Energy conserved dose summation for radiotherapy planning

Abstract

An accumulated dose distribution is generated using a mass-weighted dose summation and / or four-dimensional dose calculation. Medical image data are accessed with a computer system, where the medical image data contain at least a reference image and a daily image acquired from a patient. A deformation map is generated from the medical image data using a deformable image registration between the reference image and the daily image. Transferred dose data are generated by mapping dose data from the daily image to the reference image using the deformation map or recalculated using mapped energy and mass data. The accumulated dose distribution is generated using a mass-weighted summation of the transferred dose data, in which the transferred dose for a fraction is weighted by a mass weighting value corresponding to a ratio of a transferred mass density image for that fraction and an average over of transferred mass density images over all fractions.

Description

ENERGY CONSERVED DOSE SUMMATION FOR RADIOTHERAPY PLANNINGCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 633,550. filed on Apnl 12, 2024. and entitled “ENERGY CONSERVED DOSE SUMMATION FOR RADIOTHERAPY PLANNING,” which is herein incorporated by reference in its entirety.STATEMENT OF FEDERALLY SPONSORED RESEARCH

[0002] This invention was made with government support under EB032680 awarded by the National Institutes of Health. The government has certain rights in the invention.BACKGROUND

[0003] A treatment plan developed for adaptive radiotherapy or patient retreatment can be effectively optimized based on the total dose delivered to a patient. Because previous doses are usually calculated on different image frames, deformable dose accumulation (DDA) is often utilized. DDA involves three basic operations: performing deformable image registration (DIR), translating doses to a reference image, and performing dose summation to summate the translated doses to obtain the total dose.

[0004] Dose summation methods in commercial software packages cannot ensure consistent energy' if patients underwent mass changes such as weight loss, tumor shrinkage, or bladder volume changes, which may then result in these techniques estimating an inaccurate total dose. In these instances, the resultant total dose no longer represents the total radiation energy delivered to patients.

[0005] To address the impact of mass changes on dose translation, an energy and mass- conserved (EMC) dose mapping method can be used for doses calculated with Monte Carlo and model-based dose engines. Due to mass changes between treatment fractions or courses, however, it is challenging to appropriately define the sum of delivered doses on existing images.SUMMARY OF THE DISCLOSURE

[0006] It is an aspect of the present disclosure to provide a method for generating an accumulated dose distribution. The method includes accessing medical image data with a computer system, where the medical image data contain at least a reference image and a daily image acquired from a patient; generating a deformation map from the medical image data using a deformable image registration between the reference image and the daily image; generating transferred dose data by mapping dose data from the daily image to the reference image using the deformation map. where the transferred dose data indicate doses to be delivered to the patient according to a radiation treatment plan; and generating the accumulated dose distribution using a mass-weighted summation of the transferred dose data.

[0007] It is another aspect of the present disclosure to provide a method for updating a radiation treatment plan using a deformable dose accumulation. The method includes accessing a first medical image and a second medical image with a computer system, where the first medical image was acquired from a patient at a first time point and the second medical image was acquired from the patient at a second time point that is different from the first time point; accessing a radiation treatment plan with the computer system; generating a deformation map by registering the second medical image to the first medical image; generating a mass image from the second medical image using mass calibration data; generating an energy image from the mass image using dose data from the radiation treatment plan; computing transferred dose data by applying the deformation map to the energy image to generate a transferred energy image, applying the deformation map to the mass image to generate a transferred mass image, and dividing the transferred energy image by the transferred mass image; generating an accumulated dose distribution by summing the transferred dose data over a plurality of radiation treatment fractions while weighting each transferred dose in the transferred dose data by a mass weighting value corresponding to each of the plurality of radiation treatment fractions; and updating the radiation treatment plan based on the accumulated dose distribution.BRIEF DESCRIPTION OF THE DRAWINGS

[0008] FIGS. 1A, IB, and 1C illustrate example workflows for deformable dose accumulation methods to calculate an energy -conserved total dose (FIGS. 1A and IB) and a direct summation total dose (FIG. 1 C).

[0009] FIG. 2 is a flowchart setting forth the steps of an example method for generating an accumulated dose distribution using a mass-weighted dose summation technique.

[0010] FIGS. 3A-3F show images from an example study of a patient with lung cancer retreatment. FIGS. 3 A and 3B show the deformed mass images for the first treatment and retreatment plans. FIG. 3C shows the average of the deformed mass images. FIGS. 3D and 3Eshow the deformed energy distribution of the first treatment and retreatment plans. FIG. 3F shows the sum of the deformed energy' distribution.

[0011] FIGS. 4A and 4B show the isodose lines of the directly summed total dose overlaid on the reference image (FIG. 4A) and the isodose lines of the mass-weighted total dose overlaid on the average image (FIG. 4B) from an example study.

[0012] FIG. 5 is a block diagram of an example system for generating an accumulated dose distribution using a mass-weighted dose summation.

[0013] FIG. 6 is a block diagram of example components that can implement the system of FIG. 5.DETAILED DESCRIPTION

[0014] Described here are systems and methods for radiation treatment planning using a deformable dose accumulation that implements a mass-weighted dose summation (MWDS) technique that accounts for mass variations, thereby improving the consistency between the total deposited energy and the summed total dose.

[0015] In general, the disclosed MWDS method takes mass differences between individual doses into account when estimating dose accumulations. This method, coupled with an energy / mass mapping method, can enable estimation of a summed total dose that is consistent with the total of delivered radiation energy. In this way, the disclosed MWDS method allows for the derived total dose to be equivalent to the total delivered energy on average mass. For instance, in some implementations, an energy and mass congruent mapping method is used to transfer dose and mass from dosimetry' images where delivered doses were calculated to a reference image. The transferred masses can be averaged at each voxel to generate an average mass image. The transferred doses can then be multiplied by the ratio of their transferred mass to the average mass and summed to generate the mass-weighted total dose. Advantageously, this MWDS technique therefore enables validation of any dose accumulation program. The MWDS method can also generate an average mass or CT image that allows the summed total doses to be correctly visualized on their corresponding masses.

[0016] In radiation therapy, the goal is to deliver a prescribed dose of radiation to a tumor or other PTV while minimizing the dose to surrounding organs-at-risk (OARs) or otherhealthy tissues. Due to factors such as patient movement, changes in anatomy, and variations in treatment setup, the actual dose delivered may differ from the planned dose. Deformable dose accumulation techniques address these challenges by incorporating information from medical images (e.g., computed tomography (CT) images) of the patient that have been acquired at different time points during the treatment course. By registering or otherwise aligning these medical images and accounting for changes in anatomy and setup, deformable dose accumulation techniques can calculate the cumulative dose distribution received by the patient’s tissues.

[0017] In general, deformable dose accumulation techniques include registering medical images acquired at different time points. As a non-limiting example, a deformable image registration (DIR) can be used to register the medical images. After the medical images are coregistered, the dose distributions from each treatment session can be accumulated to create a cumulative dose distribution for the patient. Generating the cumulative dose distribution can include mapping the dose from each session onto a common reference image.

[0018] As noted above, it is an aspect of the present disclosure that a mass-weighted dose summation may be used to account for mass differences between individual doses when generating the cumulative dose distribution. As an illustrative example of the mass changes that can be accounted for using the systems and methods described in the present disclosure, suppose two fractional images are identical except for one voxel in the planned treatment volume (PTV). where the voxel is occupied by tumor cells in one fraction but by air in another fraction. If the same amount of dose was delivered to this voxel in the two fractions, they contribute different amounts of energy to the total dose at this voxel. Similar phenomena may occur in several other cases such as weight loss, bladder volume change, or anatomic increase for pediatric patients. In the latter case, the energy delivered to a voxel a few years ago could be diluted due to organ grow th in the pediatric patient. Direct dose summation overlooks such underlying mass changes, and the resultant total dose cannot represent the total energy deposited in each organ. The lack of a correlation between the summed dose and the total amount of delivered energy not only compromises treatment response assessment, but also complicates the validation of dose accumulation operations.

[0019] Supposeis the mass image calculated from a medical image, Xt, acquired at fraction i, i=l,... ,K, and Dtis a dose distribution calculated on the medical image, Xt. Atransform, can be defined from to a refence image, R . A mass-weighted total dose at voxel v can then be calculated by,

[0020] where

[0021] andthe transferred dose at voxel v. When there is no mass change in these images, (v) = 1 and the dose in equation (1) becomes the directly summed total doses,(2).

[0022] With an EMC dose mapping method, the transferred dosevoxel v is defined as the transferred energy divided by the transferred mass, i.e.,

[0023] whereare the transferred energy and mass distributions on the reference image, R . In this case, the termsin (v) and (v)arecancelled in equation (1). The total dose in equation (1) becomes the ratio of the total deposited energy and the averaged mass over all fractions, i.e.,

[0024] Equations (1) and (2) are called the mass-weighted dose summation (MWDS) and direct dose summation (DDS) methods, respectively. Equation (3) shows that the mass- weighted total dose can alternatively be defined as the total deposited energy divided by the average mass. The equivalence of the two definitions offers a solution for cross validation ofdeformable dose accumulations. Additionally or alternatively, a four-dimensional (4D) dose can be defined as follows:

[0025] This definition can alternatively be written as:

[0026] When mass is static,= 1 and / J4D= DMW= DT. When mass is dynamic. * 1 and £>4D= DuwDT.

[0027] FIGS. 1A and IB shows an example workflow of the two dose summation methods. Compared to the DDS method (FIG. IB), the MWDS method (FIG. 1A) utilize transferring mass to the reference image each time a new dose is added so that the averaged mass density image MAcan be calculated. FIG. 1C illustrates an example workflow for a direct summation total dose. Advantageously, the dose summation methods described in the present disclosure can track mass changes, thereby generating a total dose that is consistent to the total deposited energy. The dose summation methods are also capable of providing 3D information (i.e., the total dose, energy, and average mass) for adaptive radiotherapy (ART) and other radiation treatment planning techniques. As an additional advantage, because a 4D dose is defined through the total deposited energy, the accuracy of 4D dose is equivalent to the conservation of the total energy, which can be used as a gold standard for 4D dose quality assurance.

[0028] Referring now to FIG. 2. a flowchart is illustrated as setting forth the steps of an example method for generating an accumulated dose distribution for a radiation treatment plan.

[0029] The method includes accessing medical image data of a patient using a computer system, as indicated at step 202. Accessing the medical image data can include retrieving previously acquired images from a database, memory, or other data storage device or medium. Additionally or alternatively, accessing the medical image data can include acquiring one or more medical images using a medical imaging system and transferring the medical image(s) to the computer system, which may be a part of the medical imaging system, a radiation treatment planning system, or the like. The medical images may include CT images,magnetic resonance images, or other medical images obtained in the process of developing a radiation treatment plan for the patient.

[0030] The medical image data may include a first medical image (or first series of medical images) acquired at a first time point and a second medical image (or second series of medical images) acquired at a second time point that is different than the first time point. The first time point may correspond to a time point before the patient received any radiation treatment, or may correspond to a time point where a patient received an earlier fraction of radiation treatment, an earlier course or radiation treatment, or some other earlier treatment. The second time point may then correspond to a current time point when the patient is preparing to undergo a new radiation treatment or treatment fraction.

[0031] As one non-limiting example, the medical image data may include daily setup images for a patient undergoing a radiation treatment and pretreatment planning images acquired from the patient at an earlier time point.

[0032] A radiation treatment plan data are also accessed with the computer system, as indicated at step 204. The radiation treatment plan data contains information pertaining to the delivery of radiation to the patient, including doses to be delivered to the PTV, contour data, fraction data, and so on. Advantageously, using the dose accumulation techniques of the present disclosure, the radiation treatment plan can be adapted based on mass changes in the patient between the first time point and the second time point.

[0033] The first medical image and the second medical image in the accessed medical image data are then coregistered, as indicated at step 206. As an example, the first and second medical images may be coregistered using a deformable image registration (DIR). In some instances, the first medical image may be a reference image and the second medical image may be a daily image. In such instances, the daily image (i.e., the second image) may be registered to the reference image (i.e., the first image). As a result of registering the first and second images, a deformation map is generated, which may be used to construct the transform,, for transferring doses to a dose grid.

[0034] The DIR technique may implement an image intensity-based objective function (e.g., a MIM-II registration), a multimodality -based objective function (e.g., a MIM-MM registration), and / or a contour consistency-based objective function (e.g., a MIM-CC registration) as its similarity metric. MIM-CC minimizes the difference of contour surfaces between the two registered images using a modified gradient descent method and MIM-MM maximizes the correspondence of high dimensional feature descriptors at each image voxelusing a Gauss -Newton-based optimization method. MIM-II is an image intensity-based, freeform deformable registration algorithm, where the squared differences of normalized intensities are summed as a similarity metric and minimized using the modified gradient descent method.

[0035] Additionally or alternatively, the first and second medical images may be coregistered using other DIR techniques, such as contour-based registrations, finite element method (FEM)-based registrations, or the like. In some cases, a contour matching-based FEM registration technique may be used. When OARs are contoured on both reference and moving images, displacements at contoured points in the reference image can be derived. Specifically, for each contour R on the reference image, its counterpart (M) on the moving image may first be identified and then a rigid translation may be performed by moving the center of M to the center of R. The translated contour can be denoted by MT. An objective function can be defined as follows:

[0036] where G^rt, mt z) represents the geometric distance between the contour point rtand mtdenotes the local similarity between rtand mtLtis a set ofmoving points located within a given distance of rt; and the parameter A can be adjusted according to the deformation of individual OARs, which in some cases may have a default value set to 1.0. The local similarity may be defined as:

[0037] where Brrepresents the neighborhood of r ; Ir kis I if voxel k in the reference image contains a point of the contour R , otherwise Ir kis 0; Im kis 1 if voxel k in the moving image contains a point of the contour M , otherwise Im kis 0; gkrepresents the corresponding Gaussian function defined between r and k . The displacement vector at each contour point may be derived by minimizing . R,MT). Repeating the above procedure for each of these OARs, displacements at all contour points can be determined. These displacements may be used as boundary constraints in a mechanical model to calculate a displacement vector field (DVF) on the reference image.

[0038] In some other cases, a hybrid registration method for each DIR algorithm may be used. For example, a MIM registration may be performed to derive boundary constraints for mechanical modeling. Specifically, for the MIM registration, boundary voxels can be identified from contours on its reference image and displacements at these voxels can be derived from its DVF. A mesh (e.g., a cubic tetrahedral mesh containing 131,614 nodes and 747,384 tetrahedron) may be scaled to cover all the boundary’ voxels. Then, a lookup table between the indices of nodes and the image voxels covered by the mesh may be established. Consequently, given a boundary voxel, its closest node can be identified. Displacements at these nodes can be derived from the MIM registration and used as boundary constraints in a mechanical model.

[0039] A volumetric interpolation may then be employed to convert the modelgenerated displacements into a new DVF on the reference image. Different preliminary registrations can be performed for each organ, with resultant displacements integrated to generate a mixed configuration file for the subsequent mechanical modeling.

[0040] As a non-limiting example, a FEM-based mechanical modeling may be used to calculate tissue deformation for organs contoured on a reference image and / or moving image. For example, the Young’s modulus (E) and Poisson’s ratio (v) of each element can be configured in a scaled mesh (e.g., a scaled tetrahedral mesh) according to the element’s location. The governing equation of elasticity can then be converted to the following set of linear algebraic equations:

[0041] where K represents the assembled global stiffness matrix; F,...,Fkrepresent the external forces acting on driving nodes; d,, .. dkrepresent displacement constraints preassigned to the nodes; and dk+i,...,dnrepresent displacements of non-driving nodes. The material parameters used in the matrix K may be selected to be consistent with the reported data. For example, Young’s moduli may be set to 1 kPa for the lung, 10 kPa for soft tissue, and1 MPa for bones, while the Poisson ratios may be set to 0.38 for the lung, 0.45 for soft issue, and 0.49 for bones. The displacements d ,...,dkmay be interpolated from the DVF of an MIM registration, generated by a contour matching technique, or the like. After modeling tissue deformation, the derived displacements at each node may be assigned to image voxels using a volumetric interpolation method. The interpolated displacement vectors may be combined with those pre-assigned to voxels outside the mesh to generate a modified DVF.

[0042] Mass and energy images are also generated from the medical image data, as indicated at step 208. For example, mass images may be generated from the medical image data using a calibration data (e.g., a CT-to-mass density conversion table). Based on this calibration data, medical images can be converted to mass density images (Mi). Energy images may be generated from the mass density images by multiplying the doses at each voxel calculated from a radiation treatment plan by the corresponding mass densities to obtain the amounts of their deposited energy (Ei).

[0043] The mass and energy images may then be transferred from the source (daily) image to the reference image using the deformation map generated when coregistering the first and second images, as indicated at step 210. Transferred dose data are then computed or otherwise estimated for each fraction using the transferred mass and energy images, as indicated at step 212. For instance, the transferred dosea voxel can be computed as the transferred energy divided by the transferred mass, as described above. In some implementations, step 212 can include summing the transferred energy images and / or averaging the transferred mass images.

[0044] An accumulated dose distribution is then computed from the transferred dose data using a mass-weighted dose summation, as indicated at step 214. For instance, the mass- weighted dose summation in equation (1) can be used to compute the accumulated dose distribution. The dose summation includes weighting the transferred dose at each voxel by a mass weighting value, , (v) , which can be indicative of changes in mass at the voxel from the first time point to the second time point. As a non-limiting example, the mass change weighting value can be estimated by dividing a transferred mass image for a particular fraction by the average mass image. In this way, the mass weighting value indicates a portion of the average mass across time points that is attributable to the current treatment fraction, i . Thus, computing the average mass image can be performed in step 214, or in an earlier step of the method.

[0045] The accumulated dose distribution may then be stored using the computer system for later use or processing, as indicated at step 216. As one example, the accumulated dose distribution may be used to adapt the radiation treatment plan for the patient by mapping the accumulated dose distribution backward onto the pretreatment planning images, by mapping the accumulated dose distribution forward to one or more time points during treatment, or both.

[0046] In an example study, the MWDS method described herein was implemented in a dose summation program and demonstrated with CT images acquired from a patient undergoing lung cancer retreatment. The CT scans were acquired at two different time points and used for development of the initial treatment plan and retreatment plan, respectively. A CT-to-mass density conversion table was calibrated for the CT scanner using an advanced electron density phantom. Based on this calibration data, CT images were converted to mass density images (Mi). The doses (Di) at each voxel calculated from the two plans were multiplied by their corresponding mass densities to obtain the amounts of their deposited energy (Ei), respectively.

[0047] A feature-based, multi-modality deformable image registration algorithm (MIM Software Inc., Beachwood, OH) was employed to register the CT image of the first time point (reference image) to that of the second time point (source image). The resulting deformation map (^;.) can be defined on the source CT image. The images, doses, and deformation maps were then exported for dose summation. Specifically, energy (Ei) and mass (Mi) images were transferred from the source CT to the reference CT using a forward mapping method. The transferred mass density images were averaged to generate MA and the transferred energy images were summed to create the accumulated energy image Er. The deformed doseswere reconstructed using the EMC mapping method. The deformed doses were summed using equations (1) and (2) to derive the total doses DT MWSDand DT DDS, respectively.

[0048] The property7of energy7conservation was evaluated for the two dose summation methods. Recall that DT^WSDis defined on the mass of the average image and DT DDSis defined on that of the reference image. These doses were multiplied by their corresponding masses to derive the total deposited energy ETMWDSand ET DDS. To verify the accuracy of ETJVIWDSar*d ET DDS, the dose distributions of the two plans were converted to energy distributions and then transferred to the reference CT. The transferred energy distributions wereadded at each voxel to obtain the summed energy distribution ( ET). ET^WDSand ET DDSwere compared to ETand their differences represent energy changes in the process of MWDS and DDS, respectively.

[0049] This study focused on the issue of energy loss that occurs in the process of dose summation on the reference image.

[0050] In this example study, the non-small cell lung cancer patient was originally treated with 60 Gy in 30 fractions, and the recurrent cancer was retreated with 44 Gy in 22 fractions. With the replanning CT taken as a reference, the MIM-generated deformation map < / and identity mapwere used to transfer energy and mass from the two planning CTs to the reference CT. The deformed (i.e., transferred) mass imagesand ^2 (^2)areshown in FIGS. 3A and 3B, respectively. The average of the two mass images is illustrated in FIG. 3C. Note that the densities of lung tissue in the marked tumor region changed after the first course of radiotherapy. The deformed energy distributionsare displayed in FIGS. 3D and 3E, respectively, with their sum shown in FIG. 3F. The original treatment plan exhibits an islanded energy distribution around the tumor (FIG. 3D). This is attributable to the mass difference between the tumor and its surrounding lung tissue.

[0051] With the EMC mapping method, doses were reconstructed on the reference image. The deformed doses were summed using the DDS and MWDS methods to derive the total doses DDDS and D WDS, respectively. From these total doses, the energy distributions EDDS and EMWDS were calculated and their relative differences from the total energy distribution ET were determined within individual organs, as shown in Table 1.Table 1. The directly summed energy distribution ET used to benchmark the energy distributions EMWDS and EDDS calculated from DMWDS and DDDS.

[0052] The total energy distribution EDDS calculated from DDDS largely differs from the directly added energy distribution Er, with a mean difference of up to 41.8% in PTV and 11.2% in the left lung. In contrast, the total energy distribution EMWDS calculated from DMWDS was consistent with ET, with their mean differences within 0.18% for all OARs. The smalldifference may be atributed to computational round-off errors in the energy calculation and conversion process.

[0053] With the DDS and MWDS methods, the doses from the first treatment and retreatment were summed to generate the total doses DDDS and DMWDS on the reference (FIG. 4A) and the average image (FIG. 4B), respectively. The 24, 60, and 90 Gy-isodose lines, illustrated for DDDS and DMWDS. show almost identical isodose lines (e.g.. 24 Gy) in regions such as the chest wall and heart, but the two summed doses exhibit obvious differences in the lung region. This is because MWDS takes account for mass changes that occur in the lung and tumor regions.

[0054] The mean of DDDS and DMWDS were calculated for relevant organs as listed in Table 2. It can be observed that the two accumulated doses differ by 14.55% in the left lung, 10.2% in the PTV, and 1.78% in the esophagus, respectively, but remain consistent within 0.67% in other OARs.Table 2. The means of the equal-weighted and mass-weighted total doses calculated for relevant organs.

[0055] Treatment response is contingent upon the total energy delivered to a patient. If an accumulated total dose does not represent the total energy delivered to the patient, this dose could mislead treatment response assessments. When doses calculated on individual images are transferred to a reference image, the transferred doses still represent the energy deposited on their original mass, as manifested by the EMC dose mapping method. Due to mass variations between images, the directly summed total dose cannot help to derive the total energy delivered to OARs or targets. For example, two fractions deliver the same amount of dose to a target voxel, and this voxel experiences mass changes from tumor to air or lung tissue due to tumor shrinkage. As a result, the two fractions have the same amount of dose contributed to the total dose but different amounts of energy to the total energy, i.e., the total energy delivered to patients cannot be determined from the directly summed total dose. In contrast, the mass- weighted total dose is defined on the average CT. With deformed doses generated by the EMCmethod, their mass-weighted total dose is equivalent to the ratio of the total deposited energy to the average mass.

[0056] The correlation of the MWDS accumulated total dose and the total deposited energy provides a conceptually simple explanation of the total dose, as illustrated in FIG. 4B, and allows for the accumulated dose to be benchmarked with the sum of the deposited energy counted in individual fractions.

[0057] The proposed dose summation method takes into account the impact of mass variations on dose summation with the resultant total dose equivalently defined on an averaged mass image. The mass-weighted total dose is not only consistent to the delivered energy', but also opens an avenue for validation of deformable dose accumulation in radiotherapy.

[0058] FIG. 5 shows an example of a system 500 for generating an accumulated dose distribution using a mass-weighted dose summation technique in accordance with some embodiments of the systems and methods described in the present disclosure. As shown in FIG. 5, a computing device 550 can receive one or more types of data (e.g., medical image data, radiation treatment plan data) from data source 502. In some embodiments, computing device 550 can execute at least a portion of a mass-weighted dose summation system 504 to generate an accumulated dose distribution from data received from the data source 502 using a mass- weighted dose summation technique.

[0059] Additionally or alternatively, in some embodiments, the computing device 550 can communicate information about data received from the data source 502 to a server 552 over a communication network 554, which can execute at least a portion of the mass-weighted dose summation system 504. In such embodiments, the server 552 can return information to the computing device 550 (and / or any other suitable computing device) indicative of an output of the mass-weighted dose summation system 504.

[0060] The output radiation treatment plan data can be communicated or otherwise transferred to a radiation treatment system 560 to be used by the radiation treatment system 560 to control the delivery of radiation to a subject. In some implementations, the computing device 550 and / or server 552 may be a part of a radiation treatment planning system, such as an ART planning system. In these instances, the summed dose may be processed by the computing device 550 and / or server 552 to update the radiation treatment plan before communicating the updated radiation treatment plan to the radiation treatment system 560. In other implementations, the computing device 550 and / or server 552 may be distinct from the radiation treatment planning system. In these instances, the summed dose or other radiationtreatment plan data may be communicated to the radiation treatment planning system by the computing device 550 and / or server 552 (e.g., via the communication network 554). The radiation treatment planning system then receives the summed dose and / or other treatment plan data and generates an updated radiation treatment plan. The updated radiation treatment plan is then communicated by the radiation treatment planning system to the radiation treatment system 560. As one non-limiting example, the radiation treatment system 560 can be an image- guided radiotherapy (IGRT) system capable of ART. In some instances, proton and heavy charged particle treatment systems can also be used.

[0061] In some embodiments, computing device 550 and / or server 552 can be any suitable computing device or combination of devices, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable computer, a server computer, a virtual machine being executed by a physical computing device, and so on. The computing device 550 and / or server 552 can also reconstruct images from the data.

[0062] In some embodiments, data source 502 can be any suitable source of data (e.g., measurement data, images reconstructed from measurement data, processed image data, radiation treatment plan data), another computing device (e.g., a server storing measurement data, images reconstructed from measurement data, processed image data, radiation treatment plan data), and so on. In some embodiments, data source 502 can be local to computing device 550. For example, data source 502 can be incorporated with computing device 550 (e.g., computing device 550 can be configured as part of a device for measuring, recording, estimating, acquiring, or otherwise collecting or storing data). As another example, data source 502 can be connected to computing device 550 by a cable, a direct wireless link, and so on. Additionally or alternatively, in some embodiments, data source 502 can be located locally and / or remotely from computing device 550, and can communicate data to computing device 550 (and / or server 552) via a communication network (e g., communication network 554).

[0063] In some embodiments, communication network 554 can be any suitable communication network or combination of communication networks. For example, communication network 554 can include a Wi-Fi network (which can include one or more wireless routers, one or more switches, etc.), a peer-to-peer network (e.g., a Bluetooth network), a cellular network (e.g., a 3G network, a 4G network, etc., complying with any suitable standard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), other ty pes of wireless network, a wired network, and so on. In some embodiments, communication network 554 can be a local area network, a wide area network, a public network (e.g., the Internet), aprivate or semi-private network (e.g., a corporate or university intranet), any other suitable type of network, or any suitable combination of networks. Communications links shown in FIG. 5 can each be any suitable communications link or combination of communications links, such as wired links, fiber optic links, Wi-Fi links, Bluetooth links, cellular links, and so on.

[0064] Referring now to FIG. 6, an example of hardware 600 that can be used to implement data source 502, computing device 550, and server 552 in accordance with some embodiments of the systems and methods described in the present disclosure is shown.

[0065] As shown in FIG. 6, in some embodiments, computing device 550 can include a processor 602, a display 604, one or more inputs 606, one or more communication systems 608, and / or memory 610. In some embodiments, processor 602 can be any suitable hardware processor or combination of processors, such as a central processing unit (‘"CPU”), a graphics processing unit (“GPU”), and so on. In some embodiments, display 604 can include any suitable display devices, such as a liquid crystal display (“LCD”) screen, a light-emitting diode (“LED”) display, an organic LED (“OLED”) display, an electrophoretic display (e.g., an “e- ink” display), a computer monitor, a touchscreen, a television, and so on. In some embodiments, inputs 606 can include any suitable input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on.

[0066] In some embodiments, communications systems 608 can include any suitable hardware, firmware, and / or software for communicating information over communication network 554 and / or any other suitable communication networks. For example, communications systems 608 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 608 can include hardware, firmware, and / or softw are that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.

[0067] In some embodiments, memory 610 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 602 to present content using display 604, to communicate with server 552 via communications system(s) 608, and so on. Memory 610 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 610 can include random-access memory (“RAM”), read-only memory (“ROM”), electrically programmable ROM (“EPROM”), electrically erasable ROM (“EEPROM"), other forms of volatile memory, other forms of non-volatile memory, one or more forms of semi-volatile memory, one or more flash drives, one or more hard disks, one ormore solid state drives, one or more optical drives, and so on. In some embodiments, memory 610 can have encoded thereon, or otherwise stored therein, a computer program for controlling operation of computing device 550. In such embodiments, processor 602 can execute at least a portion of the computer program to present content (e.g., images, user interfaces, graphics, tables), receive content from server 552, transmit information to server 552, and so on. For example, the processor 602 and the memory 610 can be configured to perform the methods described herein (e.g., the method of FIG. 2).

[0068] In some embodiments, server 552 can include a processor 612, a display 614, one or more inputs 616, one or more communications systems 618, and / or memory7620. In some embodiments, processor 612 can be any suitable hardware processor or combination of processors, such as a CPU, a GPU, and so on. In some embodiments, display 614 can include any suitable display devices, such as an LCD screen, LED display, OLED display, electrophoretic display, a computer monitor, a touchscreen, a television, and so on. In some embodiments, inputs 616 can include any suitable input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on.

[0069] In some embodiments, communications systems 618 can include any suitable hardware, firmware, and / or software for communicating information over communication network 554 and / or any other suitable communication networks. For example, communications systems 618 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 618 can include hardware, firmware, and / or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.

[0070] In some embodiments, memory7620 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 612 to present content using display 614, to communicate with one or more computing devices 550, and so on. Memory 620 can include any suitable volatile memory7, non-volatile memory7, storage, or any suitable combination thereof. For example, memory 620 can include RAM. ROM, EPROM, EEPROM, other ty pes of volatile memory, other ty pes of non-volatile memory, one or more types of semi-volatile memory, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory 620 can have encoded thereon a server program for controlling operation of server 552. In such embodiments, processor 612 can execute at least a portion of the server program to transmit information and / or content (e.g., data, images, a userinterface) to one or more computing devices 550, receive information and / or content from one or more computing devices 550, receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone), and so on.

[0071] In some embodiments, the server 552 is configured to perform the methods described in the present disclosure. For example, the processor 612 and memory 620 can be configured to perform the methods described herein (e.g.. the method of FIG. 2).

[0072] In some embodiments, data source 502 can include a processor 622, one or more data acquisition systems 624, one or more communications systems 626, and / or memory 628. In some embodiments, processor 622 can be any suitable hardware processor or combination of processors, such as a CPU, a GPU, and so on. In some embodiments, the one or more data acquisition systems 624 are generally configured to acquire data, images, or both, and can include a medical imaging system such as a CT system or an MRI system. Additionally or alternatively, in some embodiments, the one or more data acquisition systems 624 can include any suitable hardware, firmware, and / or software for coupling to and / or controlling operations of a medical imaging system. In some embodiments, one or more portions of the data acquisition system(s) 624 can be removable and / or replaceable.

[0073] Note that, although not shown, data source 502 can include any suitable inputs and / or outputs. For example, data source 502 can include input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, a trackpad, a trackball, and so on. As another example, data source 502 can include any suitable display devices, such as an LCD screen, an LED display, an OLED display, an electrophoretic display, a computer monitor, a touchscreen, a television, etc., one or more speakers, and so on.

[0074] In some embodiments, communications systems 626 can include any suitable hardware, firmware, and / or software for communicating information to computing device 550 (and, in some embodiments, over communication network 554 and / or any other suitable communication networks). For example, communications systems 626 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 626 can include hardware, firmware, and / or software that can be used to establish a wired connection using any suitable port and / or communication standard (e.g., VGA, DVI video, USB, RS-232, etc.), Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.

[0075] In some embodiments, memory’ 628 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, forexample, by processor 622 to control the one or more data acquisition systems 624, and / or receive data from the one or more data acquisition systems 624; to generate images from data; present content (e.g., data, images, a user interface) using a display; communicate with one or more computing devices 550; and so on. Memory 628 can include any suitable volatile memory, non-volatile memory. storage, or any suitable combination thereof. For example, memory 628 can include RAM. ROM, EPROM, EEPROM, other ty pes of volatile memory, other ty pes of non-volatile memory, one or more types of semi-volatile memory, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory 628 can have encoded thereon, or otherwise stored therein, a program for controlling operation of data source 502. In such embodiments, processor 622 can execute at least a portion of the program to generate images, transmit information and / or content (e.g., data, images, a user interface) to one or more computing devices 550, receive information and / or content from one or more computing devices 550, receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone, etc.), and so on.

[0076] In some embodiments, any suitable computer-readable media can be used for storing instructions for performing the functions and / or processes described herein. For example, in some embodiments, computer-readable media can be transitory' or non-transitory. For example, non-transitory computer-readable media can include media such as magnetic media (e.g., hard disks, floppy disks), optical media (e.g., compact discs, digital video discs. Blu-ray discs), semiconductor media (e.g., RAM, flash memory, EPROM, EEPROM), any suitable media that is not fleeting or devoid of any semblance of permanence during transmission, and / or any suitable tangible media. As another example, transitory computer- readable media can include signals on networks, in wires, conductors, optical fibers, circuits, or any suitable media that is fleeting and devoid of any semblance of permanence during transmission, and / or any suitable intangible media.

[0077] As used herein in the context of computer implementation, unless otherwise specified or limited, the terms ‘“component.” “system,” “module,” “framework,” and the like are intended to encompass part or all of computer-related systems that include hardware, software, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being, a processor device, a process being executed (or executable) by a processor device, an object, an executable, a thread of execution, a computer program, or a computer. By way of illustration, both an application running on acomputer and the computer can be a component. One or more components (or system, module, and so on) may reside within a process or thread of execution, may be localized on one computer, may be distributed between two or more computers or other processor devices, or may be included within another component (or system, module, and so on).

[0078] In some implementations, devices or systems disclosed herein can be utilized or installed using methods embodying aspects of the disclosure. Correspondingly, description herein of particular features, capabilities, or intended purposes of a device or system is generally intended to inherently include disclosure of a method of using such features for the intended purposes, a method of implementing such capabilities, and a method of installing disclosed (or otherwise known) components to support these purposes or capabilities. Similarly, unless otherwise indicated or limited, discussion herein of any method of manufacturing or using a particular device or system, including installing the device or system, is intended to inherently include disclosure, as embodiments of the disclosure, of the utilized features and implemented capabilities of such device or system.

[0079] The present disclosure has described one or more preferred embodiments, and it should be appreciated that many equivalents, alternatives, variations, and modifications, aside from those expressly stated, are possible and within the scope of the invention.

Claims

CLAIMS1. A method for generating an accumulated dose distribution, the method comprising: accessing medical image data with a computer system, wherein the medical image data contain at least a reference image and a daily image acquired from a patient; generating a deformation map from the medical image data using a deformable image registration between the reference image and the daily image; generating transferred dose data by mapping dose data from the daily image to the reference image using the deformation map, wherein the transferred dose data indicate doses to be delivered to the patient according to a radiation treatment plan; and generating the accumulated dose distribution using a mass-weighted summation of the transferred dose data.

2. The method of claim 1. wherein the transferred dose data are generated by: generating a mass density image from the daily image; generating an energy image from the mass density image; generating a transferred mass density image by applying the deformation map to the mass density image; generating a transferred energy image by applying the deformation map to the energy image; and dividing the transferred energy image by the transferred mass density image.

3. The method of claim 1. wherein the mass-weighted summation of the transferred dose data comprises a summation of transferred dose data weighted by a mass weighting value for each radiation treatment fraction in the transferred dose data.

4. The method of claim 3. wherein the mass weighting value for a given radiation treatment fraction in the transferred dose data is calculated by dividing a mass density image for the given radiation treatment fraction by an average mass density image.

5. The method of claim 4. wherein the mass density' image comprises a transferred mass density image generated by converting the daily image to a mass density image and applying the deformation map to the mass density image to generate the transferred mass density image.

6. The method of claim 5. wherein the daily image is converted to the mass density image using mass calibration curve data.

7. The method of claim 4, wherein the average mass density image comprises an average of mass density images generated for each of the radiation treatment fractions.

8. The method of claim 1 , further comprising accessing the radiation treatment plan with the computer system and updating the radiation treatment plan using the accumulated dose distribution.

9. The method of claim 1, wherein the reference image comprises a pretreatment planning image of the patient.

10. The method of claim 1. wherein the reference image and the daily image are acquired using a computed tomography (CT) system.

11. A method for updating a radiation treatment plan using a deformable dose accumulation, the method comprising: accessing a first medical image and a second medical image with a computer system, wherein the first medical image was acquired from a patient at a first time point and the second medical image was acquired from the pah ent at a second time point that is different from the first time point; accessing a radiation treatment plan with the computer system; generating a deformation map by registering the first medical image and the second medical image; generating a mass image from the second medical image using mass calibration data; generating an energy image from the mass image using dose data from the radiation treatment plan;computing transferred dose data by applying the deformation map to the energy image to generate a transferred energy image, applying the deformation map to the mass image to generate a transferred mass image, and dividing the transferred energy image by the transferred mass image; generating an accumulated dose distribution by summing the transferred dose data over a plurality of radiation treatment fractions while weighting each transferred dose in the transferred dose data by a mass weighting value corresponding to each of the plurality of radiation treatment fractions; and updating the radiation treatment plan based on the accumulated dose distribution.

12. The method of claim 11, wherein the medical image data comprise medical images acquired with a computed tomography (CT) system.

13. The method of claim 11, wherein the medical image data comprise medical images acquired with a magnetic resonance imaging (MRI) system.

14. The method of claim 11, wherein the deformation map is generated by registering the first medical image and the second medical image using a deformable image registration.

15. The method of claim 14, wherein the deformation map is generated by registering the second medical image to the first medical image using the deformable image registration.

16. The method of claim 11, wherein the mass weighting value corresponding to a given one of the plurality of radiation treatment fractions comprises a transferred mass image for the given one of the plurality of radiation treatment fractions divided by an average mass image.

17. The method of claim 16, wherein the average mass image is computed as an average of transferred mass images over the plurality of radiation treatment fractions.

18. The method of any one of claims 1 or 14, wherein the deformable image registration implements an image intensity-based objective function as a similarity metric.

19. The method of any one of claims 1 or 14, wherein the deformable image registration implements a multimodality-based objective function as a similarity7metric.

20. The method of any one of claims 1 or 14, wherein the deformable image registration implements a contour consistency-based objective function as a similarity metric.

21. The method of any one of claims 1 or 14, wherein the deformable image registration comprises a contour matching-based finite element method.

22. The method of any one of claims 1 or 14, wherein the deformable image registration comprises a hybrid finite element method.

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