Sequential monoscopic tracking

Abstract

A method for sequential monoscopic tracking is described. The method includes generating a plurality of projections of an internal target region within a patient's body, the projections including projection data related to the position of the internal target region of the patient. The method further includes generating external position data related to external motion of the patient's body using one or more external sensors. The method further includes generating, by a processing device, a correlation model between the position of the internal target region and the external position data by fitting the plurality of projections of the internal target region to the external position data. The method further includes using the correlation model to estimate the position of the internal target region at a later time. [Selected figure] Figure 3

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 15 / 945,580, filed April 4, 2018, which claims priority to U.S. Provisional Patent Application No. 62 / 482,135, filed April 5, 2017, and U.S. Provisional Patent Application No. 62 / 482,604, filed April 6, 2017, the contents of which are hereby incorporated by reference in their entireties.

[0002] SUMMARY OF THE DISCLOSURE Embodiments of the present disclosure relate to tracking the position of a target using data from an X-ray imager. [Background technology]

[0003] In radiation therapy, a radiation dose delivered through a radiation therapy beam from a source outside the patient's body is delivered to a target region of the body to destroy tumor cells. Typically, the target region consists of a volume of tumor tissue. During radiation therapy, care must be taken to track the movement of the target region so that the therapeutic dose of the radiation therapy beam is directed to the intended location in the patient's body.

[0004] Embodiments of the present disclosure are illustrated by way of example and not limitation in the figures of the accompanying drawings. [Brief description of the drawings]

[0005] [Figure 1A] FIG. 1 illustrates a radiation therapy system that can be used in accordance with embodiments described herein. [Figure 1B] FIG. 1 is a cross-sectional view of a radiation therapy system according to an embodiment described herein. [Figure 2A] 1 illustrates a volumetric imaging device and projection according to embodiments described herein. [Figure 2B] 1A-1C are diagrams illustrating projected images according to embodiments described herein. [Diagram 3]13A-13C are diagrams illustrating internal markers combined with external markers to track movement of a target area according to an embodiment. [Figure 4A] 1 is a flow diagram illustrating a method for compensating for respiratory and other motion in a treatment system according to an embodiment. [Figure 4B] 1 is a first flow chart illustrating a method for sequential monoscopic tracking in a treatment system according to an embodiment. [Figure 4C] 11 is a second flow chart illustrating a method for sequential monoscopic tracking in a treatment system according to an embodiment. [Figure 4D] 11 is a third flow chart illustrating a method for sequential monoscopic tracking in a treatment system according to an embodiment. [Diagram 5] FIG. 1 illustrates a portal imaging radiation therapy system according to an embodiment described herein. [Figure 6] FIG. 1 illustrates a gantry-based intensity modulated radiation therapy system according to embodiments described herein. [Figure 7] 1 illustrates a helical radiation delivery system according to an embodiment described herein. [Figure 8] 1A-1D illustrate examples of different systems that may be used to generate or deliver radiation treatments in accordance with embodiments described herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0006] Described herein are embodiments of methods and apparatus for sequential monoscopic tracking. The embodiments of the present disclosure can be used in conjunction with radiotherapy delivery systems, such as the CyberKnife® radiosurgery system, that include stereoscopic x-ray imaging capabilities. Alternatively, other types of radiotherapy delivery systems (e.g., gantry-based, helical-based, etc.) can be used.

[0007] In one embodiment, the radiation therapy system includes a linear accelerator (LINAC) 1201 that acts as the radiation therapy source. During treatment, it is important to ensure that any movement of the patient's target area is carefully tracked so that the radiation therapy dose is directed to the intended target. Thus, in a radiation therapy system such as the CyberKnife® radiation therapy system, a sequential monoscopic tracking system such as the one described herein would be desirable.

[0008] The use of a volumetric imaging system (e.g., the medPhoton ImagingRing System (IRS)) in conjunction with a radiation therapy delivery system (e.g., the CyberKnife® Radiation Therapy System) as shown in FIG. 1A offers new image registration and image tracking opportunities. For example, in embodiments of the present disclosure, such a system can acquire two or more flat-panel x-ray images using only a single imager.

[0009] An embodiment of the present disclosure tracks the 3D position of a target inside a patient using data from sequentially acquired x-ray images from two or more flat-panel x-ray images taken at different times. Such imaging can be referred to as sequential monoscopic imaging, since single (monoscopic) images are taken sequentially (e.g., every few seconds) from different angles. In some embodiments, images can be taken between once every second and once every minute, inclusive. In other embodiments, images can be taken between once every 100 milliseconds and once every two minutes, inclusive. The difference with stereoscopic x-ray imaging is that a single monoscopic image does not completely define the location of the target in 3D space. An object visualized in a single x-ray image is located anywhere on a line connecting the x-ray source and the object's location in the x-ray image.

[0010] Information about object position from a series of individual images acquired from different positions can be simultaneously fitted to a correlation model to estimate the most likely trajectory of the target over an observation period. The correlation between the moving 3D target position and the externally detected respiration amplitude can be modeled using projections acquired from multiple individual flat-panel X-ray images (e.g., monoscopic projection images), all acquired from at least two positions at different times. The monoscopic projection images can be fitted to a 3D model using the mathematical formulas described herein to estimate the trajectory of the target over a period of time.

[0011] In one embodiment, a projection (also referred to herein as a "projection image") may be an image depicting an internal region of the body projected onto a plane (2D) outside the body from a single observation angle. In this embodiment, an X-ray point source and flat-panel detector on either side of the body may be used to acquire the projection image. The X-ray source and detector may be mounted on a ring gantry that rotates around the body to allow acquisition of projection images from a variety of imaging angles.

[0012] In another embodiment, the projection data may include both lines between the 2D detector positions of the internal target and the X-ray point source, and / or the 2D detector positions themselves. In one embodiment, a correlation model between the 3D target positions and the external sensor positions may be fitted by minimizing the distance between the model projected onto the detector and the 2D detector positions, and / or by minimizing the distance between this model and the lines between the 2D detector positions and the X-ray point source.

[0013] For example, a correlation model can be developed by fitting 2D target positions acquired at multiple time points to simultaneously acquired external measurements (e.g., external marker positions). Such correlation models can be used, for example, in radiation therapy systems, where a correlation model can be developed before and during treatment and used to estimate internal tumor positions from external measurements, and this information is used to dynamically move or shape the radiation beam along with the target.

[0014] The term "target" may refer to one or more fiducials near (within some defined proximity to) a treatment site (e.g., a tumor). In another embodiment, a target may be a bone structure. In yet another embodiment, a target may refer to a soft tissue of a patient (e.g., a tumor). A target may be any defined structure or site that can be identified and tracked as described herein.

[0015] FIG. 1A illustrates a radiation therapy system 1200 that can be used in accordance with embodiments described herein. As shown, FIG. 1A illustrates a configuration of the radiation therapy system 1200. In the illustrated embodiment, the radiation therapy system 1200 includes a linear accelerator (LINAC) 1201 that acts as a radiation therapy source. In one embodiment, the LINAC 1201 is mounted on the end of a robotic arm 1235 that has multiple (e.g., 5 or more) degrees of freedom to position the LINAC 1201 to irradiate a diseased anatomy (e.g., target 120) with beams delivered from multiple angles and in multiple planes within a working volume around the patient. Treatment may require beam paths with a single isocenter approach, a multiple isocenter approach, or a non-isocentric approach. Alternatively, other types of image-guided radiation therapy (IGRT) systems can be used. In an alternative embodiment, the LINAC 1201 can be mounted on a gantry-based system as described below.

[0016] The LINAC 1201 can be positioned at a number of different nodes (defined locations where the LINAC 1201 can stop and deliver radiation) during treatment by moving the robotic arm 1235. At these nodes, the LINAC 1201 can deliver one or more radiation therapy beams to the target. The nodes can be positioned in an approximately spherical distribution around the patient. The specific number of nodes and the number of treatment beams delivered at each node can vary as a function of the location and type of diseased anatomy to be treated.

[0017] The radiation therapy system 1200 includes an imaging system 1210 having a processing device 1230 connected to X-ray sources 1203A and 1203B (i.e., imaging sources) and stationary X-ray detectors 1204A and 1204B. Alternatively, the X-ray sources 1203A, 1203B and / or the X-ray detectors 1204A, 1204B can be movable, in which case they can be repositioned to maintain alignment with the target 120, or alternatively to image the target from different orientations, or to acquire many X-ray images and reconstruct a three-dimensional (3D) cone beam CT. In one embodiment, the X-ray source is not a point source, but an array of X-ray sources, as will be understood by those skilled in the art. In one embodiment, the LINAC 1201 serves as an imaging source, and the LINAC power level is reduced to a level that is safe for imaging.

[0018] The imaging system 1210 can perform computed tomography imaging (CT), such as cone-beam CT or helical mega-voltage computed tomography (MVCT), and the images generated by the imaging system 1210 can be two-dimensional (2D) or three-dimensional (3D). The two x-ray sources 1203A and 1203B can be mounted in fixed positions on the ceiling of the operating room and aligned to project x-ray imaging beams from two different angular positions (e.g., separated by 90 degrees) to intersect at a machine isocenter (referred to herein as the treatment center, which provides a reference point for positioning the patient on the treatment couch 1206 during treatment) and illuminate the imaging planes of the respective detectors 1204A and 1204B after passing through the patient. In one embodiment, the imaging system 1210 allows for stereoscopic imaging of the target and the surrounding volume of interest (VOI). In other embodiments, the imaging system 1210 can include more or less than two x-ray sources and more or less than two detectors, and any of the detectors can be movable rather than fixed. In yet another embodiment, the position of the X-ray source and the position of the detector can be swapped. The detectors 1204A and 1204B can be made of a luminescent material (e.g., amorphous silicon) that converts the X-rays into visible light, and a CMOS (complementary metal oxide semiconductor) or CCD (charge-coupled device) imaging cell array that converts this light into a digital image that can be compared to the reference image during an image registration process that converts the coordinate system of the digital image into the coordinate system of the reference image, as known to those skilled in the art. The reference image can be, for example, a digitally reconstructed radiograph (DRR), which is a virtual X-ray image generated from a 3D CT image based on simulating the X-ray image formation process by projecting a light beam through the CT image.

[0019] The IGRT delivery system 1200 further includes a secondary imaging system 1239. The imaging system 1239 is a cone beam computed tomography (CBCT) imaging system, such as the medPhoton Imaging Ring System. Alternatively, other types of volumetric imaging systems can be used. The secondary imaging system 1239 includes a rotatable gantry 1240 (e.g., a ring) mounted on an arm and rail system (not shown) that moves the rotatable gantry 1240 along one or more axes (e.g., along an axis extending from the head to the foot of the treatment couch 1206). An imaging source 1245 and a detector 1250 are mounted on the rotatable gantry 1240. The rotatable gantry 1240 can rotate 360 ​​degrees around an axis extending from the head to the foot of the treatment couch. Thus, the imaging source 1245 and the detector 1250 can be positioned at many different angles. In one embodiment, the imaging source 1245 is an x-ray source and the detector 1250 is an x-ray detector. In one embodiment, the secondary imaging system 1239 includes two independently rotatable rings. The imaging source 1245 can be mounted on the first ring and the detector 1250 can be mounted on the second ring. In one embodiment, the rotatable gantry 1240 is stationary at the foot of the treatment couch to avoid collision with the robotic arm 1202 during radiation treatment delivery.

[0020] 1A, the image-guided radiation therapy system 1200 can be further associated with a treatment delivery workstation 150. The treatment delivery workstation can be located remotely from the radiation therapy system 1200 in a room different from the treatment room in which the radiation therapy system 1200 and the patient reside. The treatment delivery workstation 150 can include a processing device (which can be processing device 1230 or another processing device) and memory that modifies treatment delivery to the patient 1225 based on detection of target motion based on one or more image registrations as described herein.

[0021] In some embodiments, a gantry system with helical delivery can be used to rotate the imaging system 1210. For example, the gantry system can be used to acquire two, three, or more images (e.g., x-ray images) at different angles. The radiation therapy delivery system can further include a rotating imaging system 109 positioned around the patient.

[0022] In one implementation, system 1200 includes a frameless robotic radiosurgery system (e.g., the CyberKnife treatment delivery system). In another implementation, system 1200 is coupled to a gantry-based LINAC treatment system, e.g., LINAC 1201 is coupled to the gantry of a gantry-based system. Alternatively, system 1200 can be used with other types of radiation therapy systems, such as the helical delivery system discussed below.

[0023] 1B illustrates the configuration of an image-guided radiation therapy (IGRT) system 700. In general, the IGRT system 700 may correspond to the radiation therapy system 1200 of FIG. 1A.

[0024] 1B, the IGRT system 700 can include two kilovoltage (kV) imaging sources 702A and 702B, which can be mounted on tracks 722A and 722B in the operating room ceiling 720 and further aligned to project imaging x-ray beams 704A and 704B from two different locations such that ray 712A of beam 704A intersects ray 712B of beam 704B at an imaging center 726 (i.e., isocenter) that provides a reference point for positioning a LINAC 708 for generating treatment beams 716A, 716B, and 716C during treatment and for positioning a patient 710 on a treatment couch 714. After passing through the patient 710, the imaging x-ray beams 704A and 704B can illuminate respective imaging planes of x-ray detectors 724A and 724B, which can be mounted substantially parallel (e.g., within 5 degrees) of each other on or near the operating room floor 718. The kV imaging sources 702A and 702B can be substantially coplanar such that the imaging surfaces of the kV imaging sources 702A and 702B form a single imaging plane. In one embodiment, the kV imaging sources 702A and 702B can be replaced with a single kV imaging source. With an x-ray image of the patient 710 generated, the LINAC 708 can rotate to generate a treatment beam 716 from a different angle. While the LINAC 708 rotates to a different angle, the kV imaging sources 702A and 702B can move along trajectories 722A and 722B to generate an x-ray image of the patient 710 from a new angle.

[0025] FIG. 2A illustrates a volumetric imaging device 202 and a projection 208 according to an embodiment described herein. In one embodiment, the volumetric imaging device includes a source 204 and a detector 206. In one embodiment, the source 204 and the detector 206 of the volumetric imager 202 can be used to track a target and align a patient during radiation therapy. In one embodiment, the volumetric imaging device 202 can be used to take a series of images that can be used to perform tracking and alignment. In another embodiment, the volumetric imaging device 202 can be used in combination with a second imager, such as a stationary 2D X-ray imager (e.g., 209), to perform tracking and alignment. In one embodiment, a projection (e.g., a projection line) 208 is shown extending from the source 204 to the detector 206.

[0026] In one embodiment, the projection data (e.g., from a projection image based on projection rays 208) can include both the lines 208 between the 2D detector 206 positions and the X-ray point source 204, and the detector 206 positions themselves. The projection image 210 in Figure 2B is an example of a projection image. In one embodiment, the correlation model can be fitted by minimizing the distance between the model projected onto the detector 206 and the 2D detector positions and / or by minimizing the distance between the model and the lines 208 between the 2D detector 206 positions and the X-ray point source 204.

[0027] In one embodiment, the projections can be generated by any method that maps a 3D point to a point in a 2D plane. For example, an X-ray point source 204 and a flat-panel detector 206 can be mounted on either side of the body on a rotatable platform or gantry 202 to obtain X-ray projection images of the body from different angles. Each image pixel has a scalar intensity value that approximates the integral of the linear attenuation coefficient encountered along the X-ray as it travels in a line 208 from the X-ray point source 204 to the detector 206. The X-ray imaging system projects the 3D location of the internal anatomical structure to a 2D location in the X-ray projection image. In this embodiment, the 3D location can be internal location data that identifies the location of the internal target region of the patient. As described herein, a projection (or projection data) can refer to both the 2D location in the X-ray projection image and the line 208 between the X-ray point source 204 and the 2D location in the X-ray projection image.

[0028] FIG. 3 illustrates a target (e.g., a tumor) 151 in a patient's body 150 having an internal marker 152 combined with one or more external markers 180 attached to the patient's skin. The one or more external markers 180 attached to the patient's skin allow for the determination of abdominal or chest wall motion 182. In the example of a patient's breathing, the external markers can track the external motion as the patient inhales and exhales. The external markers 180 can be tracked automatically by an external detection device using some optical method, such as infrared or visible light, and the position of the external markers can be determined more than 60 times per second. The external markers can also be attached to a belt, flexible ring, or vest that fits snugly around the patient's waist.

[0029] In one embodiment, if only external markers are used to compensate for patient motion, they may not accurately reflect the internal motion of the target organ, since the external markers may move significantly at the same time as the target organ moves only slightly, and vice versa. Furthermore, the main axis of motion of the external markers is not necessarily the same as the main axis of motion of the internal target. For example, a lung target may have a main axis of motion in the inferior / superior direction of the patient, whereas an external chest marker may have a main axis of motion in the anterior / posterior direction. External markers alone may not be accurate enough to compensate for patient motion. A combination of internal and external markers can be used to accurately track the motion of the target organ. To enable accurate tracking of the motion of the target organ, periodic x-ray imaging of the internal markers (e.g., by an internal detection device) can be correlated with continuous optical tracking of the external markers (by an external tracking device). To correlate the movement of the internal and external markers, a relationship between the positions of the internal and external markers can be determined, which can be done at the beginning of the treatment process and will be described below with reference to Figures 4A-4D. Note that in one embodiment, the Synchrony® respiratory tracking system can be used. Alternatively, other types of tracking systems can be used.

[0030] 4A is a flow diagram illustrating a method 404 for compensating for changes in the position of a target (e.g., a tumor) within a patient related to patient respiration and / or other motion during radiation treatment delivery. In general, the method 404 may be performed by processing logic, which may include hardware (e.g., a processing device, circuitry, dedicated logic, programmable logic, microcode, device hardware, etc.), software (e.g., instructions running or executed on a processing device), or a combination thereof. In some embodiments, the method 404 may be performed by processing logic of the radiation treatment system 1200 of FIG. 1.

[0031] In one embodiment, the first few operations in this method can be performed at a time prior to the actual treatment of the patient. In particular, the surgeon can insert (e.g., establish) a set of internal markers (e.g., fiducials) proximal or internal to the target organ during a short surgical procedure in block 406, and then the surgeon can attach (e.g., establish) an external marker set to the patient's chest or abdominal wall proximate to the target organ immediately prior to treatment in block 408. In one embodiment, the external marks can be optical markers in an optically based system. In another embodiment, other markers can be used. In some embodiments, targets (e.g., fiducials, soft tissue, bone, etc.) can be tracked without inserting an internal marker set. Then, in block 410, a processor of the radiation therapy device, e.g., processor 670 of FIG. 8, having received the position information of the internal and external markers, correlates the positions of the internal and external markers immediately prior to starting treatment of the patient. Methods for correlating the internal and external markers are described below. With the positions of the internal and external markers correlated, treatment of the patient can begin.

[0032] In one embodiment, in block 411, the processing device of the radiation therapy system determines whether the total elapsed time since the internal marker was last imaged is equal to a predetermined period. The predetermined period can be, for example, on the order of a few seconds. Alternatively, other periods can be used. If the total elapsed time is equal to the predetermined period, the treatment beam is stopped in block 416 and the internal marker is imaged, for example, using X-ray imaging. In another embodiment, the treatment beam is not stopped during the acquisition of a new X-ray image. The total elapsed time is then reset to zero in block 418 and the method returns to block 411. Returning to block 411, if the total elapsed time is not equal to the predetermined period, the external marker is tracked in block 412 during which the treatment beam is activated (e.g., the treatment delivery system is controlled) in block 414. The external marker can be tracked such that position data is delivered to the processing device of the radiation therapy system, for example, 60 times per second. Alternatively, other periods can be used. In some embodiments, the system can take an X-ray image when the rotating gantry reaches a predetermined angle. For example, an X-ray image may be taken each time the gantry passes through 0° and 90° in each rotation. In other embodiments, a combination of periods and angles may be used. The processing device may then correlate the positions of the external markers with the positions of the internal markers to generate position data regarding the positional changes of any of the target organs. Thus, between periodic imaging of the internal markers, the positions of the external markers are used to track the position of the target.

[0033] When target movement is detected, the radiation therapy system can compensate for this movement and control radiation therapy delivery in a number of different ways. For example, the treatment system can move the LINAC and / or the patient treatment couch to control the direction of the treatment beam relative to the target. The treatment system can fire or stop the radiation therapy beam to align with the target. The treatment system can shape or collimate the radiation therapy beam, modify the beam energy, or otherwise change the characteristics of the radiation therapy beam.

[0034] An embodiment of the present disclosure can track the 3D position of a target inside a patient using two or more flat-panel X-ray images acquired at different times. The correlation between the moving internal 3D target position and the externally detected motion (e.g., respiration) amplitude is modeled using data acquired from multiple individual flat-panel X-ray images, all acquired at different times. Various mathematical approaches to sequential monoscopic tracking (SMT) can be used, two of which are discussed below. However, the present disclosure is not limited to the two approaches discussed below. In alternative embodiments, other mathematical approaches can be used.

[0035] In one embodiment, the mathematical approach can be visualized as a projection ray from the X-ray source through the target location model (to be determined) onto the panel, and solved for the target location model using linear algebra that minimizes the minimum sum of squares difference between the projection model location on the panel and the actual location detected in 2D (hereinafter referred to as the "projection ray approach"). The model can be a single stationary tumor location (for pseudo-stationary tracking) or a function of the respiration amplitude (for respiration tracking). In the following, this projection ray mathematical approach is described with respect to (1) a helical radiotherapy delivery system using modeling of non-stationary target motion (e.g. due to respiration or cardiac motion), (2) a helical radiotherapy delivery system using modeling of pseudo-stationary target motion, and (3) a radiotherapy delivery system having a target motion tracking system using modeling of non-stationary target motion. However, the present disclosure is not limited to only the approach discussed below. In alternative embodiments, other mathematical approaches can be used.

[0036] The mathematical techniques discussed in this section may relate to helical radiation delivery systems, such as the helical delivery systems mentioned herein. When targets are expected to undergo significant respiratory motion (typically lung, liver, pancreas, breast, and kidney treatments), a model is constructed that allows real-time compensation for target motion instead of using image pairs to perform cyclic corrections. The first input to this model is a stream of 3D positions of external fiducials or markers on a vest worn by the patient. An example of an external marker may be, for example, LEDs on a vest that are tracked at approximately 30 frames per second. The second input is cyclic data from an imaging system, for example, a single projection X-ray image. In particular, as each image is taken, the fiducial (fiducial treatment) or target is localized in the X-ray image with the aid of a digitally reconstructed radiograph (DRR) using a planned computed tomography (CT) that simulates an X-ray projection at a desired angle. From this localization step, a line can be estimated that connects the X-ray source to the fiducial (or target) centroid. This line, together with the source position, is then the second input to the correlation model.

[0037] In the case where the target is in the image, it is trivial to find a line connecting the source to the target, which is simply a line connecting the identified source in the detector plane to the center of gravity of the target. However, for the case with multiple fiducials, obtaining the center of gravity of the fiducial arrangement in the detector plane would produce inaccurate results, since the fiducials may be at different distances from the source.

[0038] The distances of the fiducials from the source cannot be derived accurately within a single projection image, so we instead approximate these distances using the known 3D locations of the fiducials at the nominal alignment. In some embodiments, each fiducial can be tracked individually, and a 3D model corresponding to each tracked fiducial can be generated.

[0039] The position of the fiducial in the nominal alignment state is x j、 Denote j=1...N, the source position as S, and the 3D projection of the fiducials onto the detector plane (the 2D positions are determined by a localization algorithm, and the 3D projection is calculated by giving each fiducial an arbitrary "depth" corresponding to the detector) as f j By writing j is the line S+λ(f j -S) x on j In particular, we can see that the following equation holds: TIFF0007650622000001.tif13150Similarly this P j x j lies perpendicular to this line, i.e. TIFF0007650622000002.tif13150Solving these equations together gives the following equation. TIFF0007650622000003.tif19150This is P j It is thus possible to estimate the mean value as the estimated centre of gravity of the reference configuration. This position, in addition to the source, defines the line that is then sent to the modeller.

[0040] In embodiments where the target does not experience significant respiratory and / or cardiac motion, it may be desirable to perform periodic corrections to the translation of the target to ensure that it remains within acceptable limits, and to be able to measure the rotation of the target. To do so, image pairs can be used, typically two immediately adjacent images acquired with the gantry rotating, subject to some constraints (e.g., the angular separation between the two images must be at least 30 degrees and not more than 150 degrees).

[0041] Because these images taken by the kV snapshot imaging system on the gantry share the down / up direction, the approach described above can be extended to accommodate non-orthogonal angles, as in the case of a robot-based LINAC, as described in more detail below. However, the couch will undergo continuous motion throughout the treatment, and the effect of this motion on the projected fiducial position will vary according to the distance of each fiducial from the x-ray source. Extending the robot-based LINAC approach to account for this couch motion is not straightforward, and therefore an alternative approach is proposed.

[0042] For example, in the general case where there are N images (N equals 2 if the workflow proposed above is used), the camera projection matrix is ​​allowed to vary among these images. In reality, we can assume that the projection matrix remains constant unless bending of the gantry causes significant changes in the camera parameters.

[0043] The standard pinhole camera projection model can be represented by the projection matrix P: TIFF0007650622000004.tif19150

[0044] F is the 3D coordinate system of the camera, f is the focal length (e.g., the distance between the X-ray source and the intersection of the source-detector axis and the detector center), and k u and k vare the inverse pixel dimensions along the x and y image axes, and u 0 and v 0 are the image coordinates of the intersection of the optical axis and the imaging plane, and {x F ,y F ,z F} is the 3D position in the camera coordinate system of the object being projected, and {I jx ,I jy} are the 2D image coordinates of the object as it appears in the projection (in this case the x-ray image).

[0045] Before performing the projection, the object position can be transformed from the imaging coordinate system to the camera coordinate system. This transformation is expressed as R, where the j suffix represents the transformation from the imaging coordinate system to the camera coordinates for image j. j Then, the projection matrix for image j is P j By denoting it as such, the following equation is obtained: TIFF0007650622000005.tif13150c in previous formula j ={c jx c jy c jz} T is a vector representing the couch offset in image j, where x={xyz} T is the object position in the imaging coordinate system, and I j ={I jx I jy} T are the 2D coordinates of the projection of the object on the imaging plane. In homogeneous coordinates, we have a 3 × 4 matrix P j R j T j Writing this gives us the following formula: TIFF0007650622000006.tif19150 and TIFF0007650622000007.tif19150The previous equation can be rewritten as follows: TIFF0007650622000008.tif26150In general, then, for N fields of view, the above equation becomes: TIFF0007650622000009.tif64150

[0046] As long as N>=2, the system is overdetermined and the solution for x can be found using standard matrix minimization techniques, i.e., the general solution is X=(A T A) -1 A T If B, then ∥AX-B∥ 2 This can be found by finding X that minimizes

[0047] In one embodiment, this establishes a framework that allows for the estimated 3D position of each fiducial marker to be determined using its projection location into multiple x-ray images, still using these fiducial marker positions to estimate the 6D corrections (translation and three roll angles) required to map from the nominal alignment position to the current target position.

[0048] In one embodiment, this can be a point-based registration problem. There may be a closed-form solution to find a rigid transformation that maps one set of points to another corresponding set such that the sum of squared distances between these points is minimized. By writing a set of reference positions in image coordinates as X and the corresponding positions in the nominal registration as Y, the translation components are simply TIFF0007650622000010.tif7150 Two 3×N reference coordinate matrices, each with respect to its own center of gravity TIFF0007650622000011.tif7150Using Schönemann's solution and notation, TIFF0007650622000012.tif7150 In this case, TIFF0007650622000013.tif7150Note that both Horn's solution and Schoenemann's solution solve for orthogonal matrices that contain both rotations and reflections. A modification that allows one to find the optimal rotation is to use R=USV if the above solution produces a reflection (i.e., det(R)=-1). T It is possible to find the optimal rotation by using TIFF0007650622000014.tif13150

[0049] To derive the yaw, pitch, and roll rotation angles y, p, and r, the following general rules can be used: Assume that the x-axis corresponds to the patient's inferior-superior axis (and thus the axis about which the gantry rotates), the y-axis corresponds to the patient's left-right axis, and the z-axis corresponds to the patient's anterior-posterior axis. Further assume that these angles are applied in the following order: yaw, pitch, and roll. Writing the sines and cosines of the angles as sy, cy, sp, cp, sr, cr results in the rotation matrix: TIFF0007650622000015.tif19150This formula makes it possible to derive the angle values ​​that follow: TIFF0007650622000016.tif38150

[0050] In another embodiment, the imaging isocenter can be defined as the origin of the image coordinate system. If the geometry of the imaging system has well-defined features, then the positions of the two X-ray sources S A and S B is known and the position and orientation of the X-ray detector can be assumed to be known. Since the pixel size of the X-ray detector is known, any detected reference position can be related to coordinates in the imaging coordinate system located on the detector plane.

[0051] These coordinates, which represent the projection of the fiducial onto the plane of detectors A and B, respectively, are denoted by {F A1 ,F A2 ,…,F AN} and {F B1 ,F B2 ,…,F BN} Furthermore, the true position of criterion i can be written as lying on two lines defined by the following equations: TIFF0007650622000017.tif7150 λ in the previous formula Ai and λ Bi is a scalar parameter that defines the position along the line.

[0052] Ideally the two lines would intersect, but due to uncertainties in the fiducial localization and calibration of the imaging system, the two lines typically do not coincide exactly, and so a method must be found to use these lines to estimate the fiducial position. Because the inferior / superior directions are shared between the two imaging planes, the step of aligning the two projections is as simple as using the inferior / superior fiducial positions projected in both planes as the average of the values ​​from the two planes. With both projections having the same inferior / superior positions, this step becomes a simple inverse projection problem of finding the 3D location that represents the intersection of the two corrected fiducial projections.

[0053] In an alternative embodiment, the mathematical approach can be visualized as fuzzy cones projected from the source onto the panel instead of lines. These cones represent the uncertainty of the actual position detected in 2D by the panel. This uncertainty can be due to errors in the 2D detection (e.g. physical constraints like pixel size, imaging process constraints, etc.). Uncertainty can also be due to patient motion between the times when the images are acquired. Advantages of this approach in some embodiments include adjustable parameters that represent the physical and algorithmic uncertainty of the system, adjustable parameters that represent the rate of deviation from the predicted pseudo-static patient motion or the existing external / internal marker motion model (a time-varying parameter, i.e. the predicted standard deviation of the measurements increases as the images change over time, and older images are effectively weighted lightly), and a statistical confidence metric defined as the likelihood of a given model explaining the actual measured data given the various adjustable uncertainty parameters.

[0054] Rather than correlating respiration amplitude with 3D positions derived from stereoscopic image pairs, embodiments of the present disclosure establish a direct correlation between respiration amplitude and 2D positions detected within sequentially acquired flat-panel images.

[0055] The inside-outside correlation model can be implemented using many different functions, below are some examples: TIFF0007650622000018.tif7150 TIFF0007650622000019.tif13150 TIFF0007650622000020.tif7150 Separate functions for the inhalation and exhalation phases of breathing

[0056] Each of these functions is composed of several parameters: for example, the linear model has 6 parameters that compose its two 3D vectors, while the 5D model has 9 parameters that compose its three 3D vectors.

[0057] Generating an inner-outer correlation model is a problem of selecting a model form and then determining values ​​for all the model parameters that best fit the observed data. Using a pair of simultaneous images that give the 3D measured target positions, this problem can be mathematically stated as follows: Let's say TIFF0007650622000021.tif7150. M(x) with n parameters c 1 ,c 2 ,…c n Let the model function have the following structure: Then, optimize the model parameters to minimize the sum of squares of the differences between the modeled and measured 3D positions: JPEG0007650622000022.jpg7150

[0058] The above describes interior-exterior model construction when the measured 3D target locations are known. In an embodiment of the present disclosure, the interior-exterior correlation model is constructed using only the 2D target locations detected in one flat-panel image at a time. Model generation involves the further generation of 3D model locations relative to the 2D locations on the flat-panel detector. TIFF0007650622000023.tif7150 is a 2D target location on a flat-panel. M(x) with n parameters c 1 ,c 2 ,…cn Let the model function have the following structure: TIFF0007650622000024.tif7150 is a set of functions that projects 3D points in space onto a 2D flat-panel. Then, optimize the model parameters to minimize the sum of squares of the differences between the modeled positions projected onto 2D and the measured 2D positions on the flat-panel: JPEG0007650622000025.jpg7150

[0059] In one embodiment, the above formula is the formula utilized to construct the sequential monoscopic correlation model. In an alternative embodiment, additional modifications can be used to make the algorithm more robust to detect tracking errors. In one embodiment, errors are detected in the model construction because sequential monoscopic modeling cannot rely on shared mutual information found in simultaneous image pairs. Simultaneous image pairs have one common axis, and thus differences in tracking results along this axis indicate some tracking error.

[0060] To detect tracking errors in sequential monoscopic modeling, one can instead give estimates of the expected 2D tracking errors and 3D patient modeling errors and then calculate a statistical confidence metric for the model. Let σ be the predicted error between the projected 2D location in the model and the measured 2D location on the flat-panel. The optimization formula is Change it as TIFF0007650622000026.tif10150. As a result, the standard deviation between the projection model and the actual measured points is given by: JPEG0007650622000027.jpg19150This standard deviation between the model and the measured data can be used to verify whether the model is an adequate fit to the data.

[0061] Model building can be made more robust to changes over time by adjusting the predicted standard deviation between the projected model points and the measured 2D positions based on the age of the measurements. σ 1 is the constant prediction error in 2D position due to tracking accuracy, and σ 2 is the rate at which this prediction error, called the time-varying parameter, increases over time, then σ(Δt)=σ 1 +σ 2 Assume that Δt holds. Next, this model Optimize as TIFF0007650622000028.tif10150. This optimization allows the model to use all available measurements, but more closely fit newer measurements if the patient's breathing pattern changes over time. Although embodiments of the present disclosure are described with respect to their use in modeling respiratory motion, alternative embodiments of the method and apparatus of the present disclosure may be applied to modeling other types of motion, such as cardiac motion. In another embodiment, the present disclosure may also be applied to quasi-static motion of a target.

[0062] In another embodiment, the least squares minimization problem to derive the 3D motion model may be: The motion model f can be optimized to best match the motion of the fiducials in the patient to the 2D fiducial locations detected in the X-ray images. i,j can be the 2D reference location in the i=1..n image for the j=1..m reference. The motion model f is the model of the respiratory amplitude ai and the motion adjusted 3D position s j 3D reference position s for j The function P gi,ci is the gantry angle g corresponding to image i i and bed position c i Given,the motion adjusted 3D position,x,,, we project it to its corresponding 2D position in the,x-ray image.

[0063] In one embodiment, the system uses a fiducial detection algorithm to find the 2D fiducial locations (p i.j ) and then solve for the motion model in the above equation using a solver library. Once the optimal model function is calculated, the 3D location of the target for any breathing amplitude can be predicted within milliseconds. In one example, let a be the breathing amplitude and t be the 3D location of the target within the patient in the absence of motion. The new 3D target location t′ can then be: JPEG0007650622000030.jpg923

[0064] Motion models can take a variety of forms. For example, linear motion can be expressed as f(a i ,sj)=[x 1 ,y 1 ,z 1 ]a i +[x 2 ,y 2 ,z 2 ]+s j In this case, the minimization process involves six variables (x 1 ,y 1 ,z 1 ,x 2 ,y 2 ,z 2 ), thus requiring a minimum of three flat-panel images to build the model. Additional images can be used to improve the robustness and statistical reliability of the model. Alternative motion model equations can be used to model more complex motion paths. For example, the model function can be a higher order polynomial (e.g., cubic) to handle non-linear target motion, or a dual polynomial to handle hysteresis where the motion during inhalation is different from the motion during exhalation.

[0065] In one embodiment, the motion model can include rotations or even non-rigid spatial transformations. Models with more degrees of freedom require a larger minimum number of images to construct, and therefore more additional images to reach the same level of robustness and statistical confidence as linear models.

[0066] In reality, a patient's breathing pattern may not remain constant over time. The system adapts to changes in breathing patterns by re-optimizing the model each time a new image is acquired, taking approximately 1 second to process the image and update the model. In one embodiment, the model adaptation is made more responsive to recent breathing changes by using only the n most recent images and giving heavier weights to more recent images in the minimization objective function. In the present formulation, the weights are specified by increasing the prediction error between the modeled 2D location and the predicted 2D location proportionally to the age of the image.

[0067] In one example, let σ represent the inherent accuracy of 2D fiducial detection on the panel, resulting from, for example, finite pixel size and the accuracy of geometric registration of the kV imaging components. Let σ′ represent the expected rate of change in the patient's breathing pattern over time, and Δt i =t n -t i is the period between the i-th image and the most recent (n-th) image, Δt i σ′ represents how much the breathing pattern in the i-th image deviates from the current model. The equation for minimizing the time-varying motion model is presented below. JPEG0007650622000031.jpg2984

[0068] By including time-lapse changes, sequential monoscopic imaging can effectively track non-respiratory motion as well as respiratory motion. One complication with non-respiratory motion is believed to be the absence of a continuous external signal such as respiratory amplitude to correlate with periodic X-ray images. In one embodiment, the system first has an indication that motion has occurred when the next X-ray image is acquired. Advantageously, to increase responsiveness to target motion, the system can preferentially select position information derived from the most recent image.

[0069] In one embodiment, to model non-respiratory motion using the above model optimization framework, we first define a motion model function that is independent of respiratory amplitude, e.g., a function f(a i ,sj)=[x,y,z]+s j models the static translation of the 3D target and reference positions. The model is then optimized using only the most recent few images, typically as few as two (e.g., n=2). Finally, a time-varying parameter (σ′) can be chosen that matches the speed at which non-respiratory targets are expected to move within the patient (e.g., the prostate is observed to move slowly due to bladder filling throughout treatment). At this point, the model minimization process can accurately calculate the 3D positions of stationary targets and efficiently handle moving targets by preferentially selecting position information from more recent images.

[0070] In one embodiment, the model minimization framework also provides a method to verify the consistency of the model. Stereoscopic imaging can be used to compare the detected positions along the axis shared by two simultaneous images to verify the tracking results. This comparison may not be possible with sequential monoscopic imaging. Instead, the model confidence is calculated as the probability that the optimized motion model is consistent with the 2D detected positions, given the a priori predicted detection accuracy (σ) and image time course (σ′) parameters. This probability can be derived by calculating the area of ​​the chi-squared distribution larger than the value of the optimized motion model. The degrees of freedom of the chi-squared distribution can be twice the number of 2D images multiplied by the number of criteria (2mn). In one embodiment, the model confidence is mathematically expressed as follows: TIFF0007650622000032.tif4392

[0071] 4B is a first flow diagram illustrating a method for sequential monoscopic tracking in a treatment system according to an embodiment. In general, the method 401 can be performed by a processing logic unit that can include hardware (e.g., a processing device, a circuit, a dedicated logic unit, a programmable logic unit, a microcode, a hardware unit of a device, etc.), software (e.g., instructions that run or are executed on a processing device), or a combination thereof. In some embodiments, the method 401 can be performed by a processing logic unit of the radiation treatment system 1200 of FIG. 1.

[0072] Beginning at block 403, processing logic may generate multiple projections of an internal target region within a patient's body. In one embodiment, the multiple projections include projection data (e.g., by one or more internal detection devices) relating to the location of the internal target region of the patient. In one embodiment, the multiple projections are sequentially acquired monoscopic projection images acquired using an imager (e.g., an internal detection device) rotated on a gantry. In one embodiment, the multiple projections are acquired at different times. The internal detection device may generate one projection image data at a time. The internal detection device may generate multiple sequential images based thereon to generate a single projection. In one embodiment, the projection data identifies internal motion of the patient's body, the internal motion including motion of the internal target region. In another embodiment, the projection data identifies internal motion of the patient's body, the internal motion including motion of one or more implanted fiducial markers.

[0073] In block 405, the processing logic generates external position data relating to the external motion of the patient's body using one or more external sensors (e.g., by one or more external detection devices). In block 407, the processing logic generates, by the processing device, a correlation model between the position of the internal target region and the external position data. In one embodiment, the correlation model can be generated by fitting multiple projections (e.g., projection data) of the internal target region to the external position data. In one embodiment, the correlation model identifies an optimal fit of an analytical function to the projection data identified in the multiple projections and the corresponding external position data. In one embodiment, the processing logic generates the correlation model during acquisition of the CBCT scan (or any other type of scan). In block 409, the processing logic estimates the position of the internal target region at a later time using the correlation model. The processing logic can optionally control a radiation treatment delivery system based on the correlation model.

[0074] In one embodiment, to control the radiation therapy delivery system based on the correlation model, the processing device will direct a radiation therapy beam generated by a linear accelerator (LINAC) based on the correlation model. In another embodiment, to control the radiation therapy delivery system based on the correlation model, the processing device will control a collimator of the linear accelerator (LINAC) based on the correlation model. In one embodiment, the collimator is a multi-leaf collimator, and to control the collimator, the processing device will move one or more leaves of the multi-leaf collimator. In another embodiment, to control the radiation therapy delivery system based on the correlation model, the processing device will control a treatment couch. In another embodiment, to control the radiation therapy delivery system based on the correlation model, the processing device will gate a radiation therapy beam generated by a linear accelerator (LINAC) based on the correlation model. In another embodiment, the projection data corresponds to one or more fiducial markers located near the internal target region, and to generate the correlation model, the processing device will calculate a deformation state of the internal target region based on a relative position of the one or more fiducial markers.

[0075] 4C is a second flow diagram illustrating a method for sequential monoscopic tracking in a treatment system according to an embodiment. In general, the method 402 can be performed by processing logic that can include hardware (e.g., a processing device, a circuit, a dedicated logic, a programmable logic, a microcode, a hardware of a device, etc.), software (e.g., instructions that run or execute on a processing device), or a combination thereof. In some embodiments, the method 402 can be performed by processing logic of the radiation treatment system 1200 of FIG. 1.

[0076] In block 411, processing logic sequentially acquires multiple x-ray images of the target using a single imager on a rotating gantry. In an embodiment, the multiple x-ray images are acquired by rotating the single imager around the target. In block 413, processing logic determines, with a processing device, a three-dimensional position of the target using the multiple sequentially acquired x-ray images. In block 415, processing logic optionally controls a radiation treatment delivery system based on the correlation model.

[0077] 4D is a third flow diagram illustrating a method for sequential monoscopic tracking in a treatment system according to an embodiment. In general, the method 404 can be performed by a processing logic unit that can include hardware (e.g., a processing device, a circuit, a dedicated logic unit, a programmable logic unit, a microcode, a hardware unit of a device, etc.), software (e.g., instructions that run or are executed on a processing device), or a combination thereof. In some embodiments, the method 404 can be performed by a processing logic unit of the radiation treatment system 1200 of FIG. 1.

[0078] At block 417, the processing logic generates position data for a target location inside the patient's body. In one embodiment, the processing logic generates the position data by generating multiple projections of the internal target location. At block 419, the processing logic generates external position data for the external motion of the patient's body using one or more external sensors. In one embodiment, the external position data is generated continuously, where "continuously" is used to mean that the external position data is generated more frequently than the "periodically" generated projections. For example, continuously generated external data could mean external position data generated at 30 Hz, whereas periodically generated projection data could be generated every 30 seconds or similarly different time intervals, where the "continuously" generated data is generated several orders of magnitude more frequently than the "periodically" generated data. At block 421, the processing logic causes the processing device to generate a correspondence between the location of the internal target location and the location of the external sensor by fitting a correlation model to the multiple projections of the internal target location and the external position data. Optionally, the internal position data corresponds to one or more fiducial markers located near the internal target region, and in block 423, the processing logic calculates a deformation state of the internal target region based on the relative positions of the one or more fiducial markers to generate a correlation model. In block 425, the processing logic controls the treatment delivery system to direct radiation towards the position of the internal target location in the patient based on the correlation model to compensate for patient motion.

[0079] An embodiment of the present disclosure may be implemented in a portal imaging system 1400 as shown in FIG. 5. In one embodiment, the portal imaging system 1400 may adjust the beam energy of the LINAC during treatment, thereby allowing the LINAC to be used for both x-ray imaging (e.g., generating monoscopic images) and radiation therapy. In another embodiment, the system 1400 may include an on-board kV imaging system for generating x-ray images (e.g., monoscopic images) and a separate LINAC for generating higher energy therapeutic radiation beams. The system 1400 includes a gantry 1410, a LINAC 1420, and a portal imaging detector 1450. The gantry 1410 may be rotated to an angle corresponding to a selected projection and used to obtain x-ray images of a VOI of a patient 1430 on a treatment couch 1440. In an embodiment including a portal imaging system, the LINAC 1420 may generate x-ray beams that pass through a target on the patient 1430 and impinge on the portal imaging detector 1450 to generate an x-ray image of the target. After an x-ray image of the target is generated, the beam energy of the LINAC 1420 can be increased so that the LINAC 1420 can generate a radiation beam to treat the target area of ​​the patient 1430. In another embodiment, a kV imaging system can generate an x-ray beam that passes through a target in the patient 1430 and generates an x-ray image of the target. In some embodiments, a portal imaging system can acquire portal images while treatment is being performed. A portal imaging detector 1450 can measure the exit radiation fluence after the beam passes through the patient 1430, which can allow the location of internal or external fiducials or anatomical structures (e.g., tumors or bones) to be localized within the portal image.

[0080] Alternatively, the kV imaging source or portal imager and method of operation described herein can be used with other types of gantry-based systems. In some gantry-based systems, the gantry rotates the kV imaging source and the LINAC about an axis that passes through the isocenter. The gantry-based system includes a ring gantry having a generally toroidal shape, where the patient's body extends through the bore of the ring / toroid, and the kV imaging source and the LINAC are mounted on the circumference of the ring and rotate about an axis that passes through the isocenter. The gantry-based system can further include a C-arm gantry, where the kV imaging source and the LINAC are cantilever-mounted and rotate about an axis that passes through the isocenter. In another embodiment, the kV imaging source and the LINAC can be used in a robotic arm-based system that includes a robotic arm mounted as discussed above.

[0081] FIG. 6 illustrates a gantry-based intensity modulated radiation therapy (IMRT) system 709 according to an implementation of the present disclosure. In the gantry-based system 709, a radiation source (e.g., LINAC 1201) having a head assembly 701 is mounted on a gantry 703. In one embodiment, the radiation beam 160 can be delivered from several positions on a circular rotational plane (e.g., around a rotational axis). In one embodiment, the system 709 includes a treatment imaging system that can include a kV imaging source 705 and an x-ray detector 707. The kV imaging source 705 can be used to image the patient toward the setting and also generate x-ray images of the ROI by directing a series of x-ray beams to the patient's ROI that are incident on the x-ray detector 707 opposite the kV imaging source 705 to generate images during treatment. The resulting system generates arbitrarily shaped radiation beams 760 that intersect each other at an isocenter to deliver a dose distribution to a target location. In one implementation, the gantry-based system 700 can be a c-arm based system.

[0082] FIG. 7 illustrates a helical radiation delivery system 800 according to an embodiment of the present disclosure. The helical radiation delivery system 800 can include a linear accelerator (LINAC) 810 mounted on a ring gantry 820. The LINAC 810 can be used to generate a narrow intensity modulated pencil beam (i.e., a treatment beam) by directing an electron beam toward an X-ray emitting target. The treatment beam can deliver radiation to a target region (i.e., a tumor). The ring gantry 820 has a generally toroidal shape, through the bore of the ring / toroid a patient 830 extends, and the LINAC 810 is mounted on the periphery of the ring and rotates about an axis passing through the center to irradiate the target region with beams delivered from one or more angles around the patient. A patient 830 on a treatment couch 840 can be moved simultaneously through the bore of the gantry during treatment.

[0083] The helical radiation delivery system 800 includes a therapeutic imaging system that can include a kV imaging source 850 and an X-ray detector 870. The kV imaging source 850 can be used to image the patient 830 toward the setting and generate an X-ray image of a region of interest (ROI) by directing a series of X-ray beams at the patient 830 that are incident on an X-ray detector 870 opposite the kV imaging source 850 to generate an image during treatment. The therapeutic imaging system can further include a collimator 860. In one embodiment, the collimator 860 can be a variable aperture collimator. In another embodiment, the collimator 860 can be a multi-leaf collimator (MLC). The MLC includes a housing that contains multiple leaves that are movable to adjust the aperture of the MLC to enable shaping of the imaging X-ray beam. In another embodiment, the variable aperture collimator 860 can be an aperture collimator that includes a trapezoidal block that moves along a frame in a manner similar to a camera aperture to generate a variable size aperture that enables shaping of the imaging X-ray beam. The kV imaging source 850 and the x-ray detector 870 may be mounted orthogonally (e.g., separated by 90 degrees) to the LINAC 810 on the ring gantry 820 and aligned to project an imaging x-ray beam onto a target area and illuminate an imaging plane of the detector 870 after passing through the patient 130. In some embodiments, the LINAC 810 and / or the kV imaging source 850 may be cantilevered onto a C-arm gantry that rotates them about an axis passing through the isocenter. Aspects of the present disclosure may further be used in other such systems, such as gantry-based LINAC systems, static imaging systems associated with radiation therapy and radiosurgery, proton therapy systems using built-in image guidance, interventional radiation therapy, and intraoperative x-ray imaging systems.

[0084] The helical radiation delivery system 800 further includes a secondary imaging system 801. The imaging system 801 is a CBCT imaging system, such as the medPhoton ImagingRing System. Alternatively, other types of volumetric imaging systems can be used. The secondary imaging system 801 includes a rotatable gantry 807 (e.g., a ring) mounted on an arm and rail system (not shown) that moves the rotatable gantry 807 along one or more axes (e.g., along an axis extending from the head to the foot of the treatment couch 840). The imaging source 803 and the detector 805 are mounted on the rotatable gantry 807. The rotatable gantry 807 can rotate 360 ​​degrees around an axis extending from the head to the foot of the treatment couch. Thus, the imaging source 803 and the detector 805 can be positioned at many different angles. In one embodiment, the imaging source 803 is an x-ray source and the detector 805 is an x-ray detector. In one embodiment, the secondary imaging system 801 includes two independently rotatable rings. The imaging source 803 can be mounted on the first ring and the detector 805 can be mounted on the second ring.

[0085] 8 illustrates an example of a different system 600 within which a set of instructions can be executed to cause the system to perform any one or more of the methodologies discussed herein. In alternative implementations, the machine can be connected (e.g., networked) to other machines in a LAN, an intranet, an extranet, and / or the Internet. Each of these systems can operate as a server or client machine in a client-server network environment, as a peer machine in a peer-to-peer (or distributed) network environment, or as a server or client machine in a cloud computing infrastructure or environment.

[0086] These systems are machines capable of executing (sequentially or otherwise) a set of instructions that specify actions that the machine is to perform. Moreover, although a single machine is illustrated, the term "machine" should be taken to include any collection of machines that individually or collectively execute one or more sets of instructions to perform any one or more of the methodologies discussed herein.

[0087] The exemplary radiation therapy delivery system 110, which may represent the treatment delivery systems 1200, 800, 709, or some other system, includes a processing device 602, a main memory 604 (e.g., read only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), etc.), a static memory 606 (e.g., flash memory, static random access memory (SRAM), etc.), and a data storage device 618, which communicate with each other over a bus 630.

[0088] The processing device 602 represents one or more general-purpose processing devices, such as a microprocessor or central processing unit. The processing device may be the same or different processing device as the processing device 1230 and may represent a processing device within the therapy delivery workstation 150. More specifically, the processing device may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or a processor implementing a combination of instruction sets. The processing device 602 may also be one or more special-purpose processing devices, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor. The processing device 602 is configured to execute instructions 626 for performing the operations and steps discussed herein.

[0089] Computer system 600 may further include a network interface device 608 for communicating over a network 620. Computer system 600 may further include a video display unit 610 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 612 (e.g., a keyboard), a cursor control device 614 (e.g., a mouse), a graphics processing unit 622, a signal generation device 616 (e.g., a speaker), a video processing unit 628, and an audio processing unit 632.

[0090] Data storage device 618 may include a machine-readable storage medium 624 (also known as a computer-readable medium) having stored thereon one or more sets of instructions or software 626 that embody one or more of the methodologies or functions described herein. The instructions 626 may also reside, completely or at least partially, within main memory 604 and / or processing device 602 during execution of the instructions by computer system 600, with main memory 604 and processing device 602 also constituting machine-readable storage media.

[0091] In one implementation, the instructions 626 include an x-ray motion component 699 for performing functions corresponding to those disclosed herein. Although the exemplary implementation shows the machine-readable storage medium 624 as being a single medium, the term "machine-readable storage medium" should be taken to include a single or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) that store one or more sets of instructions. The term "machine-readable storage medium" should also be taken to include any medium that can store or encode a set of instructions for execution by a machine, causing the machine to perform any one or more of the methodologies of the present disclosure. Thus, the term "machine-readable storage medium" should be taken to include, but is not limited to, solid-state memory, optical media, and magnetic media.

[0092] From the foregoing description, it will be apparent that aspects of the present disclosure may be embodied at least in part in software. That is, the techniques of the present disclosure may be executed within a computer system or other data processing system in response to the processing device 625, 640, or 602 (see FIG. 8), for example, executing sequences of instructions contained within a memory. In various implementations, hardware circuitry may be used in combination with software instructions to implement the teachings of the present disclosure. That is, the techniques of the present disclosure are not limited to any specific combination of hardware circuitry and software, or to any particular source for the instructions executed by a data processing system. Additionally, for ease of explanation throughout this specification, various functions and operations may be described as being performed or caused by software code. However, one skilled in the art will recognize that such expressions imply that these functions result from execution of the code by the processing device 625, 640, or 602.

[0093] A machine-readable medium can be used to store software and data that, when executed by a general-purpose or special-purpose data processing system, causes the system to perform various methods of the present disclosure. The executable software and data can be stored in various places, including, for example, system memory and storage or any other device that can store software programs and / or data. Thus, a machine-readable medium includes any mechanism that delivers (i.e., stores) information in a form accessible by a machine (e.g., a computer, a network device, a personal digital assistant, a manufacturing tool, any device having a set of one or more processors, etc.). For example, a machine-readable medium includes recordable / non-recordable media, such as read-only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, etc. A machine-readable medium can be a non-transitory computer-readable storage medium.

[0094] Unless otherwise stated, as is evident from the preceding discussion, terms such as "accept," "position," "execute," "emit," or "cause" can refer to the actions and processes of a computer system or similar electronic computing device that manipulates and transforms data represented as physical (e.g., electronic) quantities in the registers and memory of the computer system into other data similarly represented as physical quantities in the memory or registers of the computer system, other such information storage, or display devices. Implementations of the methods described herein can be implemented using computer software. When written in a programming language conforming to a generally accepted standard, instruction sequences designed to implement these methods can be compiled for execution on a variety of hardware platforms and for interfacing to a variety of operating systems. Additionally, implementations of the present disclosure are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages ​​can be used to implement implementations of the present disclosure.

[0095] It should be noted that the methods and devices described herein are not limited to use only in medical diagnostic imaging and therapy. In alternative implementations, the methods and devices herein can be used in applications outside the medical technology field, such as industrial imaging and non-destructive testing of materials. In such applications, for example, "treatment" may generally refer to the performance of an operation controlled by a treatment planning system, such as the application of a beam (e.g., radiation, sound waves, etc.), and "target" may refer to a non-anatomical object or area.

[0096] In the foregoing specification, the present disclosure has been described with reference to certain exemplary implementations thereof. It will be apparent, however, that various modifications and changes can be made to those exemplary implementations without departing from the broader spirit and scope of the present disclosure as set forth in the appended claims. The specification and drawings are therefore to be regarded in an illustrative rather than a restrictive sense. [Explanation of symbols]

[0097] 150 Patient's Body 151 Target 152 Internal Markers 180 External Marker 182 Abdominal or chest wall movement

Claims

1. 1. A method performed by a processing device of a radiation therapy delivery system, comprising: generating a plurality of two-dimensional projections of an internal target area within a patient's body, the plurality of two-dimensional projections including two-dimensional projection data relating to a two-dimensional position of the internal target area of ​​the patient, each of the plurality of two-dimensional projections being generated sequentially at a different time; generating external position data relating to respiratory amplitude of the body of the patient using one or more external sensors; the processing device fits the plurality of two-dimensional projections of the internal target region, generated sequentially from different positions at different times, to the respiration amplitude to generate a correlation model that minimizes the sum of squares of the differences between the external position data and the two-dimensional position of the internal target region; the processing device estimating a three-dimensional position of the internal target region at a later time using the correlation model; Including, The method of claim 1, wherein the plurality of two-dimensional projections are sequentially acquired monoscopic projection images acquired using an imager rotated on a gantry.

2. The method of claim 1 , wherein the two-dimensional projection data identifies internal motions of the patient's body including motions of the internal target region.

3. The method of claim 1 , wherein the two-dimensional projection data identifies internal motion of the patient's body, including motion of one or more implanted fiducial markers.

4. The method of claim 1 , wherein the multiple two-dimensional projections are acquired at different times.

5. 10. The method of claim 1, further comprising steering a radiation therapy beam generated by a linear accelerator (LINAC) based on the correlation model.

6. 2. The method of claim 1, further comprising controlling a collimator of a linear accelerator (LINAC) based on the correlation model.

7. 7. The method of claim 6, wherein the collimator is a multi-leaf collimator, and to control the multi-leaf collimator, the method includes moving one or more leaves of the multi-leaf collimator.

8. 10. The method of claim 1, further comprising controlling a treatment couch.

9. 10. The method of claim 1, further comprising gating a radiation therapy beam generated by a linear accelerator (LINAC) based on the correlation model.

10. the two-dimensional projection data corresponds to one or more fiducial markers positioned proximate the internal target region; and calculating a deformation state of the internal target region based on the relative positions of the one or more fiducial markers to generate the correlation model.

2. The method of claim 1 .

11. 1. A radiation therapy delivery device comprising: a first detection device for generating two-dimensional projection data relating to an internal target region within a patient's body, the two-dimensional projection data including two-dimensional projections generated sequentially at different times; a second detection device for generating positional data of respiration amplitude relative to one or more sensors external to the body of the patient; a processing device for receiving the two-dimensional projection data for the internal target region and the position data from the sensor, and for generating a correlation model that minimizes the sum of squares of the differences between the position data and the two-dimensional position of the internal target region by fitting the two-dimensional projection data of the internal target region sequentially generated from different positions at different times to the respiration amplitude in order to control a radiation therapy delivery device based on the position data obtained from the sensor, and for estimating the three-dimensional position of the internal target region at a later time point using the correlation model; Including, The apparatus, wherein the plurality of two-dimensional projections are sequentially acquired monoscopic projection images acquired using an imager rotated on a gantry.

12. The apparatus of claim 11 , wherein the first detection device comprises a rotatable single imager.

13. a linear accelerator (LINAC) including an on-board kilovoltage (kV) imager coupled to the gantry; the first detection device is coupled to the gantry; 13. The apparatus of claim 12.

14. The first detection device further comprises: generating a plurality of sequential images; and generating a single projection based on the plurality of sequential images; For the purpose of 12. The apparatus of claim 11 .

15. a linear accelerator (LINAC) coupled to the movable stage; the first detection device includes a cone beam CT (CBCT) imager coupled to a gantry; 12. The apparatus of claim 11 .

16. 16. The apparatus of claim 15, wherein the movable stage is the gantry.

17. 16. The apparatus of claim 15, wherein the movable stage is separate from the gantry.

18. 16. The apparatus of claim 15, wherein the correlation model is generated during acquisition of a scan by the cone beam CT (CBCT) imager.

19. 16. The apparatus of claim 15, wherein the first detection device includes a portal imager.

20. A non-transitory computer-readable medium, comprising: When executed by a processing device of a radiation therapy delivery system, the processing device generating a plurality of two-dimensional projections of an internal target area of ​​a patient, the plurality of two-dimensional projections including two-dimensional projection data relating to a two-dimensional location of the internal target area, each of the plurality of two-dimensional projections being generated sequentially at a different time; generating external position data relating to respiratory amplitude of the body of the patient using one or more external sensors; generating, by the processing device, a correlation model that minimizes the sum of squares of the differences between the external position data and the two-dimensional position of the internal target region by fitting the plurality of two-dimensional projections of the internal target region, generated sequentially from different positions at different times, to the respiratory amplitude; using the correlation model to estimate the three-dimensional location of the internal target region at a later time. An order to do something, Including, 11. A non-transitory computer readable medium, wherein the plurality of two-dimensional projections are sequentially acquired monoscopic projection images acquired using an imager rotated on a gantry.

21. 21. The non-transitory computer readable medium of claim 20, wherein the two-dimensional projection data identifies internal motion of the patient's body including motion of the internal target region.

22. 21. The non-transitory computer readable medium of claim 20, wherein the two-dimensional projection data identifies internal motion of the patient's body including motion of one or more implanted fiducial markers.

23. 1. A method performed by a processing device, comprising: generating a plurality of two-dimensional projections of an internal target area within a patient's body, the plurality of two-dimensional projections including two-dimensional projection data relating to a two-dimensional position of the internal target area of ​​the patient, each of the plurality of two-dimensional projections being generated sequentially at a different time; generating external position data relating to respiratory amplitude of the body of the patient using one or more external sensors; the processing device fits the plurality of two-dimensional projections of the internal target region, generated sequentially from different positions at different times, to the respiration amplitude to generate a correlation model that minimizes the sum of squares of the differences between the external position data and the two-dimensional position of the internal target region; the processing device estimating a three-dimensional position of the internal target region at a later time using the correlation model; determining a three-dimensional location of the internal target region based on the correlation model; Including, The method of claim 1, wherein the plurality of two-dimensional projections are sequentially acquired monoscopic projection images acquired using an imager rotated on a gantry.

24. 24. The method of claim 23, further comprising the processing device controlling a radiation treatment delivery system based on the correlation model.

25. 1. A method performed by a processing device, comprising: generating two-dimensional position data relating to an internal two-dimensional position of a target within a patient's body by generating a plurality of two-dimensional projections of the internal two-dimensional position of the target, the plurality of two-dimensional projections being generated sequentially at different times; said processing device generating external position data relating to respiratory amplitude of said body of said patient using one or more external sensors, said external position data being generated more frequently than said plurality of two-dimensional projections; the processing device fits the plurality of two-dimensional projections of the internal two-dimensional position of the target, generated sequentially from different positions at different times, to the respiration amplitude to generate a correlation model that minimizes the sum of squares of the differences between the external position data and the internal two-dimensional position of the target; the processing device controlling a treatment delivery system to direct radiation toward the three-dimensional location of the target based on the correlation model to compensate for motion of the patient; Including, The method of claim 1, wherein the plurality of two-dimensional projections are sequentially acquired monoscopic projection images acquired using an imager rotated on a gantry.

26. the two-dimensional position data corresponds to one or more fiducial markers positioned proximate an internal two-dimensional position of the target; generating the correlation model includes calculating a deformation state of the internal target location based on the relative positions of the one or more fiducial markers; 26. The method of claim 25.

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