Patent Description:
Intensity modulated radiation therapy (IMRT) is an external beam radiation therapy technique that utilizes computer planning software to produce a three-dimensional radiation dose map, specific to a target tumor's shape, location, and motion characteristics. Various regions within a tumor and within the subject's overall anatomy may receive varying radiation dose intensities through IMRT, which treats a subject with multiple rays of radiation, each of which may be independently controlled in intensity and energy. Each of these rays or beams is composed of a number of sub-beams or beamlets, which may vary in their individual intensity, thereby providing the overall intensity modulation. Because of the high level of precision required for IMRT methods, detailed data must be gathered about tumor locations and their motion characteristics. In doing so, the radiation dose imparted to healthy tissue can be reduced while the dose imparted to the affected region, such as a tumor, can be increased. In order to achieve this, accurate geometric precision is required during the treatment planning stage.

Image-guided radiation therapy (IGRT) employs medical imaging, such as magnetic resonance (MR) or computed tomography (CT), concurrently with the delivery of radiation therapy to a subject undergoing treatment. In general, IGRT is employed to accurately direct radiation therapy using positional information from the medical images to supplement a prescribed radiation delivery plan. The advantage of using IGRT is twofold. First, it provides a means for improved accuracy in delivering radiation fields. Second, it provides a method for reducing the dose imparted to healthy tissue during treatment. Moreover, higher accuracy in delivering radiation fields allows for dose escalation in tumors, without appreciably increasing dose levels to the surrounding healthy tissue. Also, dose escalation allows for treatments to be completed in fewer fractions, creating greater throughput and fewer subject visits.

Forms of active respiratory motion compensation for radiotherapy, consisting of gating or tracking, have been in clinical use for almost two decades. In respiratory gating, the radiation beam is turned on or off based on tumor location and/or respiratory phase. In respiratory tracking, the radiation beam direction is changed based upon the measured or estimated tumor position. Intrafractional variations present additional challenges. In particular, large translations, rotations, and deformations occurring due to respiration, peristalsis, organ filling, or muscle relaxation can cause deviations between the planned and delivered dose. Respiratory gating is one approach to compensate for respiratory motion. On conventional linear accelerators, a one-dimensional motion surrogate signal (e.g. from a respiratory bellows or reflective marker placed on the chest of a patient) is monitored over time. When the signal falls within a pre-determined threshold, the beam is turned on. On state-of-the-art hybrid MR imaging and RT treatment devices, gating can be based on rapidly acquired 2D cross-sectional images intersecting the target and/or organs at risk (OAR). The MR images reduce the uncertainty in estimating target position from motion surrogate signals.

The use of 4D CT or MR imaging (3D + respiratory phase) during simulation can also help to account for intrafraction motion. An internal target volume (ITV) may be created to encompass the total extent of the gross target volume (GTV) throughout the respiratory cycle. The GTV moves within this ITV while the beam is on, resulting in larger irradiated volumes that include OAR.

More recently, the use of pre-beam 4D-MRI has been proposed to be used in conjunction with beam-on cine imaging to accumulate dose on MR-guided RT systems. In this approach, a motion model is generated from the 4D-MRI and dynamically updated throughout the treatment fraction through deformable registration with the cine images. The dynamic update of the motion model facilitates generation of a synthetic 3D volume at the frame rate of the cine images, while also permitting control for deviations from the average respiratory cycle determined from the pre-beam 4D-MRI. While this is a powerful method, it has two key limitations. First, the frame rate of the cine images must be rapid enough to capture respiratory motion, limiting the pulse sequences that can be used and preventing interleaved functional imaging. Second, this method has no provision for validating model accuracy during treatments.

A plethora of methods exist for acquiring 4D-MRI. They can include 2D multi-slice approaches or volumetric (3D) approaches. Most commonly, the data sorting is performed retrospectively. A rapidly emerging 4D-MRI approach is that of a 3D stack-of-stars radial acquisition with a golden angle increment. This technique permits the use of combined parallel imaging and sparsity constraints to reconstruct high quality 3D volumes at multiple respiratory phases from highly undersampled acquisitions with durations under <NUM> minutes.

If the use of deformable registration is to be avoided, dynamically updating 4D-MRIs must be reconstructed throughout the treatment fraction. The simplest solution would be to acquire continuous 3D stack-of-stars data during beam-on and reconstruct 4D-MRI epochs that account for short-term variations in respiratory pattern or physiological effects such as organ filling. However, with this approach, the ability to acquire real-time images for instantaneous determination of the current respiratory phase is lost, as is the ability to track or perform respiratory gating based on 2D images.

Radiation therapy systems having a LINAC disposed on an articulated arm have been shown to be advantageous platforms for delivering radiation dose that is highly conformal to a tumor while minimizing dose to the surrounding normal tissue. However, to accurately treat the tumor, the tumor's precise location needs to be determined. In some existing radiation therapy systems with image-guidance, X-ray sources, typically mounted on the ceiling of the treatment room, are used to image the subject. Although these sources can provide real time 2D radiographic images for treatment alignment, they cannot be used to generate clinical images of the subject. In addition, 2D radiographic images lack volumetric information and cannot image tumors with only soft tissue contrast, which in many cases is highly desired. The article <NPL>, discloses a magnetic resonance imaging system using a surrogate respiratory apparatus for generating a surrogate respiratory signal of a subject contemporaneously with acquiring images of a tissue.

The present disclosure addresses the aforementioned drawbacks by providing a system in accordance with claim <NUM> for respiratory gated radiotherapy using a respiratory motion model. The motion model can be a continuously updated model that represents a patient's internal anatomy as a mathematical function of an external respiratory surrogate. In one configuration, the model can be implemented on a low-field MRI system, such as a <NUM>. 35T system, using amplitude and velocity of a respiratory bellows as a surrogate. In another configuration, multi-planar model-based respiratory gating can be performed.

In one configuration, a method for magnetic resonance imaging (MRI) guided respiratory gated radiotherapy is provided. The method includes acquiring images of a tissue in a subject and measuring, using the images, a position of the tissue in the images to determine motion of the tissue. A surrogate respiratory signal is acquired contemporaneously with acquiring the images. Motion of the tissue and the surrogate respiratory signal are correlated to create a motion model for the subject. A gated radiotherapy treatment is then administered where the gating is based upon the motion model.

The motion model includes a surrogate respiratory signal and a time derivative of the surrogate respiratory signal. The motion model is trained using the images prior to correlating motion of the tissue and the surrogate respiratory signal. The motion model updated by adding a recently acquired image to the model and by removing an earliest acquired image from the motion model. An agreement between gating based upon the motion model and a direct image gating may be determined and gating may be adjusted if a disagreement exists between the motion model gating and the direct image gating. Functional images may be acquired with the images of the tissue and the acquisition of these function images may be interleaved with the acquisition of the tissue images.

The system for performing image guided respiratory gated radiotherapy includes a magnetic resonance imaging system for acquiring images of a tissue in a subject and includes a surrogate respiratory apparatus for generating a surrogate respiratory signal of the subject contemporaneously with the images. A radiotherapy treatment system is also included and is configured to deliver radiotherapy treatment to the subject. A computer system is included and is configured to: i) measure a position of the tissue in the images; ii) determine motion of the tissue using the images; iii) correlate the motion of the tissue and the surrogate respiratory signal using a respiratory motion model; and iv) gate the radiotherapy treatment delivered to the subject using the motion model.

The computer system is further configured to train the motion model using the images prior to correlating motion of the tissue and the surrogate respiratory signal. The computer system is further configured to update the motion model by adding a recently acquired image to the model and removing an earliest acquired image from the model. The computer system is further configured to determine an agreement between the gating based upon the motion model and a direct image gating. Gating may be adjusted if a disagreement exists between the motion model gating and the direct image gating. The magnetic resonance imaging system may acquire functional images in addition to the images of the tissue and the acquisition of the functional images may be interleaved with the images of the tissue. In some configurations, the surrogate respiratory apparatus may include a bellows device. The magnetic resonance system may also be configured to acquire a stack of adjacent slices in a cyclic sequential fashion, and motion in each slice may be separately correlated to the external surrogate.

A system and method is provided for respiratory gated radiotherapy using a respiratory motion model where MRI-guided respiratory gating is performed with a continuously updated model that represents a patient's internal anatomy as a mathematical function of an external respiratory surrogate. The model represents the patient's internal tissue as if it were to be continuously imaged, such as at a high frame rate, allowing a high signal to noise ratio 3D image to be reconstructed at any breathing phase with magnetic resonance image (MRI). In one configuration, amplitude and velocity of a respiratory bellows acts as the respiratory surrogate. In one configuration, the model is built and updated by fitting anatomical motion measured using MRI images that are periodically acquired at a low frame rate. In another configuration, multi-planar model-based respiratory gating may be performed by sequentially imaging a stack of adjacent slice positions.

The technique can be used to perform MRI-image gated radiotherapy using sequences that do not allow rapid repeated imaging as is required for direct image-based gating, but may provide better image contrast (example: T2-weighted images), or the technique can be used to enable acquisition of MRI functional imaging concurrently interleaved with gated radiotherapy by reducing the frequency of images needed for accurate gating. Functional imaging, such as diffusion-weighted imaging, may be interleaved during gated treatments to assess tumor response. The model may also be used to estimate accumulated dose during a radiotherapy fraction subject to respiratory motion, and/or extract local measures of tissue properties.

Referring to <FIG>, a radiation therapy system is depicted which may be used in conjunction with example implementations of the present invention. An example of an image-guided radiation therapy (IGRT) system <NUM> includes a therapeutic (treatment) source <NUM> and a diagnostic (imaging) MRI system <NUM>, both of which may be contained in housing <NUM>. The system <NUM> allows the therapeutic source <NUM> and the diagnostic MRI system <NUM> to be focused in a desired manner with respect to a target volume <NUM> in a subject <NUM> positioned on a patient table <NUM>.

In some configurations, positioned opposite the treatment source <NUM> is an optional electronic portal imaging device (EPID), such as x-ray imager detector <NUM>. The detector <NUM> functions as a portal image device when receiving radiation from the therapeutic source <NUM>. The detector <NUM> may contain a number of detector elements (e.g., an array of detector elements) that together sense the projected radiation that passes through the subject <NUM>. Each detector element produces an electrical signal that represents the intensity of a beam impinging on that detector element and, hence, the attenuation of the beam as it passes through the subject <NUM>.

The table <NUM> may allow for moving a subject <NUM> into and out of the system <NUM> through use of table motion controller <NUM>. A control mechanism <NUM> controls the operation of the therapeutic source <NUM> and the diagnostic system <NUM>. The IGRT system <NUM> includes an operator workstation <NUM>, which may include a computer <NUM> that receives commands and scanning parameters from an operator via an input or from a memory or other suitable storage medium <NUM>. The input may be a keyboard, a mouse, a touch screen, or other suitable input mechanism. An associated display <NUM> allows the operator to observe data from the computer <NUM>, including images of the subject <NUM> that may be used to review or modify the treatment plan, and to position the subject <NUM> by way of appropriately adjusting the position of the patient table <NUM>. The operator supplied commands and parameters may also be used by the computer <NUM> to provide control signals and information to the control mechanism <NUM>.

The therapeutic source <NUM> is controlled by a radiotherapy controller <NUM> that forms a part of the control mechanism <NUM> and which provides power and timing signals to the therapeutic source <NUM>. The controller <NUM> also provides power and timing signals to the diagnostic imaging system <NUM> through imaging controller <NUM>. In some configurations, the controller <NUM> can include two independent controllers for controlling the therapeutic source <NUM> and the diagnostic imaging system <NUM>, and in other configurations a single controller can control both systems.

The therapeutic source <NUM> produces a radiation beam <NUM>, or "field," in response to control signals received from the controller <NUM> focused on a target volume <NUM>. The diagnostic imaging system <NUM> acquires MR imaging data of the subject <NUM> for a target volume <NUM>. The position of the patient table <NUM> may also be adjusted to change the position of the target volume <NUM> with respect to the therapeutic source <NUM>, the diagnostic imaging system <NUM>, and the detector <NUM> by way of a table motion controller <NUM>, which is in communication with the computer <NUM> and operator workstation <NUM>.

A data acquisition system (DAS) <NUM> samples data from the detector <NUM>. In some configurations, the data sampled from the detector <NUM> is analog data and the DAS <NUM> converts the data to digital signals for subsequent processing. In other configurations, the data sampled from the detector <NUM> is digital data. The operator workstation <NUM>, or a separate image reconstructor <NUM>, receives x-ray data from the DAS <NUM> and performs image reconstruction. The reconstructed images can be stored in a mass storage device <NUM>, or can be displayed on the display <NUM> of the operator workstation <NUM>.

Referring to <FIG>, a flowchart depicting one configuration for respiratory gated radiotherapy using a respiratory motion model is shown. The process begins with images of a location of a tumor, lesion, tissue, or anatomy of interest being acquired during a training phase, and are subsequently acquired periodically during gated radiotherapy at step <NUM>. At the same time, an external respiratory surrogate is continuously acquired at <NUM>. A typical time between images would be <NUM>-<NUM> seconds. Initial model training is conducted at step <NUM>. For initial model training, a sequence of N images is acquired. A value of N may be <NUM>, but other values are possible including between <NUM>-<NUM>, and the duration of the training period may be about <NUM>-<NUM> seconds. The position of the tumor is measured in each of the N training images at step <NUM>. A patient-specific respiratory motion model is fit to the acquired images at step <NUM>. The model is a mathematical function that establishes a correlation between tissue motion and the respiratory surrogate signal. After the training period, treatment commences and the gating decision is based on the external surrogate, using the model at step <NUM>. The model is updated at step <NUM>. In-between images acquired for model building/updating, functional imaging can be performed depending on time requirements and the functional imaging sequence used and the sequence used for model building/updating.

Referring to <FIG>, one configuration for a process of model updating during image guided radiotherapy is depicted. The most recently acquired image is added to the training sequence at step <NUM>, and the earliest acquired image is dropped from the training sequence at step <NUM>. The model is then rebuilt from the new training set at step <NUM>. The model-based gating decision may be compared to the direct image gating decision based on the newly acquired image at step <NUM>. Information on agreement between the model-based and direct image-based gating could be used to trigger an action to improve the accuracy of the system at step <NUM>. In the event of a disagreement between the model-based and direct image-based gating, information may be used to adjust the gating at step <NUM>, such as to pause to avoid an irregular breathing cycle that is not accurately described by the model but was detected via direct imaging. After gating is adjusted at step <NUM>, or after improving the accuracy of the system at step <NUM>, the process may be repeated by returning to step <NUM>. The process may be repeated for the duration of an imaging procedure, the course of a radiotherapy treatment, or for any desired period of time.

In one configuration, a high-speed MRI sequence (non-T2 weighted) may be used to acquire sequential images of a subject. In this way, only a fraction of the acquired images may be needed to build the model, allowing the remaining images to be used to assess the model accuracy. Referring to <FIG>, a respiratory surrogate signal <NUM> is shown. Images used for model building <NUM> are reflected as filled-in dots, while images acquired for accuracy assessment <NUM> are shown as open dots. In one example, the liver of a human volunteer was imaged at a rate of <NUM> seconds between images. A liver vessel was tracked to simulate a tumor. To simulate a T2-weighted image frame rate, only every 10th image was used to build the model. The remaining images were used to evaluate the accuracy of the model-based gating decision. The model in the present example was <NUM>% specific compared to ground truth, meaning that over all evaluated image time points, if the model-based gating decision was 'beam-on', the direct image-based gating decision was 'beam-on' <NUM>% of the time.

In one configuration, initial model training may consist of <NUM> images, with a frame rate of <NUM> seconds between images. Deformable image registration may be used to measure the motion of the tumor and surrounding tissue in each training image relative to a breath hold image acquired at the beginning of the session. A simulated tumor contour may be drawn on the reference image and a boundary may be formed by an isotropic margin about the tumor contour. Margins may take on any appropriate size, such as <NUM> about the tumor. A respiratory bellows may be used as the external respiratory surrogate. The bellows may be strapped to the patient's abdomen and a pressure change resulting from abdominal motion may be electronically recorded.

In one configuration, the motion model may take the form of x→ = α→V + β→f, where V is the amplitude of the bellows signal, f is the time derivative of the bellow signal, and α→ and β→ are vector-valued tissue-specific parameters extracted using least-squares fitting. During simulated treatment, the motion model may be used to deform the tumor contour to the current breathing phase as determined by the respiratory surrogate. The beam may be turned off if the model-deformed contour is outside the boundary by <NUM>% or more of its area. When an image is acquired, such as every <NUM> seconds, for example, the model may be updated. Then, the model-based gating decision may be compared to the actual image-based gating decision. If the model decision is 'beam-on', but the actual image decision is 'beam-off', a beam veto may be enforced, such as for <NUM> second, for example. This technique may be used to mitigate the occurrence of irregular breaths that are not accurately described by the model. To evaluate model performance, for the images not used for model building, the model-based gating decision may be compared to the direct image-based gating decision without updating the model.

Referring to <FIG>, a flowchart depicting one configuration of the process of building the respiratory motion model is shown. The surrogate amplitude and velocity may be fit along with the image deformation vectors obtained from a 2D non-rigid image registration to the 5D motion model: x→ = αv + βf + x<NUM> where x→ is the estimated tissue position, a and β establish a correlation between the surrogate amplitude v and velocity f to the tissue position, and x<NUM> is the initial tissue position. The 2D images are acquired and registered at step <NUM>. A 3D reference volume is acquired using a 3D MR sequence at step <NUM> during a breath hold. In one example, a 3D reference image may include a <NUM> sec. balanced steady state free precession (bSSFP) 3D breath-hold sequence with <NUM> x <NUM> x <NUM> voxel resolution and using deformable image registration via Elastix with bilateral filtering. The 2D images are then combined to form a high SNR volume at step <NUM>, which may have a higher SNR than the 3D reference volume. The model is fit using the respiratory surrogate at step <NUM>. The motion model fit may be a voxel-specific motion model and may be smoothed with a Gaussian kernel to reduce the influence of noise. The 3D model volume is generated at <NUM>.

In some configurations, error evaluation may be performed using leave-one-out landmarks, or with a deformation vector field. This may establish model robustness by providing a user with the agreement or disagreement between different models. Surrogate estimation error may also be analyzed using a cross-validation error routine and the like. Image registration evaluation may also be performed where landmarks may be assessed for how much spatial displacement they experience. 3D deformation using a 2D-3D slice to volume registration may be used to suppress errors, and may also improve registration at sliding tissue interfaces. Accelerated image acquisition may also be performed to reduce over-sampling and decrease the acquisition times.

Referring to <FIG> one configuration where the respiratory bellows surrogate <NUM> is correlated to a k-space imaging surrogate <NUM> derived from a single imaging plane is shown. This correlation allows the imaging surrogate to be estimated using the bellows surrogate while imaging at different lateral planes, as shown with estimated points <NUM>. This technique will allow the model to be constructed with an imaging based surrogate that may more accurately model anatomical motion, and would be accessible during radiotherapy gating. Referring to <FIG>, a correlation between the imaging and bellows surrogates is shown with example data.

Referring to <FIG>, in one configuration the radiotherapy treatment beam may be turned off if the model-deformed contour is outside the boundary by a predetermined amount, such as <NUM>% or more of its area. Examples are shown in <FIG> of: raw image and image-based gating decision 'beam-on' is shown in the upper left, model-based image and model-based gating decision 'beam-on' is shown in the upper right, raw image and image-based gating decision 'beam-off is shown in the lower left, and model-based image and image-based gating decision 'beam-off' is shown in the lower right.

In another configuration, multi-planar model-based respiratory gating may be performed. Clinical MRI-based respiratory gating is performed with single sagittal plane images only, because imaging in multiple planes is not fast enough to turn the beam off in time to accommodate respiratory motion with current MRI technology. This is a significant limitation because for some tumor types, the spatial relationship of the tumor and nearby radio-sensitive normal organs is complex and dynamic and therefore not easily captured by single plane imaging. For example, pancreatic tumors are adjacent to the stomach and wrapped by the duodenum, organs which are very sensitive to high radiation doses and which move and deform with respiration as well as digestive processes.

The multi-planar model-based approach overcomes the imaging speed limitation on multi-plane imaging by sequentially imaging a stack of adjacent slice positions. These raw images cannot be used directly for gating because the anatomy they represent is inconsistent image-to-image until the entire stack has been imaged and the sequence starts again with a period of a few seconds. In one configuration, a motion model is used to interpolate a virtual image at every slice position at all times when a raw image is not available.

Claim 1:
A system for performing image guided respiratory gated radiotherapy, comprising:
a magnetic resonance imaging system for acquiring images of a tissue in a subject;
a surrogate respiratory apparatus for generating a surrogate respiratory signal of the subject contemporaneously with acquiring the images of the tissue, wherein the surrogate respiratory apparatus includes a bellows;
a radiotherapy treatment system configured to deliver radiotherapy treatment to the subject;
a computer system configured to:
i) control the magnetic resonance imaging system to acquire image slices of the tissue in the subject;
ii) measure a position of the tissue in the image slices;
iii) determine motion of the tissue using the image slices;
iv) correlate the motion of the tissue and the surrogate respiratory signal using a respiratory motion model that includes an amplitude of the surrogate respiratory signal and a time derivative of the surrogate respiratory signal;
v) gate the radiotherapy treatment delivered to the subject using the motion model;
vi) determine an agreement between the gating based upon the motion model and a direct image gating and adjust gating upon determining a disagreement between the motion model gating and the direct image gating;
vii) train the motion model using the image slices of the tissue prior to correlating motion of the tissue and the surrogate respiratory signal; and
vii) update the motion model by adding a recently-acquired image to the model and removing an earliest acquired image from the model.