TECHNICAL FIELD

Embodiments of the present invention pertain generally to determining machine parameters in a radiation therapy treatment system. In particular, the present invention pertains to using deep learning technologies to determine machine parameters for a treatment plan in a radiation therapy system.

BACKGROUND

Radiation therapy or “radiotherapy” can be used to treat cancers or other ailments in mammalian (e.g., human and animal) tissue. One such radiotherapy technique is a Gamma Knife, by which a patient is irradiated by a large number of low-intensity gamma rays that converge with high intensity and high precision at a target (e.g., a tumor). In another embodiment, radiotherapy is provided using a linear accelerator, whereby a tumor is irradiated by high-energy particles (e.g., electrons, protons, ions, high-energy photons, and the like). The placement and dose of the radiation beam must be accurately controlled to ensure the tumor receives the prescribed radiation, and the placement of the beam should be such as to minimize damage to the surrounding healthy tissue, often called the organ(s) at risk (OARs). Radiation is termed “prescribed” because a physician orders a predefined amount of radiation to the tumor and surrounding organs similar to a prescription for medicine. Generally, ionizing radiation in the form of a collimated beam is directed from an external radiation source toward a patient.

A specified or selectable beam energy can be used, such as for delivering a diagnostic energy level range or a therapeutic energy level range. Modulation of a radiation beam can be provided by one or more attenuators or collimators (e.g., a multi-leaf collimator). The intensity and shape of the radiation beam can be adjusted by collimation avoid damaging healthy tissue (e.g., organs at risk) adjacent to the targeted tissue by conforming the projected beam to a profile of the targeted tissue.

The treatment planning procedure may include using a three-dimensional image of the patient to identify a target region (e.g., the tumor) and to identify critical organs near the tumor. Creation of a treatment plan can be a time consuming process where a planner tries to comply with various treatment objectives or constraints (e.g., dose volume histogram (DVH), overlap volume histogram (OVH)), taking into account their individual importance (e.g., weighting) in order to produce a treatment plan that is clinically acceptable. This task can be a time-consuming trial-and-error process that is complicated by the various organs at risk (OARs) because as the number of OARs increases (e.g., up to thirteen for a head-and-neck treatment), so does the complexity of the process. OARs distant from a tumor may be easily spared from radiation, while OARs close to or overlapping a target tumor may be difficult to spare.

Traditionally, for each patient, the initial treatment plan can be generated in an “offline” manner. The treatment plan can be developed well before radiation therapy is delivered, such as using one or more medical imaging techniques. Imaging information can include, for example, images from X-rays. Computed Tomography (CT), nuclear magnetic resonance (MR), positron emission tomography (PET), single-photon emission computed tomography (SPECT), or ultrasound. A health care provider, such as a physician, may use three-dimensional imaging information indicative of the patient anatomy to identify one or more target tumors along with the organs at risk near the tumor(s). The health care provider can delineate the target tumor that is to receive a prescribed radiation dose using a manual technique, and the health care provider can similarly delineate nearby tissue, such as organs, at risk of damage from the radiation treatment. Alternatively or additionally, an automated tool (e.g., ABAS provided by Elekta AB, Sweden) can be used to assist in identifying or delineating the target tumor and organs at risk. A radiation therapy treatment plan (“treatment plan”) can then be created using an optimization technique based on clinical and dosimetric objectives and constraints (e.g., the maximum, minimum, and fraction of dose of radiation to a fraction of the tumor volume (“95% of target shall receive no less than 100% of prescribed dose”), and like measures for the critical organs).

The treatment planning procedure may include using a three-dimensional image of the patient to identify a target region (e.g., the tumor) and to identify critical organs near the tumor. Creation of a treatment plan can be a time consuming process where a planner tries to comply with various treatment objectives or constraints (e.g., dose volume histogram (DVH) objectives), taking into account their individual importance (e.g., weighting) in order to produce a treatment plan that is clinically acceptable. This task can be a time-consuming trial-and-error process that is complicated by the various organs at risk (OARs) because as the number of OARs increases (e.g., up to thirteen for a head-and-neck treatment), so does the complexity of the process. OARs distant from a tumor may be easily spared from radiation, while OARs close to or overlapping a target tumor may be difficult to spare. The treatment plan can then be later executed by positioning the patient in the treatment machine and delivering the prescribed radiation therapy. The radiation therapy treatment plan can include dose “fractioning,” whereby a sequence of radiation therapy deliveries are provided over a predetermined period of time (e.g., 45 fractions), with each therapy delivery including a specified fraction of a total prescribed dose. However, during treatment the position of the patient and the position of the target tumor in relation to the treatment machine (e.g., linear accelerator—“linac”) is very important in order to ensure the target tumor and not healthy tissue is irradiated.

OVERVIEW

Intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) have become the standards of care in modern cancer radiation therapy. Determining individual patient IMRT or VMAT treatment plans can include a trial-and-error process, and can include weighing target dose versus organ at risk (OAR) sparing tradeoffs, and adjusting program constraints where effects of the program constraints on the plan quality metrics and the dose distribution can be very difficult to predict. The order in which the planning constraints can be adjusted can introduce dose differences. Treatment plan quality can depend on subjective judgements by the dosimetrist that can depend on an experience or skill level of the dosimetrist. Even the most skilled dosimetrists still have no assurances that their plans are close to optimal, or whether a little or a lot of effort will result in a better plan. In certain approaches, a computationally intensive algorithm can be used to determine a treatment plan, however such approaches can require hours to compute a treatment plan. Computational approaches can include comparing one-dimensional target-organ overlap measures in a new patient (DVHs, OVHs) with overlap measures associated with high-quality plans from previous patients to find plans similar to that of the new patient, and exploring plan quality space to determine optimal, or even Pareto-optimal, families of plans that the dosimetrist may then select for use in a radiation therapy treatment. The present inventors have recognized, among other things, the need for a treatment planning system that can determine a high-quality treatment plan in real time (e.g. providing a treatment plan or updated treatment plan during a patient workflow), such as to improve patient workflows (e.g., reduce a patient wait time to receive a radiation treatment) and to provide for rapid radiation treatment therapy replanning (e.g., a treatment plan can be recalculated or updated based changes in a patient). A high quality treatment plan can be a treatment plan that satisfies the treatment plan prescription, e.g., a plan that irradiates the target to the extent intended by the prescribing physician, and that spares the OARs as much as possible or to the extent specified in the treatment plan constraints. A treatment plan can include an intensity modulated radiotherapy treatment plan or a volumetric modulated arc therapy treatment plan. These constraints are usually depicted as dose volume histograms (DVHs) displaying the cumulative dose per volume fraction of target or OAR volume, and fractional dose to fractional target/OAR volume as described above. The present inventors have also recognized that it is possible to train a neural network, such as a deep convolutional neural network to determine machine parameters for a radiation therapy treatment plan based on imaging information. The neural network can learn the properties of treatment plans for a common diagnosis and can predict the likely treatment machine parameter settings, such as to deliver the intended dose distributions. The machine parameters can include gantry angles, beam apertures (e.g., beam section shapes) through which the therapeutic X-ray beams can be projected at a target, and the aperture intensities. The neural network can estimate the machine parameters to drive the treatment machine, based on the image of the patient and the target and OAR delineations. The neural network can include a model with linkages between patient anatomies and treatment constraints, and the treatment machine parameter settings that can deliver the intended 3D dose distribution. The trained neural network can provide a model of the treatment planning process that can encapsulate the many subjective decisions made during plan creation, and can enable the production of plan templates to initiate plan creation, provide an assessment of plan quality, provide aid for treatment clinics lacking deep local IMRT/VMAT expertise, and perhaps in the future provide fully automated treatment planning.

In an aspect, the disclosure can feature a method for training a deep convolutional neural network, such as to provide a patient radiation treatment plan. The method can include collecting patient data from a group of patients. The patient data can include at least one image of patient anatomy and a prior treatment plan. The treatment plan can include predetermined machine parameters. The method can also include training a deep convolution neural network for regression by using the prior treatment plans and the corresponding collected patient data, such as to determine a new treatment plan. The new treatment plan can include predicted machine parameters. The method can also include training the deep convolutional neural network by adjusting one or more parameters of the deep convolutional neural network, such as to minimize a cost function that includes a difference between the predetermined sets of machine parameters and predicted sets of machine parameters. The predetermined machine parameters can include at least one of a gantry angle, a multi-leaf collimator leaf position, or a radiation therapy beam intensity. The predicted machine parameters can include at least one of a gantry angle, a multi-leaf collimator leaf position, or a radiation therapy beam intensity. The method can also include collecting patient data including at least one signed distance map from each patient in a group of patients. The at least one image of patient anatomy can include at least one of a planning CT image, an anatomy label map, a determined object distance such as a signed distance map from the patient.

In an aspect, the disclosure can feature a method of using a deep convolutional neural network, such as to provide a radiation treatment plan. The method can include retrieving a trained deep convolution neural network previously trained on patient data from a group of patients. The method can also include collecting new patient data. The new patient data can include at least one image of patient anatomy. The method can also include determining a new treatment plan for the new patient using the trained deep convolutional neural network for regression. The new treatment plan can include a new set of machine parameters. The trained deep convolutional neural network can provide the new treatment plan including the set of machine parameters. The set of machine parameters can include at least one of a gantry angle, a multi-leaf collimator leaf position, or a radiation therapy beam intensity. The new treatment plan can be created in real-time. The new treatment plan can be created in real-time during a radiation therapy treatment. A treatment plan that can be created in real-time during a radiation therapy treatment can include an online adaptive plan.

In an aspect, the disclosure can feature a radiation therapy treatment system. The system can include an image acquisition device, such as to collect patient data including at least one image of patient anatomy. The system can also include a radiation therapy device, such as to deliver radiation therapy to a patient. The system can also include a non-transitory machine-readable medium, such as to store a trained deep convolution neural network and radiation therapy treatment plans. The system can also include a processor, such as to generate a new treatment plan based on the collected patient data using the trained deep convolution neural network for regression. The new treatment plan can include a set of machine parameters. The system can also include a radiation therapy control circuit that can be configured to instruct the radiation therapy device to deliver radiation therapy to the patient in accordance with the new radiation treatment plan having a new set of machine parameters. The trained deep convolution neural network can be configured to be previously trained on patient data from a group of patients. The new set of machine parameters can include predicted machine parameters. The new set of machine parameters can include at least one of a gantry angle, a multi-leaf collimator leaf position, or a radiation therapy beam intensity. The deep convolutional neural network can be trained by adjusting one or more parameters of the deep convolutional neural network, such as to minimize a cost function that can include a difference between a predetermined set of machine parameters and a predicted set of machine parameters. Patient data can include at least one signed distance map from each patient in a group of patients. The at least one image of patient anatomy can include at least one of a planning CT image, an anatomy label map, a determined object distance such as a signed distance map from the patient. The new treatment plan can be created in real-time. The new treatment plan can be created in real-time during a radiation therapy treatment.

In an aspect, the disclosure can feature a non-transitory machine-readable medium including instructions, which when executed by an image processor, cause the image processor to receive patient data from a group of patients. The patient data can include at least one image of patient anatomy and a prior treatment plan. The treatment plan can include predetermined machine parameters. The non-transitory machine readable medium can also include instructions, which when executed by the image processor, cause the image processor to train a deep convolution neural network for regression by using the prior treatment plans and the corresponding collected patient data. The at least one or more parameters of the deep convolution neural network can be adjusted to minimize a cost function. The non-transitory machine readable medium can also include instructions, which when executed by the image processor, cause the image processor to determine a new treatment plan that can include a new set of predicted machine parameters using the trained deep convolution neural network. The machine parameters can include at least one of a gantry angle, a multi-leaf collimator leaf position, or a radiation therapy beam intensity. The non-transitory machine readable medium can also include instructions, which when executed by the image processor, cause the image processor to receive patient data that can include at least one signed distance map from each patient in a group of patients. The at least one image of patient anatomy can include at least one of a planning CT image, an anatomy label map, or a determined object distance. The new treatment plan can be created in real-time during a radiation therapy treatment.

