Bed calculation with isotoxic planning

Systems and methods are disclosed for performing operations comprising: receiving dose information representing dose delivered during a first radiotherapy treatment fraction; accessing one or more previous dose information representing dose delivered during one or more previous radiotherapy treatment fractions; computing a measure of biologically effective dose (BED) based on a combination of the dose information delivered during a first radiotherapy treatment fraction and the dose delivered during the one or more previous radiotherapy treatment fractions; and performing an isotoxic planning process for delivering a second radiotherapy treatment fraction following the first radiotherapy treatment fraction based on the computed measure of BED.

TECHNICAL FIELD

Embodiments of the present disclosure pertain generally to performing isotoxic planning in radiotherapy treatment sessions.

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 involves irradiation with a Gamma Knife, whereby a patient is irradiated by a large number of low-intensity gamma ray beams 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 (MLC)). The intensity and shape of the radiation beam can be adjusted by collimation to avoid damaging healthy tissue (e.g., OARs) 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 (3D) 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 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), 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 3D imaging information indicative of the patient anatomy to identify one or more target tumors along with the OARs 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 optimized plan is comprised of numerical parameters that specify the direction, cross-sectional shape, and intensity of each radiation beam.

The treatment plan can then be later executed by positioning the patient in the treatment machine and delivering the prescribed radiation therapy directed by the optimized plan parameters. The radiation therapy treatment plan can include dose “fractioning,” whereby a sequence of radiation treatments is provided over a predetermined period of time (e.g., 30-45 daily fractions), with each treatment 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

In some aspects, systems and methods are provided for performing operations including: receiving dose information representing delivery of a dose distribution to a target corresponding to a first radiotherapy treatment fraction; determining a target type associated with the target; applying a machine learning technique, associated with the determined target type, to the dose information to predict a measure of biologically effective dose (BED), the machine learning technique being trained to establish a relationship between a set of prior dose distributions delivered for the target type and one or more ground truth BED measurements; and performing an isotoxic planning process based on the predicted measure of BED.

In some aspects, the operations further comprise: storing a plurality of machine learning techniques each associated with a different target type and configured to predict BED for the associated target type.

In some aspects, the operations further comprise selecting the machine learning technique from the plurality of machine learning techniques based on the determined target type.

In some aspects, the operations further comprise: computing a measured BED by: accumulating a plurality of doses delivered across a plurality of radiotherapy treatment fractions into a first value, the plurality of radiotherapy treatment fractions comprising the first radiotherapy treatment fraction; accumulating an adjusted sum of squares of the plurality of doses; and combining the accumulated plurality of doses and the adjusted sum of squares of the plurality of doses into the measure of BED; and comparing the measured BED to the predicted measure of BED.

In some aspects, the operations further comprise: computing a difference between the measured BED to the predicted measure of BED; comparing the difference to a threshold; and controlling delivery of a radiotherapy treatment plan based on a result of comparing the difference to the threshold.

In some aspects, the operations further comprise training the machine learning technique by: obtaining a set of training data comprising a set of training dose distributions and a ground truth measure of BED; applying the machine learning technique to the set of training dose distributions to predict a training measure of BED; computing a deviation between the predicted training measure of BED and the ground truth measure of BED; and updating one or more parameters of the machine learning technique based on the deviation.

In some aspects, the set of training data is generated by: obtaining the set of training dose distributions associated with multiple radiotherapy treatment fractions; accumulating the set of training dose distributions into a first value; accumulating an adjusted sum of squares of the set of training dose distributions; combining the accumulated set of training dose distributions and the adjusted sum of squares of the set of training dose distributions into the ground truth measure of BED; and associating the ground truth measure of BED with the set of training dose distributions.

In some aspects, systems and methods are provided for performing operations comprising: receiving dose information representing dose delivered during a first radiotherapy treatment fraction; accessing one or more previous dose information representing dose delivered during one or more previous radiotherapy treatment fractions; computing a measure of biologically effective dose (BED) based on a combination of the dose information delivered during a first radiotherapy treatment fraction and the dose delivered during the one or more previous radiotherapy treatment fractions; and performing an isotoxic planning process for delivering a second radiotherapy treatment fraction following the first radiotherapy treatment fraction based on the computed measure of BED.

In some aspects, the one or more previous radiotherapy treatment fractions were delivered prior to the first radiotherapy treatment fraction.

In some aspects, the operations further comprise: computing a total survival fraction of tumor cells based on the computed measure of BED.

In some aspects, the operations further comprise: obtaining a first constant corresponding to a tumor cell associated with a patient being treated by the first radiotherapy treatment fraction; and computing the total survival fraction as an exponential of the first constant and the computed measure of BED.

In some aspects, the operations further comprise: accumulating a plurality of doses delivered across a plurality of radiotherapy treatment fractions into a first value, the plurality of radiotherapy treatment fractions comprising the first radiotherapy treatment fraction and the one or more previous radiotherapy treatment fractions; accumulating an adjusted sum of squares of the plurality of doses; and combining the accumulated plurality of doses and the adjusted sum of squares of the plurality of doses into the measure of BED.

In some aspects, the operations further comprise: obtaining first and second constants corresponding to a tumor cell associated with a patient being treated by the first radiotherapy treatment fraction; computing a ratio of the first and second constants; and multiplying the sum of squares of the plurality of doses by the ratio to compute the adjusted sum of squares of the plurality of doses.

In some aspects, the operations further comprise: presenting the measure of BED to an operator before delivery of the second radiotherapy treatment fraction.

In some aspects, the operations further comprise: receiving magnetic resonance imaging information associated with the second radiotherapy treatment fraction prior to delivery of the second radiotherapy treatment fraction; computing, based on the magnetic resonance imaging information, a distance between a critical organ and a tumor cell associated with a patient being treated by the second radiotherapy treatment fraction; and setting a dose to be delivered during the second radiotherapy treatment fraction based on the distance and the measure of BED.

In some aspects, the operations further comprise: adjusting the set dose during delivery of the second radiotherapy treatment fraction; and updating the measure of BED based on the adjusted set dose delivered during the second radiotherapy treatment fraction.

In some aspects, the measure of the BED comprises a second order function of a plurality of doses delivered across a plurality of radiotherapy treatment fractions.

In some aspects, the measure of the BED is computed in accordance with: BED=Σdi+(β/α)Σdi2where BED is the measure of BED, direpresents dose delivered during a radiotherapy treatment fraction number i, β and α represent respectively first and second constants associated with a tumor cell associated with a patient being treated by the first radiotherapy treatment fraction.

In some aspects, a total survival fraction is computed in accordance with: exp[−α{BED}] where exp is an exponential function.

DETAILED DESCRIPTION

In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and which is shown by way of illustration-specific embodiments in which the present disclosure may be practiced. These embodiments, which are also referred to herein as “examples,” are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that the embodiments may be combined, or that other embodiments may be utilized and that structural, logical and electrical changes may be made without departing from the scope of the present disclosure. The following detailed description is, therefore, not be taken in a limiting sense, and the scope of the present disclosure is defined by the appended claims and their equivalents.

Typical radiotherapy treatment sessions rely on MR images (MRI) to visualize very small tumor cells. To treat such small tumor cells, the radiotherapy dose per day or fraction can be increased which consequently decreases the number of times a patient has to undergo radiotherapy treatment (e.g., reduce the number of radiotherapy treatment sessions). The drawback of increasing the dose per day or fraction is the potential harm to nearby critical organs. Given that such nearby organs and/or tumor move from one day to the next, the MRI images can be captured just before or during a radiotherapy treatment fraction in order to adjust (increase or decrease) the amount of radiotherapy dose to the tumor that is being or will be delivered in the given radiotherapy treatment session. In some cases, where the distance between the tumor cells and critical organs is small, the radiotherapy dose to the tumor can be decreased while keeping the dose to nearby critical organs or OARs unchanged, and where the distance between the tumor cells and critical organs is relatively large, the radiotherapy dose to the tumor can be increased by keeping the dose to nearby critical organs or OARs identical. This process of modifying the radiotherapy dose distribution relative to the prescribed radiotherapy dose distribution specified in a radiotherapy treatment plan is referred to as isotoxic planning. Namely, isotoxic planning involves an automated or operator guided radiotherapy dose distribution adjustment based on MR or even CT images captured just prior or during a given radiotherapy treatment fraction based on a distance between a tumor cell and a critical organ, in order to maximize allowable dose to the tumor while giving the same radiation toxicity to the nearby critical organs in each treatment delivery or fraction

Certain isotoxic planning techniques (or isoeffective dose calculations) can utilize a BED measure. Specifically, the BED represents the efficiency at which cancer cells are killed with radiation or radiotherapy, such that when the BED is increased, the cancer cells are killed more efficiently with improved survival rates. The BED is indicative of how much radiotherapy dose was applied to the tumor cells rather than the actual physical radiotherapy dose distribution that was delivered by the radiotherapy treatment device. In this way, the BED is a measure of the true biological dose delivered by a particular combination of dose per fraction and total dose to a particular tissue characterized by a specific constant associated with a type of tumor cell. The BED is the amount of an absorbed radiation that reaches targets or sites of action within the body to cause a biologic effect.

The traditional BED formula that is typically computed to represent the cancer cell destruction presumes isoeffective planning whereby the dose to the tumor remains constant for all the treatment fractions. In other words, if we employ the traditional BED formula for isotoxic planning, an approximation or mean of the radiotherapy dose to the tumor needs to be used to calculate the BED. Such an approximation or mean, though, can result in quadratically inaccurate representations of the BED. This can severely impact the isotoxic planning and can end up causing inaccurate BED calculation. Because the BED directly and theoretically relates to cancer cells' survival rates, inaccuracy in the BED may result in incorrect cancer research outcomes after collecting a large number of clinical data including the relationship between the BED and treatment outcome (overall survival, local control etc) for each patient.

The disclosed embodiments provide an advanced technique for accurately computing and measuring BED based on an accumulation of dose delivered during a current radiotherapy treatment fraction and the dose delivered during one or more prior radiotherapy treatment fractions. Based on the improved measure of the BED, a more accurate representation is provided of the amount of dose distribution delivered to the cancer cells in a patient. This more accurate BED measure can be used to perform an isotoxic planning process. In some implementations, a plurality of doses delivered across a plurality of radiotherapy treatment fractions is accumulated into a first value. A quadratic term of the plurality of doses is additionally accumulated and combined with the accumulated plurality of doses to generate the measure of BED. In some implementations, a total survival fraction of tumor cells can also be computed based on the measure of BED.

In this way, the accuracy of the BED delivered to the tumor of a patient over entire radiotherapy treatment fractions can be improved which can provide more accurate prediction of the patient's treatment outcome.

FIG.1illustrates an example radiotherapy system100for providing radiation therapy to a patient, according to some embodiments. The radiotherapy system100includes an image processing device112(also referred to as a dose processing device). The image processing device112may be connected to a network120. The network120may be connected to the Internet122. The network120can connect the image processing device112with one or more of a database124, a hospital database126, an oncology information system (OIS)128, a radiation therapy device130, an image acquisition device132, a display device134, and a user interface136. The image processing device112can be configured to generate radiation therapy treatment plans142to be used by the radiation therapy device130.

The image processing device112may include a memory device116, a processor114, and a communication interface118. The memory device116may store computer-executable instructions, such as an operating system143, radiation therapy treatment plans142(e.g., original treatment plans, adapted treatment plans and the like), software programs144(e.g., artificial intelligence, deep learning, neural networks, radiotherapy treatment plan software), and any other computer-executable instructions to be executed by the processor114.

in some embodiments, the software programs144may convert medical images of one format MRI) to another format (e.g., CT) by producing synthetic images, such as pseudo-CT images. For instance, the software programs144may include image processing programs to train a predictive model for converting a medical image146in one modality (e.g., an MRI image) into a synthetic image of a different modality (e.g., a pseudo CT image); alternatively, the trained predictive model may convert a CT image into an MRL image. In other embodiments, the software programs144may register the patient image (e.g., a CT image or an MR image) with that patient's dose distribution (also represented as an image) so that corresponding image voxels and dose voxels are associated appropriately by the network. In yet other embodiments, the software programs144may substitute functions of the patient images or processed versions of the images that emphasize some aspect of the image information. Such functions might emphasize edges or differences in voxel textures, or any other structural aspect useful to neural network learning. In other embodiments, the software programs144may substitute functions of the dose distribution that emphasize some aspect of the dose information. Such functions might emphasize steep gradients around the target or any other structural aspect useful to neural network learning. The memory device116may store data, including medical images146, patient data145, and other data required to create and implement a radiation therapy treatment plan142.

In some embodiments, the software programs144can compute BED for a particular patient and/or type of tumor cell being treated in a radiotherapy treatment session. Specifically, the software programs144receive dose information representing dose distribution delivered during a first radiotherapy treatment fraction. The software programs144access one or more previous dose information representing dose distribution delivered during one or more previous radiotherapy treatment fractions. The software programs144compute a measure of BED based on a combination of the dose information delivered during a first radiotherapy treatment fraction and the dose delivered during the one or more previous radiotherapy treatment fractions. For example, the software programs144can compute the BED in accordance with Equation 1 below:
BED=Σdi+(β/α)Σdi2Equation 1
where BED is the measure of BED, direpresents dose delivered during a radiotherapy treatment fraction number i, β and α represent respectively first and second constants associated with a tumor cell associated with a patient being treated by the first radiotherapy treatment fraction with isotoxic planning, whereby the dose to nearby critical organs is predetermined by a radiation oncologist according to published literatures.

In addition to the memory device116storing the software programs144, it is contemplated that software programs144may be stored on a removable computer medium, such as a hard drive, a computer disk, a CD-ROM, a DVD, a HD, a Blu-Ray DVD, USB flash drive, a SD card, a memory stick, or any other suitable medium; and the software programs144when downloaded to image processing device112may be executed b image processor114.

The processor114may be communicatively coupled to the memory device116, and the processor114may be configured to execute computer-executable instructions stored thereon. The processor114may send or receive medical images146to memory device116. For example, the processor114may receive medical images146from the image acquisition device132via the communication interface118and network120to be stored in memory device116. The processor114may also send medical images146stored in memory device116via the communication interface118to the network120be either stored in database124or the hospital database126.

Further, the processor114may utilize software programs144(e.g., a treatment planning software) along with the medical images146and patient data145to create the radiation therapy treatment plan142. Medical images146may include information such as imaging data associated with a patient anatomical region, organ, or volume of interest segmentation data. The medical images146may include or be associated with metadata that specifies estimated motion, such as the periodic motion phase of a target region depicted in the medical images146(e.g., the respiratory phase of a breathing cycle depicted in the medical images146). Such estimated motion for the medical images can be generated or computed by a trained ML model and/or by performing some image registration techniques or processes on the medical images. Patient data145may include information such as (1) functional organ modeling data (e.g., serial versus parallel organs, appropriate dose response models, etc.); (2) radiation dosage data (e.g., DVH information); (3) other clinical information about the patient and course of treatment (e.g., other surgeries, chemotherapy, previous radiotherapy, etc.); (4) type of tumor cells being treated by the radiotherapy treatment session. The medical images146may be received from or be associated with a device that measures or determines a motion phase of periodic motion (e.g., estimated motion) of a target region depicted in the images.

In addition, the processor114may utilize software programs to generate intermediate data such as updated parameters to be used, for example, by a machine learning model, such as a neural network model; or generate intermediate 2D or 3D images, which may then subsequently be stored in memory device116. The processor114may subsequently transmit the executable radiation therapy treatment plan142via the communication interface118to the network120to the radiation therapy device130, where the radiation therapy plan will be used to treat a patient with radiation. In addition, the processor114may execute software programs144to implement functions such as image conversion, image segmentation, deep learning, neural networks, and artificial intelligence. For instance, the processor114may execute software programs144that train or contour a medical image such software programs144when executed may train a boundary detector or utilize a shape dictionary.

The processor114may be a processing device, include one or more general-purpose processing devices such as a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), or the like. More particularly, the processor114may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction Word (VLIW) microprocessor, a processor implementing other instruction sets, or processors implementing a combination of instruction sets. The processor114may also be implemented by one or more special-purpose processing devices such as an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP). a System on a Chip (SoC), or the like. As would be appreciated by those skilled in the art, in some embodiments, the processor114may be a special-purpose processor, rather than a general-purpose processor. The processor114may include one or more known processing devices, such as a microprocessor from the Pentium™, Core™, Xeon™, or Itanium® family manufactured by Intel™, the Turion™, Athlon™, Sempron™, Opteron™, FX™, Phenom™ family manufactured by AMD™, or any of various processors manufactured by Sun Microsystems. The processor114may also include graphical processing units such as a GPU from the GeForce®, Quadro®, Tesia® family manufactured by Nvidia™, GMA, Iris™ family manufactured by Inel™, or the Radeon™ family manufactured by AMD™. The processor114may also include accelerated processing units such as the Xeon Phi™ family manufactured by Intel™. The disclosed embodiments are not limited to any type of processor(s) otherwise configured to meet the computing demands of identifying, analyzing, maintaining, generating, and/or providing large amounts of data or manipulating such data to perform the methods disclosed herein. In addition, the term “processor” may include more than one processor (for example, a multi-core design or a plurality of processors each having a multi-core design). The processor114can execute sequences of computer program instructions, stored in memory device116, to perform various operations, processes, methods that will he explained in greater detail below.

The memory device116can store medical images146. In some embodiments, the medical images146may include one or more MRI images (e.g., 2D 3D MRI, 2D streaming MRI, four-dimensional (4D) MRI, 4D volumetric MRI, 4D cine MRI, etc.), functional MRI images (e.g., fMRI, DCE-MRI, diffusion MRI), CT images (e.g., 2D CT, cone beam CT, 3D CT, 4D CT), ultrasound images (e.g., 2D ultrasound, 3D ultrasound, 4D ultrasound), one or more projection images representing views of an anatomy depicted in the MRI, synthetic CT (pseudo-CT), and/or CT images at different angles of a gantry relative to a patient axis, PET images, X-ray images, fluoroscopic images, radiotherapy portal images, SPECT images, computer-generated synthetic images (e.g., pseudo-CT images), aperture images, graphical aperture image representations of MLC leaf positions at different gantry angles, and the like. Further, the medical images146may also include medical image data, for instance, training images, ground truth images, contoured images, and dose images. In an embodiment, the medical images146may be received from the image acquisition device132. Accordingly, image acquisition device132may include an MRI imaging device, a CT imaging device, a PET imaging device, an ultrasound imaging device, a fluoroscopic device, a SPECT imaging device, an integrated linac and MRI imaging device, or other medical imaging devices for obtaining the medical images of the patient. The medical images146may be received and stored in any type of data or any type of format that the image processing device112may use to perform operations consistent with the disclosed embodiments.

The memory device115may be a non-transitory computer-readable medium, such as a read-only memory (ROM), a phase-change random access memory (PRAM), a static random access memory (SRAM), a flash memory, a random access memory (RAM), a dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM), an electrically erasable programmable read-only memory (EEPROM), a static memory (e.g., flash memory, flash disk, static random access memory) as well as other types of random access memories, a cache, a register, a CD-ROM, a DVD or other optical storage, a cassette tape, other magnetic storage device, or any other non-transitory medium that may be used to store information including image, data, or computer executable instructions (e.g., stored in any format) capable of being accessed by the processor114, or any other type of computer device. The computer program instructions can be accessed by the processor114, read from the ROM, or any other suitable memory location, and loaded into the RAM for execution by the processor114. For example, the memory device116may store one or more software applications. Software applications stored in the memory device116may include, for example, an operating system143for common computer systems as well as for software-controlled devices. Further, the memory device116may store an entire software application, or only a part of a software application, that is executable by the processor114. For example, the memory device116may store one or more radiation therapy treatment plans142.

The image processing device112can communicate with the network120via the communication interface118, which can be communicatively coupled to the processor114and the memory device116. The communication interface118may provide communication connections between the image processing device112and radiotherapy system100components (e.g., permitting, the exchange of data with external devices). For instance, the communication interface118may, in some embodiments, have appropriate interfacing circuitry to connect to the user interface136, which may be a hardware keyboard, a keypad, or a touch screen through which a user may input information into radiotherapy system100.

Communication interface118may include, for example, a network adaptor, a cable connector, a serial connector, a USB connector, a parallel connector, a high-speed data transmission adaptor (e.g., such as fiber, USB 3.0, thunderbolt, and the like), a wireless network adaptor (e.g., such as a WiFi adaptor), a telecommunication adaptor 3G, 4G/LTE and the like), and the like. Communication interface118may include one or more digital and/or analog communication devices that permit image processing device112to communicate with other machines and devices, such as remotely located components, via the network120.

The network120may provide the functionality of a local area network (LAN), a wireless network, a cloud computing environment (e.g., software as a service, platform as a service, infrastructure as a service, etc.), a client-server, a wide area network (WAN), and the like. For example, network120may be a LAN or a WAN that may include other systems S1(138), S2(140), and S3(141). Systems S1, S2, and S3may be identical to image processing device112or may be different systems. In some embodiments, one or more systems in network120may form a distributed computing/simulation environment that collaboratively performs the embodiments described herein. In some embodiments, one or more systems S1, S2, and S3may include a CT scanner that obtains CT images (e.g., medical images146). In addition, network120may be connected to Internet122to communicate with servers and clients that reside remotely on the internet.

Therefore, network120can allow data transmission between the image processing device112and a number of various other systems and devices, such as the OIS128, the radiation therapy device130, and the image acquisition device132. Further, data generated by the OIS128and/or the image acquisition device132may be stored in the memory device116, the database124, and/or the hospital database126. The data may be transmitted/received via network120, through communication interface118in order to be accessed by the processor114, as required.

The image processing device112may communicate with database124through network120to send/receive a plurality of various types of data stored on database124. For example, database124may include machine data (control points) that includes information associated with a radiation therapy device130, image acquisition device132, or other machines relevant to radiotherapy. Machine data information may include control points, such as radiation beam size, arc placement, beam on and off time duration, machine parameters, segments, MLC configuration, gantry speed, MRI pulse sequence, and the like. Database124may be a storage device and may be equipped with appropriate database administration software programs. One skilled in the art would appreciate that database124may include a plurality of devices located either in a central or a distributed mariner.

In some embodiments, database124may include a processor-readable storage medium (not shown). While the processor-readable storage medium in an embodiment may be a single medium, the term “processor-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of computer-executable instructions or data. The term “processor-readable storage medium” shall also be taken to include any medium that is capable of storing or encoding a set of instructions for execution by a processor and that cause the processor to perform any one or more of the methodologies of the present disclosure. The term “processor-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories and optical and magnetic media. For example, the processor-readable storage medium can be one or more volatile, transitory, non-transitory, or non-volatile tangible computer-readable media

Image processor114may communicate with database124to read images into memory device116or store images from memory device116to database124. For example, the database124may be configured to store a plurality of images (e.g., 3D MRL 4D MRI, 2D MRI slice images, CT images, 2D Fluoroscopy images, X-ray images, raw data from MR scans or CT scans, Digital Imaging and Communications in Medicine (DICOM) data, projection images, graphical aperture images, etc.) that the database124received from image acquisition device132. Database124may store data to be used by the image processor114when executing software program144or when creating radiation therapy treatment plans142. Database124may store the data produced by the trained machine learning mode, such as a neural network including the network parameters constituting the model learned by the network and the resulting estimated data. As referred to herein, “estimate” or “estimated” can be used interchangeably with “predict” or “predicted” and should be understood to have the same meaning. The image processing device112may receive the imaging data, such as a medical image146(e.g., 2D MRI slice images, CT images, 2D Fluoroscopy images, X-ray images, 3DMRI images, 4D MRI images, projection images, graphical aperture images, image contours, etc.) from the database124, the radiation therapy device130(e.g., an MR-linac), and/or the image acquisition device132to generate a treatment plan142. The image data that is received can be associated with estimated motion of each image. The estimated motion can be received together with the images and/or can be computed upon receipt of the images. In some examples, a distance between a target (e.g., tumor cells) and one or more critical organs can be measured based on the images received from the image acquisition device132. This distance, together with a computed BED, can be used to perform isotoxic planning for one or more radiotherapy treatment fractions delivered to the patient associated with the images.

In an embodiment, the radiotherapy system100can include an image acquisition device132that can acquire medical images (e.g., MRI images, 3D MRI, 2D streaming MRI, 4D volumetric MRI, CT images, cone-Beam CT, PET images, functional MRI images (e.g., fMRI, DCE-MRI and diffusion MRI). X-ray images, fluoroscopic image, ultrasound images, radiotherapy portal images, SPECT images, and the like) of the patient. mage acquisition device132may, for example, be an MRI imaging device, a CT imaging device, a PET imaging device, an ultrasound device, a fluoroscopic device, a SPECT imaging device, or any other suitable medical imaging device for obtaining one or more medical images of the patient. Images acquired by the image acquisition device132can be stored within database124as either imaging data and/or test data. By way of example, the images acquired by the image acquisition device132can be also stored by the image processing device112, as medical image146in memory device116. In some cases, the image acquisition device132can generate or provide estimated motion for each image or group of images captured by the image acquisition device132.

In some embodiments, the image acquisition device132may be integrated with the radiation therapy device130as a single apparatus (e.g., an MR-linac). Such an MR-linac can be used, for example, to determine a location of a target organ or a target tumor in the patient, so as to direct radiation therapy accurately according to the radiation therapy treatment plan142to a predetermined target.

The image acquisition device132can be configured to acquire one or more images of the patient's anatomy for a region of interest (e.g., a target organ, a target tumor, or both). Each image, typically a 2D image or slice, can include one or more parameters (e.g., a 2D slice thickness, an orientation, and a location, etc.). In some embodiments, the image acquisition device132can acquire a 2D slice in any orientation. For example, an orientation of the 2D slice can include a sagittal orientation, a coronal orientation, or an axial orientation. The processor114can adjust one or more parameters, such as the thickness and/or orientation of the 2D slice, to include the target organ and/or target tumor. In an embodiment, 2D slices can be determined from information such as a 3D MRI volume. Such 2D slices can be acquired by the image acquisition device132in “real-time” while a patient is undergoing radiation therapy treatment.

The image processing device112may generate and store radiation therapy treatment plans142for one or more patients. The radiation therapy treatment plans142may provide information about a particular radiation dose (or dose distribution) to be applied to each patient at each radiotherapy treatment fraction. The radiation therapy treatment plans142may also include other radiotherapy information, such as control points including beam angles, gantry angles, beam intensity, dose-histogram-volume information, the number of radiation beams to be used during therapy, the dose per beam, and the like.

The image processor114may generate the radiation therapy treatment plan142by using software programs144such as treatment planning software (such as Monaco®, manufactured by Elekta AB of Stockholm, Sweden). In order to generate the radiation therapy treatment plans142, the image processor114may communicate with the image acquisition device132(e.g., a CT device, an MRI device, a PET device, an X-ray device, an ultrasound device, etc.) to access images of the patient and to delineate a target such as a tumor, to generate contours of the images. In some embodiments, the delineation of one or more OARs, such as healthy tissue surrounding the tumor or in close proximity to the tumor, may be required. Therefore, segmentation of the OAR may be performed when the OAR is close to the target tumor. In addition, if the target tumor is close to the OAR (e.g., prostate in near proximity to the bladder and rectum), then by segmenting the OAR from the tumor, the radiotherapy system100may study the dose distribution not only in the target but also in the OAR.

In order to delineate a target organ or a target tumor from the OAR, medical images, such as MRI images, CT images, PET images, fMRI images, X-ray images, ultrasound images, radiotherapy portal images, SPECT images, and the like, of the patient undergoing radiotherapy may be obtained non-invasively by the image acquisition device132to reveal the internal structure of a body part. Based on the information from the medical images, a 3D structure of the relevant anatomical portion may be obtained and used to generate a contour of the image. Contours of the image can include data overlaid on top of the image that delineates one or more structures of the anatomy. In some cases, the contours can be files associated with respective images that specify the coordinates or 2D or 3D locations of various structures of the anatomy depicted in the images.

In addition, during a treatment planning process, many parameters may be taken into consideration to achieve a balance between efficient treatment of the target tumor (e.g., such that the target tumor receives enough radiation dose for an effective therapy) and low irradiation of the OAR(s) (e.g., the OAR(s) receives as low a radiation dose as possible). Other parameters that may be considered include the location of the target organ and the target tumor, the location of the OAR, a computed measure of BED, and the movement of the target in relation to the OAR. For example, the 3D structure may be obtained by contouring the target or contouring the OAR within each 2D layer or slice of an MRI or CT image and combining the contour of each 2D layer or slice. The contour may be generated manually (e.g., by a physician, dosimetrist, or health care worker using a program such as MONACO™ manufactured by Elekta AB of Stockholm, Sweden) or automatically (e.g., using a program such as the Atlas-based auto-segmentation software, ABAS™, manufactured by Elekta AB of Stockholm, Sweden). In certain embodiments, the 3D structure of a target tumor or an OAR may be generated automatically by the treatment planning software.

After the target tumor and the OAR(s) have been located and delineated, a dosimetrist, physician, or healthcare worker may determine a dose of radiation to be applied to the target tumor, as well as any maximum amounts of dose that may be received by the OAR proximate to the tumor (e.g., left and right parotid, optic nerves, eyes, lens, inner ears, spinal cord, brain stem, and the like). After the radiation dose is determined for each anatomical structure (e.g., target tumor, OAR), a process known as inverse planning may be performed to determine one or more treatment plan parameters that would achieve the desired radiation dose distribution. Examples of treatment plan parameters include volume delineation parameters (e.g., which define target volumes, contour sensitive structures, etc.), margins around the target tumor and OARs, beam angle selection, collimator settings, and beam-on times. During the inverse-planning process, the physician may define dose constraint parameters that set bounds on how much radiation an OAR may receive (e.g., defining full dose to the tumor target and zero dose to any OAR; defining 95% of dose to the target tumor: defining that the spinal cord, brain stem, and optic structures receive ≤45 Gy, ≤55 Gy and <54 Gy, respectively). The result of inverse planning may constitute a radiation therapy treatment plan142that may be stored in memory device116or database124. Some of these treatment parameters may be correlated. For example, tuning one parameter (e.g., weights for different objectives, such as increasing the dose to the target tumor) in an attempt to change the treatment plan may affect at least one other parameter, which in turn may result in the development of a different treatment plan. Thus, the image processing device112can generate a tailored radiation therapy treatment plan142having these parameters in order for the radiation therapy device130to provide radiotherapy treatment to the patient.

In addition, the radiotherapy system100may include a display device134and a user interface136. The display device134may include one or more display screens that display medical images, interface information, treatment planning parameters (,e.g., projection images, graphical aperture images, contours, dosages, beam angles, etc.) treatment plans, a measure of BED, a target, localizing a target and/or tracking a target, or any related information to the user. The user interface136may be a keyboard, a keypad, a touch screen or any type of device that a user may use to input information to radiotherapy system100. Alternatively, the display device134and the user interface136may be integrated into a device such as a tablet computer (e.g., Apple iPad®, Lenovo ThinkPad®, Samsung Galaxy®, etc.).

Furthermore, any and all components of the radiotherapy system100may be implemented as a virtual machine (e.g., VMWare, Hyper-V, and the like). For instance, a virtual machine can be software that functions as hardware. Therefore, a virtual machine can include at least one or more virtual processors, one or more virtual memories, and one or more virtual communication interfaces that together function as hardware. For example, the image processing device112, the OIS128, and the image acquisition device132could be implemented as a virtual machine. Given the processing power, memory and computational capability available, the entire radiotherapy system10could be implemented as a virtual machine.

FIG.2Aillustrates an example radiation therapy device202that may include a radiation source, such as an X-ray source or a linear accelerator, a couch216, an imaging detector214, and a radiation therapy output204. The radiation therapy device202may be configured to emit a radiation beam208to provide therapy to a patient. The radiation therapy output204can include one or more attenuators or collimators, such as an MLC as described in the illustrative embodiment ofFIG.5, below.

Referring back toFIG.2A, a patient can be positioned in a region212and supported by the treatment couch216to receive a radiation therapy dose, according to a radiation therapy treatment plan. The radiation therapy output204can be mounted or attached to a gantry206or other mechanical support. One or more chassis motors (not shown) may rotate the gantry206and the radiation therapy output204around couch216when the couch216is inserted into the treatment area. In an embodiment, gantry206may be continuously rotatable around couch216when the couch216is inserted into the treatment area. In another embodiment, gantry206may rotate to a predetermined position when the couch216is inserted into the treatment area. For example, the gantry206can be configured to rotate the therapy output204around an axis (“A”). Both the couch216and the radiation therapy output204can be independently moveable to other positions around the patient, such as moveable in transverse direction (“T”), moveable in a lateral direction (“L”), or as rotation about one or more other axes, such as rotation about a transverse axis (indicated as “R”). A controller communicatively connected to one or more actuators (not shown) may control the couch's216movements or rotations in order to properly position the patient in or out of the radiation beam208according to a radiation therapy treatment plan. Both the couch216and the gantry206are independently moveable from one another in multiple degrees of freedom, which allows the patient to be positioned such that the radiation beam208can precisely target the tumor. The MLC may be integrated and included within gantry206to deliver the radiation beam208of a certain shape.

The coordinate system (including axes A, T, and L) shown inFIG.2Acan have an origin located at an isocenter210. The isocenter210can be defined as a location where the central axis of the radiation beam208intersects the origin of a coordinate axis, such as to deliver a prescribed radiation dose to a location on or within a patient. Alternatively, the isocenter210can be defined as a location where the central axis of the radiation beam208intersects the patient for various rotational positions of the radiation therapy output204as positioned by the gantry206around the axis A. As discussed herein, the gantry angle corresponds to the position of gantry206relative to axis A, although any other axis or combination of axes can be referenced and used to determine the gantry angle.

Gantry206may also have an attached imaging detector214. The imaging detector214is preferably located opposite to the radiation source, and in an embodiment, the imaging detector214can be located within a field of the therapy beam208.

The imaging detector214can be mounted on the gantry206(preferably opposite the radiation therapy output204), such as to maintain alignment with the therapy beam208. The imaging detector214rotates about the rotational axis as the gantry206rotates. In an embodiment, the imaging detector214can be a flat panel detector (e.g., a direct detector or a scintillator detector). In this manner, the imaging detector214can be used to monitor the therapy beam208or the imaging detector214can be used for imaging the patient's anatomy, such as portal imaging. The control circuitry of radiation therapy device202may be integrated within system100or remote from it.

In an illustrative embodiment, one or inure of the couch216, the therapy output204, or the gantry206can be automatically positioned, and the therapy output204can establish the therapy beam208according to a specified dose for a particular therapy delivery instance. A sequence of therapy deliveries can be specified according to a radiation therapy treatment plan, such as using one or more different orientations. or locations of the gantry206, couch216, or therapy output204. The therapy deliveries can occur sequentially, but can intersect in a desired therapy locus on or within the patient, such as at the isocenter210A prescribed cumulative dose of radiation therapy can thereby be delivered to the therapy locus while damage to tissue near the therapy locus can be reduced or avoided.

FIG.2Billustrates an example radiation therapy device202that may include a combined lilac and an imaging system, such as can include a CT imaging system. The radiation therapy device202can include an MLC (not shown). The CT imaging system can include an imaging X-ray source218, such as providing X-ray energy in a kilo electron-Volt (keV) energy range. The imaging X-ray source218can provide a fan-shaped and/or a conical beam208directed to an imaging detector222, such as a fiat panel detector. The radiation therapy device202can be similar to the system described in relation toFIG.2A, such as including a radiation therapy output204, a gantry206, a couch216, and another imaging detector214(such as a flat panel detector). The X-ray source218can provide a comparatively lower-energy X-ray diagnostic beam, for imaging.

In the illustrative embodiment ofFIG.2B, the radiation therapy output204and the X-ray source218can be mounted on the same rotating gantry206, rotationally separated from each other by 90 degrees. In another embodiment, two or more X-ray sources can be mounted along the circumference of the gantry206, such as each having its own detector arrangement to provide multiple angles of diagnostic imaging concurrently. Similarly, multiple radiation therapy outputs204can be provided.

FIG.3depicts an example radiation therapy system300that can include combining a radiation therapy device202and an imaging system, such as an MR imaging system (e.g., known in the art as an MR-linac) consistent with the disclosed embodiments. As shown, system300may include a couch216, an image acquisition device320, and a radiation delivery device330. System300delivers radiation therapy to a patient in accordance with a radiotherapy treatment plan. In some embodiments, image acquisition device320may correspond to image acquisition device132inFIG.1that may acquire origin images of a first imaging modality MRI image shown inFIG.4A) or destination images of a second imaging modality (e.g., CT image shown inFIG.4B).

Couch216may support a patient (not shown) during a treatment session. In some implementations, couch216may move along a horizontal translation axis (labelled “I”), such that couch216can move the patient resting on couch216into and/or out of system300. Couch216may also rotate around a central vertical axis of rotation, transverse to the translation axis. To allow such movement or rotation, couch216may have motors (not shown) enabling the couch to move in various directions and to rotate along various axes. A controller (not shown) may control these movements or rotations in order to properly position the patient according to a treatment plan.

In some embodiments, image acquisition device320may include an MRI machine used to acquire 2D or 3D MRI images of the patient before, during, and/or after a treatment session. Image acquisition device320may include a magnet321for generating a primary magnetic field for magnetic resonance imaging. The magnetic field lines generated by operation of magnet321may run substantially parallel to the central translation axis I. Magnet321may include one or more coils with an axis that runs parallel to the translation axis I. In some embodiments, the one or more coils in magnet321may be spaced such that a central window323of magnet321is free of coils. In other embodiments, the coils in magnet321may be thin enough or of a reduced density such that they are substantially transparent to radiation of the wavelength generated by radiotherapy device330. Image acquisition device320may also include one or more shielding coils, which may generate a magnetic field outside magnet321of approximately equal magnitude and opposite polarity in order to cancel or reduce any magnetic field outside of magnet321. As described below, radiation source331of radiotherapy device330may be positioned in the region where the magnetic field is cancelled, at least to a first order, or reduced.

Image acquisition device320may also include two gradient coils325and326, which may generate a gradient magnetic field that is superposed on the primary magnetic field. Coils325and326may generate a gradient in the resultant magnetic field that allows spatial encoding of the protons so that their position can be determined. Gradient coils325and326may be positioned around a common central axis with the magnet321and may be displaced along that central axis. The displacement may create a gap, or window, between coils325and326. In embodiments where magnet321can also include a central window323between coils325and326, the two windows may be aligned with each other.

In some embodiments, image acquisition device320may be an imaging device other than an MRI, such as an X-ray, a CT, a CBCT, a spiral CT, a PET, a SPECT, an optical tomography, a fluorescence imaging, ultrasound imaging, radiotherapy portal imaging device, or the like. As would be recognized by one of ordinary skill in the art, the above description of image acquisition device320concerns certain embodiments and is not intended to be limiting.

Radiotherapy device330may include the radiation source331, such as an X-ray source or a linac, and an MLC332(shown below inFIG.5). Radiotherapy device330may be mourned on a chassis335. One or more chassis motors (not shown) may rotate chassis335around couch216when couch216is inserted into the treatment area. In an embodiment, chassis335may be continuously rotatable around couch216, when couch216is inserted into the treatment area. Chassis335may also have an attached radiation detector (not shown), preferably located opposite to radiation source331and with the rotational axis of chassis335positioned between radiation source331and the detector. Further, device330may include control circuitry (not shown) used to control, for example, one or more of couch216, image acquisition device320, and radiotherapy device330. The control circuitry of radiotherapy device330may be integrated within system300or remote from it.

During a radiotherapy treatment session, a patient may be positioned on couch216. System300may then move couch216into the treatment area defined by magnet321, coils325and326, and chassis335. Control circuitry may then control radiation source331, MLC332, and the chassis motors) to deliver radiation to the patient through the window between coils325and326according to a radiotherapy treatment plan.

FIG.2A,FIG.2B, andFIG.3illustrate generally embodiments of a radiation therapy device configured to provide radiotherapy treatment to a patient, including a configuration where a radiation therapy output can be rotated around a central axis (e.g., an axis “A”). Other radiation therapy output configurations can be used. For example, a radiation therapy output can be mounted to a robotic arm or manipulator having multiple degrees of freedom. In another embodiment, the therapy output can be fixed, such as located in a region laterally separated from the patient, and a platform supporting the patient can be used to align a radiation therapy isocenter with a specified target locus within the patient.

As discussed above, radiation therapy devices described byFIG.2A,FIG.2B, andFIG.3include an MLC for shaping, directing, or modulating an intensity of a radiation therapy beam to the specified target locus within the patient.FIG.5illustrates an example MLC332that includes leaves532A through532J that can be automatically positioned to define an aperture approximating ii tumor540cross section or projection. The leaves532A through532J permit modulation of the radiation therapy beam. The leaves532A through532J can be made of a material specified to attenuate or block the radiation beam in regions other than the aperture, in accordance with the radiation treatment plan. For example, the leaves532A through532J can include metallic plates, such as comprising tungsten, with a long axis of the plates oriented parallel to a beam direction and having ends oriented orthogonally to the beam direction (as shown m the plane of the illustration ofFIG.2A). A “state” of the MLC332can be adjusted adaptively during a course of radiation therapy treatment, such as to establish a therapy beam that better approximates a shape or location of the tumor540or another target locus. This is in comparison to using a static collimator configuration or as compared to using an MLC332configuration determined exclusively using an “offline” therapy planning technique. A radiation therapy technique using the MLC332to produce a specified radiation dose distribution to a tumor or to specific areas within a tumor can be referred to as IMRT.

FIG.6illustrates an embodiment of another type of radiotherapy device630(e.g., a Leksell Gamma Knife), according to some embodiments0f the present disclosure. As shown inFIG.6, in a radiotherapy treatment session, a patient602may wear a coordinate frame620to keep stable the patient's body part (e.g., the head) undergoing surgery or radiotherapy. Coordinate frame620and a patient positioning system622may establish a spatial coordinate system, which may be used while imaging a patient or during radiation surgery. Radiotherapy device630may include a protective housing614to enclose a plurality of radiation sources612. Radiation sources612may generate a plurality of radiation beams (e.g., beamlets) through beam channels616. The plurality of radiation beams may be configured to focus on an isocenter210from different directions. While each individual radiation beam may have a relatively low intensity, isocenter210may receive a relatively high level of radiation when multiple doses from different radiation beams accumulate at isocenter210. In certain embodiments, isocenter210may correspond to a target under surgery or treatment, such as a tumor.

In some examples, the image processing device112implements a dose processing device. The dose processing device can compute a measure of BED for one or more prior radiotherapy treatment session. For example, the dose processing device can receive or access dose information representing a dose distribution delivered during a first radiotherapy treatment fraction. The dose distribution can be received from the radiotherapy treatment device used to treat a given patient. The received dose distribution can be different from what was actually prescribed by the radiotherapy treatment plan for treating the particular type of tumor or target.

In some examples, an operator can receive a BED measure, one or more images of the patient and a prescribed dose to be delivered during the first radiotherapy treatment fraction. Prior to instructing the radiotherapy treatment device to begin delivering the first radiotherapy treatment fraction, the operator can make adjustments to the prescribed dose based on the BED measure and the one or more images. For example, the operator can increase or decrease the dose distribution based on the distance between the tumor or target and the OAR.

In some examples, the radiotherapy device can deliver the first radiotherapy treatment fraction to the patient (e.g., after adjusting the dose distribution). The dose processing device can then update the measure of BED in accordance with Equation 1 based on the dose distribution delivered during the first radiotherapy treatment fraction. For example, the dose processing device can determine a type of tumor cell or target being treated and access a database to obtain first and second constants representing biological dose effects of delivering radiotherapy dose distribution to the type of tumor cell. The dose processing device, can, for example, obtain the p and a constants from the database based on the type f tumor cell or target.

The measure of BED can take into account the current dose distribution delivered during the first radiotherapy treatment fraction and the dose distribution delivered during prior radiotherapy treatment fractions. Namely, the dose processing device can store a record associated with the patient being treated. The dose processing device can generate a list of radiotherapy treatment fractions in the record and associate the actual physical dose distribution delivered to the patient during each respective radiotherapy treatment fraction. The dose processing device, in computing the measure of BED for a most recent radiotherapy treatment fraction, obtains the first and second constants and the list of actual physical dose distributions delivered during each particular previous radiotherapy treatment fraction for the patient. The dose processing device then computes a first value representing a sum of all of the retrieved actual physical dose distributions and the currant dose distribution of the latest radiotherapy treatment fraction. The dose processing device also computes a second value representing a multiplication of a ratio of the first and second constants by a sum of the square of all of the retrieved actual physical dose distributions and the current dose distribution of the latest radiotherapy treatment fraction. The dose processing device computes the measure of BED as a sum of the first and second values. In this way, the BED is a second order function of a plurality of doses delivered across a plurality of radiotherapy treatment fractions.

In some examples, the dose processing device can present this current measure of BED to a dosimetrist or operator prior to delivery of a subsequent radiotherapy treatment fraction to the patient. The dosimetrist or operator can perform an isotoxic planning process or the dose processing device can perform an automated isotoxic planning process to adjust a prescribed dose delivered during the subsequent radiotherapy treatment fraction based on the current measure of BED. After the subsequent radiotherapy treatment fraction is delivered the measure of BED is again updated and the record associated with the patient is updated to reflect the actual dose distribution delivered to the patient during the subsequent radiotherapy treatment fraction for use in computing future measures of BED.

In some examples, the dose processing device can compute a total survival fraction of tumor cells based on the computed measure of BED. The total survival fraction can also be used to adjust a dose distribution delivered during a subsequent treatment fraction. The total survival fraction can be computed in accordance with Equation 2 below:
exp[−α{BED}]  Equation 2
where α represents the second constant of the BED formulation defined by Equation 1 and exp is an exponential function.

In some examples, the computed BED can be stored as part of training data used to train one or more machine learning techniques, as explained below. For example, the training data can be generated and stored representing different types of tumors or targets and specified actual dose distributions delivered across a set of radiotherapy treatment fractions. As each radiotherapy treatment fraction is delivered, the actual dose distribution for that radiotherapy treatment fraction is received, stored in the training data among a plurality of other dose distributions delivered to the patient and the BED computed according to Equation 1. The BED that is stored can be the ground truth BED that is computed for a particular type of target or tumor and a set of radiotherapy treatment fraction dose distributions.

In some examples, the dose processing device can receive a dose distribution for an upcoming (or past) radiotherapy treatment fraction and a type of target being treated. The dose processing device can apply a trained machine learning technique to the dose distribution and the type of target being treated to estimate the BED for the upcoming (or past) radiotherapy treatment fraction. Based on the estimated BED, an operator or the dose processing device can adjust the dose distribution that will be delivered during the radiotherapy treatment fraction or a subsequent radiotherapy treatment fraction. For example, the dose processing device can implement a plurality of machine learning techniques. Each of the machine learning techniques (e.g., neural networks) can be trained to estimate or predict a BED for a given dose distribution associated with a different target type or tumor cell type. The dose processing device can select a machine learning technique associated with a target type of a patient. The dose processing device can apply the prescribed or adjusted dose to the selected machine learning technique to estimate the BED for the prescribed or adjusted dose. The estimated BED can then be used in an isotoxic planning process or to perform quality assurance on computed BED measures.

In some examples, after delivery of a radiotherapy treatment fraction, the actual dose distribution is obtained together with past dose distributions from prior radiotherapy treatment fractions. The dose processing device can compute the BED based on Equation 1 and the dose distributions of the current and prior radiotherapy treatment fraction. The dose processing device can also apply a machine learning technique on the current dose distribution (the actual dose distribution applied by the radiotherapy treatment fraction or the dose distribution specified in the radiotherapy treatment plan for the radiotherapy treatment fraction) to estimate the BED for the radiotherapy treatment fraction. The dose processing device can compare the computed BED based on Equation 1 with the estimated BED to perform a quality assurance check. In some cases, the dose processing device can compute a deviation or difference between the computed BED and the estimated BED and compare the difference to a threshold. In response to determining that the difference is greater than the threshold, the dose processing device can alert an operator to adjust the dose distribution for a future radiotherapy treatment fraction.

FIG.7illustrates an example flow diagram for deep learning, where a deep learning model (or a machine learning model), such as a deep convolutional neural network (DCNN), autoencoder neural network, or variational autoencoder neural network, can be trained and used to generate predictions or estimations of BED based on one or more training data, such as training dose distributions and tumor/target types. After the DCNN is trained, the DCNN can be applied to a new dose distribution (or collection of dose distributions) to estimate BED the new dose distribution.

Inputs704can include a defined deep learning model (which can include one or more sub-networks or one or more individual and independent machine learning models) having an initial set of values and training data. The training data can include multiple training dose distributions for different types of targets and corresponding ground truth BED measurements.

The deep learning model can include one or more neural networks (referred to as sub-networks), such as a DCNN or autoencoder neural network. The deep learning network can be trained on the training data to establish a relationship between a given set of the dose distributions for a given target type and the corresponding ground truth BED measurement. In some examples, training data can be generated by applying the given set of the dose distributions to Equation 1 to obtain the corresponding ground truth BED measurement of the given set of the dose distributions. In this way, the DCNN can be applied to a set of dose distributions or one dose distribution associated with a particular target type to generate an estimation of BED for the target type and the dose distribution. This estimation can be compared (e.g., for quality assurance purposes) to an actual measured BED for a treatment fraction or set of treatment fractions to ensure accuracy of the measured BED.

During training of deep learning (DL) model708, a batch of training data can be selected from the training data of known patients or of a patient undergoing radiotherapy. The DL model708(e.g., the neural network) is applied to the batch of training data to generate an estimate of BED. The ground truth BED associated with the batch of training data is retrieved (e.g., the BED that has been calculated using Equation 1 based on a set of dose distributions associated with a set of treatment fractions). A deviation is computed based on a comparison of the ground truth BED and the estimated BED and parameters of the DL model708(e.g., the neural network) are updated based on the computed deviation.

The errors or result of computing the deviation (loss) can be used during a procedure called backpropagation to update the parameters of the deep learning network (e.g., layer node weights and biases of each or of certain sub-networks of the model708), in order to reduce or minimize errors during subsequent trials. The errors or result of computing the deviation can be compared to predetermined criteria, such as proceeding to a sustained minimum for a specified number of training iterations. If the errors or result of computing the loss function do not satisfy the predetermined criteria, then model parameters of the deep learning model708can be updated using backpropagation, and another batch of training data can be selected from the other sets of training data (of the same patient or other patients) and expected results for another iteration of deep learning model training. If the errors or result of computing the loss function satisfy the predetermined criteria, then the training can be ended, and the trained model708can then be used during a deep learning testing or inference stage712to generate BED estimates for subsequently received dose distributions.

After updating the parameters of the DCNN, the iteration index can be incremented by a value of one. The iteration index can correspond to a number of times that the parameters of the DCNN have been updated. Stopping criteria can be computed, and if the stopping criteria are satisfied, then the DCNN model can be saved in a memory, such as a memory device, and the training can be halted. If the stopping criteria are not satisfied, then the training can continue by obtaining another batch of training data from the same training subject or another training subject. In an embodiment, the stopping criteria can include a value of the iteration index (e.g., the stopping criteria can include whether the iteration index is greater than or equal to a determined maximum number of iterations). The steps recited above for training the DL model708can be repeated for each set or batch of training data associated with the same or different patient and/or region of interest.

After the DL model708is trained, the DL model708is applied to anew dose distribution (data sample for an upcoming radiotherapy treatment fraction or dose distributions for the entire set of treatment fractions associated with a patient) to generate an estimated BED for the new dose distribution associated with a particular type of tumor or target. If a difference between an estimated BED and a measured BED that is computed according to Equation 1 (such as based on prior dose distributions of prior radiotherapy treatment fractions) exceeds a threshold, the output of DL model708can be applied to update or control delivery of the radiotherapy treatment plan, such as by modifying the dose distribution for the upcoming radiotherapy treatment fraction.

FIG.8is a flowchart illustrating example operations of the image processing device112in performing process800, according to example embodiments. The process800may be embodied in computer-readable instructions for execution by one or more processors such that the operations of the process800May be performed in part or in whole by the functional components of the image processing device112; accordingly, the process800is described below by way of example with reference thereto. However, in other embodiments, at least some of the operations of the process800may be deployed on various other hardware configurations. The process800is therefore not intended to be limited to the image processing device112(e.g., dose processing device) and can be implemented in whole, or in part, by any other component. Some or all of the operations of process1100can be in parallel, out of order, or entirely omitted.

At operation810, image processing device112receives training data (paired dose distributions and ground truth BED measurements). For example, image processing device112receives training data, which may include paired training data sets (e.g., input-output training pairs).

At operation820, image processing device112receives one or more cost functions for training the model.

At operation830, image processing device112performs training of the model based on the received training data and one or more cost functions.

At operation850, image processing device112outputs the trained model.

At operation860, image processing device112utilizes the trained model to generate a prediction of a BED measurement based on an input dose distribution.

FIG.9Ais a flowchart illustrating example operations of the image processing device112in performing process900, according to example embodiments. The process900may be embodied in computer-readable instructions for execution by one or more processors such that the operations of the process900may be performed in part or in whole by the functional components of the image processing device112accordingly, the process900is described below by way of example with reference thereto. However, in other embodiments, at least some of the operations of the process900may be deployed on various other hardware configurations. The process900is therefore not intended to be limited to the image processing device112and can be implemented in whole, or in part, by any other component. Some or all of the operations of process900can be in parallel, out of order, or entirely omitted.

At operation910, image processing device112receives dose information representing dose delivered during a first radiotherapy treatment fraction, as discussed above.

At operation920, image processing device112accesses one or more previous dose information representing dose delivered during one or more previous radiotherapy treatment fractions, as discussed above.

At operation930, image processing device112computes a measure of BED based on a combination of the dose information delivered during a first radiotherapy treatment fraction and the dose delivered during the one or more previous radiotherapy treatment fractions, as discussed above.

At operation940, image processing device112performs an isotoxic planning process for delivering a second radiotherapy treatment fraction following the first radiotherapy treatment fraction based on the computed measure of BED, as discussed above.

FIG.9Bis a flowchart illustrating example operations of the image processing device112in performing process901, according to example embodiments. The process901may be embodied in computer-readable instructions for execution by one or more processors such that the operations of the process901may be performed in part or in whole by the functional components of the image processing device112accordingly, the process901is described below by way of example with reference thereto. However, in other embodiments, at least some of the operations of the process901may be deployed on various other hardware configurations. The process901is therefore not intended to be limited to the image processing device112and can be implemented in whole, or in part, by any other component. Some or all of the operations of process901can be in parallel, out of order, or entirely omitted.

At operation911, image processing device112receives tumor dose information representing isotoxic-planned dose prior to a first radiotherapy treatment fraction, as discussed above.

At operation921, image processing device112iterates or causes operation911to be repeated for all of the treatment fractions to generate an isotoxic-planned dose for all treatment fractions, as discussed above.

At operation931, image processing device112computes a measure of BED by applying the isotoxic-planned dose for all treatment fractions to Equation 1, as discussed above.

FIG.10illustrates a block diagram of an embodiment of a machine1000on which one or more of the methods as discussed herein can be implemented. In one or more embodiments, one or more items of the image processing device112can be implemented by the machine1000. In alternative embodiments, the machine1000operates as a standalone device or may be connected (e.g., networked) to other machines. In one or more embodiments, the image processing device112can include one or more of the items of the machine1000. In a networked deployment, the machine1000may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer for distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

The example machine1000includes processor1002(e.g., a CPU, a GPU, an ASIC, circuitry, such as one or more transistors, resistors, capacitors, inductors, diodes, logic gates, multiplexers, buffers, modulators, demodulators, radios (e.g., transmit or receive radios or transceivers), sensors1021(e.g., a transducer that converts one form of energy (e.g., light, heat, electrical, mechanical, or other energy) to another form of energy), or the like, or a combination thereof), a main memory1004and a static memory1006, which communicate with each other via a bus1008. The machine1000(e.g., computer system) may further include a video display unit1010(e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The machine1000also includes an alphanumeric input device1012(e.g., a keyboard), a user interface (UI) navigation device1014(e.g., a mouse), a disk drive or mass storage unit1016, a signal generation device1018(e.g., a speaker), and a network interface device1020.

The disk drive or mass storage unit1016includes a machine-readable medium1022on which is stored one or more sets of instructions and data structures (e.g., software)1024embodying or utilized by any one or more of the methodologies or functions described herein. The instructions1024may also reside, completely or at least partially, within the main memory1004and/or within the processor1002during execution thereof by the machine1000, the main memory1004and the processor1002also constituting machine-readable media.

The machine1000as illustrated includes an output controller1028. The output controller1028manages data flow to/from the machine1000. The output controller1028is sometimes called a device controller, with software that directly interacts with the output controller1028being called a device driver.

As used herein, “communicatively coupled between” means that the entities on either of the coupling must communicate through an item therebetween and that those entities cannot communicate with each other without communicating through the item.

ADDITIONAL NOTES

In this document, the terms “a,” “an,” “the,” and “said” are used when introducing elements of aspects of the disclosure or in the embodiments thereof, as is common in patent documents, to include one or more than one of the elements, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated.

In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “comprising,” “including,” and “having” are intended to be open-ended to mean that there may be additional elements other than the listed elements, such that elements after such a term (e.g., comprising, including, having) in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms “first,” “second,” and “third,” and so forth, are used merely as labels, and are not intended to impose numerical requirements on their objects.

Embodiments of the disclosure may be implemented with computer-executable instructions. The computer-executable instructions (e.g., software code) may be organized into one or more computer-executable components or modules. Aspects of the disclosure may be implemented with any number and organization of such components or modules. For example, aspects of the disclosure are not limited to the specific computer-executable instructions or the specific components or modules illustrated in the figures and described herein. Other embodiments of the disclosure may include different computer-executable instructions or components having more or less functionality than illustrated and described herein.

Method examples (e.g., operations and functions) described herein can be machine or computer-implemented at least in part (e.g., implemented as software code or instructions). Some examples can include a computer-readable medium or machine-readable medium encoded with instructions operable to configure an electronic device to perform methods as described in the above examples. An implementation of such methods can include software code, such as microcode, assembly language code, a higher-level language code, or the like (e.g., “source code”). Such software code can include computer readable instructions for performing various methods (e.g., “object” or “executable code”). The software code may form portions of computer program products. Software implementations of the embodiments described herein may be provided via an article of manufacture with the code or instructions stored thereon, or via a method of operating a communication interface to send data via a communication interface (e.g., wirelessly, over the internet, via satellite communications, and the like).

Further, the software code may be tangibly stored on one or more volatile or non-volatile computer-readable storage media during execution or at other times. These computer-readable storage media may include any mechanism that stores information in a form accessible by a machine (e.g., computing device, electronic system, and the like), such as, but not limited to, floppy disks, hard disks, removable magnetic disks, any form of magnetic disk storage media, CD-ROMS, magnetic-optical disks, removable optical disks (e.g., compact disks and digital video disks), flash memory devices, magnetic cassettes, memory cards or sticks (e.g., secure digital cards), RAMs (e.g., CMOS RAM and the like), recordable/non-recordable media (e.g., ROMs), EPROMS, EEPROMS, or any type of media suitable for storing electronic instructions, and the like. Such computer-readable storage medium may be coupled to a computer system bus to be accessible by the processor and other parts of the OIS.

In an embodiment, the computer-readable storage medium may have encoded a data structure for a treatment planning, wherein the treatment plan may be adaptive. The data structure for the computer-readable storage medium may be at least one of a Digital Imaging and Communications in Medicine (DICOM) format, an extended DICOM format, an XML format, and the like. DICOM is an international communications standard that defines the format used to transfer medical image-related data between various types of medical equipment. DICOM RT refers to the communication standards that are specific to radiation therapy.

In various embodiments of the disclosure, the method of creating a component or module can be implemented in software, hardware, or a combination thereof. The methods provided by various embodiments of the present disclosure, for example, can be implemented in software by using standard programming languages such as, for example, Compute Unified Device Architecture (CUDA), C, C++, Java, Python, and the like; and using standard machine learning/deep learning library (or API), such as tensorflow, torch and the like; and combinations thereof. As used herein, the terms “software” and “firmware” are interchangeable, and include any computer program stored in memory for execution by a computer.

A communication interface includes any mechanism that interfaces to any of a hardwired, wireless, optical, and the like, medium to communicate to another device, such as a memory bus interface, a processor bus interface, an Internet connection, a disk controller, and the like. The communication interface can be configured by providing configuration parameters and/or sending signals to prepare the communication interface to provide a data signal describing the software content. The communication interface can be accessed via one or more commands or signals sent to the communication interface.

The present disclosure also relates to a system for performing the operations herein. This system may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. The order of execution or performance of the operations in embodiments of the disclosure illustrated and described herein is not essential, unless otherwise specified. That is, the operations may be performed in any order, unless otherwise specified, and embodiments of the disclosure may include additional or fewer operations than those disclosed herein. For example, it is contemplated that executing or performing a particular operation before, contemporaneously with, or after another operation is within the scope of aspects of the disclosure.

The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the disclosure without departing from its scope. While the dimensions, types of materials and coatings described herein are intended to define the parameters of the disclosure, they are by no means limiting and are example embodiments. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the disclosure should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment. The scope of the disclosure should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. Further, the limitations of the following claims are not written in means-plus-function format and are not intended to be interpreted based on 35 U.S.C. § 112, sixth paragraph, unless and until such claim limitations expressly use the phrase “means for” followed by a statement of function void of further structure.