SYSTEMS AND METHODS FOR DYNAMIC MULTILEAF COLLIMATOR TRACKING

The present disclosure provides systems and methods for dynamic multileaf collimator (MLC) tracking. A method may include identifying a plurality of working leaves of the MLC at a control point; determining, for the control point, a signal acquisition region of an electronic portal imaging device (EPID) based on a plurality of planned position trajectories of the plurality of working leaves, wherein the signal acquisition region is part of an imaging plane of the EPID and includes a plurality of acquisition rows; and obtaining an image from the EPID at the control point, wherein the image includes information acquired in the signal acquisition region.

TECHNICAL FIELD

The present disclosure generally relates to radiation technologies, and more particularly, to systems and methods for dynamic multileaf collimator (MLC) tracking in radiation.

BACKGROUND

Radiation is widely used in imaging and treatment, e.g., cancer treatment and several other health conditions. MLC plays an important role in dynamic conformal radiation therapy. The movement precision of the MLC is desired to ensure the efficacy of the radiation therapy, especially for a stereotactic radiosurgery (SRS), a stereotactic body radiation therapy (SBRT), etc. Thus, it may be desirable to provide systems and methods for dynamic MLC tracking.

SUMMARY

According to an aspect of the present disclosure, a system for dynamic MLC tracking is provided. The system may include at least one storage device storing executable instructions, and at least one processor in communication with the at least one storage device. When executing the executable instructions, the at least one processor may cause the system to perform one or more of the following operations. The operations may include identifying a plurality of working leaves of the MLC at a control point; determining, for the control point, a signal acquisition region of an electronic portal imaging device (EPID) based on a plurality of planned position trajectories of the plurality of working leaves, wherein the signal acquisition region is part of an imaging plane of the EPID and includes a plurality of acquisition rows; and obtaining an image from the EPID at the control point, wherein the image includes information acquired in the signal acquisition region.

In some embodiments, the operations may further include determining a sampling rate of the EPID at the control point based on a planned speed profile of each of the plurality of working leaves, wherein the image is captured by the EPID using the sampling rate at the control point.

In some embodiments, the determining, for a control point, a signal acquisition region of an EPID includes: obtaining leaf information of the plurality of working leaves based on a planned position trajectory of each of the plurality of working leaves; and determining a start acquisition row and an end acquisition row of the plurality of acquisition rows based on the leaf information of the plurality of working leaves and EPID information of the EPID.

In some embodiments, the leaf information of the plurality of working leaves includes information of a start working leaf, information of an end working leaf, position information of a center leaf of the MLC, and a projection width of a leaf projected upon an isocenter plane of the MLC.

In some embodiments, the EPID information of the EPID includes a pixel size of the EPID, an image size of an image captured by the EPID, a source image distance (SID), and an offset value of a center of the EPID with respect to a beam central axis at the control point.

In some embodiments, the determining a sampling rate of the EPID at the control point includes: obtaining a maximum leaf speed of a leaf among all leaves of the MLC; obtaining a maximum sampling rate of the EPID; obtaining, among the plurality of working leaves, a maximum working leaf speed of a working leaf at the control point based on a planned speed profile of each of the plurality of working leaves; and determining the sampling rate of the EPID at the control point based on the maximum leaf speed, the maximum sampling rate of the EPID, and the maximum working leaf speed at the control point.

In some embodiments, the plurality of planned position trajectories of the plurality of working leaves are determined based on a treatment plan.

In some embodiments, the plurality of planned speed profiles of the plurality of working leaves are determined based on a treatment plan.

In some embodiments, the operations may further include: obtaining a plurality of images each of which corresponds to data acquired by the EPID in the signal acquisition region using the sampling rate at one of a plurality of control points; and for each of the plurality of working leaves, determining a measured position trajectory and a measured speed profile of the each working leaf based on the plurality of images; determining a position trajectory error of the each working leaf based on the planned position trajectory and the measured position trajectory of the each working leaf; and determining a speed profile error of the each working leaf based on the planned speed profile and the measured speed profile of the each working leaf.

In some embodiments, the determining a measured position trajectory and a measured speed profile of each of the plurality of working leaves based on the plurality of images includes: obtaining a plurality of corrected images by correcting, based on an image correction algorithm, the plurality of images; and determining the measured position trajectory and the measured speed profile of the each working leaf based on the plurality of corrected images.

In some embodiments, the determining the measured position trajectory and the measured speed profile of the each working leaf based on the plurality of corrected images includes: for each of the plurality of working leaves, obtaining pixel positions with respect to the each working leaf in each of the plurality of corrected images; determining a field position corresponding to each pixel position based on a predetermined mapping relationship between the pixel positions and the field positions; and obtaining the measured position trajectory and the measured speed profile of the each working leaf based on the field position corresponding to each pixel position and a time synchronization signal, wherein the time synchronization signal is configured to synchronize a first measured time of the measured position trajectory with a first planned time of the planned position trajectory, and synchronize a second measured time of the measured speed profile with a second planned time of the planned speed profile.

According to another aspect of the present disclosure, a method for dynamic MLC tracking is provided. The method may include identifying a plurality of working leaves of the MLC at a control point; determining, for the control point, a signal acquisition region of an electronic portal imaging device (EPID) based on a plurality of planned position trajectories of the plurality of working leaves, wherein the signal acquisition region is part of an imaging plane of the EPID and includes a plurality of acquisition rows; and obtaining an image from the EPID at the control point, wherein the image includes information acquired in the signal acquisition region.

According to still another aspect of the present disclosure, a non-transitory readable medium is provided. The non-transitory readable medium may include at least one set of instructions for dynamic MLC) tracking. When executed by at least one processor of an electrical device, the at least one set of instructions directs the at least one processor to perform a method. The method may include identifying a plurality of working leaves of the MLC at a control point; determining, for the control point, a signal acquisition region of an electronic portal imaging device (EPID) based on a plurality of planned position trajectories of the plurality of working leaves, wherein the signal acquisition region is part of an imaging plane of the EPID and includes a plurality of acquisition rows; and obtaining an image from the EPID at the control point, wherein the image includes information acquired in the signal acquisition region.

DETAILED DESCRIPTION

The term “image” in the present disclosure is used to collectively refer to image data (e.g., scan data, projection data) and/or images of various forms, including a two-dimensional (2D) image, a three-dimensional (3D) image, a four-dimensional (4D), etc. The term “pixel” and “voxel” in the present disclosure are used interchangeably to refer to an element of an image. The term “anatomical structure” in the present disclosure may refer to gas (e.g., air), liquid (e.g., water), solid (e.g., stone), cell, tissue, organ of a subject, or any combination thereof, which may be displayed in an image (e.g., a second image, or a first image, etc.) and really exist in or on the subject's body. The term “region,” “location,” and “area” in the present disclosure may refer to a location of an anatomical structure shown in the image or an actual location of the anatomical structure existing in or on the subject's body, since the image may indicate the actual location of a certain anatomical structure existing in or on the subject's body.

Provided herein are systems and components for non-invasive imaging and/or treatment, such as for disease diagnosis, treatment or research purposes. In some embodiments, the systems may include a radiotherapy (RT) system, a computed tomography (CT) system, an emission computed tomography (ECT) system, an X-ray photography system, a positron emission tomography (PET) system, a magnetic resonance imaging (MRI) system, or the like, or any combination thereof. For illustration purposes, the disclosure describes systems and methods for radiotherapy. The term “image” used in this disclosure may refer to a 2D image, a 3D image, or a 4D image. In some embodiments, the term “image” may refer to an image of a region, e.g., a region of interest (ROI), of a patient. The term “region of interest” or “ROI” used in this disclosure may refer to a part of an image along a line, in two spatial dimensions, in three spatial dimensions, or any of the proceeding as they evolve as a function of time. The image may be an Electronic Portal Imaging Device (EPID) image, a CT image, a fluoroscopy image, an ultrasound image, a PET image, or an MR image. This is not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, a certain number of variations, changes, and/or modifications may be deduced under the guidance of the present disclosure. Those variations, changes, and/or modifications do not depart from the scope of the present disclosure.

An aspect of the present disclosure relates to systems and methods for dynamic MLC tracking. The systems and methods may use an electronic portal imaging device (EPID) to track positions of leaves of the MLC at a control point during a radiotherapy treatment on or radiation based imaging of a subject. The systems and methods may determine a region (a part of the imaging plane) on an imaging plane of the EPID at the control point. The EPID may only acquire imaging data of the region along a direction parallel to movement directions of the leaves of the MLC, rather than acquiring imaging data of the whole imaging plane. In some embodiments, working leaves may be identified that are used for controlling the shape of the radiation beam at a control point. In some embodiments, a start acquisition row and an end acquisition row of the region may be determined based at least in part on planned position trajectories of the working leaves. In some embodiments, a sampling rate of the EPID at the control point may be determined based on planned speed profiles of the working leaves that are used for controlling the shape of the radiation beams at the control point. By controlling the EPID to acquire the imaging data on the region using the sampling rate at the control point, the amount of imaging data may be reduced and a processing speed may be improved, thereby reducing motion blur effects of the MLC and improving a measurement accuracy of the working leaves during the dynamic MLC tracking.

FIG.1is a schematic diagram illustrating an exemplary RT system100according to some embodiments of the present disclosure. The RT system100may include an RT device110, a network120, one or more terminals130, a processing device140, and a storage device150. In some embodiments, two or more components of the RT system100may be connected to and/or communicate with each other via a wireless connection (e.g., the network120), a wired connection, or a combination thereof. The connection between the components of the RT system100may be variable. Merely by way of example, the RT device110may be connected to the processing device140through the network120or directly. As a further example, the storage device150may be connected to the processing device140through the network120or directly.

The RT device110may be configured to deliver a radiotherapy dose to a subject. For example, the RT device110may deliver one or more radiation beams to a treatment region (e.g., a tumor) of a subject for causing an alleviation of the subject's symptom. A radiation beam may include a plurality of radiation beamlets. In the present disclosure, “subject” and “object” are used interchangeably. The subject may include any biological subject (e.g., a human being, an animal, a plant, or a portion thereof) and/or a non-biological subject (e.g., a phantom). For example, the subject may include a specific portion of a body, such as the head, the thorax, the abdomen, or the like, or a combination thereof, of the subject. In some embodiments, the RT device110may be an image-guided radiation therapy (IGRT) device, a conformal radiation therapy device, an intensity-modulated radiation therapy (IMRT) device, an intensity-modulated arc therapy (IMAT) device, an emission guided radiation therapy (EGRT), a stereotactic radiosurgery (SRS), a stereotactic body radiation therapy (SBRT), or the like.

In some embodiments, the RT device110may include a treatment radiation source111, an electronic portal imaging device (EPID)112, a couch113, a gantry114, and a collimator assembly115. The treatment radiation source111may be configured to emit treatment radiations towards the subject. In some embodiments, the treatment radiation source111may be mounted on the gantry114. The couch113may be configured to support the subject to be treated and/or imaged. In some embodiments, the couch113may be movable relative to the gantry114.

The EPID112may be configured to acquire an image of the subject and/or the collimator assembly115. In some embodiments, the EPID112may be mounted on the gantry114and rotate with the gantry114. In some embodiments, the EPID112may include an imaging plane116and a detector (not shown inFIG.1). In some embodiments, the detector may acquire signals from the whole or part of the imaging plane116. In some embodiments, the detector may include one or more detector units. The detector unit(s) may include a scintillation detector (e.g., a cesium iodide detector, a gadolinium oxysulfide detector), a solid detector, a liquid ionization chamber, or the like, or any combination thereof. In some embodiments, an imaging radiation source of the EPID may be the treatment radiation source111.

In some embodiments, the EPID112may be at any position associated with the Source Image Distance (SID). For example, the EPID112may be at100cm SID (i.e., a distance between the treatment radiation source111and the imaging plane116of the EPID may be 100 cm). In some embodiments, a center of the treatment radiation source111and a center of the imaging plane116may align. For example, a center of the treatment radiation source111and a center of the imaging plane116aligning indicates that an offset between the center of the treatment radiation source111and the center of the imaging plane116is less than a distance threshold (e.g., 1 mm, 2 mm, etc.).

The collimator assembly115may be configured to control the shape of the treatment radiations generated by the treatment radiation source111. In some embodiments, the collimator assembly115may include a multi-leaf collimator (MLC) situated in an MLC plane and at least one jaw situated in a jaw plane other than the MLC plane. The MLC may include at least one first group of leaves and at least one second group of leaves opposing each other and being moveable to form an aperture corresponding to a treatment field by blocking pathways of a first portion of the radiation beam within the treatment field. A second portion of the radiation beam may impinge on a radiation target or a portion thereof located in the treatment field. In some embodiments, a gap may exist between the projection of the at least one jaw along a direction of the radiation beam and the treatment field. The at least one jaw may shield or block a part of the first portion of the radiation beam. The MLC and/or the at least one jaw may be made of a radiation-impermeable material. Exemplary radiation-impermeable materials may include tungsten, lead, steel, or the like, or an alloy thereof, or a combination thereof. In some embodiments, a projection of the at least one jaw along the direction of the radiation beam may partially overlap the treatment field, i.e., forming an aperture corresponding to the treatment field together with the MLC.

The network120may include any suitable network that can facilitate the exchange of information and/or data for the RT system100. In some embodiments, one or more components (e.g., the RT device110, the terminal(s)130, the processing device140, the storage device150, etc.) of the RT system100may communicate information and/or data with one or more other components of the RT system100via the network120. For example, the processing device140may obtain image data from the RT device110(e.g., the EPID112of the RT device110) via the network120. As another example, the processing device140may obtain user (e.g., a doctor, a radiologist) instructions from the terminal(s)130via the network120. The network120may be or include a public network (e.g., the Internet), a private network (e.g., a local area network (LAN)), a wired network, a wireless network (e.g., an 802.11 network, a Wi-Fi network), a frame relay network, a virtual private network (VPN), a satellite network, a telephone network, routers, hubs, switches, server computers, and/or any combination thereof. For example, the network120may include a cable network, a wireline network, a fiber-optic network, a telecommunications network, an intranet, a wireless local area network (WLAN), a metropolitan area network (MAN), a public telephone switched network (PSTN), a Bluetooth™ network, a ZigBee™ network, a near field communication (NFC) network, or the like, or any combination thereof. In some embodiments, the network120may include one or more network access points. For example, the network120may include wired and/or wireless network access points such as base stations and/or internet exchange points through which one or more components of the RT system100may be connected to the network120to exchange data and/or information.

The terminal(s)130may enable user interaction between a user and the RT system100. In some embodiments, the terminal(s)130may be connected to and/or communicate with the RT device110, the processing device140, and/or the storage device150. For example, the terminal(s)130may display a treatment image of the subject obtained from the processing device140. In some embodiments, the terminal(s)130may include a mobile device131, a tablet computer132, a laptop computer133, or the like, or any combination thereof. In some embodiments, the mobile device131may include a smart home device, a wearable device, a mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. Merely by way of example, the terminal(s)130may include a mobile device as illustrated inFIG.3. In some embodiments, the smart home device may include a smart lighting device, a control device of an intelligent electrical apparatus, a smart monitoring device, a smart television, a smart video camera, an interphone, or the like, or any combination thereof. In some embodiments, the wearable device may include a bracelet, footwear, eyeglasses, a helmet, a watch, clothing, a backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the mobile device may include a mobile phone, a personal digital assistant (PDA), a gaming device, a navigation device, a point of sale (POS) device, a laptop, a tablet computer, a desktop, or the like, or any combination thereof. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, virtual reality glasses, a virtual reality patch, an augmented reality helmet, augmented reality glasses, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or the augmented reality device may include a Google Glass™, an Oculus Rift™, a Hololens™, a Gear VR™, etc. In some embodiments, the terminal(s)130may be part of the processing device140.

The processing device140may process information obtained from the RT device110, the terminal(s)130, and/or the storage device150. For example, the processing device140may identify a plurality of working leaves of the MLC at a control point and obtain a plurality of planned position trajectories and/or a plurality of planned speed profiles of the plurality of working leaves. The processing device140may determine a signal acquisition region (a part of the imaging plane) of the EPID112based on the plurality of planned position trajectories for the control point. As another example, the processing device140may determine a sampling rate of the EPID112at the control point based on the planned speed profiles of the plurality of working leaves. As still another example, the processing device140may obtain an image captured by the EPID112using the sampling rate at the control point. In some embodiments, the processing device140may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processing device140may be local or remote. For example, the processing device140may access information stored in the RT device110, the terminal(s)130, and/or the storage device150via the network120. As another example, the processing device140may be directly connected to the RT device110, the terminal(s)130and/or the storage device150to access stored information. In some embodiments, the processing device140may be implemented on a cloud platform. Merely by way of example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof. In some embodiments, the processing device140may be implemented by a computing device200having one or more components as illustrated inFIG.2.

In some embodiments, the storage device150may be connected to the network120to communicate with one or more other components (e.g., the RT device110, the processing device140, the terminal(s)130) of the RT system100. One or more components of the RT system100may access the data and/or instructions stored in the storage device150via the network120. In some embodiments, the storage device150may be directly connected to or communicate with one or more other components (e.g., the RT device110, the processing device140, the terminal(s)130) of the RT system100. In some embodiments, the storage device150may be part of the processing device140.

It should be noted that the above description regarding the RT system100is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations and modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure. In some embodiments, the RT system100may include one or more additional components and/or one or more components of the RT system100described above may be omitted. For example, the RT device110may further include an imaging component for positioning the radiation target or a portion thereof. The imaging component may include a computed tomography (CT) device (e.g., a cone beam CT (CBCT) device, a fan beam CT (FBCT) device, a multi-slice CT (MSCT) device, etc.), a magnetic resonance imaging (MM) device, an ultrasound imaging device, a fluoroscopy imaging device, a single-photon emission computed tomography (SPECT) device, a positron emission tomography (PET) device, an X-ray imaging device, or the like, or any combination thereof.

FIG.2is a schematic diagram illustrating exemplary hardware and/or software components of a computing device200according to some embodiments of the present disclosure. The computing device200may be used to implement any component of the RT system100as described herein. For example, the processing device140and/or the terminal(s)130may be implemented on the computing device200, respectively, via its hardware, software program, firmware, or a combination thereof. Although only one such computing device is shown, for convenience, the computer functions relating to the RT system100as described herein may be implemented in a distributed fashion on a number of similar platforms, to distribute the processing load. As illustrated inFIG.2, the computing device200may include a processor210, a storage device220, an input/output (I/O)230, and a communication port240.

Merely for illustration, only one processor is described in the computing device200. However, it should be noted that the computing device200in the present disclosure may also include multiple processors, thus operations and/or method operations that are performed by one processor as described in the present disclosure may also be jointly or separately performed by the multiple processors. For example, if in the present disclosure the processor of the computing device200executes both operation A and operation B, it should be understood that operation A and operation B may also be performed by two or more different processors jointly or separately in the computing device200(e.g., a first processor executes operation A and a second processor executes operation B, or the first and second processors jointly execute operations A and B).

The storage device220may store data obtained from one or more components of the RT system100. In some embodiments, the storage device220may include a mass storage device, a removable storage device, a volatile read-and-write memory, a read-only memory (ROM), or the like, or any combination thereof. In some embodiments, the storage device220may store one or more programs and/or instructions to perform exemplary methods described in the present disclosure. For example, the storage device220may store a program to be executed by the processing device140to determine signal acquisition regions and sampling rates of the EPID112. As another example, the storage device220may store a program to be executed by the processing device140to cause the EPID112to capture images based on the signal acquisition regions and sampling rates. As still another example, the processing device140may store a program to be executed by the processing device140to determine whether the MLC is moved according to a planned speed profile and/or a planned position trajectory.

The I/O230may input and/or output signals, data, information, etc. In some embodiments, the I/O230may enable a user interaction with the processing device140. In some embodiments, the I/O230may include an input device and an output device. The input device may include alphanumeric and other keys that may be input via a keyboard, a touch screen (for example, with haptics or tactile feedback), a speech input, an eye tracking input, a brain monitoring system, or any other comparable input mechanism. The input information received through the input device may be transmitted to another component (e.g., the processing device140) via, for example, a bus, for further processing. Other types of the input device may include a cursor control device, such as a mouse, a trackball, or cursor direction keys, etc. The output device may include a display (e.g., a liquid crystal display (LCD), a light-emitting diode (LED)-based display, a flat panel display, a curved screen, a television device, a cathode ray tube (CRT), a touch screen), a speaker, a printer, or the like, or a combination thereof.

The communication port240may be connected to a network (e.g., the network120) to facilitate data communications. The communication port240may establish connections between the processing device140and the RT device110, the terminal(s)130, and/or the storage device150. The connection may be a wired connection, a wireless connection, any other communication connection that can enable data transmission and/or reception, and/or any combination of these connections. The wired connection may include, for example, an electrical cable, an optical cable, a telephone wire, or the like, or any combination thereof. The wireless connection may include, for example, a Bluetooth™ link, a Wi-Fi™ link, a WiMax™ link, a WLAN link, a ZigBee™ link, a mobile network link (e.g., 3G, 4G, 5G), or the like, or a combination thereof. In some embodiments, the communication port240may be and/or include a standardized communication port, such as RS232, RS485, etc. In some embodiments, the communication port240may be a specially designed communication port. For example, the communication port240may be designed in accordance with the digital imaging and communications in medicine (DICOM) protocol.

FIG.3is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary mobile device300according to some embodiments of the present disclosure. In some embodiments, one or more terminals130and/or a processing device140may be implemented on a mobile device300, respectively.

FIG.4is a block diagram illustrating an exemplary processing device140according to some embodiments of the present disclosure. As shown inFIG.4, the processing device140may include an identifying module410, a region determining module420, a sampling rate determining module430, an image obtaining module440, and a trajectory error determining module450.

The identifying module410may be configured to identify a plurality of working leaves of the MLC at a control point. As used herein, the plurality of working leaves of the MLC at the control point refer to leaves that form an aperture corresponding to a treatment filed at the control point.

The region determining module420may be configured to determine a signal acquisition region of the EPID112. For example, the region determining module420may determine, for the control point, the signal acquisition region of the EPID112based on a plurality of planned position trajectories of the plurality of working leaves. In some embodiments, the signal acquisition region may be a region on the imaging plane116of the EPID112, from which one or more detectors of the EPID112acquire signals. For example, the signal acquisition region may be part of the imaging plane116of the EPID112.

The sampling rate determining module430may be configured to determine a sampling rate of the EPID112. In some embodiments, the sampling rate determining module430may determine the sampling rate of the EPID112at the control point based on a planned speed profile of each of the plurality of working leaves. For example, the sampling rate determining module430may obtain a maximum leaf speed of a leaf among all leaves of the MLC and a maximum sampling rate of the EPID112. As another example, the sampling rate determining module430may obtain, among the plurality of working leaves, a maximum working leaf speed of a working leaf at the control point based on a planned speed profile of each of the plurality of working leaves. As still another example, the sampling rate determining module430may determine the sampling rate of the EPID112at the control point based on the maximum leaf speed, the maximum sampling rate of the EPID, and the maximum working leaf speed at the control point.

The image obtaining module440may be configured to obtain an image from the EPID112at the control point. For example, the image obtaining module440may obtain the image include information acquired from the signal acquisition region using the sampling rate from the EPID112

The trajectory error determining module450may be configured to determine a position trajectory error and/or a speed profile error of the each working leaf. For example, the trajectory error determining module450may determine the position trajectory error of the each working leaf based on a planned position trajectory and a measured position trajectory of the each working leaf. As another example, the trajectory error determining module450may determine the speed profile error based on a planned speed profile and a measured speed profile of the each working leaf.

It should be noted that the above descriptions of the processing device140are provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, various modifications and changes in the forms and details of the application of the above method and system may occur without departing from the principles of the present disclosure. In some embodiments, the processing device140may include one or more other modules and/or one or more modules described above may be omitted. For example, the processing device140may also include a transmission module configured to transmit signals (e.g., electrical signals, electromagnetic signals) to one or more components (e.g., the RT device110, the terminal(s)130, the storage device150) of the RT system100. As a further example, the processing device140may include a storage module (not shown) used to store information and/or data (e.g., a treatment plan, images, etc.) associated with the dynamic MLC tracking. Additionally or alternatively, two or more modules may be integrated into a single module and/or a module may be divided into two or more units. For example, the region determining module420and the sampling rate determining module430may be combined as an EPID parameter determining module to determine the signal acquisition regions and sampling rates of the EPID. As another example, the trajectory error determining module450may be divided into a position trajectory error determining unit and a speed profile error determining unit to determine the position trajectory error and the speed profile error of a leaf of the MLC, respectively. However, those variations and modifications also fall within the scope of the present disclosure.

FIG.5is a flowchart illustrating an exemplary process500for dynamic MLC tracking according to some embodiments of the present disclosure. In some embodiments, process500may be executed by the RT system100. For example, the process500may be implemented as a set of instructions (e.g., an application) stored in a storage device (e.g., the storage device150, the storage device220, and/or the storage390). In some embodiments, the processing device140(e.g., the processor210of the computing device200, the CPU340of the mobile device300, and/or one or more modules illustrated inFIG.4) may execute the set of instructions and may accordingly be directed to perform the process500.

In510, the processing device140(e.g., the identifying module410) may identify a plurality of working leaves of the MLC at a control point.

In some embodiments, during a radiotherapy session of the subject, the RT device110(e.g., the treatment radiation source111, the EPID112, the gantry114, the collimator assembly115) may rotate around the subject using a plurality of rotation angles relative to an axis perpendicular to the couch113. In some embodiments, each control point may correspond to a rotation angle among the plurality of rotation angles. In some embodiments, each control point may correspond to a treatment field.

In some embodiments, the MLC may include at least one first group of leaves and at least one second group of leaves opposing each other and being moveable to form an aperture corresponding to a treatment field by blocking pathways of a first portion of the radiation beam within the treatment field. A second portion of the radiation beam may impinge on a radiation target (e.g., a subject or a portion thereof) located in the treatment field. In some embodiments, the subject may include a biological subject (e.g., a human, an animal), a non-biological subject (e.g., a phantom), or the like, or a combination thereof. For example, the subject may include a patient. As another example, the subject may include a specific portion, such as the chest, a breast, and/or the abdomen of the patient. In some embodiments, the radiation target may be an anatomical structure. For example, the radiation target may include an organ, tissue, a blood vessel, or the like, or a combination thereof, of the subject.

As used herein, a plurality of working leaves of the MLC at the control point refer to leaves that form the aperture corresponding to the treatment filed at the control point. In some embodiments, before the radiotherapy session is performed on the subject (e.g., days or weeks before the treatment commences), a treatment plan of the subject may be determined. For example, the treatment plan may be determined based on a planned image of the subject captured by an imaging device (e.g., the EPID112or an imaging component of the RT device110). Using the planned image, one or more radiation targets of the subject may be identified and located. In some embodiments, the treatment plan may describe at least one treatment field to be applied to the subject at each of the plurality of control points. For example, the treatment plan may include a planned fraction duration, a planned radiation dose, a planned radiation energy delivery direction, a planned beam shape of a radiation beam, a planned cross-sectional area of the radiation beam at a specific location along the direction that the radiation beam travels, a planned region of interest (ROI) (e.g., the radiation target in the subject), or the like, or any combination thereof.

In some embodiments, the processing device140may identify the plurality of working leaves based on the treatment plan. For example, the processing device140may identify leaves that form the planned beam shape of the radiation beam as the plurality of working leaves. In some embodiments, the processing device140may identify a position (or a sequence number) of each working leaf among the plurality of working leaves. The sequence number and the position of a working leaf may be determined mutually.

In520, the processing device140(e.g., the region determining module420) may determine, for the control point, a signal acquisition region of the EPID112based on a plurality of planned position trajectories of the plurality of working leaves.

In some embodiments, the signal acquisition region may be a region on the imaging plane116of the EPID112, from which one or more detectors of the EPID112acquire signals. For example, the signal acquisition region may be part of the imaging plane116of the EPID112. The signal acquisition region may at least include image information of the treatment field shaped by the plurality of working leaves of the MLC. In some embodiments, the one or more detectors may scan data on the imaging plane116row by row to acquire the signals for imaging. A direction of the row scanning may be parallel to a moving direction of the working leaves of the MLC. In some embodiments, the signal acquisition region may include a plurality of acquisition rows. The plurality of acquisition rows may include a start acquisition row and an end acquisition row by the scanning time. The start acquisition row may be a first row from which the one or more detectors of the EPID112acquire signals. The end acquisition row may be a last row from which the one or more detectors of the EPID112acquire signals. For example, the imaging plane116may be divided into20rows along the direction that is within the plane the leaves of the MLC are arranged and perpendicular to the moving direction of the working leaves. For instance, the leaves of the MLC are arranged in an X-Y plane and working leaves move along the X direction to form an aperture conforming to a treatment field, the imaging plane116may be divided into20rows along the Y direction. The leaves of the MLC may be numbered from the first to the twentieth. The plurality of acquisition rows may include10rows. The start acquisition row may be the fifth row, and the end acquisition row may be the fourteenth row among the20rows. The signal acquisition region may be a rectangular region between the fifth row and the fourteenth row, and two borders the imaging plane116. The two borders may be perpendicular to the scanning direction that is from the first row to the twentieth row of the leaves of the MLC, or vice versa. In some embodiments, each control point may correspond to a signal acquisition region. For example, the processing device140may determine a signal acquisition region for each control point.

In some embodiments, each working leaf of the plurality of working leaves may have a planned position trajectory. The planned position trajectory may indicate a planned position of the working leaf at each time point or each control point during the radiotherapy session of the subject. If each working leaf moves to the corresponding planned position at the corresponding time point or the corresponding control point, the plurality of working leaves may form the treatment field of the treatment plan at the control point. In some embodiments, the plurality of planned position trajectories of the plurality of working leaves may be determined based on the treatment plan.

In some embodiments, the processing device140may obtain leaf information of the plurality of working leaves based on a planned position trajectory of each of the plurality of working leaves. The leaf information may include information of a start working leaf, information of an end working leaf, information of a center leaf of the MLC, a projection width of a leaf projected upon an isocenter plane of the MLC, or the like, or any combination thereof. In some embodiments, the start acquisition row may be a first row regarding which the one or more detectors of the EPID112acquire signals. In some embodiments, the end acquisition row may be a last row regarding which the one or more detectors of the EPID112acquire signals. In some embodiments, the information of the start working leaf and/or the end working leaf may include a planned position of the start working leaf and/or the end working leaf, the numbering of the start working leaf and/or the end working leaf, or the like, or any combination thereof. In some embodiments, the information of a center leaf of the MLC may include information regarding a position of the center leaf of the MLC. In some embodiments, the center leaf may be a leaf at the center of the MLC. In some embodiments, the projection width of a leaf projected upon the isocenter plane of the MLC may be a width of the leaf that is projected on the isocenter plane. The isocenter plane may be a plane that passes through an isocenter and is perpendicular to a beam central axis of the treatment radiation source111. The isocenter plane may be parallel to the imaging plane116. The beam central axis may be a line passing through a center of the treatment radiation source111and a center of a plane formed by the radiation beam emitted from the treatment radiation source111, which is perpendicular to a propagation direction of the radiation beam. The isocenter may be a point in space where beam central axis of the treatment radiation source111intersect when the gantry114rotates during the radiotherapy session.

In some embodiments, the processing device140may determine the start acquisition row and the end acquisition row of the plurality of acquisition rows (or the signal acquisition region) based on the leaf information of the plurality of working leaves and EPID information of the EPID112. In some embodiments, the EPID information of the EPID112may include a pixel size of the EPID112, an image size of an image captured by the EPID112, a source image distance (SID), an offset value of a center of the EPID112with respect to a beam central axis at the control point, or the like, or any combination thereof. In some embodiments, the pixel size of the EPID112may be a size of a pixel on the imaging plane116. The pixel size may be calculated by dividing the size of the imaging plane116by a predetermined resolution. In some embodiments, the imaging size of the image captured by the EPID112may be a size of an image captured by the EPID112. For example, the imaging size may be equal to a size of the imaging plane116of the EPID112. In some embodiments, the SID may be a distance between the treatment radiation source111and the imaging plane116. For example, the SID may be100cm. In some embodiments, the offset value of the center of the EPID112with respect to the beam central axis may be a distance between the center of the imaging plane116and the beam central axis. In some embodiments, different rotation angles may correspond to different offset values. For example, if the beam central axis of the treatment radiation source111is perpendicular to the ground (i.e., the rotation angle is 0), the center of the imaging plane116may align with the beam central axis (the center of the imaging plane116is on the beam central axis). If the gantry114rotates to an angle other than 0, the center of the imaging plane116may be away from the beam central axis due to gravity. The distance between the center of the imaging plane116and the beam central axis may be the offset value. In some embodiments, the offset values corresponding to different rotation angles may be predetermined and stored in a storage device (e.g., the storage device150, the storage device220, the storage390, etc.).

In some embodiments, the processing device140may determine the start acquisition row and the end acquisition row of the signal acquisition region based on the leaf information of the plurality of working leaves and the EPID information. For example, the processing device140may determine a start acquisition row an end acquisition row at a control point A according to Equation (1) and Equation (2), respectively:

where StartRow denotes a position of the acquisition row, CenterLeaf denotes a sequence number of a center leaf of the MLC, StartLeaf denotes a sequence number of a start working leaf at the control point A, EndLeaf denotes a sequence number of an end working leaf at the control point A, LeafWidth denotes a projection width of a leaf projected upon an isocenter plane of the MLC at the control point A, SID denotes a source image distance at the control point A, PanelShift denotes an offset value of a center of the EPID112with respect to a beam central axis at the control point A, PixelSize denotes a pixel size of the EPID112, and ImageSize denotes an image size of an image captured by the EPID112at the control point A. In some embodiments, the sequence number and the position of a working leaf may be determined mutually.

In530, the processing device140(e.g., the image obtaining module440) may obtain an image from the EPID112at the control point.

In some embodiments, the image may include information acquired from the signal acquisition region. For example, for the control point, the one or more detectors of the EPID112may scan the imaging plane116between the start acquisition row and the end acquisition row to acquire the information of the signal acquisition region.FIG.6is a schematic diagram illustrating an exemplary signal acquisition region620and an exemplary image650according to some embodiments of the present disclosure. As shown inFIG.6, the signal acquisition region620may be part of an imaging plane610. The signal acquisition region620may include a start acquisition row630and an end acquisition row640, and the region between the start acquisition row630and the end acquisition row640. The one or more detectors of the EPID112may scan the imaging plane610between the start acquisition row630and the end acquisition row640, and may obtain the image650. The remaining regions on the imaging plane116except for the signal acquisition region620may be assigned with a value (e.g., a gray value of each pixel in the remaining regions) equal to a value of a pixel being shaded by the leaves of the MLC.

In some embodiments, the processing device140(e.g., the sampling rate determining module430) may determine a sampling rate of the EPID112at the control point based on a planned speed profile of each of the plurality of working leaves. In some embodiments, the image may be captured by the EPID112using the sampling rate at the control point. In some embodiments, each control point may correspond to a sampling rate. The EPID112may capture images using different sampling rates at different control points. In some embodiments, the sampling rate at a control point may be determined based on speeds of working leaves at the control point. Since the working leaves are not always moved at high speeds (e.g., no less than 25 mm/s), redundant images may be obtained if the EPID112captures images using high sampling rates (e.g., no less than 120 frames/s) all the time, thereby causing unnecessary burden on image processing. In some embodiments, the sampling rate at a control point may be proportional to a maximum speed of a working leaf among the plurality of working leaves at the control point. For example, more images may be captured by the EPID112at a control point M than at a control point N, where the maximum speed among the plurality of working leaves at control point M is higher than the maximum speed among the plurality of working leaves at the control point N.

In some embodiments, each working leaf of the plurality of working leaves may have a planned speed profile. The planned speed profile may indicate a planned speed of the working leaf at each time point or each control point during the radiotherapy session of the subject. If each working leaf moves at the corresponding planned speed at the corresponding time point or the corresponding control point, the plurality of working leaves may form the treatment field specified in the treatment plan at the control point. In some embodiments, the plurality of planned speed profiles of the plurality of working leaves may be determined based on the treatment plan. In some embodiments, the processing device140may obtain a maximum working leaf speed of a working leaf at the control point from the planned speed profile of each of the plurality of working leaves. In some embodiments, the processing device140may obtain the maximum speed of a working leaf among the plurality of working leaves at the control point from the planned speed profile of each of the plurality of working leaves. In some embodiments, an exemplary process for determining a sampling rate at a control point may be found elsewhere (e.g.,FIG.7and the descriptions thereof) in the present disclosure.

It should be noted that the above description regarding the process500is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations and modifications may be made under the teachings of the present disclosure. In some embodiments, the process500may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed above. For example, the processing device140may further store the image obtained from the EPID112in a storage device (e.g., the storage device150, the storage device220, and/or the storage390). However, those variations and modifications do not depart from the scope of the present disclosure.

FIG.7is a flowchart illustrating an exemplary process700for determining a sampling rate at a control point according to some embodiments of the present disclosure. In some embodiments, process700may be executed by the RT system100. For example, the process700may be implemented as a set of instructions (e.g., an application) stored in a storage device (e.g., the storage device150, the storage device220, and/or the storage390). In some embodiments, the processing device140(e.g., the processor210of the computing device200, the CPU340of the mobile device300, and/or one or more modules illustrated inFIG.4) may execute the set of instructions and may accordingly be directed to perform the process700.

In710, the processing device140(e.g., the sampling rate determining module430) may obtain a maximum leaf speed of a leaf among all leaves of the MLC.

In some embodiments, the maximum leaf speed may be a maximum speed at which a leaf, among all leaves of the MLC, can move. The maximum leaf speed may be a predetermined value and stored in a storage device (e.g., the storage device150, the storage device220, and/or the storage390). For example, the maximum leaf speed may be 25 mm/s. In some embodiments, the processing device140may access the storage device to obtain the maximum speed.

In720, the processing device140(e.g., the sampling rate determining module430) may obtain a maximum sampling rate of the EPID112.

In some embodiments, the maximum sampling rate of the EPID112may be a maximum rate that the EPID112captures images. The maximum sampling rate may be a predetermined value and stored in a storage device (e.g., the storage device150, the storage device220, and/or the storage390). For example, the maximum sampling rate may be 120 frames/s. In some embodiments, the processing device140may access the storage device to obtain the maximum sampling rate.

In730, the processing device140(e.g., the sampling rate determining module430) may obtain, among the plurality of working leaves, a maximum working leaf speed of a working leaf at the control point based on a planned speed profile of each of the plurality of working leaves.

In some embodiments, the maximum working leaf speed may be a maximum speed of a working leaf among the plurality of working leaves. In some embodiments, the processing device140may obtain speeds of the plurality of working leaves from the planned speed profile of each of the plurality of working leaves. The processing device140may compare the speeds of the plurality working leaves to identify the maximum working leaf speed of the working leaf at the control point.

In740, the processing device140(e.g., the sampling rate determining module430) may determine the sampling rate of the EPID112at the control point based on the maximum leaf speed, the maximum sampling rate of the EPID, and the maximum working leaf speed at the control point.

In some embodiments, the processing device140may determine the sampling rate of the EPID112at the control point according to an algorithm based on the maximum leaf speed, the maximum sampling rate of the EPID, and the maximum working leaf speed at the control point. For example, the processing device140may determine the sampling rate of the EPID112at the control point according to Equation (3):

where F denotes the sampling rate of the EPID112at the control point, VMAX-Wdenotes the maximum working leaf speed of a working leaf at the control point, VMAXdenotes the maximum leaf speed of a leaf among all leaves of the MLC, and FMAXdenotes the maximum sampling rate of the EPID112.

It should be noted that the above description regarding the process700is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations and modifications may be made under the teachings of the present disclosure. In some embodiments, the process700may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed above. For example, the processing device140may store the sampling rate of the EPID112at the control point in a storage device (e.g., the storage device150, the storage device220, and/or the storage390). However, those variations and modifications do not depart from the scope of the present disclosure.

FIG.8is a flowchart illustrating an exemplary process800for determining a position trajectory error and a speed profile error of each working leaf according to some embodiments of the present disclosure. In some embodiments, process800may be executed by the RT system100. For example, the process800may be implemented as a set of instructions (e.g., an application) stored in a storage device (e.g., the storage device150, the storage device220, and/or the storage390). In some embodiments, the processing device140(e.g., the processor210of the computing device200, the CPU340of the mobile device300, and/or one or more modules illustrated inFIG.4) may execute the set of instructions and may accordingly be directed to perform the process800.

In810, the processing device140(e.g., the trajectory error determining module450) may obtain a plurality of images each of which corresponds to data acquired by the EPID112in the signal acquisition region using the sampling rate at one of a plurality of control points.

In some embodiments, the processing device140may identify the plurality of control points during the radiotherapy session of the subject. The EPID112may obtain at least one image using the sampling rate at each control point. Each of the at least one image may include data acquired in the signal acquisition region corresponding to the each control point.

In820, for each of the plurality of working leaves, the processing device140(e.g., the trajectory error determining module450) may determine a measured position trajectory and a measured speed profile of the each working leaf based on the plurality of images.

In some embodiments, the measured position trajectory may indicate a measured position of the each working leaf at each time point or each control point during the radiotherapy session of the subject. In some embodiments, the processing device140may synchronize time points of the plurality of images and time points of the RT system100. For instance, the time point of an image may be recorded as the time the image is acquired by the RT system100, thereby synchronizing the time points of the plurality of images and the time points of the RT system100. In some embodiments, after the time synchronization between the plurality of images and the RT system100, the processing device140may correct one or more of the plurality of images with respect to errors caused by, e.g., a geometric offset of the EPID112, the RT system100, etc. For example, the processing device140may obtain a plurality of corrected images by correcting, based on an image correction algorithm, the plurality of images. The image correction algorithm may include a bad point correction algorithm, a dark field correction algorithm, a gain correction algorithm, a plate offset correction algorithm, a tilt correction algorithm, a SID correction algorithm, or the like, or any combination thereof. In some embodiments, the image correction algorithm may be a correction chart or a correction table. For example, the correction chart or the correction table may include a correction value corresponding to each control point. The processing device140may look up the correction chart or a correction table to obtain the corresponding correction value using the corresponding control point. In some embodiments, the image correction algorithm may be predetermined and stored in a storage device (e.g., the storage device150, the storage device220, and/or the storage390).

In some embodiments, the processing device140may determine the measured position trajectory and the measured speed profile of the each working leaf based on the plurality of corrected images. In some embodiments, the processing device140may identify each working leaf in the plurality of corrected images and obtain a measured position of the each working leaf The processing device140may record the measured position of the each working leaf and the corresponding time point to obtain the measured position trajectory of the each working leaf. The processing device140may identify a position changes with time from the plurality of corrected images to obtain the measured speed profile of the each working leaf. Exemplary processes for determining the measured position trajectory and the measured speed profile of the each working leaf may be found elsewhere (e.g.,FIG.11and the descriptions thereof) in the present disclosure.

In830, for each of the plurality of working leaves, the processing device140(e.g., the trajectory error determining module450) may determine a position trajectory error of the each working leaf based on the planned position trajectory and the measured position trajectory of the each working leaf.

In some embodiments, the position trajectory error of a working leaf may indicate a difference between a measured position and a planned position of each working leaf at each time point or each control point. In some embodiments, the processing device140may compare the planned position trajectory and the measured position trajectory of a same working leaf to obtain a position trajectory error of the same working leaf.FIG.9is a schematic diagram illustrating an exemplary planned position trajectory and an exemplary measured position trajectory of a working leaf according to some embodiments of the present disclosure.FIG.10is a schematic diagram illustrating an exemplary position trajectory error of a working leaf according to some embodiments of the present disclosure. As shown inFIG.9, the planned position trajectory of the working leaf is presented as a smooth line, and the measured position trajectory of the working leaf is presented as a dotted line. The measured position trajectory and the planned position trajectory may be compared, and a difference between the planned position trajectory and the measured position trajectory may be designated as the position trajectory error as shown inFIG.10.

In840, for each of the plurality of working leaves, the processing device140(e.g., the trajectory error determining module450) may determine a speed profile error of the each working leaf based on the planned speed profile and the measured speed profile of the each working leaf

In some embodiments, the speed profile error of a working leaf may indicate a difference between a measured speed and a planned speed of each working leaf at each time point or each control point. In some embodiments, the processing device140may compare the planned speed profile and the measured speed profile of a same working leaf to obtain a speed profile error of the same working leaf.

In some embodiments, the planned position and/or speed of each of one or more working leaves may be determined from the planned position trajectory and/or the planned speed profile, and a time point corresponding to the planned position and/or speed of each of the one or more working leaves according to the treatment plan may be determined. The time point corresponding to the planned position and/or speed may be designated as a first time point of the RT system. The measured position and/or speed of each of one or more working leaves may be determined from the measured position trajectory and/or the measured speed profile, and a time point corresponding to the measured position and/or speed of each of the one or more working leaves may be determined. The time point corresponding to the measured position and/or speed of each of the one or more working leaves may be designated as a second time point of the RT system100. The first time point of the RT system100and the second time point of the RT system100may be synchronized. In some embodiments, a planned position and/or speed and a measured position and/or speed of the same working leaf at each time point may be compared to obtain a position error and/or speed error at each time point, thereby obtaining the position trajectory error and/or speed profile error of the working leaf.

In some embodiments, the speed profile error and/or the position trajectory error of the each working leaf may be used for evaluating a dynamic error of the each working leaf during the radiotherapy session of the subject. In some embodiments, the speed profile error and/or the position trajectory error of the each working leaf may be compared with a predetermined threshold to determine whether the each working leaf is moved according to the treatment plan.

It should be noted that the above description regarding the process800is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations and modifications may be made under the teachings of the present disclosure. In some embodiments, the process800may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed above. For example, the processing device140may store the plurality of images at the plurality of control points in a storage device (e.g., the storage device150, the storage device220, and/or the storage390). However, those variations and modifications do not depart from the scope of the present.

FIG.11is a flowchart illustrating an exemplary process1100for obtaining a measured position trajectory and a measured speed profile according to some embodiments of the present disclosure. In some embodiments, process1100may be executed by the RT system100. For example, the process1100may be implemented as a set of instructions (e.g., an application) stored in a storage device (e.g., the storage device150, the storage device220, and/or the storage390). In some embodiments, the processing device140(e.g., the processor210of the computing device200, the CPU340of the mobile device300, and/or one or more modules illustrated inFIG.4) may execute the set of instructions and may accordingly be directed to perform the process1100.

In some embodiments, the process1100may be performed for each working leaf of a plurality of working leaves. In1110, the processing device140(e.g., the trajectory error determining module450) may obtain pixel positions with respect to the each working leaf in each of the plurality of corrected images.

In some embodiments, each working leaf may be represented on a corrected image as a plurality of pixels. For example, the processing device140may identify the plurality of pixels according to an edge gradient algorithm or a50% gray value point algorithm. The processing device140may identify the pixel positions of the plurality of pixels in each corrected image.

In1120, the processing device140(e.g., the trajectory error determining module450) may determine a field position corresponding to each pixel position based on a predetermined mapping relationship between the pixel positions and the field positions.

In some embodiments, the predetermined mapping relationship may include a plurality mapping pairs. A mapping pair may include a pixel position and a corresponding field position. The predetermined mapping relationship may be predetermined and stored in a storage device (e.g., the storage device150, the storage device220, and/or the storage390). The processing device140may look up the predetermined mapping relationship using each pixel position to obtain the corresponding field position.

In1130, the processing device140(e.g., the trajectory error determining module450) may obtain the measured position trajectory and the measured speed profile of the each working leaf based on the field position corresponding to each pixel position and a time synchronization signal.

In some embodiments, the time synchronization signal may be configured to synchronize a first measured time of the measured position trajectory with a first planned time of the planned position trajectory. In some embodiments, the time synchronization signal may be configured to synchronize a second measured time of the measured speed profile with a second planned time of the planned speed profile. The processing device140may determine a trajectory of measured positions with time as the measured position trajectory based on the field position corresponding to the each pixel position. The processing device140may determine a trajectory of measured speeds with time as the measured speed profile based on the field position corresponding to the each pixel position. In some embodiments, the processing device140may determine a trajectory of measured positions with radiation dose and/or with rotation angles as the measured position trajectory. The measured position trajectory with radiation dose and/or with rotation angles may be compared with the corresponding planned position trajectory with radiation dose and/or with rotation angles to evaluate the dynamic errors of the plurality of working leaves at each control point.