROI. The pre-scan rate of change of maximum BTZ signal activity can be calculated per control point by modeling (e.g., simulating) the total maximum BTZ signal activity for the shifted pre-scan image set corresponding to the control point. For example, the maximum BTZ signal activity for a volume of shifted pre-scan image subsets within the LTS index corresponding to a control point can be calculated for each of the multiple subsets included. The calculation may involve analyzing and / or processing each pixel / voxel, or each of multiple groups of pixels / voxels, to determine the maximum signal intensity value. The values ​​for each subset within the volume of shifted pre-scan image subsets can then be modeled so that the maximum BTZ signal activity value is linearly fitted. Therefore, the gradient of the maximum BTZ signal activity can be determined for each control point. Furthermore, a threshold for the rate of change of the maximum BTZ signal activity can be calculated by averaging the gradients of the maximum BTZ signal activity for all control points. As will be further described throughout, this threshold for the rate of change of the maximum BTZ can be used for all control points during treatment delivery to determine whether the real-time rate of change of the maximum BTZ should trigger a beam interlock.

[0072] In some variants, hybrid images (a combination of all available real-time images and remaining real-time images from pre-scan images or previous treatment passes, as described above) may be generated during the treatment delivery session to calculate the real-time maximum BTZ value change rate. The real-time maximum signal activity within the BTZ can be modeled (e.g., using linear fitting) using any previously calculated real-time maximum BTZ activity for any preceding control point to determine the real-time maximum change rate. If this metric exceeds a predetermined threshold for the control point (e.g., a threshold determined based on pre-scan imaging data), a significant deviation of the target volume from the BTZ may occur in real time. Notifications may be generated to provide the user with information about the target volume deviation.

[0073] BTZ-PTV intensity change As described above, anatomical objects entering the BTZ-PTV (the shell region of the BTZ excluding the PTV) can increase the mean value of activity within the region (e.g., the mean value of signal intensity within each group of each pixel / voxel of interest or multiple groupings of pixels / voxels). In some examples, this increase can be detected as an increase in the mean image intensity value within the region. Thus, the BTZ-PTV signal intensity change metric can provide information on any BTZ intrusion by OAR and / or any other PET affinity (avid) non-target regions. In some variant forms, BTZ intrusion by OAR can be simulated using the calibration procedure described above. The mean BTZ-PTV intensity can then be calculated and used as a threshold for significant changes indicating OAR intrusion during therapeutic delivery.

[0074] At each control point during treatment delivery, the real-time average BTZ-PTV signal intensity value can be calculated and compared to the pre-scan average BTZ-PTV intensity threshold. Some methods may involve identifying areas of imaging data (e.g., pixels or voxels) that are likely to be PTV, and then determining the shell area by subtracting the identified PTV area from the BTZ area. In some variations, a weighting coefficient may be applied to the real-time average BTZ shell intensity to reduce detection sensitivity for low imaging treatment sessions and enhance detection sensitivity for high imaging treatment sessions. In some variations, the pre-scan average BTZ-PTV intensity may be constant and used for comparison at all control points. A rate of change in the calculated real-time average BTZ-PTV intensity value exceeding the pre-scan average BTZ shell intensity threshold (e.g., a threshold determined based on pre-scan imaging data) may indicate a significant intrusion of OAR into the ROI. Notifications may be generated to provide users with information about OAR intrusion into the BTZ.

[0075] BTZ Activity Standard Deviation Change Rate When anatomical objects enter the BTZ, the standard deviation of activity within the BTZ may change, for example, increase. In some cases where the standard deviation of activity increases, this increase may be detected as an increase in the standard deviation of signal activity within the BTZ (e.g., the standard deviation of signal intensity values ​​within each group of each pixel / voxel of interest within the BTZ or within each of multiple groups of pixels / voxels). Thus, by measuring the BTZ activity standard deviation as a function of control points, it is possible to provide an indication that OARs and / or non-target regions may be entering the BTZ. In some variant forms, the calibration procedure (described above) can be used with a set of pre-scan images to simulate BTZ intrusion by OARs. The pre-scan rate of change of the BTZ activity standard deviation can be calculated per control point by modeling the BTZ activity standard deviation value calculated for each subset of images within the volume of the shifted pre-scan image subset in the LTS index corresponding to the control point. For example, the BTZ activity standard deviation for the volume of the shifted pre-scan image subset in the LTS index corresponding to the control point can be calculated for each of the multiple subsets included. The calculation may involve analyzing and / or processing each pixel / voxel of interest, or each of multiple groups of pixels / voxels, to determine the BTZ activity standard deviation. The values ​​for each subset within the volume of shifted pre-scan image subsets (relative to the control points) can then be modeled so that the BTZ activity standard deviation is linearly fitted. Thus, the slope of the BTZ signal activity standard deviation can be determined for each control point. Furthermore, a threshold for the rate of change of the maximum BTZ signal activity can be calculated by averaging the slopes of the BTZ activity standard deviations for all control points. As further described throughout, this BTZ activity standard deviation threshold can be used for all control points during treatment delivery to determine whether the real-time rate of change of the maximum BTZ should trigger a beam interlock.

[0076] In some variants, hybrid images (a combination of all available real-time images and remaining real-time images from pre-scan images or previous treatment passes, as described above) may be generated during the treatment delivery session to calculate the rate of change of the real-time BTZ activity standard deviation. The real-time value of the BTZ signal activity standard deviation may be determined for the real-time image corresponding to a given control point. To calculate the rate of change of the real-time BTZ activity standard deviation, the real-time BTZ signal activity standard deviation may be modeled (e.g., using linear fitting) by combining any available real-time BTZ activity standard deviations for preceding control points. A rate of change of the calculated real-time BTZ activity standard deviation exceeding a predetermined threshold for a control point (e.g., an OAR intrusion threshold calculated using pre-scan imaging data) may indicate significant OAR intrusion into the BTZ. Notifications may be generated to provide the user with information about OAR intrusion into the BTZ.

[0077] BTZ Peak Activity As described above regarding preceding image metrics, the calculation and recording of prescan image metrics representing signal activity within the BTZ (e.g., image pixel intensity) may be useful for analyzing real-time signal activity within the BTZ to determine (qualify) any tumor displacement from the BTZ and / or OAR intrusion into the BTZ. Thus, by evaluating a set of prescan images for each of several control points, the peak signal activity of the BTZ, the PTV within the BTZ, and / or BTZ-PTV (i.e., the shell region of the BTZ excluding the PTV) can be determined. In some variations, a mask may be applied to the prescan images so that the BTZ-PTV can be evaluated. In some variations, the calculated prescan peak PTV signal activity (e.g., the maximum signal intensity of a pixel or group of pixels indicating PTV) and the calculated prescan peak BTZ-PTV activity (e.g., the maximum signal intensity of a pixel or group of pixels indicating BTZ-PTV) may be compared to determine a threshold for use in therapeutic delivery. In some variations, the ratio of prescan peak PTV activity to prescan peak BTZ-PTV activity may be determined. The pre-scan peak activity ratio can be classified as meaningful or significant if it exceeds a threshold (e.g., greater than 1, e.g., approximately 1.0–2.0, approximately 1.03–1.8, approximately 1.06–1.6, approximately 1.09–1.4, or approximately 1.12–1.2, etc.). In some variations, the pre-scan peak activity ratio may be defined as meaningful if it is greater than 1.15. In some variations, the determined smallest meaningful pre-scan PTV peak activity value (one pre-scan PTV peak activity is calculated for each control point) may be set as the minimum activity threshold for comparison with the real-time PTV peak activity.

[0078] During a treatment session, the real-time PTV peak activity value calculated using the real-time image volume at each control point can be compared to a minimum activity threshold (e.g., a threshold determined based on pre-scan imaging data) to determine whether the PTV is currently within the BTZ. Notifications may be generated to provide the user with information on the target volume deviation.

[0079] Normalized mutual information Normalized cross-information metrics can be evaluated during real-time treatment delivery and used to spatially register real-time images to corresponding pre-scan images (or aggregated pre-scan images) for real-time tracking of PTV. Normalized cross-information can be measured to indicate the level of similarity between registered real-time images and corresponding pre-scan or aggregated pre-scan images. In some variations, cross-information between images is maximized when the entropy of their joint probability distribution (JPD) is minimized. Thus, in some variations, a JPD can be generated and analyzed comparing a set of real-time images to a set of pre-scan images. In some variations, images may be scanned, and clusters of signal types within each may be identified (e.g., organized into histogram bins) so that the JPD can show the distribution of each cluster. For example, three histogram bins may be used to classify the signals within pixels or voxels in each set of images as "background" (low signal), "foreground" (high signal), and "intermediate" (neither low nor high signal). If the JPD plot shows that entropy is minimized for all clusters, the real-time image can be determined to have converged to its registered location relative to the pre-scan image. This measurement can be useful for tracking PTV during treatment delivery. If the PTV is determined to be missing more than a certain amount of BTZ coverage (e.g., more than 50%, more than 75%) during tracking, a beam interlock may be triggered.

[0080] Overlap Percentage The overlap percentage metric is calculated during real-time treatment delivery and can be compared to a predetermined threshold (e.g., a threshold selected by a medical professional or automatically determined by the radiotherapy system) to determine the amount of overlap between the PTV and prescanned BTZ acquired in real time. The overlap percentage can be measured to indicate the level of similarity between images after a set of real-time images has been registered with a corresponding set of prescanned images or a combined prescanned image. In some variations, the overlap percentage between two registered images can be calculated by scanning one of the images (e.g., a set of real-time images) and counting the number of pixels or voxels having a specific signal type (e.g., having a background (low) signal), and then scanning the other image (e.g., a set of prescanned images) and counting the number of pixels or voxels having a specific signal type, and then comparing the counts. If the overlap percentage is greater than a predetermined threshold (e.g., greater than approximately 50% overlap), the real-time images of the PTV can be determined to overlap sufficiently with the prescanned images of the BTZ. A beam interlock may be triggered if the overlap percentage is below a predetermined threshold (e.g., less than 50% overlap, which may indicate that the PTV has almost completely deviated from the BTZ). In some variants, the threshold overlap percentage may be set to 100% if it is determined that there is no difference in the entirety of the PTV being within the BTZ for radiation delivery. The percentage threshold may be selected by the clinician based on one or more factors, including but not limited to respiratory behavior, anatomical location, radiosensitivity of surrounding non-target tissues, and / or clinical practice guidelines.

[0081] Z-score A z-test can be performed to calculate a z-score indicating how well a real-time image of an ROI matches the corresponding pre-scan image of the ROI. A z-test is a statistical test used to determine whether two population means are different (including known variances and large sample sizes), and the resulting z-score is a numerical representation of the z-test's results. A higher z-score suggests that the data is more far from the standard. Therefore, a z-test can be used to calculate a z-score indicating the degree to which a real-time image metric (e.g., real-time BTZ peak signal activity) deviates from the corresponding pre-scan image metric (e.g., pre-scan BTZ peak signal activity). For example, using BTZ peak signal activity as the image metric, the z-score may provide information about whether the OAR entered the BTZ and whether the signal in the real-time BTZ matches the signal in the pre-scan BTZ. That is, a higher z-score suggests a higher probability that at least a portion of the OAR entered the BTZ and / or at least a portion of the PTV left the BTZ. The acceptable range of z-score values ​​may be determined during the acquisition of pre-scan images 506, and a calculated z-score outside the predefined range may indicate a significant difference between the real-time image and the pre-scan image. In some variant forms, the acceptable range of z-score values ​​may be approximately -1.5 to 1.5, approximately -2.0 to 2.0, approximately -2.5 to 2.5, approximately -3.0 to 3.0, approximately -3.5 to 3.5, or approximately -4.0 to 4.0.

[0082] Voxel or pixel count In some variations, the number of voxels or pixels with signal intensity values ​​exceeding a minimum signal activity threshold (e.g., a threshold calculated using the pre-scan BTZ peak activity metric) may be counted and compared to a pixel or voxel count threshold (e.g., a voxel count threshold determined based on pre-scan imaging data) to provide information about any OAR intrusion into the BTZ. For example, the number of pixels or voxels in a BTZ-PTV shell with signal activity values ​​greater than the minimum signal activity threshold may be relatively small if there is no OAR intrusion into the shell, as the BTZ-PTV constitutes a background (low-signal) region of the ROI image. Therefore, if the number of pixels or voxels in a shell with signal activity values ​​greater than the minimum signal activity threshold exceeds the pixel or voxel count threshold compared to the pre-scan imaging data, the increased signal in the background region may suggest an OAR intrusion into the BTZ. Thus, a beam interlock may be triggered when the real-time voxel count exceeds a predetermined voxel count threshold. A threshold for voxel or pixel count may be predetermined, and the voxel or pixel count of real-time images acquired during treatment delivery may be compared to the threshold to determine whether the OAR has entered the BTZ region. In particular, the voxel or pixel count threshold may be the maximum number of voxels in an ROI (e.g., BTZ-PTV) that has signal activity exceeding a minimum signal activity threshold (e.g., a threshold calculated using pre-scan BTZ peak activity). For example, the voxel count threshold may be between approximately 60 and 200 voxels, such as approximately 90 voxels, approximately 100 voxels, or approximately 150 voxels. In some variations, the voxel count threshold may be determined in part on one or more of the following factors: the volume of the BTZ, the size or volume of neighboring OARs (maybe multiples), the statistical noise distribution, and / or the intensity of the PTV (e.g., foreground) signal relative to the signal in the BTZ-PTV (background).

[0083] 510 delivering therapeutic radiation Treatment during a radiotherapy treatment session (or radiation delivery during quality assurance or calibration procedures) may include delivery of a prescribed dose of radiation to a patient (e.g., a target volume(s) within the patient) across several beam stations, across several treatment passes, and in some variations, across several treatment fractions. Real-time imaging of treatment delivery and ROI may be performed simultaneously or substantially simultaneously, and the real-time images can be used to provide information in real time whether the treatment should be modified or permanently or temporarily terminated. As stated throughout, several control points may be used in the treatment, and they may vary at least partially based on the size or volume of the target volume. In some variations, a treatment session may include more than one revisit to each control point (and in some variations, each irradiation position relative to the control point) to deliver the prescribed total amount of fluence to the treatment volume. For example, a treatment delivery session may include about two, three, four, five, or six passes at each control point to treat the patient.

[0084] During treatment delivery, for example during BgRT treatment delivery, real-time images of the ROI can be continuously acquired and reconstructed to reveal the biological signature of the target volume. In some variations, real-time images for a given LTS index can be continuously acquired and updated at the current control point where the treatment is delivered. At each given control point, radiation delivery may be further decomposed (i.e., segmented, split, binned) into subgroups of gantry angles or irradiation position groups, each of which may contain multiple irradiation positions. At each irradiation position group, the ROI location status and / or occlusion status can be updated using real-time reconstructed images of the LTS index. In some variations, the ROI status can be updated for a given control point using real-time PET images reconstructed from the last 100–1000 ms (e.g., approximately 250 ms, approximately 500 ms, or approximately 750 ms) of PET data, which may be updated every 1–500 ms (e.g., approximately 10 ms, approximately 50 ms, approximately 100 ms, or approximately 150 ms). The radiation fluence is calculated against the updated ROI status using the irradiation filter and can be normalized to match the planned fluence (which may be determined during treatment planning step 504). The fluence can then be segmented into machine-deliverable fluences, which may include the number of beam pulses and / or MLC leaf patterns at each irradiation site in the group. Thus, the fluence can be redirected to tumors that have moved within the BTZ using the LTS PET image. As described throughout this specification, if a comparison of the LTS PET image and / or image data with the prescan image and / or image data indicates that the inner ROI has shifted significantly out of the outer ROI (e.g., the real-time image metric value exceeds a given threshold) or that the OAR has entered the ROI significantly (e.g., the real-time image metric value exceeds a given threshold), the radiotherapy system may enter an interlock state.

[0085] 512 shows the real-time status of ROI. Signals indicating the real-time status of a patient's ROI may be transmitted to a display and / or a user interface on the display. The real-time status may include ROI displacement status (e.g., PTV location status relative to BTZ) and / or ROI intrusion status (e.g., BTZ blockage status relative to one or more OARs). The notification may be graphical and interpretable to a medical professional. In some variations, the image metrics and / or thresholds described herein may be used to generate notifications of unsafe and / or safe levels of tumor displacement from the ROI and / or OAR intrusion into the ROI. The notifications may inform a medical professional about the real-time status of the ROI via digital, visual, auditory, or tactile outputs (or a combination thereof) and may be generated via a controller or a device communicably coupled to the controller (e.g., a computer). In some variations, the radiotherapy treatment system may automatically trigger beam and / or system interlocks if real-time image metrics exceed predefined thresholds. In other words, if the real-time image metric exceeds a predetermined threshold for the metric, it may indicate that the real-time ROI (which may be continuously imaged and reconstructed during a treatment session) does not match the pre-scan ROI, and that irradiating the real-time ROI may increase the risk of radiation therapy complications (e.g., toxicity) for the patient. Therefore, the generation and / or emission of radiation beams from a compatible radiation therapy system may be permanently or temporarily terminated based on the real-time ROI status indication.In some variations, a notification indicating a significant difference between a real-time image and a corresponding prescan image (i.e., significant real-time tumor displacement from the ROI considering prescan image data and / or OAR intrusion into the ROI) may differ from a notification indicating a non-significant difference between a real-time image and a corresponding prescan image (i.e., non-significant real-time tumor displacement from the ROI considering prescan image data and / or OAR intrusion into the ROI). For example, a notification that a real-time image significantly deviates from a prescan image may be represented by the activation of a first LED on one or more elements of the radiotherapy system (e.g., controller, computer, gantry, etc.), while a notification that the real-time image does not significantly deviate from a prescan image may be represented by the activation of a second LED on one or more elements of the radiotherapy system. The first LED may indicate to a medical professional that it is not safe to deliver radiotherapy, while the second LED may have a different color from the first LED to indicate that it is safe to deliver the prescribed radiation to the patient.

[0086] 514 to trigger beam interlock at will As described above, if a comparison of real-time images with corresponding pre-scan images indicates that treatment delivery may not be safe for the patient, a beam interlock may be triggered to prevent or stop the generation of the prescribed radiation dose to the patient. The radiotherapy systems described herein may be used to enforce the interlock state of the radiotherapy treatment system. For example, a notification indicating that it is not safe to deliver radiation to an ROI may cause a first software node of the treatment system to send a delivery command to a second software node that controls the delivery hardware to execute the transmitted command. Thus, the second software node may, in response to a notification indicating that it is not safe to deliver treatment to the patient, execute a command to pause or shut down at least part of the radiotherapy treatment system (e.g., close all leaves of the collimator, stop radiation generation from LINAC). In some variants, the treatment plan may be updated during the beam interlock, taking into account the real-time location and / or occlusion status of the ROI. The treatment delivery session may then be resumed using the updated treatment plan. [Examples]

[0087] The following describes in further detail various exemplary variations of methods for monitoring ROI during radiotherapy treatment sessions.

[0088] Example 1 Variations of the method for monitoring ROI during radiotherapy sessions using the highest correlation location and intensity metrics are summarized as steps 602–618 of Method 600 in Figure 6 and are described in detail below. In some variations, some or all of steps 602–618 of Method 600 (e.g., steps 608–618) may be repeated for all treatment paths during a treatment session.

[0089] In one variant, method 600 may include acquiring a prescan PET image of the ROI with the patient in the treatment position 602. The prescan PET image may include a volume of multiple PET images that can be acquired at each of a plurality of corresponding beam stations (where j is the j-th beam station). In some variants, each image volume may include a combination of a prescan image in an LTS volume for the current beam station and a volume of prescan images in an LTS index for two preceding beam stations. The combined prescan image may be generated by combining a subset of multiple PET images as a single reconstructed image of the entire ROI 604. Method 600 may include calculating true and shifted prescan image metrics using cross-correlation (described in detail above) 604, and determining beam interlock thresholds using the prescan image metrics and the OAR penetration simulation procedure (described in detail above) 606. For example, in one variant, to calculate the true prescan image metric for a beam station, 604 the volume of a prescan image subset (corresponding to a beam station) may be cross-correlated with the aggregated prescan image, and the maximum signal location (Lo(j)) and maximum signal intensity (Io(j)) may be recorded for each beam station. In another variant, to calculate the shifted prescan image metric for a beam station, 604 the volume of a prescan image subset may be shifted such that there is a 50% overlap between the PTV and BTZ, and then cross-correlated with the aggregated prescan image. The maximum shifted signal location (Ls(j)) is determined and obtained as the shifted threshold for each beam station. 606 Furthermore, the pre-scan averaged signal (Mo(j)) in the BTZ-PTV shell may be recorded for each beam station, and the simulation method described above may be performed to determine the OAR intrusion threshold (R(j), increased averaged signal) for each beam station (where PTV may be used to simulate OAR with 50% overlap with BTZ) to obtain 606.Furthermore, in some variants, the mean maximum intensity correlation (Io(mean)) and minimum intensity correlation (Io(minimum)) may be determined by considering all pre-scan images, and the following signal intensity auto-interlock trigger criterion is determined for use at all beam stations during treatment delivery: [[Io(mean) - Io(minimum)] / Io(mean)]. In other variants, the signal intensity auto-interlock trigger criterion may be determined by the user and / or calculated based on other characteristics of the pre-scan images.

[0090] Method 600 may include acquiring real-time images of the ROI at each of several beam stations during treatment delivery 608. Then, at each beam station, real-time image metrics are calculated using cross-correlation to obtain 610. For example, real-time maximum correlation location (Li(j)) and maximum correlation intensity (Ii(i)) metrics are calculated using the volume of real-time PET images corresponding to the volume of pre-scan images to obtain 610. In some variant forms, weighting coefficients may be applied to Ii(i)) to reduce detection sensitivity for low imaging treatment sessions and enhance detection sensitivity for high imaging treatment sessions. Figure 7 shows images from a simulation of Method 600 and provides an example of how real-time maximum correlation location and intensity were identified for each LTS relative to the treatment path. Each LTS (1, 2, and 3 as shown) was acquired and locally cross-correlated with the aggregated pre-scan images shown. Cross-correlation was performed by generating signals 702 in the correlation domain and analyzing these signals to identify the maximum cross-correlation (Cmax(j)), including both location and intensity, for each beamstation at points 704, 706, and 708. Li(j) and Ii(j) were determined from Cmax(j) and recorded for use in steps 612-618 of Method 600.

[0091] Method 600 may include performing a calculation using pre-scan and real-time image metrics 612 and comparing the result with a beam interlock threshold determined in step 606 614. For example, in some variations, the calculation [Li(j)-Lo(j)] may be performed 612 and the result may be compared with Ls(j) 614. If the result is greater than Ls(j), a beam interlock notification is shown 616 and the beam interlock is triggered 618. In some variations, the calculation [Ii(i) / Io(mean)] may be performed 612 and the result may be compared with a signal strength auto-trigger criterion 614. If the result is greater than the signal strength auto-trigger criterion, a beam interlock notification is shown 616 and the beam interlock is triggered 618. In some variations, the calculation [Mi(j)-Mo(j)] may be performed 612 and the result may be compared with R(j) 614. If the result is R(j) or higher, a notification of the ROI's real-time status is displayed, and a beam interlock is triggered. Optionally, if the comparison results indicate that the real-time image metrics are within acceptable limits and / or thresholds, a notification may be generated to indicate to the user that ROI monitoring has been successfully completed and that it is safe to continue radiation delivery.

[0092] Example 2 Variations of the method for monitoring ROI during radiotherapy sessions using the BTZ maximum value change rate and BTZ activity standard deviation change rate metrics are summarized in steps 802–822 of Method 800 in Figure 8 and are described in detail below. In some variations, some or all of steps 802–822 of Method 800 may be repeated for all treatment paths prescribed to the patient.

[0093] Method 800 may include generating a shifted prescan image of a patient's ROI (e.g., BTZ and PTV) by acquiring a subset of multiple prescan PET images at each of a plurality of corresponding beam stations 802, where each subset may be a combined volume of images in the LTS index for the corresponding beam station. Generating the shifted prescan image 802 may include shifting each subset of images so that the acquired PTV can half-overlap with the BTZ. Method 800 may include stitching the shifted subset of prescan images with any available (preceding) shifted subsets of images that constitute the LTS index for the beam station 804. Statistics describing the signal intensity in voxels of the current image volume in the LTS index are then calculated and obtained 806. In some variations, the statistics may include, for example, the BTZ maximum signal activity metric and / or the BTZ standard deviation of the signal activity metric, which may be determined using all available stitched shifted subsets of prescan images for the current beam station. Method 800 may involve simulating or modeling all calculated statistics for the beam station (e.g., the BTZ maximum signal activity metric and / or the BTZ standard deviation of the signal activity metric for each subset of prescan images in the complete LTS index for the beam station) 808. The modeling may be, for example, a linear fitting of the statistics, thereby obtaining prescan image metrics calculated based on those fittings 808 (e.g., the gradient values ​​of the statistics can be any image metric, as described above). Once all prescan image metrics have been calculated for the beam station (e.g., the metrics for each subset of prescan images in the LTS index for the beam station) 808, they are averaged to obtain a beam interlock threshold 810.Such thresholds may be, for example, a PTV deviation threshold (e.g., the mean slope of the BTZ maximum signal activity value, as described above) and / or an OAR intrusion threshold (e.g., the mean slope of the BTZ standard deviation of signal activity, as described above) for a given beam station. In some variations, multiple subsets of prescan images may be combined to generate an aggregated prescan image for use in generating and analyzing real-time hybrid images of ROIs.

[0094] Method 800 may include 812 acquiring a set of real-time PET images corresponding to a subset of prescan images for each of several beam stations during a radiation delivery session (e.g., a therapeutic delivery session). The set of real-time images is stitched 814 to generate a dynamic hybrid image (i.e., the corresponding subset of prescan images in the aggregated prescan image may be replaced to update the hybrid image, as described above). Furthermore, statistics describing the signal intensity in voxels of the subset of real-time images may be calculated 816 to calculate real-time image metrics for ROI. For example, the real-time BTZ maximum signal activity value and / or the BTZ standard deviation of the signal activity value may be determined, and once all subsets of real-time images for the LTS index have been acquired and added to the hybrid image, linear fitting may be performed 816 to calculate the rate of change of the real-time BTZ maximum signal activity value, and the rate of change of the real-time BTZ standard deviation of signal activity for the current beam station has been calculated (as described above). Method 800 may include comparing a real-time image metric with a beam interlock threshold(s) calculated in step 810 818. If the comparison 818 finds that the real-time image metric (e.g., real-time BTZ maximum signal activity value change rate and / or real-time BTZ standard deviation change rate of signal activity) exceeds a beam station beam interlock threshold (e.g., PTV deviation threshold and / or OAR intrusion threshold), then a real-time status notification of the ROI is shown 820 and a beam interlock is triggered 822.

[0095] Figures 9A and 9B show plots and results from the simulation of Method 800. Figure 9A shows data from three treatment passes of a radiotherapy treatment delivery session in the simulation. Figure 9B shows simulation data from treatment pass 2 (904 in Figure 9A), indicating that a beam interlock was triggered during the pass. In particular, Figure 9A shows the aggregated prescan image 900, the aggregated image 902 of the first treatment pass (aggregation of images acquired in real time during the first treatment pass), and the aggregated image 904 of the second treatment pass (aggregation of images acquired in real time during the second treatment pass). PTV 920 is shown as a circle around the center of each image. As shown in the figure, the high-signal target volume remained substantially within PTV 920 during the first treatment pass 902, but did not remain within PTV 920 during the second treatment pass 904. In this example, the beam interlock was triggered at beam station 51 (approximately midway through the second treatment pass 904, where PTV 920 is shown) as a result of comparing the PTV image metric with the PTV deviation threshold. Figure 9B shows two graphical representations of the PTV deviation and OAR intrusion metrics described and calculated above for treatment pass 904 in Figure 9A. Each graph 906, 908 shows how the respective monitoring metrics changed as a function of the beam station during the second treatment pass 904. The PTV deviation threshold 930 and OAR intrusion threshold 932 for beam station 51 are also shown. As shown, the PTV monitoring metric exceeded the PTV deviation threshold at beam station 51, triggering the beam interlock. The OAR intrusion threshold 932 for beam station 51 was never exceeded during this treatment pass. This is consistent with the information observed in the aggregated image 904 in Figure 9A, namely that the OAR did not intrude into the BTZ.

[0096] Example 3 One variation of the method for monitoring ROI during radiotherapy sessions may use a BTZ peak activity monitoring metric, a minimum signal activity threshold, a normalized mutual information metric, a z-score threshold, and a voxel count threshold. The BTZ peak activity monitoring metric, minimum signal activity threshold, normalized mutual information metric, and z-score threshold can be used to track PTV characteristics and deviations from the BTZ during treatment delivery. The voxel count threshold may be used to determine OAR intrusion into the BTZ during treatment delivery. This method is summarized as steps 1002-1022 of Method 1000 in Figure 10 and is described in detail below. In some variations, some or all of steps 1002-1022 of Method 1000 may be repeated for all treatment paths prescribed to the patient.

[0097] Method 1000 may include obtaining a subset of multiple PET prescan images at each of a plurality of corresponding beam stations 1002, where each subset may be contained in one or more beam station LTS indices (for example, each LTS index may contain four subsets of prescan images corresponding to four consecutive beam stations). In some variations, enhancement filters may be applied to the subsets of prescan images. Prescan image metrics are calculated using the (enhanced) prescan images 1004. In some variations, prescan image metrics may include, for example, PTV peak activity values ​​and BTZ-PTV peak activity values ​​for each volume of images in the LTS index. Method 1000 may include calculating a signal threshold using the prescan image metrics 1006. For example, a foreground signal to background signal (PTV peak activity to BTZ-PTV peak activity) ratio may be calculated and classified as meaningful if it is greater than a given threshold (e.g., about 1.15 or greater). Next, the lowest meaningful PTV peak activity value (i.e., the lowest PTV peak activity value from the group of PTV peak activity values ​​used to calculate a meaningful foreground signal-to-background signal ratio) is set as the signal threshold (e.g., minimum signal activity threshold) for use when evaluating the real-time image. Additionally or alternatively, aggregated pre-scan images may be generated, and / or thresholds for normalized mutual information, z-scores, and / or voxel counts may be determined for use when evaluating the real-time image.

[0098] Method 1000 may include acquiring a real-time image LTS volume 1008 of the ROI at each of several corresponding beam stations. The real-time image may be acquired, for example, during radiation delivery. In some variations, acquiring a real-time image volume may include enhancing the image (e.g., using an enhancement filter) to facilitate the calculation of real-time image metrics (e.g., real-time PTV peak activity and / or BTZ-PTV peak activity) using the real-time image (as described with respect to step 1016 below). In some variations, calculating the real-time image metrics 1010 may include using the image metrics to determine representative signal values. For example, the real-time foreground signal to background signal ratio may be calculated using real-time PTV peak activity and BTZ-PTV peak activity (as described above). In some variations, the ratio may be classified as meaningful (i.e., there is enough signal that Method 1000 can proceed) if, for example, it is about 1.15 or greater. The representative signal value may then be compared to a signal threshold 1012. For example, the signal threshold may be the minimum activity threshold calculated in step 1006. If the real-time PTV peak activity value is less than the minimum activity threshold, real-time image acquisition and / or treatment delivery may continue. If the real-time PTV peak activity is greater than or equal to the minimum activity threshold, the current set of real-time images is enhanced (e.g., by an enhancement mask) to obtain (e.g., by an enhancement mask) such that fewer voxels than all voxels in the BTZ are considered in the next step of method 1000. The volume of the enhanced real-time images is registered with the corresponding volume of the prescan images so that the prescan PTV (PTV reconstructed in the aggregated prescan images) can be mapped to the current beamstation frame of reference.In some variant forms, the image volume is registered using normalized mutual information and a joint probability distribution containing three clusters of voxels (voxels with foreground (PTV) signal levels, voxels with background (BTZ-PTV) signal levels, and voxels with signal levels between the foreground threshold and the background threshold) and the following formula: NMI(X;Y)=[[I(X;Y)] / [max(H(X),H(Y))]] (where I(X;Y) is mutual information and max(H(X),H(Y)) is the maximum entropy of the real-time image (X) and the pre-scan image (Y)) to obtain 10¹⁴. Figure 11 shows an example of a plot representing a misaligned image (left panel) and a plot representing an aligned image (right panel). The X and Y axes of the plots in Figure 11 represent the intensity bins of two images, A and B. For a given pixel in image A, the intensity is plotted in appropriate bins along the X-axis, and the intensity bins for the corresponding (same) pixel in image B are plotted along the Y-axis. The result is a scatter plot of image pixel pairs representing JPD. Compared to the aligned plot 1104, there is increased variance for the cluster distribution in the misaligned plot 1102. The relative decrease in variance (entropy) on plot 1104 indicates that the compared image has more mutual information than the image compared to plot 1102. Method 1000 may include calculating a real-time image metric (e.g., a measure of normalized mutual information) using the registered image 1016 and comparing the real-time metric to a predetermined beam interlock threshold (e.g., a measure of NMI less than or equal to about 0.5) 1018. Simultaneously, a notification of the real-time status of the ROI is shown 1018, and a beam interlock is triggered 1018 if the real-time image metric is less than the predetermined beam interlock threshold.Method 1000 may include performing further calculations 1020 using the real-time image metrics calculated in the previous step (e.g., using the real-time PTV peak activity and / or real-time BTZ-PTV peak activity values ​​calculated in step 1010). For example, using the real-time PTV signal activity values, the mean and standard deviation of the real-time foreground (PTV) signal can be calculated for each set of real-time images and the corresponding volume of prescan images 1020, and the similarity of PTV activity in the real-time images and prescan images can be compared via z-scores using a z-test 1022. That is, a lower z-score may indicate that the real-time image matches the prescan image. A notification of the real-time status of the ROI may be shown, and a beam interlock may be triggered if the result of the calculation using the real-time image metrics is outside a given beam interlock range (e.g., outside the acceptable range of z-scores). As another example, Method 1000 may optionally include performing further calculations 1020 using the real-time images acquired in step 1008. For example, the number of voxels in the BTZ-PTV region of a real-time image volume with a signal value greater than the minimum activity threshold can be counted. Next, the voxel count can be compared to a predetermined voxel count threshold to determine whether the total voxel count is low enough to deliver treatment (i.e., no OAR in the BTZ, as described above). Simultaneously, a notification of the real-time status of the ROI is given, and if the result of calculations using the real-time image is outside a predetermined beam interlock range, a beam interlock is triggered (for example, if the total voxel count is too high, a beam interlock may be triggered).

[0099] While specific variations are described and illustrated herein, those skilled in the art will readily conceive of a wide variety of other means and / or structures for performing the functions described herein and / or obtaining one or more of the results and / or benefits. Each such variation and / or modification is considered to fall within the scope of the variations of the invention described herein. More generally, those skilled in the art will readily understand that all parameters, dimensions, materials, and configurations described herein are intended to be illustrative, and that actual parameters, dimensions, materials, and / or configurations will depend on the specific application(s) in which the teachings of the invention are used. Those skilled in the art will be able to recognize or confirm many equivalents to specific variations of the invention described herein using only routine experimentation. Therefore, it should be understood that the above variations are presented merely as examples, and that variations of the invention may be carried out in ways different from those specifically described and claimed, within the scope of the appended claims and their equivalents. Variations of the invention of this disclosure cover each individual feature and / or method described herein. Furthermore, any combination of two or more such features and / or methods is included within the scope of the invention of this disclosure, provided that such features and / or methods are not contradictory to each other.

[0100] The methods described above can be implemented in any of many ways. For example, at least some methods of this technology can be implemented using hardware, firmware, software, or a combination thereof. When implemented in firmware and / or software, the firmware and / or software code can run on any suitable set of processors or logical components, whether provided on a single device or distributed across multiple devices.

[0101] In this regard, the various embodiments described herein may be embodied as computer-readable storage media (or multiple computer-readable storage media) (e.g., computer memory, one or more floppy disks, compact disks, optical disks, magnetic tapes, flash memory, circuit configurations in field-programmable gate arrays or other semiconductor devices, or other non-temporary or tangible computer storage media), the computer-readable storage media may be encoded with one or more programs that, when executed on one or more computers or other processors, perform methods for implementing the various embodiments of the invention discussed above. The computer-readable media(s) may be transportable so that the programs(s) stored on the computer-readable media can be loaded onto one or more different computers or other processors to implement the various embodiments of the invention discussed above.

[0102] The terms “program” or “software” are used herein in a general sense and refer to any type of computer code or set of computer executable instructions that can be used to program a computer or other processor to implement various aspects of the embodiments discussed above. Furthermore, it should be understood that, according to one aspect, one or more computer programs that, when executed, perform the methods disclosed herein do not need to reside on a single computer or processor, but may be modularly distributed across several different computers or processors to implement various aspects of the invention disclosed herein.

[0103] Computer executable instructions can take many forms, such as program modules, which are executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. Typically, the functions of program modules may be combined or distributed as desired in different variant forms.

[0104] Furthermore, data structures can be stored in a computer-readable medium in any preferred format. For simplicity of explanation, a data structure may be shown as having related fields through locations within the data structure. Such relationships can also be realized by assigning storage for fields to locations in a computer-readable medium that can convey one or more relationships between fields. However, relationships between information in the fields of a data structure can be established using any preferred mechanism, including the use of pointers, tags, or other mechanisms to establish relationships between data elements.

[0105] Furthermore, the actions performed as part of the method may be ordered in any preferred manner. Thus, various methods may be constructed in which the actions are performed in an order different from that described, and these may include performing several actions simultaneously, even if they are shown as sequential actions in the exemplary embodiment.

Claims

1. A method for real-time radiotherapy treatment, To generate a set of pre-scan images that include a region of interest (ROI) within the patient, To acquire a set of real-time images including the aforementioned ROI, The set of real-time images is compared with the set of pre-scan images, If the set of real-time images deviates from the set of pre-scan images by a predetermined threshold, the radiotherapy treatment session will be terminated. The method, including the method described above.

2. Calculating pre-scan image metrics, The method according to claim 1, further comprising calculating a real-time image metric, wherein comparing the set of real-time images of the ROI with the set of pre-scan images of the ROI includes comparing the pre-scan image metric with the real-time image metric.

3. The method according to claim 1, further comprising generating a graphical notification or other notification of the deviation status of the set of real-time images from the set of pre-scan images.

4. The generation of the aforementioned pre-scan image set is Acquiring a subset of pre-scan images at each of multiple control points, The method according to claim 1, comprising combining a subset of the pre-scan images.

5. The method according to claim 4, further comprising combining a plurality of subsets of prescan images corresponding to a plurality of control points in order to generate a summed prescan image of the ROIs.

6. The method according to claim 4, wherein each of the plurality of control points includes a beam station in a radiotherapy treatment system configured to deliver radiotherapy to a portion of the ROI in the patient.

7. The method according to claim 1, wherein obtaining the set of real-time images includes obtaining one or more subsets of real-time images at a control point.

8. The method according to claim 1, further comprising delivering radiotherapy treatment to the patient from a control point if the set of real-time images does not deviate from the set of pre-scan images by a predetermined threshold amount.

9. The method according to claim 1, wherein the ROI includes an inner ROI within an outer ROI.

10. Identifying the medial ROI before the real-time radiotherapy treatment, The method according to claim 9, further comprising localizing the lateral ROI before the real-time radiotherapy treatment.

11. The method according to claim 2, wherein the set of prescan images includes a sum of multiple subsets of prescan images, the calculation of the prescan image metric includes calculating the autocross-correlation between each of the sum of the multiple subsets of prescan images, and the calculation of the real-time image metric includes calculating the local cross-correlation between the set of real-time images and the sum of the prescan images.

12. The method according to claim 11, wherein each of the pre-scan image metric and the real-time image metric includes one or more of the maximum cross-correlation signal intensity for the control point and the location of the maximum cross-correlation signal intensity for the control point.

13. The method according to claim 2, wherein each of the real-time image metric and the pre-scan image metric includes the rate of change of the maximum signal intensity.

14. The set of pre-scan images includes a combined pre-scan image comprising multiple subsets of pre-scan images, and the method is To determine the pre-scan image metric, the rate of change of the maximum signal intensity is calculated for a plurality of subsets of the pre-scan images, A hybrid image including the ROI is generated by replacing the corresponding subset of pre-scan images in the aggregated pre-scan image with the aforementioned set of real-time images. The method according to claim 13, further comprising calculating the rate of change of the maximum signal intensity for the hybrid image in order to determine the real-time image metric.

15. The method according to claim 13, wherein the ROI includes an inner ROI within an outer ROI, and the maximum signal intensity includes the maximum signal intensity within the inner ROI.

16. The method according to claim 15, wherein the rate of change of the maximum signal intensity indicates the movement of the inner ROI relative to the outer ROI.

17. The method according to claim 2, wherein each of the real-time image metric and the pre-scan image metric includes a rate of change in the standard deviation of the signal.

18. The set of pre-scan images includes a combined pre-scan image comprising multiple subsets of pre-scan images, and the method is To determine the pre-scan image metric, the rate of change of the standard deviation of the signal is calculated for each of a plurality of subsets of the pre-scan images, A hybrid image including the ROI is generated by replacing the corresponding subset of pre-scan images in the aggregated pre-scan image with the aforementioned set of real-time images. The method according to claim 17, further comprising calculating the standard deviation of the signal for the hybrid image in order to determine the real-time image metric.

19. The method according to claim 18, wherein the prescan image metric includes the maximum rate of change of the standard deviation of the signal intensity in a plurality of subsets of the prescan images.

20. The method according to claim 17, wherein the ROI includes an inner ROI within an outer ROI, and calculating the standard deviation of the signal includes excluding the signal in the inner ROI and calculating the standard deviation of the signal in the outer ROI.

21. The method according to claim 20, wherein the rate of change of the standard deviation of the signal indicates the movement of an anatomical object to the outer ROI when the set of real-time images deviates from the set of pre-scan images by a predetermined threshold amount.

22. The method according to claim 2, wherein the ROI includes a volumetric shell containing an inner ROI within an outer ROI, and each of the real-time image metric and the pre-scan image metric includes the average intensity of the ROI.

23. Adjusting the signal intensity of the aforementioned internal ROI, To determine the pre-scan image metric, the average intensity of the ROI is calculated using the regulated signal of the inner ROI, The method according to claim 22, further comprising calculating the average intensity of the ROI for the set of real-time images in order to determine the real-time image metric.

24. Adjusting the signal intensity of the inner ROI is To duplicate the signal of the aforementioned inner ROI, The method of claim 23, comprising shifting the replicated signal to a plurality of locations within the outer ROI, wherein the calculation of the prescan image metric includes calculating the average intensity of the ROI at each of the plurality of locations.

25. The method according to claim 24, wherein the replicated signal of the internal ROI is used to simulate the presence of anatomical objects within the ROI.

26. The method according to claim 23, wherein calculating the average intensity of the ROI for the set of real-time images includes applying weighting coefficients to the calculation of the average intensity of the ROI.

27. The method according to claim 2, wherein each of the pre-scan image metrics and the real-time image metrics includes two or more image metrics.

28. The method according to claim 1, wherein the method is performed by a first module in a radiotherapy treatment system, and terminating the radiotherapy treatment session includes sending a command from the first module to a second module configured to control the hardware of the radiotherapy treatment system.

29. The method according to claim 1, further comprising generating one or more visual, auditory, or tactile notifications indicating a deviation status of the set of real-time images from the set of pre-scan images.

30. Terminating the aforementioned radiotherapy treatment session means Automatically save records of radiation therapy treatments delivered to patients, The method according to claim 1, comprising generating an updated set of pre-scan images including the ROI.

31. The method according to claim 1, wherein each of the set of pre-scan images and the set of real-time images includes a 3D image of at least a portion of the ROI.

32. The method according to claim 1, further comprising defining the predetermined threshold based on the set of prescan images, wherein the predetermined threshold includes one or more predetermined thresholds.

33. The method according to claim 1, wherein each of the set of prescan images and the set of real-time images includes one of PET images, SPECT images, CT images, MRI images, X-ray images, ultrasound images, or a combination thereof.

34. A method for monitoring a region of interest (ROI) within a patient during a radiotherapy treatment session, Acquiring multiple subsets of pre-scan images containing at least a portion of the ROI at multiple corresponding beam stations, To generate a combined pre-scan image of the ROI, multiple subsets of the pre-scan images corresponding to the multiple beam stations are combined, For each of the aforementioned beam stations, To generate a subset prescan image of the aforementioned combined prescan image, one or more sets of consecutive prescan images are combined, The auto-cross-correlation between the subset image corresponding to the beam station and the aggregated pre-scan image is calculated. The beam station acquires a set of real-time images, To generate a subset of real-time images of the ROI, the set of real-time images is combined with the remainder of one or more sets of consecutive real-time images, wherein the one or more sets of consecutive real-time images correspond to one or more sets of consecutive pre-scan images for the beam station. The local cross-correlation between the subset real-time images of the ROI and the aggregated pre-scan images of the ROI is calculated. In order to determine whether the local cross-correlation is within an acceptable range, the local cross-correlation is compared with the autocross-correlation, To indicate whether the local cross-correlation is within the acceptable range, a graphical notification is generated. If the local cross-correlation is not within the acceptable range, the radiotherapy for the patient may be optionally modified. The method, including the method described above.

35. A method for monitoring a region of interest (ROI) within a patient during a radiotherapy treatment session, Acquiring multiple subsets of pre-scan images containing at least a portion of the ROI at multiple corresponding beam stations, To generate a combined pre-scan image of the ROI, multiple sets of the pre-scan images corresponding to the multiple beam stations are combined, For each of the aforementioned beam stations, in order to generate a subset prescan image of the aggregated prescan image, one or more sets of consecutive prescan images are combined, Using multiple subset pre-scan images corresponding to the multiple beam stations, the rate of change of the ROI with respect to the aggregated pre-scan image is calculated. To acquire a set of real-time images that include at least a portion of the ROI, To calculate the maximum real-time image signal for the set of real-time images, The hybrid image of the ROI is generated by replacing the corresponding set of pre-scan images in the aggregated pre-scan image with the set of real-time images. To calculate the rate of change of the maximum real-time image signal for the hybrid image, The rate of change of the maximum real-time image signal is compared with the rate of change of the maximum pre-scan image signal, To indicate whether the aforementioned maximum real-time image signal is within an acceptable range, a graphical notification is generated. If the maximum real-time image signal is not within the acceptable range, the radiotherapy treatment for the patient may be optionally modified. The method, including the method described above.

36. A method for monitoring a region of interest (ROI) within a patient during a radiotherapy treatment session, Acquiring multiple subsets of pre-scan images containing at least a portion of the ROI at multiple corresponding beam stations, To generate a pre-scan image with the aforementioned ROIs aggregated, In each of the aforementioned beam stations, the pre-scan minimum signal activity metric is calculated, Determining the overall minimum signal activity threshold, To acquire a set of real-time images of the aforementioned ROI, Calculating real-time signal activity metrics, The real-time signal activity metric is compared with the overall minimum signal activity threshold, If the real-time signal activity metric is greater than the overall minimum signal activity threshold, The set of real-time images is registered with the corresponding set of pre-scan images. Using the aforementioned set of registered real-time and corresponding pre-scan images, the normalized mutual information metric (NMI) is calculated. If the NMI metric exceeds a predetermined NMI threshold, the z-score is calculated in order to compare the set of real-time images with the aggregated pre-scan images. To indicate whether the aforementioned z-score is within an acceptable range, a graphical notification is generated, If the z-score is not within the acceptable range, the radiotherapy treatment for the patient may be optionally modified. The method, including the method described above.

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