Patent Description:
Radiation therapy (also referred to as radiotherapy) may be used in the treatment of cancer or other pathologies. A linear accelerator ("Linac") may be used in radiotherapy to direct a radiation beam to a desired location on a patient. The Linac may direct photons (e.g., as an X-ray), electrons, or other subatomic particles toward a target, such as a cancerous tumor. The radiation beam may be shaped to match a shape of the tumor, such as by using a multileaf collimator (e.g., which may include multiple tungsten leaves that may move independently of one another to create one or more specified radiation beam shapes).

Because healthy cells may be harmed or killed during radiotherapy treatment of a specified target, it may be desirable to minimize radiation to healthy tissue. Medical imaging may aid in this pursuit. Imaging systems such as computed tomography (CT), fluoroscopy, and magnetic resonance imaging (" MRI" or "MR imaging") may be used to determine the location of (localize) or track a target. An example of a radiotherapy treatment system integrated with an imaging system may include an MRI-Linac system (such as can be used for MRI-guided radiotherapy), which may be configured to use three-dimensional (3D) images of a target, such as a tumor, in radiotherapy to provide radiation to the target while reducing or minimizing radiation to other tissue.

The MRI-Linac system may include an accelerator, such as may be configured to rotate on a ring gantry around an MRI system. The patient to be treated may be positioned on a surface (e.g., a table, a bed, or a couch), such as may be centered inside the MRI-Linac system. MRI can provide a spatial map of hydrogen nuclei in tissues of the patient, and images may be acquired in a two-dimensional (2D) plane or 3D volume. Health care providers, such as oncologists, may prefer MRI- Linac imaging techniques because MRI may provide excellent soft tissue contrast without using ionizing radiation.

In an MRI-guided LINAC, for example, it can be desirable to localize the target position of the target and organs at risk (OARs) during the treatment itself. This can enable gating or tracking strategies to compensate for motion while the beam is on. In some modes of operation, this can be accomplished by the acquisition of sequential 2D MRI slices, for example alternating axial, coronal and sagittal slices. These 2D slices can be used to directly infer 3D target motion using direct segmentation or registration techniques. These approaches may have the following limitations: <NUM>) there can be significant out-of-plane motion, which can be difficult to localize with 2D slices; <NUM>) slices are generally centered on the target, rendering it difficult to simultaneously track OARs; and <NUM>) only information in the 2D slices is gathered during treatment, which makes it difficult to perform dose calculations, e.g., offline retrospective calculation of dosimetry for adaptive radiotherapy (dose compensation utilizes full 3D information of the patient's anatomy over time).

<CIT> describes a method of obtaining a three-dimensional deformation of an organ which is deformable over time and extends in a three-dimensional space from at least two sets of data representing points of said organ and corresponding to distinct times in the deformation of the organ. A correspondence between the points in the sets of data being determined, said method uses a definition of notional planes on which there are defined explicit equations of the deformation of the organ including unknown parameters. The parameters are calculated for each equation and the explicit equations obtained are then utilized in order to define the deformation in the three-dimensional space by means of weighting functions defined for the points in the space.

MR imaging can be performed in "real-time" (e.g., "online," "ongoing," or "continuously") during radiotherapy, such as to provide target location and motion information, e.g., 3D deformation and/or 3D rotation, for the radiation beam delivery. A target to be tracked can include an organ, such as a prostate, or a tumor relating to all or part of the organ. In image processing, one way in which a target can be determined to be in motion is if the location of the target changes relative to its background in the image. Image processing techniques to localize, track, or predict a location of a target can include image subtraction, such as can include using one or more absolute differences, or using edge, corner, or region of interest (ROI) image feature detection.

Fast and accurate 3D localization and tracking of the target can be important during radiotherapy, such as to account for patient motion (e.g., organ motion and/or tumor motion). Motion of a target, e.g., 3D deformation and/or 3D rotation, can be caused by one or more sources, such as patient respiration (e.g., a breathing cycle), a reflex (e.g., a cough, passing gas, etc.), intentional or unintentional patient movement, or other expected or unexpected target motion.

This disclosure describes techniques that can estimate 3D motion from a series of 2D MRI slices. As described in detail below, these techniques can include two main stages: <NUM>) a learning stage where a conversion model is built that links 2D slices to the 3D motion; and <NUM>) a tracking stage where 3D real-time tracking is performed based on the conversion model built in the learning stage. These techniques can estimate full 3D motion from 2D slices to provide the current change, e.g., one or more of 3D location, 3D deformation, and/or 3D rotation, of the target in real-time.

This disclosure is directed to a computer-implemented method of determining at least one real-time change of at least a portion of a region of a patient. The computer-implemented method comprises obtaining a plurality of real-time image data corresponding to <NUM>-dimensional (2D) magnetic resonance imaging (MRI) images including at least a portion of the region, performing 2D motion field estimation on the plurality of image data, approximating a <NUM>-dimensional (3D) motion field estimation, including applying a conversion model to the 2D motion field estimation, determining at least one real-time change of at least a portion of the region based on the approximated 3D motion field estimation, and optionally controlling the treatment of at least a portion of the region using the determined at least one change.

This disclosure is directed to a system for controlling real-time image-guided adaptive radiation treatment of at least a portion of a region of a patient. The system comprises a treatment adaptation system and a therapy controller circuit. The treatment adaptation system is configured to obtain a plurality of real-time image data corresponding to <NUM>-dimensional (2D) magnetic resonance imaging (MRI) images including at least a portion of the region, perform 2D motion field estimation on the plurality of image data, approximate a <NUM>-dimensional (3D) motion field estimation, including applying a conversion model to the 2D motion field estimation, and determine at least one real-time change of at least a portion of the region based on the approximated 3D motion field estimation. The therapy controller circuit is optionally configured to control the treatment of at least a portion of the region using the determined at least one change.

This Overview is an overview of some of the teachings of the present application and not intended to be an exclusive or exhaustive treatment of the present subject matter. Further details about the present subject matter are found in the detailed description and appended claims. Other aspects of the invention will be apparent to persons skilled in the art upon reading and understanding the following detailed description and viewing the drawings that form a part thereof, each of which are not to be taken in a limiting sense. The scope of the present invention is defined by the appended claims and their legal equivalents.

Like numerals having letter suffixes or different letter suffixes may represent different instances of similar components. Such embodiments are demonstrative and not intended to be exhaustive or exclusive embodiments of the present apparatuses, systems, or methods.

1A illustrates an example of a radiotherapy device, e.g., a linear accelerator <NUM>, according to some embodiments of the present disclosure. Using a linear accelerator <NUM>, a patient <NUM> may be positioned on a patient table <NUM> to receive the radiation dose determined by the treatment plan. The linear accelerator <NUM> may include a radiation head <NUM> that generates a radiation beam <NUM>. The entire radiation head <NUM> may be rotatable, such as around a horizontal axis <NUM>. In an example, below the patient table <NUM> there may be provided a flat panel scintillator detector <NUM>, which may rotate synchronously with radiation head <NUM>, such as around an isocenter <NUM>. The intersection of the axis <NUM> with the center of the beam <NUM>, produced by the radiation head <NUM>, can be referred to as the "isocenter. " The patient table <NUM> may be motorized so that the patient <NUM> can be positioned with the tumor site at or close to the isocenter <NUM>. The radiation head <NUM> may rotate about a gantry <NUM>, such as to provide patient <NUM> with a plurality of varying dosages of radiation, such as according to the treatment plan.

<FIG> is an example of portions of an imaging or radiotherapy system <NUM>, e.g., MRI-guided LINAC, that can be used to provide real-time image guidance in accordance with various techniques of this disclosure. More particularly, the system <NUM> of <FIG> can use images obtained in real-time to control or adapt a radiation therapy treatment plan in real-time. The system <NUM> can include a treatment apparatus <NUM> (e.g., a radiotherapeutic apparatus, such as can include a linear accelerator ("Linac")).

The patient <NUM> can be positioned on a patient support <NUM>, such as a table, a couch, or other surface. The patient support <NUM> can be configured to change position such as relative to one or more other components of the treatment apparatus <NUM>, such as to elevate or change the longitudinal position of the patient <NUM>. Radiation can be emitted from a therapeutic radiation source <NUM> (e.g., accelerated particles such as x-rays or protons) toward the patient <NUM>. In an example, the radiation source <NUM> can be configured to move, such as to rotate around the patient <NUM>, such as by using a rotational support <NUM> (e.g., gantry) to which the therapeutic radiation source <NUM> can be attached. The therapeutic radiation source <NUM> can be configured to move, such as to rotate, such as by using a member or a mechanical arm, which can be connected to the treatment apparatus <NUM> and the therapeutic radiation source <NUM>. The treatment apparatus <NUM> in an embodiment may be a linear accelerator "Linac" (e.g., as shown and described with respect to FIG. 1A) that can be configured to direct an x-ray beam toward a target (e.g., a cancer tumor) of the patient <NUM>.

In addition, the system <NUM> can include an imaging and control system <NUM> (e.g., a magnetic resonance imaging (MRI) machine) that includes an imaging system <NUM> and a therapy controller circuit <NUM> (also referred to in this disclosure as "controller circuit <NUM>" or "controller <NUM>") in communication with the treatment apparatus <NUM>, as depicted by lightning bolt <NUM> (e.g., lightning bolt <NUM> may be a wired or wireless connection). The imaging and control system <NUM> can also include a database <NUM>, for example, to store acquired images. The imaging system <NUM> can include a magnetic resonance imaging (MRI) machine that can be used in combination with the treatment apparatus <NUM> (e.g., such as to provide an MRI linear accelerator ("MRI-Linac"). The MRI apparatus can be used to provide imaging information that can be used to control or adapt treatment of the patient <NUM>. One or more other imaging systems can additionally or alternatively be included in or used with the system <NUM> or the imaging system <NUM>, such as a computed tomography (CT) system.

The imaging system <NUM> can acquire, for example, three-dimensional (3D) images of the patient. For example, during a treatment planning phase, a health care worker, e.g., physician, nurse, physicist, or technician, can control the system <NUM> to acquire 3D planning image data prior to treatment of the patient, e.g., via the imaging system <NUM>. The 3D planning image data can be useful in determining a precise location of a region of interest of the patient, e.g., a target. As another example, immediately prior to treatment, e.g., several days after the 3D planning image was acquired, the health care worker can control the system <NUM> to acquire a new 3D image that can be used to during the treatment. In addition, during the treatment of the patient <NUM>, the imaging system <NUM> can acquire a plurality of <NUM>-dimensional (1D) lines or <NUM>-dimensional (2D) slices or 3D volume of MRI images including at least a portion of the region (which when combined could form a 3D image of the region).

The controller <NUM> can control one or more aspects of the system <NUM>. For example, the controller <NUM> can control the position of the patient, e.g., via the patient support <NUM>, control the radiation dosage emitted from the radiation source <NUM>, control or adapt a beam aperture shape to track the target, and/or control the movement and/or positioning of the radiation source <NUM>.

As described above, an MRI-Linac system can have its own controller circuit <NUM> to control both the imaging and Linac. However, in example implementations in which the imaging system <NUM> is a CT system, the controller of the CT system may not control the Linac. As such, separate controllers control a CT system and the Linac.

The system <NUM> can include a treatment adaptation system (TAS) <NUM> in communication with the imaging and control system <NUM>, as depicted by lightning bolt <NUM>. The TAS <NUM> can receive a previously obtained 3D image data volume, e.g., from MRI or CT scans, that corresponds to the 3D image acquired by the imaging system <NUM>. The TAS can include an input/output circuit <NUM> for receiving and transmitting data, a memory circuit <NUM> for buffering and/or storing data, and a processor circuit <NUM>. The memory circuit <NUM>, which may be any suitably organized data storage facility can receive image data from the imaging and control system <NUM>. The memory circuit <NUM> may receive the image data via a wireless or wired connection, through conventional data ports and may also include circuitry for receiving analog image data and analog-to-digital conversion circuitry for digitizing the image data. The memory circuit <NUM> can provide the image data to the processor circuit <NUM>, which can implement the functionality of the present invention in hardware or software, or a combination of both on a general-purpose computer. In an embodiment, the processor circuit <NUM> may be a graphical processing unit (GPU).

As described in more detail below and in accordance with this disclosure, the TAS <NUM> can estimate 3D motion from a series of 2D slices acquired in real-time, e.g., using an MRI, to adapt a radiation therapy treatment plan in real-time. In a learning stage, the TAS <NUM> can build a conversion model that links 2D slices to previously obtained 3D image data volumes, e.g., acquired using MRI or CT. In a tracking stage, the TAS <NUM> can perform 3D real-time tracking based on the conversion model built in the learning stage. The TAS <NUM> can determine whether a region, e.g., a target, has changed position, and then output information to the imaging and control system <NUM> that can allow the therapy controller circuit <NUM> to control the therapy in response to a determined change in position.

<FIG> is a flow diagram illustrating an example of a technique that can be used to build a conversion model that can link 2D slices to previously obtained 3D image data volumes. The flow diagram of <FIG> can represent the learning stage in which the TAS <NUM> can build the conversion model that can link 2D slices to 3D motion. First, the TAS <NUM> can obtain a set of acquired 4D image data (block <NUM>) from the imaging and control system <NUM>. The image data can be acquired using MR or CT imaging techniques. The 4D image data includes 3D image data volumes obtained over a period of time. Optionally, the TAS <NUM> can use the 4D image data from the learning stage to fill in any parts of the image that are missing when the TAS <NUM> later uses 2D slices during the tracking stage.

From the 4D image data, the TAS <NUM> can extract 2D slices (block <NUM>) and perform 3D motion field estimation between times such as can serve as endpoints of a time frame (block <NUM>). Referring first to the 3D motion field estimation (block <NUM>), to quantify motion in the 4D image data <NUM>, the TAS <NUM> can extract a first reference 3D image data volume. As 3D image data volumes are progressing in time, the changes between two image data volumes can be characterized as a deformation defined by a deformation vector field. The TAS <NUM> can perform 3D motion field estimation by, for example, calculating deformation vector fields (DVF) to find the deformation between each successive 3D image data volume and the reference 3D image data volume. In some examples, the deformation can be a pixel-to-pixel (or voxel-to-voxel) deformation in time where each pixel (or voxel) can have a deformation vector that defines its movement from one 3D image to the next 3D image, e.g., if a patient had a very small calcification the vector can define how that calcification moved. If there is no deformation, all pixel (or voxel) deformation vectors point are null. If there is deformation, the pixel (or voxel) deformation vectors point in various directions.

In an example, the processor circuit <NUM> of the TAS <NUM> can use a nonlinear registration technique to determine the deformation. In an example, the processor circuit <NUM> can calculate a DVF for each pixel (or voxel) in an image. In an example, the processor circuit <NUM> can calculate a DVF for pixels (or voxels) in an area of interest, e.g., specific to a target or organ at risk, such as of a segmented or other image. In some cases, for reduced computational complexity, the TAS <NUM> can use rigid registration instead of deformable registration.

After the TAS <NUM> calculates the DVFs, the TAS <NUM> has a set of DVFs that describe how the organ moves, e.g., translates and/or rotates, and/or deforms during respiration. The set of DVFs can include a substantial amount of information, which can be computationally difficult to process. To simply the computation, the processor circuit <NUM> of the TAS <NUM> can reduce the dimensionality of the set of DVFs, if desired.

First, the processor circuit <NUM> of the TAS <NUM> can apply a dimensionality reduction technique to the DVFs. As seen in <FIG>, the dimensionality reduction technique can include applying a principal component analysis (PCA) to the 3D motion field data (block <NUM>). Application of PCA to the DVFs results in a set of principal components or coefficients, which define vectors. Then, using a predefined criterion, such as a predefined amount of variability, or a predefined desired accuracy of a reconstructed deformation field, the TAS <NUM> can reduce the dimensionality by selecting one or more PCA components from the set of principal components (block <NUM>). In an example, the accuracy can be defined as a measure of the difference between a reconstructed deformation field and the ones given by the registration.

Dimensionality reduction techniques are not limited to the use of PCA. Other non-limiting examples of dimensionality reduction techniques include independent component analysis (ICA), kernel PCA, canonical correlation analysis, locally linear embedding (LLE), Hessian LLE, Laplacian eigenmaps, local tangent space alignment, maximum variance unfolding, and maximally informative dimensions.

As indicated above, the TAS <NUM> can extract 2D slices from the 4D image data volume (block <NUM>). As with the 3D image data volumes, the TAS <NUM> can perform 2D motion field estimation by, for example, calculating DVFs to find the deformation between successive 2D image data (2D slices)(block <NUM>).

In some examples, the TAS <NUM> can select arbitrary slices within the 4D image data volume. In other examples, the TAS <NUM> can determine and select an orientation such as a plane, e.g., sagittal, axial, coronal, such as that having the most motion information and select slices from that plane or other orientation. The "plane" associated with a particular MRI slice need not be strictly planar, and may include some curvature, such as due to MRI distortion artifacts, or a slice that has been at least partially compensated for the MRI distortion. For example, the TAS <NUM> can train on three planes and determine which plane provides the better prediction of 3D motion. In some examples, the TAS <NUM> can select slices from planes in three orthogonal directions and calculate a DVF in each of those planes.

After the TAS <NUM> calculates the DVFs for the 2D image data, the TAS <NUM> has a set of DVFs. To simply the computation, the processor circuit <NUM> of the TAS <NUM> can reduce the dimensionality of the set of DVFs by applying a dimensionality reduction technique to the DVFs. The dimensionality reduction technique can include the TAS <NUM> applying a PCA to the 2D motion field data (block <NUM>) to generate a set of principal components. Then, using a predefined criterion, such as a predefined amount of variability, or a predefined desired accuracy of a reconstructed deformation field, the TAS <NUM> can reduce the dimensionality by selecting one or more PCA components from the set of principal components (block <NUM>).

For example, during the PCA analysis, the TAS <NUM> can determine the main components variation. By way of specific example, the <NUM>st principal component may be the largest and can explain <NUM>% of variability and the <NUM>nd principal component can explain <NUM>%. If a predefined amount of variability is <NUM>%, then the TAS <NUM> can select the <NUM>st and the <NUM>nd principal components.

In an example, the accuracy can be defined as a measure of the difference between a reconstructed deformation field and the ones given by the registration.

After the TAS <NUM> has optionally reduced the dimensionality of both the 3D motion field PCA and the 2D motion field PCA, the processor <NUM> of the TAS <NUM> can generate a multivariate, multidimensional function f that establishes a relation between, or links, the 2D PCA components and the 3D PCA components. The function f can be, for example, a linear regression between a column Y that contains the 3D PCA components of the deformation vector fields, and a column X that contains the 2D PCA components, as shown below: <MAT>.

The linear regression can be shown by the following: <MAT>.

In some examples, the linear regression technique is principal component regression. Although a linear regression technique was described any type of regression analysis can be used, such as one or more non-linear regression techniques. The process is not restricted to linear regression, such as where f is a multivariate, multidimensional function.

Once the TAS <NUM> has calculated the function f (block <NUM>), the TAS can calculate the model that links the 2D slices to the 3D motion (at block <NUM>). The model can include the components of the 2D PCA and the 3D PCA and the function f that links them.

During the tracking stage, the TAS <NUM> can obtain 2D slices in any orientation, e.g., sagittal, sagittal-axial, sagittal-axial-coronal, as long as the slices are in the same anatomical location as the one used during the learning stage. Then, the TAS <NUM> can calculate a PCA of the obtained image data and use the model to map the image data back to see what an estimate of full 3D motion should be.

In some examples, the model in the learning stage can be built from a set of 4D MRI data. In some such examples, the set of 4D MRI data can be obtained from a phase or amplitude-binned 4D MRI scan acquired at an earlier time, or just prior to treatment. Image data can be obtained over a plurality of respiratory cycles, where individual respiratory cycles include a plurality of portions, and the TAS <NUM> can generate at least two 3D image data volumes using a central tendency of the image data in like-portions. For example, the respiration cycle can be binned and the TAS <NUM> can generate a 3D image by taking information from the same bins at different respiratory phases. In this manner, the TAS <NUM> can generate a 4D image averaged over multiple respiratory cycles.

In other examples, the 4D MRI data can be obtained from a series of fast 3D MRI scans. In some cases, e.g., if 4D image data is not available, the 4D MRI data can be simulated from a static 3D MRI image, such as with some additional hypotheses, such as modeling the motion dynamics.

<FIG> is a flow diagram illustrating an example of a technique that can be used to estimate a real-time 3D image of a patient using the conversion model built according to the flow diagram of <FIG>. The flow diagram of <FIG> represents the real-time tracking stage in which the TAS <NUM> can approximate a 3D motion field estimation, including applying the conversion model to the 2D motion field estimation, and determine at least one real-time change, e.g., 3D location, 3D deformation, and/or 3D rotation, of at least a portion of the target or region based on the approximated 3D motion field.

In <FIG>, the TAS <NUM> can obtain a plurality of real-time image data corresponding to 2D images, e.g., 2D MRI slices (block <NUM>). In some examples, the data images can include at least a portion of the target. The TAS <NUM> can perform 2D motion field estimation on the plurality of image data by, for example, estimating the real-time DVFs (and hence the real-time 3D image of the patient) to find the deformation between successive 2D image data, e.g., 2D slices, (block <NUM>). Next, the TAS <NUM> can approximate 3D motion field estimation, which can include applying the conversion model to the 2D motion field estimation. For example, the TAS <NUM> can compute the 2D PCA of the newly obtained 2D image data, e.g., 2D slices, (block <NUM>). Using the conversion model estimated by the function f that links the 2D PCA and the 3D PCA (block <NUM>), the TAS <NUM> can estimate the 3D PCA components (block <NUM>). Using the estimated 3D PCA components, the TAS <NUM> can approximate a real-time 3D motion field estimation of a region of the patient (block <NUM>), and thus estimate motion of a target, e.g., an organ at risk (block <NUM>).

In some examples, the TAS <NUM> can determine the best orientation and position of 2D slices to image the patient during treatment. For example, the TAS <NUM> can determine a subspace containing the maximum information for each 3D PCA component. This subspace can contain deformation information that is the most correlated to the 3D image data volume and that provides give the most accurate prediction of motion. The TAS <NUM> can automatically select the best orientation for the choice of the 2D slice using this deformation information.

In some examples, the TAS <NUM> can enable real-time estimation of the 2D PCA components. For example, instead of computing a deformable registration between the 2D slices, the TAS <NUM> can perform an optimization process that can directly estimate the coordinates of the current slices in the 2D PCA, which will generate the best coordinates that deform the current slices to the model slice.

By determining the estimated motion of the target, the TAS <NUM> can control treatment by accurately gating the treatment if the at least a portion of the region is outside a predefined spatial gating window. In addition, the TAS <NUM> can control treatment by controlling an emitted radiation direction of a treatment delivery device to track the region.

It should be noted that although the techniques are described as subject-specific, the techniques of this disclosure can be extended to a general statistical 3D PCA. In that case, the 3D PCA determined during the learning stage can be determined on several subjects.

Before real-time tracking, it can be desirable for the TAS <NUM> to perform pre-alignment in a pre-processing stage to ensure that the originally acquired 4D image data from which the conversion model was determined is aligned to the patient's current position. It can be desirable to make sure that the slices used during the tracking stage are the same as was used during the learning stage. Misalignment can occur, for example, if the 4D image data was acquired on a previous day.

In the pre-alignment act, the TAS <NUM> can determine a correction for patient movement in between a first patient session at a first time, e.g., a learning stage on a first day, and a second patient session at a second time, e.g., a tracking stage on a second day. The TAS <NUM> can perform rigid alignment of the 3D PCA to the current patient. The TAS <NUM> can correct the 3D PCA components through various reorientation strategies, in the case of non-linear registration in the learning stage. In one example, the TAS <NUM> can determine which slices to use during the tracking stage based on the slices used during the modelling stage to ensure consistency.

<FIG> illustrates an example of portions of a radiotherapy system <NUM>, e.g., MRI-guided LINAC. The radiotherapy system <NUM> can include a treatment system <NUM>, an imaging system <NUM>, and an end-user interface <NUM>. The treatment system <NUM> can include a treatment apparatus, such as can include a linear accelerator ("linac"). The linac can be configured to deliver a radiotherapy treatment to a patient <NUM>. The patient <NUM> can be positioned on a patient support <NUM>, such as a table, a couch, or other surface. The patient support <NUM> can be configured to change position, such as relative to one or more other components of the linac, such as to elevate or change the longitudinal position of the patient <NUM>. In an example, the patient support <NUM> can be configured to be motorized such that the patient <NUM> can be positioned with the target at or close to a center of the treatment apparatus.

Radiation can be emitted from a radiation source <NUM> toward the patient <NUM>. In an example, the radiation source <NUM> can be configured to move, such as to rotate around the patient <NUM>, such as by using a rotational support <NUM> (e.g., a gantry or a mechanical arm) to which the radiation source <NUM> can be attached. The radiation source <NUM> can be configured to direct an x-ray (or other particle) beam toward a target (e.g., a cancer tumor) of the patient <NUM>. The radiation source <NUM> can be configured to rotate, such as to provide the patient <NUM> with a plurality of dosages of radiation (e.g., varying dosages), such as according to a treatment plan.

The imaging system <NUM> can include an imaging apparatus <NUM> such as a magnetic resonance imaging (MRI) machine that can be used with the treatment system <NUM> (e.g., such as to provide an MRI linear accelerator ("MRI-linac")). The MRI apparatus can be used to provide imaging information that can be used to determine a location of the target in the patient <NUM>, such as to direct radiotherapy to a specified location of the patient <NUM>, such as to the target. The imaging system <NUM> can additionally or alternatively include a computed tomography (CT) system, or another imaging system. The imaging system <NUM> can include one or more sensors <NUM>. The one or more sensors <NUM> can include a flat panel detector (e.g., an X-ray detector), such as can be arranged opposite an X-ray source. The imaging system <NUM> can include one or more inputs <NUM>, one or more outputs <NUM>, a processor circuit <NUM>, a memory circuit <NUM>, a database <NUM>, a communication circuit <NUM>, a timer circuit <NUM>, and a controller circuit <NUM>.

The imaging system <NUM> can acquire, for example, a reference image (e.g., a treatment planning image) of the patient <NUM> with at least three dimensions (e.g., the 3D MR reference image or a 4D MR reference image). In an example, information about the 3D MR reference image can be acquired by the imaging system. The 3D MR reference image can be useful in determining a location of a region of interest of the patient (e.g., the target). In an example, during the treatment session of the patient <NUM>, the imaging system <NUM> can acquire a plurality of one-dimensional (1D) lines, two-dimensional (2D) slice or projection images, a 3D MR image (e.g., a 3D image of a volume), or a 4D MR image (e.g., a sequence of 3D MR images over time).

The treatment system <NUM> can be communicatively coupled to the imaging system <NUM> and the end-user interface <NUM>. The imaging system <NUM> can include or be communicatively coupled to the end-user interface <NUM>. This communicative coupling can include using one or more communication links (e.g., communication link <NUM>), such as can include a wired or wireless transmitter, receiver or transceiver circuits (such as at each end of the communication link), a communication bus, a communication network, or a computer network.

The processor circuit <NUM> can be configured to determine information about a location (e.g., a position) of the target in the patient <NUM>. The output <NUM> can be configured to provide information, such as about the position of the target, such as to the treatment system <NUM>, such as during a radiotherapy session of the patient <NUM>. The end-user interface <NUM> can be used by a caregiver, for example, a radiation oncologist, a radiation dosimetrist, or a radiation therapist (e.g., a radiographer). In an example, the end-user interface <NUM> can include an audio/visual indicator (e.g., a monitor). The controller circuit <NUM> can be configured to control one or more aspects of the imaging system <NUM>. In an example, the controller circuit <NUM> can control the use or operation of the gradient coils of the imaging apparatus <NUM>, such as to specify an orientation of the real-time 2D MR image slice. The memory circuit <NUM> can provide information to the processor circuit <NUM>, which can implement the techniques described herein in hardware or software, or a combination of both on a general-purpose computer. In an example, the processor circuit <NUM> can include graphical processing unit (GPU).

This document describes, among other things, applying a conversion model to a 2D target or OAR motion estimation to obtain an estimated 3D target or OAR motion estimation, one or more other techniques for 3D motion estimation can be used in combination with the techniques described herein. For example, one or more aspects of various techniques described in this disclosure can be combined with one or more of aspects described in the following U. Patent Applications: <NUM>) <CIT> (Attorney Docket No <NUM>. 006PRV); <NUM>) <CIT> (Attorney Docket No <NUM>. 003PRV); <NUM>) <CIT> (Attorney Docket No <NUM>. 009PRV); and <NUM>) <CIT> (Attorney Docket No <NUM>.

Claim 1:
A computer-implemented method of determining at least one real-time change of at least a portion of a region of a patient, the computer-implemented method comprising:
obtaining (<NUM>) a plurality of real-time image data corresponding to <NUM>-dimensional, 2D, magnetic resonance imaging, MRI, images, including at least a portion of the region;
performing (<NUM>) 2D motion field estimation on the plurality of image data; approximating a <NUM>-dimensional, 3D, motion field estimation, including applying a conversion model to the 2D motion field estimation; and
determining the at least one real-time change of the at least a portion of the region based on the approximated 3D motion field estimation;
characterised in that the conversion model is specified by:
performing a 3D motion field estimation (<NUM>) on at least two 3D image data volumes obtained during a first time frame, wherein the at least two 3D image data volumes include at least a portion of the region;
performing a 2D motion field estimation (<NUM>) on 2D image data corresponding to at least two 2D images obtained during the first time frame, wherein the 2D image data includes at least a portion of the region; and
determining (<NUM>) the conversion model using the 3D motion field and the 2D motion field.