Patent Description:
Embodiments of the present disclosure relate to an optical camera based tracking system for a multi-leaf collimator (MLC) used in radiation treatment systems and, in particular, to suppressing effects of ambient light entering the MLC housing on the camera based tracking system.

Collimators are frequently used in radiation treatment for shaping a beam of highly energized particles, referred to as a treatment beam. Some radiation treatment systems use a variable aperture collimator called a multi-leaf collimator (MLC). A multi-leaf collimator is a collimator that is made up of a collection of individual leaves that can move independently in and out of the path of the treatment beam. For conformal radiation treatments, the MLC enables conformal shaping of the treatment beam. Accordingly, an image-based feedback can be used to ensure accurate placement of the individual leaves.

Prior art document <CIT> discloses a collimating arrangement for use in radiotherapy apparatus, which a set of mutually contiguous members for substantially attenuating radiation. The positions of the members are adjustable so that edges thereof form an adjustable aperture for defining the cross-sectional shape of the beam. A lamp illuminates an area including the beam-defining edges. Each of the members is provided with a respective retroreflector for reflecting illumination from the lamp into the camera substantially independently of the position of the beam-defining edges. A detecting arrangement, including a video camera, more responsive to the higher intensity of illumination reflected from the retroreflectors than from respectively adjacent regions in the illuminated area, is used to detect the positions of the retroreflectors which are respectively representative of the positions of the edges of the members.

Prior art document <CIT> discloses a multi-leaf collimator for a radiation beam the particles of which is selected from the group consisting of high energy photons, electrons, protons and heavy ions which are emitted from a small effective radiation source. The collimator comprises low height, elongated, curved, in cross section wedge-shaped leaves arranged side by side in opposed pairs. The configuration is such that the parts of the surfaces intersecting the irradiation field will always be directed generally towards the radiation source. A protective casing surrounding a frame on which the collimator is mounted can be filled with helium gas to optimize the collimator for use with electron and photon beams in the energy range from <NUM> up to <NUM> MeV. The individual settings of the leaves are supervised by a TV-camera which also can be used to set the leaves in predetermined positions.

The present disclosure is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings.

The method according to the present invention is defined by claim <NUM>. The radiation system according to the present invention is defined by claim <NUM>.

Described herein is a method for suppressing effects of ambient light on a camera based tracking system. In one embodiment, to enable visual tracking and verification of MLC leaf positions in linear accelerator of a radiation treatment system, a camera-based leaf positon feedback system having a camera located inside the collimator housing, is implemented. The camera-based leaf tracking system operates by detecting leaf features (e.g., notch, edge, protrusion, etc.) visible on images of leaf surface. Because the camera is located inside the collimator housing where lighting is minimal, an LED lighting system located inside the housing is used to illuminate the surface of the leaves with light. External light may enter the collimator housing and reflect off the sides of the leaves on the inside of the aperture. This creates undesirable illumination in the aperture, including near the leading edges of the leaves that shape the aperture. Such illumination can make visual tracking and verification of leaf position difficult.

Embodiments of the present disclosure describe ambient light suppressing techniques that may include white balancing operations, determining color saturation, producing an ambient light attenuation map and applying the ambient light attenuation map to the original image. Various embodiments allow for an accurate visual verification of the location of the leaves by reducing undesired illumination throughout the aperture and, in particular, near the leading edges of the leaves that shape the aperture. In addition, these embodiments may be applied to other types of imaging systems that are subject to ambient light effects. In one embodiment, the camera-based feedback system may be a primary feedback system. In another embodiment, the camera-based feedback system may be a secondary feedback system.

It should be noted that while certain embodiments may be described herein in relation to white light, the method described herein may also be used with any characterized light source (knowing the primary color wavelength). The color may be selected based on the operating environment in order to maximize color separation between the internal system and ambient light. The color selection may be done at the time of installation of the LINAC <NUM> at a treatment facility or at the time of manufacturing. For example, if a lighting system is also installed in the treatment room, a desired internal color could be selected for the installation. Alternatively, at the time of installation of LINAC <NUM>, a sensor could be held in the MLC aperture (e.g., illustrated by aperture <NUM>) with MLC lighting system <NUM> turned off to measure the color of the ambient light in the MLC. In this embodiment, which is not part of the claimed invention, any color deemed too close to the measured color (e.g., using a metric such as the Delta-E metric used by the International Commission on Illumination (CIE) to compute the most different color, or an L2 distance in RGB/chromaticity space) would be considered ambient. This may eliminate a need for a white balancing operation (e.g., operation <NUM> of <FIG>) discussed below that may be performed in accordance with one embodiment. In one particular embodiment, to maximize system performance, the ambient lighting conditions of a radiation treatment room, in which the MLC housing is used, may be analyzed to determine the most dissimilar MLC lighting color (wavelength) which may be performed in various ways. In one embodiment, if the ambient light is BLUE ([<NUM>,<NUM>,<NUM>]) in RGB space, for example, the optimal internal light may be YELLOW ([<NUM>,<NUM>,<NUM>]) which is a combination of RED and GREEN resulting in the largest separation along the three independent color axes. Another way to quantify the separation is in Hue, Saturation and Value (HSV) space where the first channel is hue/chrome. <FIG> and <FIG> illustrate different views of embodiments of components of a radiation treatment system having a robot based linear accelerator (LINAC) <NUM> with a changeable collimator <NUM>. In one embodiment, the radiation treatment system <NUM> includes a radiation treatment robot having a LINAC <NUM> mounted on a robotic arm <NUM>. Collimator housing <NUM> may contain any one of various types of collimators (e.g., an iris collimator, an MLC, etc.) of different apertures that may be detachably mounted to the LINAC <NUM>. The different collimators may reside in collimator table <NUM>, where the radiation treatment robotic may be moved to pick up and drop off collimators based on the collimator type. The particular aperture is matched to the specifics of a radiation treatment plan. In the embodiments described below, collimator housing <NUM> represents a collimator housing that includes a MLC. In alternative embodiments, the methods described herein may be used with other types of variable aperture collimators and other types of radiation treatment systems (e.g., gantry based LINAC treatment systems).

<FIG> illustrates a cross sectional side view 1B-1B' taken from <FIG> of MLC housing <NUM>. MLC housing <NUM> includes an internal (e.g., LED) lighting system <NUM>, including LED lights <NUM> and diffusers <NUM>, that is used to illuminate the interior of the MLC housing <NUM>. The camera feedback system utilizes a camera <NUM> to capture live images of the individual leaf locations of the MLC banks (e.g., <NUM>). It should be noted that camera <NUM> may include optics and an image sensor including other electronics associated with camera <NUM>, located farther away from the beam path so as not to be effected by the radiation beam. Camera <NUM>, and its associated electronics, and lighting system <NUM>, may be part of the camera feedback system that is connected to the digital processing system <NUM>. Digital processing system <NUM> is further described in relation to <FIG> with respect to the implementation of the operations described in relation to <FIG>.

According to the claimed invention, the radiation treatment system comprises the MLC disposed within the housing, the lighting system disposed within the housing and the imaging system having optics situated inside the housing.

During operation of the LINAC <NUM>, ambient light <NUM> enters the MLC housing <NUM> (represented by vector <NUM>) through an aperture between two banks of leaves in the MLC housing <NUM>. It should be noted that since <FIG> is a cross section side view of the MLC housing <NUM>, only one of the banks of leaves is shown in the figure. The aperture is an opening between two banks of leaves that allows for the passage of radiation beams generated by the LINAC <NUM>, as illustrated in <FIG>. As the ambient light <NUM> passes through the aperture formed by the MLC bank <NUM> and opposing bank <NUM>, it reflects off the sides of the leaves on the inside of the aperture.

<FIG> illustrates a view from inside the MLC housing <NUM> as captured by the camera <NUM> of <FIG>. As the ambient light <NUM> enters the MLC housing <NUM> it reflects off the sides of the leaves of the MLC bank <NUM> on the inside of the aperture in the general region indicated by the dashed ellipse <NUM>. It should be noted that the aperture may be the region formed by the central, leading edges of the leaves of the MLC banks <NUM> and <NUM>. <FIG> illustrates the undesired illumination <NUM>. near the central, leading edges of the leaves of the MLC banks <NUM> and <NUM> caused by the ambient light <NUM>. The undesired illumination <NUM> (whose shape is more illustrative of the aperture generally identified by reference numeral <NUM>) can affect the ability of the camera feedback system to determine and verify the location of the individual leaves of the MLC banks <NUM> and <NUM> by creating visual patterns in the aperture that may falsely be identified as a trackable leaf feature (which, in one embodiment, may be a notch in the leaf but can also be other types of features such as an edge of a leaf) and by changing the appearance of the image near the actual trackable leaf feature.

<FIG> illustrates an image <NUM> of the inside surface of the MLC housing <NUM> from the perspective of the optical imaging system. Ambient light enters through the aperture <NUM> (whose reference numeral arrow pointing to the central area formed by the leading edges of the leaves of MLC bank <NUM>), illuminating the metallic surfaces located inside the MLC housing <NUM> of <FIG>. Here, ambient light is shown as more greatly illuminating the lower area <NUM> of aperture <NUM>.

<FIG> illustrates a flow diagram of a method <NUM> for suppressing effects of ambient light in an imaging system. Method <NUM> is described in relation to the suppression of ambient light entering MLC housing and affecting an optical camera based imaging system used to track MLC leaf positions. However, it should be understood that method <NUM> may also be used to suppress ambient light in other imaging systems that are subject to ambient light effects and, in particular, where ambient light is constrained in a region and the region is not intended to be illuminated or it is desirable to force the affected region to be dark. The method <NUM> may be performed by processing logic that comprises hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions run on a processing device to perform hardware simulation), or a combination thereof.

At block <NUM>, processing logic captures an image of the MLC banks surface and aperture using the feedback system camera <NUM>. At block <NUM>, in one embodiment, optional white balancing operations may be performed if the camera and the internal lighting system do not already result in mechanical surfaces (e.g., leaves of MLC banks) within the collimator housing appearing grayscale (approximately colorless) when viewed from the camera with only internal light present. As the lens of camera <NUM> discolors and becomes more opaque and the performance of the image sensor of camera <NUM> changes due to the radiation environment in which the camera operates, a change in color can occur. A white balancing operation may be performed to compensate for such radiation induced changes in the camera system.

In one embodiment, a white balance operation may be performed, for example, if the internal lighting system of the MLC housing <NUM> is not approximately gray or mapped to gray by some operations. To perform white balancing, a region of interest (ROI) is selected, where the ROI is a mechanical surface of the MLC banks <NUM>, <NUM> illuminated only by the internal lighting system. For example, an ROI may be designated as the visible surface area of the leaves of MLC bank <NUM> with a closed aperture (e.g., when the leaves are in a location that prevents ambient light from entering the housing). Following the selection of the ROI, an average RGB pixel value, v, is calculated over all pixels located within the ROI, resulting in an average red pixel value, average green pixel value and an average blue pixel value. For example, in an ROI of two pixels, pixel A has RGB values of [<NUM>; <NUM>; <NUM>] and pixel B has RGB values of [<NUM>; <NUM>; <NUM>]. The resultant average RGB pixel value is [<NUM>; <NUM>; <NUM>]. It should be noted that the pixels' value provided herein are only examples solely intended to aid in explanation of the methods described herein. Although an embodiment using RGB space is discussed herein, in other embodiments, other ways may be used to achieve the effect of minimizing average color saturation for an ROI that may not involve the RGB color space.

Using the average RGB pixel value, a scalar intensity factor, s, is calculated. In one embodiment, the scalar intensity factor includes the average of the three values of the average RGB pixel value. Using the previous example with an average RGB pixel value of [<NUM>; <NUM>; <NUM>], the scalar intensity factor would be the average of these three values, resulting in a scalar intensity factor of <NUM>. The values of the average RGB pixel value, v, and the scalar intensity factor, s, may be used to calculate a white balance parameter vector, g. The white balance parameter vector is calculated as the inverse of the average RGB pixel value multiplied by the scalar intensity factor, as shown in the equation below. <MAT> For example, using the values calculated above, where s = <NUM> and v = [<NUM>; <NUM>; <NUM>], the resulting calculation yields a white balance parameter vector of g = <NUM> / [<NUM>; <NUM>; <NUM>] or [<NUM>; <NUM>; <NUM>]. The white balance parameter vector may be applied by pixel-wise multiplication to correct the pixels in subsequent images (as such, the white balance operation <NUM> may consist only of applying the computed white balance, which may be computed less frequently to account for changes in the system hardware). It should be noted that although there is a single correction g, it is applied to all pixels in subsequent images. For example, given a pixel of RGB values [<NUM>; <NUM>; <NUM>], the corrected pixel RGB values would be [<NUM>*<NUM>; <NUM>*<NUM>; <NUM>*<NUM>] = [<NUM>; <NUM>; <NUM>]. The result is that the corrected pixel has a saturation of zero (i.e., the same values for red, green and blue) and the same average intensity over the three color channels. This gives the appearance of the leaves and other metal surfaces inside the MLC housing <NUM> as nearly being grayscale (i.e., having a small computed color saturation value). In some embodiments, a different algorithm may be used for the white balancing operation. Examples may be, but are not limited to, normalizing pixel intensities by their L2-norm, perceptual intensity or a metric other than the average RGB value. In one alternative embodiment, the metric may be computed in a different color space, for example, if the camera <NUM> has a native color space other than RGB. In another embodiment, a camera hardware-based white balance operation may be used when settings are obtained based on an ROI with no ambient light.

At block <NUM> of method <NUM>, processing logic determines a saturation map for the image captured at block <NUM>. In one embodiment, a saturation map is a representation of the color saturation of each pixel in an image. In another embodiment, the white balancing step may be omitted and the saturation map may be a representation of a distance in color space between each pixel's color and a reference color (i.e. a well-characterized color pertaining to the appearance of the MLC leaf surfaces illuminated by the internal lighting system as captured by the camera in the absence of ambient light).

At block <NUM> of method <NUM> a series of morphological and non-linear transformations are applied to the saturation map (or a map of distance in color space to a reference color), S, to produce an attenuation map, A, to suppress ambient light. In one embodiment, the saturation is modulated by pixel brightness using equation <NUM>, shown below, where V is an intensity (brightness) map. <MAT> Using the result of equation <NUM>, a sharp non-linear attenuation curve is created using equation <NUM>, shown below. The desired sharp non-linear attenuation curve may initially have a high slope value with the slope value decreasing rapidly when a specified saturation value is reached. The result is an attenuation curve that highly attenuates pixels with high saturation values due to ambient light, while not attenuating pixels that represent leaf locations. <MAT> An alternative to the method described in <NUM> is to compute the attenuation map as <NUM> / (<NUM>+S). Alternatively, the first step of <NUM> (S = S * (<NUM>+V)) could be skipped, using the saturation map "directly" as an input to the cubic function.

The cubic coefficients (a, b, c, d) may be determined empirically or computed for a certain type of ambient light (e.g., by fitting a curve to a desired response, where the x axis is saturation and the y axis is the inverse of desired attenuation) to obtain the desired attenuation curve or response for a certain type of ambient light. In this way, the sensitivity of the attenuation can be tuned to ambient light with a certain distribution of saturation value. In another embodiment, any type of suitable transfer function may be used, such as an exponential or higher order polynomial or combination of transcendental functions. A morphological operation (i.e., grayscale opening or erosion of the attenuation map, A) is performed to reduce noise and high frequency artifacts in the corrected image. The use of the morphological operation may be helpful if the source image has a large amount of noise or when the noise of color channels is independent. The ambient light attenuation map is then applied to the original image via pixel-wise multiplication (V = V * A). At block <NUM>, a final image is provided with ambient light being suppressed.

It should be noted that the above described operations are just one method of suppressing effects of ambient light and that, in alternative embodiments, certain ones of the operations of <FIG> may be optional or take a simpler form, for example, by use of a linear transformation instead of a non-linear transformation in block <NUM>.

<FIG> illustrates the final image <NUM> created at block <NUM> for the image <NUM> shown in <FIG> where the darker regions signify higher attenuation values relative to the lighter regions that signify lower attenuation values. The ambient light attenuation map determined at block <NUM> of <FIG> is applied to the entire original image <NUM> through pixel-wise multiplication (V = V *A). The ambient light attenuation map highly attenuates pixels with high saturation values caused by ambient light, while minimally attenuating pixels that represent leaf locations (that were appropriately white balanced via block <NUM> or by other technique). The resulting image <NUM> shows the suppression of ambient light (along with a geometric correction), entering the MLC housing <NUM> through the aperture <NUM>, for example as more prominently seen in lower area <NUM> as compared to lower area <NUM> of <FIG>. With the ambient light suppressed, the camera-based feedback system can more reliably verify and track the location of the individual leaves of the MLC bank <NUM>.

<FIG> illustrates one embodiment of systems that may be used in performing radiation treatment. These systems may be used to perform, for example, the methods described above. As described below and illustrated in <FIG>, a system <NUM> may include a diagnostic imaging system <NUM>, a treatment planning system <NUM>, a treatment delivery system <NUM> and a motion detecting system (not shown). In one embodiment, the diagnostic imaging system <NUM> and the motion detecting system are combined into a single unit.

Diagnostic imaging system <NUM> may be any system capable of producing medical diagnostic images of a patient that may be used for subsequent medical diagnosis, treatment planning, treatment simulation and/or treatment delivery. For example, diagnostic imaging system <NUM> may be a computed tomography (CT) system, a magnetic resonance imaging (MRI) system, a positron emission tomography (PET) system, or the like. For ease of discussion, diagnostic imaging system <NUM> may be discussed below at times in relation to an x-ray imaging modality. In other embodiments, other imaging modalities such as those discussed above may also be used.

In one embodiment, diagnostic imaging system <NUM> includes an imaging source <NUM> to generate an imaging beam (e.g., x-rays) and an imaging detector <NUM> to detect and receive the beam generated by imaging source <NUM>, or a secondary beam or emission stimulated by the beam from the imaging source (e.g., in an MRI or PET scan).

In one embodiment, imaging source <NUM> and imaging detector <NUM> may be coupled to a digital processing system <NUM> to control the imaging operation and process image data. In one embodiment, diagnostic imaging system <NUM> may receive imaging commands from treatment delivery system <NUM>.

Diagnostic imaging system <NUM> includes a bus or other means <NUM> for transferring data and commands among digital processing system <NUM>, imaging source <NUM> and imaging detector <NUM>. Digital processing system <NUM> may include one or more general-purpose processors (e.g., a microprocessor), special purpose processor such as a digital signal processor (DSP) or other type of device such as a controller or field programmable gate array (FPGA). Digital processing system <NUM> may also include other components (not shown) such as memory, storage devices, network adapters and the like. Digital processing system <NUM> may be configured to generate digital diagnostic images in a standard format, such as the Digital Imaging and Communications in Medicine (DICOM) format, for example. In other embodiments, digital processing system <NUM> may generate other standard or non-standard digital image formats. Digital processing system <NUM> may transmit diagnostic image files (e.g., the aforementioned DICOM formatted files) to treatment delivery system <NUM> over a data link <NUM>, which may be, for example, a direct link, a local area network (LAN) link or a wide area network (WAN) link such as the Internet. In addition, the information transferred between systems may either be pulled or pushed across the communication medium connecting the systems, such as in a remote diagnosis or treatment planning configuration. In remote diagnosis or treatment planning, a user may utilize embodiments of the present disclosure to diagnose or treat a patient despite the existence of a physical separation between the system user and the patient.

In one embodiment, treatment delivery system <NUM> includes a therapeutic and/or surgical radiation source <NUM> to administer a prescribed radiation dose to a target volume in conformance with a treatment plan. Treatment delivery system <NUM> may also include imaging system <NUM> to perform computed tomography (CT) such as cone beam CT, and images generated by imaging system <NUM> may be two-dimensional (2D) or three-dimensional (3D).

Treatment delivery system <NUM> may also include a digital processing system <NUM> to control radiation source <NUM>, receive and process data from diagnostic imaging system <NUM> and/or treatment planning system <NUM>, and control a patient support device such as a treatment couch <NUM>. Digital processing system <NUM> may be connected to or a part of the camera feedback system described above and operate on the images captured by camera <NUM> of <FIG>. Digital processing system <NUM> may be configured to register 2D radiographic images received from diagnostic imaging system <NUM>, from two or more stereoscopic projections, with digitally reconstructed radiographs (DRRs) generated by digital processing system <NUM> in diagnostic imaging system <NUM> and/or DRRs generated by processing device <NUM> in treatment planning system <NUM>. Digital processing system <NUM> may include a processing device that represents one or more general-purpose processors (e.g., a microprocessor), special purpose processor such as a digital signal processor (DSP) or other type of device such as a controller or field programmable gate array (FPGA). The processing device of digital processing system <NUM> may be configured to execute instructions to perform treatment delivery operations, for example, the method <NUM> described above in regards to <FIG>.

In one embodiment, digital processing system <NUM> includes system memory that may include a random access memory (RAM), or other dynamic storage devices, coupled to a processing device, for storing information and instructions to be executed by the processing device. The system memory also may be used for storing temporary variables or other intermediate information during execution of instructions by the processing device. The system memory may also include a read only memory (ROM) and/or other static storage device for storing static information and instructions for the processing device.

Digital processing system <NUM> may also include a storage device, representing one or more storage devices (e.g., a magnetic disk drive or optical disk drive) for storing information and instructions. The storage device may be used for storing instructions for performing the treatment delivery steps discussed herein. Digital processing system <NUM> may be coupled to radiation source <NUM> and treatment couch <NUM> by a bus <NUM> or other type of control and communication interface.

Digital processing system <NUM> may implement methods to manage timing of diagnostic x-ray imaging in order to maintain alignment of a target with a radiation treatment beam delivered by the radiation source <NUM>.

In one embodiment, the treatment delivery system <NUM> includes an input device <NUM> and a display <NUM> connected with digital processing system <NUM> via bus <NUM>. The display <NUM> can show trend data that identifies a rate of target movement (e.g., a rate of movement of a target volume that is under treatment). The display can also show a current radiation exposure of a patient and a projected radiation exposure for the patient. The input device <NUM> can enable a clinician to adjust parameters of a treatment delivery plan during treatment.

Treatment planning system <NUM> includes a processing device <NUM> to generate and modify treatment plans and/or simulation plans. Processing device <NUM> may represent one or more general-purpose processors (e.g., a microprocessor), special purpose processor such as a digital signal processor (DSP) or other type of device such as a controller or field programmable gate array (FPGA). Processing device <NUM> may be configured to execute instructions for performing simulation generating operations and/or treatment planning operations discussed herein.

Treatment planning system <NUM> may also include system memory <NUM> that may include a random access memory (RAM), or other dynamic storage devices, coupled to processing device <NUM> by bus <NUM>, for storing information and instructions to be executed by processing device <NUM>. System memory <NUM> also may be used for storing temporary variables or other intermediate information during execution of instructions by processing device <NUM>. System memory <NUM> may also include a read only memory (ROM) and/or other static storage device coupled to bus <NUM> for storing static information and instructions for processing device <NUM>.

Treatment planning system <NUM> may also include storage device <NUM>, representing one or more storage devices (e.g., a magnetic disk drive or optical disk drive) coupled to bus <NUM> for storing information and instructions. Storage device <NUM> may be used for storing instructions for performing the treatment planning steps discussed herein.

Processing device <NUM> may also be coupled to a display device <NUM>, such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information (e.g., a 2D or 3D representation of the VOI) to the user. An input device <NUM>, such as a keyboard, may be coupled to processing device <NUM> for communicating information and/or command selections to processing device <NUM>. One or more other user input devices (e.g., a mouse, a trackball or cursor direction keys) may also be used to communicate directional information, to select commands for processing device <NUM> and to control cursor movements on display <NUM>.

Treatment planning system <NUM> may share its database (e.g., data stored in storage <NUM>) with a treatment delivery system, such as treatment delivery system <NUM>, so that it may not be necessary to export from the treatment planning system prior to treatment delivery. Treatment planning system <NUM> may be linked to treatment delivery system <NUM> via a data link <NUM>, which in one embodiment may be a direct link, a LAN link or a WAN link.

It should be noted that when data links <NUM>, <NUM>, and <NUM> are implemented as LAN or WAN connections, any of diagnostic imaging system <NUM>, treatment planning system <NUM> and/or treatment delivery system <NUM> may be in decentralized locations such that the systems may be physically remote from each other. Alternatively, any of diagnostic imaging system <NUM>, treatment planning system <NUM>, and/or treatment delivery system <NUM> may be integrated with each other in one or more systems.

<FIG> and <FIG> illustrate configurations of image-guided radiation treatment systems <NUM> and <NUM>, in accordance with embodiments of the present disclosure. In the illustrated embodiments, the radiation treatment systems <NUM> and <NUM> include a linear accelerator (LINAC) <NUM> that acts as a radiation treatment source, and an MLC housing <NUM>. In one embodiment, the LINAC <NUM> and MLC housing <NUM> are mounted on the end of a robotic arm <NUM> having multiple (e.g., <NUM> or more) degrees of freedom in order to position the LINAC <NUM> and MLC housing <NUM> to irradiate a pathological anatomy (e.g., target <NUM>) with beams delivered from many angles, in many planes, in an operating volume around a patient. Treatment may involve beam paths with a single isocenter, multiple isocenters, or with a non-isocentric approach. Alternatively, other types of image guided radiation treatment (IGRT) systems may be used. In one alternative embodiment, the LINAC <NUM> and MLC housing <NUM> may be mounted on a gantry based system to provide isocentric beam paths. In one particular embodiment, the IGRT system is the Vero SBRT System (referred to as TM200 in Japan), a joint product of Mitsubishi Heavy Industries Ltd. , of Tokyo Japan and BrainLAB AG of Germany, that utilizes a rigid O-ring based gantry.

In one embodiment, the LINAC <NUM> and MLC housing <NUM> may be positioned at multiple different nodes (predefined positions at which the robot stops and radiation may be delivered) during treatment by moving the robotic arm <NUM>. At the nodes, the LINAC <NUM> can deliver one or more radiation treatment beams to a target. The nodes may be arranged in an approximately spherical distribution about a patient. The particular number of nodes and the number of treatment beams applied at each node may vary as a function of the location and type of pathological anatomy to be treated. For example, the number of nodes may vary from <NUM> to <NUM>, or more preferably <NUM> to <NUM> nodes and the number of beams may vary from <NUM> to <NUM>, or more preferably <NUM> to <NUM>.

Referring to <FIG>, radiation treatment system <NUM>, in accordance with one embodiment of the present disclosure, includes an imaging system <NUM> having a processor <NUM> connected with x-ray sources 703A and 703B and fixed x-ray detectors 704A and 704B. Alternatively, the x-ray sources 703A, 703B and/or x-ray detectors 704A, 704B may be mobile, in which case they may be repositioned to maintain alignment with the target <NUM>, 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 sources are not point sources, but rather x-ray source arrays, as would be appreciated by the skilled artisan. In one embodiment, LINAC <NUM> serves as an imaging source (whether gantry or robot mounted), where the LINAC power level is reduced to acceptable levels for imaging.

Imaging system <NUM> may perform computed tomography (CT) such as cone beam CT, and images generated by imaging system <NUM> may be two-dimensional (2D) or three-dimensional (3D). The two x-ray sources 703A and 703B may be mounted in fixed positions on the ceiling of an operating room and may be aligned to project x-ray imaging beams from two different angular positions (e.g., separated by <NUM> degrees) to intersect at a machine isocenter (referred to herein as a treatment center, which provides a reference point for positioning the patient on a treatment couch <NUM> during treatment) and to illuminate imaging planes of respective detectors 704A and 704B after passing through the patient. In one embodiment, imaging system <NUM> provides stereoscopic imaging of the target <NUM> and the surrounding volume of interest (VOI). In other embodiments, imaging system <NUM> may include more or less than two x-ray sources and more or less than two detectors, and any of the detectors may be movable rather than fixed. In yet other embodiments, the positions of the x-ray sources and the detectors may be interchanged. Detectors 704A and 704B may be fabricated from a scintillating material that converts the x-rays to visible light (e.g., amorphous silicon), and an array of CMOS (complementary metal oxide silicon) or CCD (charge-coupled device) imaging cells that convert the light to a digital image that can be compared with a reference image during an image registration process that transforms a coordinate system of the digital image to a coordinate system of the reference image, as is well known to the skilled artisan. The reference image may be, for example, a digitally reconstructed radiograph (DRR), which is a virtual x-ray image that is generated from a 3D CT image based on simulating the x-ray image formation process by casting rays through the CT image.

Referring to <FIG>, in alternative embodiments an imaging system <NUM> includes a motion detection device <NUM> to determine target motion, the motion detecting device <NUM> having a detection field <NUM>. The motion detecting device <NUM> may detect external patient motion (such as chest movement during respiration) that occurs within an <NUM>. The motion detecting device <NUM> can be any sensor or other device capable of identifying target movement. The motion detecting device <NUM>, may be, for example an optical sensor such as a camera, a pressure sensor, an electromagnetic sensor, or some other sensor that can provide motion detection without delivering ionizing radiation to a user (e.g., a sensor other than an x-ray imaging system). In one embodiment, the motion detecting device <NUM> acquires measurement data indicative of target motion in real-time. Alternatively, the measurement data may be acquired at a frequency that is higher (potentially substantially higher) than can be achieved or than is desirable with x-ray imaging (due to ionizing radiation delivered to the patient with each x-ray image). In one embodiment, the motion detecting device <NUM> does not provide a high absolute position accuracy. Instead, the motion detecting device <NUM> may provide sufficient relative position accuracy to detect patient movement and/or target movement.

In one embodiment, the motion detecting device <NUM> is an optical system, such as a camera. The optical system may track the position of light-emitting diodes (LEDs) situated on patient <NUM>. Alternatively, the optical system may directly track a surface region of patient <NUM>, as distinguished from tracking LEDs on the patient. There may be a correlation between movement of the target and movement of the LEDs and/or surface region of the patient <NUM>. Based on the correlation, when motion of the LEDs and/or surface region is detected, it can be determined that the target <NUM> has also moved sufficiently to require another diagnostic x-ray image to precisely determine the location of the target.

<FIG> illustrates one embodiment of a gantry based (isocentric) intensity modulated radiotherapy (IMRT) system <NUM>. In a gantry based system <NUM>, a radiation source (e.g., a LINAC) <NUM> having a head assembly <NUM> and MLC housing <NUM> are mounted on the gantry in such a way that they rotate in a plane corresponding to an axial slice of the patient. Radiation is then delivered from several positions on the circular plane of rotation. In IMRT, the shape of the radiation beam is defined by a multi-leaf collimator (MLC) that allows portions of the beam to be blocked, so that the remaining beam incident on the patient has a pre-defined shape. The resulting system generates arbitrarily shaped radiation beams that intersect each other at the isocenter to deliver a dose distribution to the target. In one embodiment, the gantry based system <NUM> may be a c-arm based system.

It will be apparent from the foregoing description that aspects of the present disclosure may be embodied, at least in part, in software. That is, the techniques may be carried out in a computer system or other data processing system in response to its processor, such as digital processing system <NUM>, for example, executing sequences of instructions contained in a memory. In various embodiments, hardware circuitry may be used in combination with software instructions to implement the present disclosure. Thus, the techniques are not limited to any specific combination of hardware circuitry and software or to any particular source for the instructions executed by the data processing system. In addition, throughout this description, various functions and operations may be described as being performed by or caused by software code to simplify description. However, those skilled in the art will recognize what is meant by such expressions is that the functions result from execution of the code by a processor or controller, such as digital processing system <NUM>.

A machine-readable medium can be used to store software and data which when executed by a general purpose or special purpose data processing system causes the system to perform various methods of the present disclosure. This executable software and data may be stored in various places including, for example, system memory and storage or any other device that is capable of storing software programs and/or data. Thus, a machine-readable medium includes any mechanism that provides (i.e., stores) information in a form accessible by a machine (e.g., a computer, network device, personal digital assistant, manufacturing tool, any device with 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..

Unless stated otherwise as apparent from the foregoing discussion, it will be appreciated that terms such as "processing," "computing," "generating," "comparing" "determining," "calculating," "performing," "identifying," or the like may 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 within the computer system's registers and memories into other data similarly represented as physical within the computer system memories or registers or other such information storage or display devices. Embodiments of the methods described herein may be implemented using computer software. If written in a programming language conforming to a recognized standard, sequences of instructions designed to implement the methods can be compiled for execution on a variety of hardware platforms and for interface to a variety of operating systems. In addition, embodiments of the present disclosure are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement embodiments of the present disclosure.

Claim 1:
A method comprising:
illuminating, with a lighting system (<NUM>) disposed inside a housing (<NUM>) of a multi-leaf collimator MLC, leaves of the MLC with a first light;
receiving ambient light inside the housing (<NUM>) of the MLC through an aperture (<NUM>) of the MLC;
capturing, using an imaging system (<NUM>) having optics situated inside the housing (<NUM>) of the MLC, a first image of the leaves of the MLC illuminated with the first light and the ambient light;
suppressing, by a processing device (<NUM>), the effect of ambient light in the first image using a reference image of the leaves of the MLC illuminated by the internal lighting system and captured using the imaging system in the absence of ambient light to generate a second image of the leaves of the MLC; and
detecting a feature of the leaves of the MLC in the second image.