Heavy ion radiotherapy RBE-weighted dose optimization device using group sparsity algorithm, and control method therefor
Patent Information
- Application Number
- PCT/KR2025/008447
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2025-03-25
- Filing Date
- 2025-06-18
- Publication Date
- 2026-10-01
Smart Images
Figure KR2025008447_01102026_PF_FP_ABST
Abstract
Description
Heavy ion radiation therapy RBE weighted dose optimization device using group sparsity algorithm and control method thereof
[0001] The present disclosure relates to a heavy ion radiation therapy RBE weighted dose optimization device. More specifically, it relates to a heavy ion radiation therapy RBE weighted dose optimization device utilizing a group sparsity algorithm, a heavy ion radiation therapy RBE weighted dose optimization device, and a method for controlling the same.
[0002] Heavy Ion Therapy is a state-of-the-art radiation therapy that accelerates heavy ions—that is, heavy atomic nuclei (usually carbon ions)—to precisely attack cancer cells.
[0003] While conventional radiation therapy (X-rays, gamma rays) can affect not only cancer cells but also surrounding normal tissues, heavy ion beams have the following characteristics.
[0004] First is the Bragg peak effect. Heavy ions emit almost no energy while passing through the body, but then emit a strong burst of energy at the location of cancer cells and stop. Thanks to this, cancer cells can be precisely targeted while minimizing damage to normal tissues.
[0005] Second, it has high lethality. It has a DNA destruction effect more than three times stronger than X-rays, so it can treat even radiation-resistant cancers.
[0006] Heavy ion therapy has the advantage of being effective for cancers that respond poorly to conventional radiation therapy (e.g., sarcoma, pancreatic cancer, salivary gland cancer, chordoma) and cancers with a high risk of recurrence. In addition, it has the advantage of being effective for cancers located in areas that are difficult to operate on (e.g., brain tumor, spinal cancer, head and neck cancer).
[0007] The advantages of heavy ion therapy include minimal damage to normal tissue, fewer treatment sessions (usually completed within 1 to 4 weeks), and effectiveness against radiation-resistant cancer.
[0008] As for the disadvantages of heavy ion therapy, the treatment cost is high and there is the problem that it can only be received at specific hospitals.
[0009] However, in the case of conventional technology, due to the characteristics of radiation therapy, it is often difficult to perform radiation therapy with a single field (beam direction), so a rotary gantry radiation therapy capable of treating from multiple angles is required. However, since the RBE weighted dose is nonlinear and difficult to predict, it has been difficult to set an appropriate angle.
[0010] In addition, numerous energy layers are required to treat 3D volumes, and one of the most time-consuming aspects of radiation therapy is energy layer switching. Having too many energy layers increases treatment time and can also affect the accuracy of radiation therapy for cancers such as lung cancer, liver cancer, and pancreatic cancer, where respiration is a factor.
[0011] The embodiment disclosed in this disclosure aims to provide a heavy ion radiation therapy RBE weighted dose optimization device that enhances the optimization of existing heavy ion radiation therapy plans through a group sparse algorithm.
[0012] The embodiment disclosed in this disclosure aims to provide a heavy ion radiation therapy RBE weighted dose optimization device capable of increasing treatment accuracy and enhancing treatment efficacy through an RBE weighted dose optimization algorithm.
[0013] The embodiments disclosed in this disclosure aim to provide a heavy ion radiation therapy RBE weighted dose optimization device capable of improving optimization problems by converting physical dose into RBE weighted dose.
[0014] The problems that this disclosure aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by a person skilled in the art from the description below.
[0015] A heavy ion radiation therapy RBE weighted dose optimization device utilizing a group sparsity algorithm according to the present disclosure for achieving the aforementioned technical problem comprises: a memory storing at least one process for performing heavy ion radiation therapy on a patient; and a processor performing a control operation according to said process, wherein the processor receives an image including a CT image of a patient, analyzes the input image, models a treatment site and normal tissue in three dimensions based on the analysis result of said image, sets a path of a heavy ion radiation beam to be irradiated on the modeled treatment site, calculates an optimized dose determining how much of the heavy ion radiation beam to be irradiated on the treatment site according to the set path based on the RBE weighted dose, predicts the effect of irradiating the heavy ion radiation beam to the treatment site by the calculated optimized dose before the actual treatment of the patient, and outputs the predicted result.
[0016] In addition, a method for optimizing RBE-weighted dose for heavy ion radiation therapy using a group sparsity algorithm, performed by a processor of a device according to the present disclosure for achieving the technical problem described above, comprises the steps of: receiving an image including a CT image of a patient; analyzing the input image; modeling a treatment site and normal tissue in three dimensions based on the analysis results of the image; setting a path for a heavy ion radiation beam to be irradiated to the modeled treatment site; calculating an optimized dose to determine how much the heavy ion radiation beam will be irradiated to the treatment site according to the set path based on the RBE-weighted dose; predicting the effect that occurs by irradiating the treatment site with the calculated optimized dose of the heavy ion radiation beam before the actual treatment of the patient; and outputting the predicted result.
[0017] In addition, a computer-readable recording medium for recording a computer program for executing a method for implementing the present disclosure may be further provided.
[0018] According to the present invention, the optimization of existing heavy ion radiation therapy plans can be advanced through a group sparse algorithm, thereby increasing the accuracy of treatment and enhancing the therapeutic effect.
[0019] According to the present invention, by combining beam angle selection and RBE dose-based treatment planning optimization through an RBE weighted dose optimization algorithm, the accuracy of treatment and the effect of treatment can be increased, thereby improving user convenience.
[0020] According to the present invention, by optimizing (minimizing) the energy layer through the RBE weighted dose optimization algorithm, the accuracy of treatment can be increased and the interplay effect improved, thereby enhancing user convenience.
[0021] According to the present invention, by converting the physical dose into an RBE-weighted dose, the optimization problem can be improved, thereby increasing the accuracy of treatment and enhancing the therapeutic effect.
[0022] The effects of the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description below.
[0023] FIG. 1 is a configuration diagram of a heavy ion radiation therapy RBE weighted dose optimization device according to the present disclosure.
[0024] FIG. 2 is a flowchart illustrating a method for optimizing RBE-weighted dose for heavy ion radiation therapy according to the present disclosure.
[0025] FIG. 3 is a drawing illustrating a TPS for carbon heavy particle therapy according to the present disclosure.
[0026] FIG. 4 is a drawing illustrating CT to SPR conversion according to the present disclosure.
[0027] FIG. 5 is a diagram illustrating RT structure DICOM processing according to the present disclosure.
[0028] FIG. 6 is a diagram illustrating an example of coordinate transformation from CT image coordinates to BEV coordinates according to the present disclosure.
[0029] FIG. 7 is a diagram illustrating coordinate transformation using a Ray Tracing algorithm according to the present disclosure.
[0030] FIG. 8 is a drawing illustrating an embodiment defining a dose effect matrix according to the present disclosure.
[0031] FIG. 9 is a diagram illustrating an example of dose calculation based on the MC technique according to the present disclosure.
[0032] FIG. 10 is a diagram illustrating an embodiment of generating a dose deposition matrix number from an MC dose calculation according to the present disclosure.
[0033] FIG. 11 is a diagram illustrating an example of MC dose implementation of spot weighting optimization and RBE weighted dose calculation according to the present disclosure.
[0034] FIG. 12 is a drawing illustrating an example of estimating Z1D* based on the MC technique according to the present disclosure.
[0035] FIG. 13 is a diagram illustrating a spot weight optimization algorithm according to the present disclosure.
[0036] FIG. 14 is a drawing illustrating an example of beam spot weighting optimization according to the present disclosure.
[0037] FIG. 15 is a diagram illustrating the optimization results in a homogeneous medium according to the present disclosure.
[0038] FIG. 16 is a diagram illustrating the optimization results in a heterogeneous medium according to the present disclosure.
[0039] FIG. 17 is a drawing illustrating the occurrence of scarcity of component units according to the present disclosure.
[0040] FIG. 18 is a drawing illustrating scarcity occurring in a specific group unit according to the present disclosure.
[0041] FIG. 19 is a drawing illustrating an example of calculating an optimized dose using intensity-modulated brachytherapy according to the present disclosure.
[0042] FIG. 20 is a drawing illustrating an embodiment of a beam angle and an energy layer according to the present disclosure.
[0043] FIG. 21 is a drawing illustrating the technical features of the present invention according to the present disclosure.
[0044] FIG. 22 is a drawing illustrating the effects of the present invention according to the present disclosure.
[0045] FIG. 23 is a diagram illustrating the effect of a group sparse optimization algorithm according to the present disclosure.
[0046] Throughout this disclosure, the same reference numerals denote the same components. This disclosure does not describe all elements of the embodiments, and general content in the art to which this disclosure pertains or content that overlaps between embodiments is omitted. The terms 'part, module, component, block' as used in the specification may be implemented in software or hardware, and depending on the embodiments, a plurality of 'parts, modules, components, blocks' may be implemented as a single component, or a single 'part, module, component, block' may include a plurality of components.
[0047] Throughout the specification, when a part is described as being "connected" to another part, this includes not only cases where they are directly connected but also cases where they are indirectly connected, and indirect connections include connections made via a wireless communication network.
[0048] Furthermore, when it is stated that a part "includes" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.
[0049] Throughout the specification, when it is stated that a component is located "on" another component, this includes not only cases where a component is in contact with another component, but also cases where another component exists between the two components.
[0050] The terms first, second, etc. are used to distinguish one component from another, and the components are not limited by the aforementioned terms.
[0051] Singular expressions include plural expressions unless there is an obvious exception in the context.
[0052] In each step, identification codes are used for convenience of explanation and do not describe the order of the steps; the steps may be performed differently from the specified order unless a specific order is clearly indicated in the context.
[0053] The operating principles and embodiments of the present disclosure will be described below with reference to the attached drawings.
[0054] In this specification, the present invention may be implemented not only as a server system but also as various devices capable of performing computational processing and providing results to a user. For example, the present invention may include a computer, a server device, and a portable terminal, or may take the form of any one of them.
[0055] Here, the computer may include, for example, a notebook, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.
[0056] The above server device is a server that processes information by communicating with an external device, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, and a web server.
[0057] The above portable terminal may include, for example, all types of handheld-based wireless communication devices such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminals, smartphones, etc., as well as wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMDs).
[0058] FIG. 1 is a configuration diagram of a heavy ion radiation therapy RBE weighted dose optimization device according to the present disclosure.
[0059] Referring to FIG. 1, the heavy ion radiation therapy RBE weighted dose optimization device (100) includes a processor (130) and a memory (150).
[0060] The input module (110) collects data.
[0061] The input module (110) receives an image including a CT scan and an image of the patient.
[0062] The sensor module (120) senses data.
[0063] The processor (130) performs operations according to the process.
[0064] The processor (130) receives an image including a CT image of a patient, analyzes the input image, models the treatment area and normal tissue in three dimensions based on the analysis results of the image, sets the path of the heavy ion radiation beam to be irradiated to the modeled treatment area, calculates an optimal dose to determine how much the heavy ion radiation beam will be irradiated to the treatment area according to the set path based on the RBE weighted dose, predicts the effect that occurs by irradiating the heavy ion radiation beam to the treatment area by the calculated optimal dose before the actual treatment of the patient, and outputs the predicted result.
[0065] The display module (140) displays a graphic image according to a control command from the processor (130).
[0066] Memory (150) stores at least one process of performing heavy ion radiation therapy on a patient.
[0067] The communication module (160) transmits and receives data with an external device (200).
[0068] The communication module (160) can receive data from an external device (200).
[0069] Here, the external device (200) includes an external device such as a smartphone, PC, laptop, tablet PC, etc. The external device (200) includes an external mobile device (200) that includes a memory (260) for storing data.
[0070] The camera module (170) captures an image of the front.
[0071] The camera module (170) photographs a subject in front according to a control command from the processor (130).
[0072] The processor (130) calculates the optimal dose using the groupable parameters of the group-wise sparsity technique.
[0073] Groupable parameters include beam angle and energy layer.
[0074] A detailed explanation of this is given in Fig. 18.
[0075] The processor (130) calculates the optimized dose as the sum of physical doses (Dose constraints), element sparsity, and group sparsity, and the optimized dose is expressed by the following mathematical formula.
[0076]
[0077] A detailed explanation of this is shown in Fig. 19.
[0078] The processor (130) converts the physical dose into an RBE weighted dose to calculate an optimized dose, and the optimized dose is expressed by the following mathematical formula.
[0079]
[0080] A detailed explanation of this is shown in Fig. 21.
[0081] The processor (130) uses L2,p-norm to remove unnecessary groups and retain only meaningful groups, wherein L2,p-norm is expressed by the following mathematical formula.
[0082]
[0083] A detailed explanation of this is given in Fig. 18.
[0084] The processor (130) generates a dose influence matrix Dij to calculate an optimized dose, wherein the dose influence matrix is expressed by the following mathematical formula.
[0085]
[0086] Here, the above w represents the spot weight to be optimized.
[0087] The above d represents an ideal dose distribution.
[0088] A detailed explanation of this is shown in Fig. 8.
[0089] The processor (130) calculates the optimal dose by applying a non-linear operation that converts the physical dose into an RBE weighted dose when optimizing the weight of the beam spot, and the optimal dose is expressed by the following mathematical formula.
[0090]
[0091] Here, T[ㆍ] represents a conversion function that converts physical dose into RBE weighted dose.
[0092] A detailed explanation of this is shown in Fig. 13.
[0093] The processor (130) reduces the beam angle through group sparse optimization.
[0094] A detailed explanation of this is given in Fig. 22.
[0095] When the processor (130) selects a beam angle, it calculates an optimized dose by applying an optimal RBE weighted dose corresponding to the selected beam angle.
[0096] A detailed explanation of this is shown in Fig. 23.
[0097] However, the components illustrated in FIG. 1 are not essential for implementing the present invention according to the present disclosure, so the present invention described herein may have more or fewer components than the components listed above.
[0098] The communication module (160) may include one or more components that enable communication with an external device, and may include, for example, at least one of a broadcast receiving module, a wired communication module, a wireless communication module, a short-range communication module, and a location information module.
[0099] The input module (110) is for inputting video information (or signal), audio information (or signal), data, or information input from a user, and may include at least one of at least one camera, at least one microphone, and a user input unit. Voice data or image data collected by the input module (110) may be analyzed and processed into a user control command.
[0100] The display module (140) displays (outputs) information processed in the present invention. For example, the present invention may display execution screen information of a running application (e.g., an application), or UI (User Interface) and GUI (Graphic User Interface) information based on such execution screen information.
[0101] The memory (150) can store data supporting various functions of the present invention and a program for the operation of the control unit, and can store input / output data (e.g., music files, still images, videos, etc.), and can store a plurality of application programs (or applications) running on the artificial intelligence-based user behavior pattern analysis device (100), data for the operation of the device, and commands. At least some of these application programs can be downloaded from an external server via wireless communication.
[0102] Such memory (150) may include at least one type of storage medium among flash memory type, hard disk type, SSD type (Solid State Disk type), SSD type (Silicon Disk Drive type), multimedia card micro type, card type memory (e.g., SD or XD memory, etc.), RAM (random access memory; RAM), SRAM (static random access memory), ROM (read-only memory; ROM), EEPROM (electrically erasable programmable read-only memory), PROM (programmable read-only memory), magnetic memory, magnetic disk, and optical disk. Additionally, the memory (150) may be a database connected via wired or wireless connection, although separate from the present invention, and may be implemented as a database system.
[0103] The processor (130) may be implemented with at least one core, a memory that stores data for an algorithm or a program that reproduces the algorithm for controlling the operation of the components within the present invention, and at least one processor (not shown) that performs the aforementioned operation using the data stored in the memory. In this case, the memory and the processor may each be implemented as separate chips. Alternatively, the memory and the processor may be implemented as a single chip.
[0104] In addition, the processor (130) can control any one or a combination of the components described above in order to implement various embodiments according to the present disclosure described in FIGS. 2 to 23 below.
[0105] At least one component may be added or removed in response to the performance of the components illustrated in FIG. 1. Additionally, it will be readily understood by those skilled in the art that the relative positions of the components may be changed in response to the performance or structure of the system.
[0106] Meanwhile, each component illustrated in Fig. 1 refers to a software and / or hardware component such as a Field Programmable Gate Array (FPGA) and an Application Specific Integrated Circuit (ASIC).
[0107] FIG. 2 is a flowchart illustrating a method for optimizing RBE-weighted dose for heavy ion radiation therapy according to the present disclosure.
[0108] The present invention is performed by a heavy ion radiation therapy RBE weighted dose optimization device (100) or a processor (130) of a heavy ion radiation therapy RBE weighted dose optimization device (100).
[0109] Referring to FIG. 2, the processor (130) receives an image including a CT image of a patient (S210).
[0110] The processor (130) analyzes the input image (S220).
[0111] The processor (130) models the treatment area and normal tissue in three dimensions based on the analysis results of the above image (S230).
[0112] The processor (130) sets the path of the heavy ion radiation beam to be irradiated onto the modeled treatment site (S240).
[0113] The processor (130) calculates an optimized dose to determine how much of the heavy ion radiation beam to irradiate the treatment site according to the set path based on the RBE weighted dose (S250).
[0114] The processor (130) predicts the effect that occurs by irradiating the treatment site with the heavy ion radiation beam by the calculated optimized dose before the actual treatment of the patient (S260).
[0115] The processor (130) outputs the predicted result (S270).
[0116] FIG. 3 is a drawing illustrating a TPS for carbon heavy particle therapy according to the present disclosure.
[0117] Referring to Fig. 3 (310), the carbon heavy ion therapy TPS will be described.
[0118] A TPS (Treatment Planning System) refers to a software system that establishes a treatment plan to provide the optimal dose to a patient in radiation therapy (particularly heavy ion therapy, proton therapy, X-ray therapy, etc.).
[0119] The functions of TPS are as follows.
[0120] The processor (130) analyzes patient CT / MRI data (S10).
[0121] The processor (130) models the area to be treated (cancer tumor) and normal tissue in 3D (S20).
[0122] The processor (130) designs the optimal path of the radiation beam (S30).
[0123] The processor (130) strikes the tumor as much as possible while minimizing damage to surrounding normal tissue (S40).
[0124] The processor (130) calculates the dose distribution (Dose Calculation) (S50).
[0125] Specifically, calculate how strong the radiation to irradiate a specific area.
[0126] In the case of the present invention, the treatment process is simulated by taking into account RBE (Relative Biological Effectiveness) in heavy ion therapy.
[0127] In other words, it has the advantage of predicting the effect by performing virtual radiation therapy before actual treatment.
[0128] Explain the simulation process in more detail.
[0129] CT simulation is performed (S1).
[0130] Set the path (Contouring, target location, normal organ) (S2).
[0131] Convert CT to Density (CT to Density) (S3).
[0132] The treatment plan is optimized by calculating the dose (Dose Calculation) (S4).
[0133] Creates a quality assurance plan (QA Plan generation) (S5).
[0134] Perform quality assurance procedures (Pre RT QA) before radiation therapy (S6).
[0135] The TPS of the present invention includes a CT to SPR conversion process, an RT structure DICOM process, a coordinate conversion process, a dose deposition matrix generation process, a spot weight optimization process, and an RBE-weighted dose calculation process.
[0136] FIG. 4 is a drawing illustrating CT to SPR conversion according to the present disclosure.
[0137] Referring to Fig. 4 (410), the CT to SPR conversion will be explained.
[0138] It means converting HU (Housefield Unit) from a CT image into SPR (Stopping Power Ratio). In other words, it means converting the HU values obtained from the CT image into SPR values to prepare them for use in radiation therapy, etc.
[0139] This means that it is essential to convert image information into water-equivalent thickness (WET).
[0140] Specifically, HU (Housefield Unit) is a unit used in CT that indicates the degree of X-ray attenuation of tissue.
[0141] The HU value of water is 0, air is -1,000, and bone is 1,000 or higher.
[0142] The HU value of each pixel reflects the density and nature of the defrosting operation.
[0143] Explain SPR (Stopping Power Ratio).
[0144] CT scans measure the X-ray attenuation coefficient to calculate the HU value. In radiation therapy, the HU value is not used directly but must be converted into an SPR value. This is because the SPR value is necessary to accurately calculate how radiation is attenuated and scattered within the body.
[0145] Explain WET (Water-equivalent thickness).
[0146] WET refers to a value indicating how much tissue attenuates radiation compared to water. In other words, it signifies "how many millimeters of water thickness radiation passing through a specific tissue has the same effect."
[0147] For example, bone has a higher WET value than water of the same thickness because it has a higher density and attenuates X-rays more.
[0148] FIG. 5 is a diagram illustrating RT structure DICOM processing according to the present disclosure.
[0149] Referring to Fig. 5 (510), RT structure DICOM processing is described.
[0150] The processor (130) reads the RT structure DICOM file.
[0151] The processor (130) extracts local information of the target and OARs from the RT structure DICOM file.
[0152] The processor (130) arranges the CT image into coordinates.
[0153] The processor (130) matches the contouring information of the tumor and organ to the SPR (Stopping power ratio, the degree to which radiation is attenuated in tissues during radiation therapy).
[0154] Explain RT structure DICOM.
[0155] RT Structure is a DICOM (medical imaging standard) file format used to store and share treatment plans in radiation therapy (RT).
[0156] DICOM (Digital Imaging and Communications in Medicine) refers to an international standard format for storing and exchanging medical images and related data. It is used to store medical images such as CT, MRI, and PET, and is also used extensively in radiation therapy (RT).
[0157] FIG. 6 is a diagram illustrating an example of coordinate transformation from CT image coordinates to BEV coordinates according to the present disclosure.
[0158] Referring to Fig. 6 (610), the coordinate transformation from CT image coordinates to BEV coordinates is explained.
[0159] The processor (130) converts CT image coordinates (x, y, z) into beam's eye view (BEV) coordinates. This is a process necessary to combine dose information and beam spot location.
[0160] Here, the beam eye view refers to an image of the patient's treatment site viewed from the perspective of the radiation beam in radiotherapy. In other words, it is a visual representation seen from the direction in which the radiation is irradiated.
[0161] Beam's Eye View (BEV) refers to visualizing the tumor and surrounding tissues from the direction in which radiation is actually delivered. It is a crucial concept in treatment planning, and generating a BEV based on images such as CT and MRI can improve treatment accuracy.
[0162] FIG. 7 is a diagram illustrating coordinate transformation using a Ray Tracing algorithm according to the present disclosure.
[0163] Referring to Fig. 7 (710), a coordinate transformation using a Ray Tracing algorithm is described.
[0164] Ray tracing refers to an algorithm that calculates light reflection, refraction, and shadows by simulating the path of rays. Ray tracing algorithms are used in various fields, such as radiation therapy.
[0165] Different voxels can be matched by rotating the gantry, but this can be solved using a ray tracing algorithm.
[0166] Referring to Fig. 7 (710), the dose distribution and beam's eye view are shown when the gantry is at 0 degrees.
[0167] When the gantry is 45 degrees, the dose distribution and beam eye view were plotted.
[0168] Here, a gantry refers to a structure that supports and allows a device to irradiate radiation in a radiation therapy device or medical imaging equipment to rotate.
[0169] FIG. 8 is a drawing illustrating an embodiment defining a dose effect matrix according to the present disclosure.
[0170] Referring to Fig. 8 (810), the definition of the dose effect matrix is explained.
[0171] The processor (130) matches dose information to the beam spot location for spot weighting optimization.
[0172] The processor (130) generates a dose effect matrix Dij for individual structures.
[0173] Specifically, the processor (130) generates a dose influence matrix Dij to calculate the optimal dose.
[0174] The dose effect matrix is expressed by the following mathematical formula 1.
[0175] [Mathematical Formula 1]
[0176]
[0177] Here, w represents the spot weight to be optimized, and d represents the ideal dose distribution.
[0178] Further explanation of the dose effect matrix is provided.
[0179] A Dose Influence Matrix refers to a matrix in radiation therapy that indicates how the dose at a specific location (e.g., a tumor or normal tissue) is influenced by the input value (beam intensity) of the radiation beam.
[0180] In other words, a dose-impact matrix is a tool that calculates the dose received at each location for a given beam intensity. In radiation therapy, a dose-impact matrix is a matrix that indicates how each beam intensity affects the dose at a specific location in a patient; it is essential for linear model-based radiation therapy planning and is utilized to find the optimal beam intensity in precision treatments such as IMRT.
[0181] In summary, the dose effect matrix can be viewed as a mathematical representation of how much a beam affects a specific location.
[0182] FIG. 9 is a diagram illustrating an example of dose calculation based on the MC technique according to the present disclosure.
[0183] Referring to Fig. 9 (910), a dose calculation based on the MC method is explained.
[0184] The upper part of Fig. 9 (910) is a diagram illustrating a comparison between the measured values and the MC technique.
[0185] The integrated depth dose and spot profile were plotted in that order.
[0186] The lower part of Fig. 9 (910) is a diagram illustrating a comparison of the TPS and MC techniques.
[0187] It was illustrated in the order of lung, muscle, and bone.
[0188] FIG. 10 is a diagram illustrating an embodiment of generating a dose deposition matrix from MC dose calculation according to the present disclosure.
[0189] Referring to Fig. 10 (1010), it can be seen that a dose deposition matrix is generated from MC dose calculation.
[0190] In the case of Gantry 0, various spot locations are formed on different energy layers.
[0191] Here, a gantry refers to a structure that supports and allows a radiation irradiating device to rotate in radiation therapy equipment or medical imaging equipment. In other words, a gantry refers to a large mechanical structure that moves to send a radiation beam or particle beam in a desired direction.
[0192] FIG. 11 is a diagram illustrating an example of spot weighting optimization and RBE weighted dose calculation according to the present disclosure.
[0193] Referring to Fig. 11 (1110), spot weight optimization and RBE weighted dose calculation are explained.
[0194] The pencil beam dose distribution is expressed by Equation 2.
[0195] [Mathematical Formula 2]
[0196]
[0197] Here, b represents a beam in a beamset. e represents energy layers in a beam. J represents a spot in an energy layer.
[0198] Weight optimization in the beam spot is expressed by Equation 3.
[0199] [Mathematical Formula 3]
[0200]
[0201] Here, T[ㆍ] represents a conversion function that converts physical dose to RBE-weighted dose.
[0202] FIG. 12 is a drawing illustrating an example of estimating Z1D* based on the MC technique according to the present disclosure.
[0203] Referring to Fig. 12 (1210), the estimation of Z1D* based on the Monte Carlo method is explained.
[0204] The Monte Carlo Method is a technique that simulates complex physical systems using probabilistic sampling and is utilized in fields such as radiation physics and particle simulation optimization problems.
[0205] Here, Z1D* refers to a variable representing the dose distribution for a specific depth in radiation dose delivery and particle simulations, and is used to predict doses according to depth, particularly in particle therapy (proton therapy, heavy ion therapy, etc.).
[0206] In other words, Z1D* is used to predict how much energy is delivered at what depth as radiation passes through a patient's tissue.
[0207] FIG. 13 is a diagram illustrating a spot weight optimization algorithm according to the present disclosure.
[0208] Referring to Fig. 13, the spot weighting algorithm is explained.
[0209] First, we will explain beam spot weight optimization.
[0210] Spot Weight Optimization refers to the process of optimizing the dose of each individual spot in particle therapy. It is primarily used in proton therapy and heavy ion therapy.
[0211] In particle therapy, a "spot" refers to the irradiation of radiation using a pencil beam scanning method. A spot is the smallest unit where a beam is irradiated to a single point, and numerous spots combine to form a 3D dose distribution.
[0212] Spot weight refers to the dose contribution (weight) of each spot, and optimizing this is called spot weight optimization.
[0213] The processor (130) calculates the optimized dose by applying a non-linear operation that converts the physical dose into an RBE weighted dose when optimizing the weight of the beam spot.
[0214] The above optimized dose is expressed by the following mathematical formula 4.
[0215] [Mathematical Formula 4]
[0216]
[0217] Here, T[ㆍ] represents a conversion function that converts physical dose into RBE weighted dose.
[0218] The convex optimization of mathematical equation 4 is expressed by mathematical equation 5.
[0219] [Mathematical Formula 5]
[0220] FIG. 14 is a drawing illustrating an example of beam spot weighting optimization according to the present disclosure.
[0221] With reference to Fig. 14 (1410), beam spot weight optimization will be explained.
[0222] First, update the spot weight w.
[0223] The spot weight w is expressed by the following mathematical formula 6.
[0224] [Mathematical Formula 6]
[0225]
[0226] Second, update Z1d*.
[0227] Z1d* is expressed by the following mathematical formula 7.
[0228] [Mathematical Formula 7]
[0229]
[0230] Here, A represents the line influence matrix.
[0231] FIG. 15 is a diagram illustrating the optimization results in a homogeneous medium according to the present disclosure.
[0232] Figure 15 (1510) shows the results of the planning optimization for the target in a water phantom for a uniform medium.
[0233] A water phantom refers to the use of water as a medium with properties similar to the human body to study how radiation passes through and is absorbed by human tissues. Water is suitable for simulating the behavior of radiation within the human body because its density (approximately 1.0 g / cm³) and atomic number are similar to those of soft tissues (muscles, skin, etc.).
[0234] FIG. 16 is a diagram illustrating the optimization results in a heterogeneous medium according to the present disclosure.
[0235] Figure 16 (1610) shows the results of planning optimization for the target in the patient's CT image in the case of a non-uniform medium.
[0236] FIG. 17 is a drawing illustrating the occurrence of scarcity of component units according to the present disclosure.
[0237] Referring to Fig. 17 (1710), the scarcity of component units is explained.
[0238] Element-wise sparsity means that each individual element of a matrix or vector is likely to be zero or a small value.
[0239] The Lk-norm consists of the following mathematical formula 8.
[0240] [Mathematical Formula 8]
[0241]
[0242] Here, A represents the line influence matrix.
[0243] FIG. 15 is a diagram illustrating the optimization results in a homogeneous medium according to the present disclosure.
[0244] Figure 15 (1510) shows the results of the planning optimization for the target in a water phantom for a uniform medium.
[0245] A water phantom refers to the use of water as a medium with properties similar to the human body to study how radiation passes through and is absorbed by human tissues. Water is suitable for simulating the behavior of radiation within the human body because its density (approximately 1.0 g / cm³) and atomic number are similar to those of soft tissues (muscles, skin, etc.).
[0246] FIG. 16 is a diagram illustrating the optimization results in a heterogeneous medium according to the present disclosure.
[0247] Figure 16 (1610) shows the results of planning optimization for the target in the patient's CT image in the case of a non-uniform medium.
[0248] FIG. 17 is a drawing illustrating the occurrence of scarcity of component units according to the present disclosure.
[0249] Referring to Fig. 17 (1710), the scarcity of component units is explained.
[0250] Element-wise sparsity means that each individual element of a matrix or vector is likely to be zero or a small value.
[0251] The Lk-norm consists of the following mathematical formula 9.
[0252] [Mathematical Formula 9]
[0253]
[0254] FIG. 18 is a drawing illustrating scarcity occurring in a specific group unit according to the present disclosure.
[0255] With reference to Fig. 18 (1810), group sparse optimization will be described.
[0256] Group-wise sparsity refers to the phenomenon where sparsity occurs at the level of specific groups in data or models. In other words, it is sparsity that leads the entire specific group to become zero or a small value, rather than individual elements.
[0257] The meaning of "Group is sparse" is that only a small number of groups are meaningful within the entire population of x.
[0258] Referring to Fig. 18, in the entire group of x, there are a first group (x1, x2, x3), a second group (xn, xn-1, xn-2), a third group (x4, x5, x6), and a fourth group (x7, x8, x9), and among these, only the first group is meaningful.
[0259] The processor (130) calculates the optimal dose using the groupable parameters of the group-wise sparsity technique.
[0260] Here, groupable parameters include beam angle and energy layer.
[0261] The processor (130) uses L2,p-norm to remove unnecessary groups and keep only meaningful groups.
[0262] L2,p-norm is expressed by the following mathematical formula 10.
[0263] [Mathematical Formula 10]
[0264]
[0265] Carbon heavy ion radiation therapy optimizes the radiation treatment plan based on weighted doses that consider biological effects (Relative Biological Effectiveness (RBE) weighted dose).
[0266] Due to the nature of radiation therapy, it is often difficult to perform radiation therapy with a single field (beam direction), so rotational gantry radiation therapy that can treat from multiple angles is required. However, since the RBE-weighted dose is nonlinear and difficult to predict, it is difficult to set an appropriate angle.
[0267] In addition, treating a 3D volume requires numerous energy layers, and one of the most time-consuming aspects of radiation therapy is energy layer switching. Having too many energy layers increases treatment time and can also affect the accuracy of radiation therapy for cancers affected by respiration, such as lung cancer, liver cancer, and pancreatic cancer.
[0268] Therefore, carbon heavy ion radiation therapy requires a new treatment plan optimization algorithm.
[0269] The present disclosure aims to optimize and minimize parameters that can be grouped (rotary treatment device treatment angle, or energy layer) through a group sparse-based optimization algorithm.
[0270] FIG. 19 is a drawing illustrating an example of calculating an optimized dose using intensity-modulated brachytherapy according to the present disclosure.
[0271] Referring to FIG. 19, the processor (130) performs an intensity modulated brachytherapy (IMBT) optimization process.
[0272] The processor (130) calculates the optimized dose as the sum of the physical dose (Dose constraints), element sparsity, and group sparsity.
[0273] The optimal dose is expressed by the following mathematical formula 11.
[0274] [Mathematical Formula 11]
[0275]
[0276] FIG. 20 is a drawing illustrating an embodiment of a beam angle and an energy layer according to the present disclosure.
[0277] FIG. 20 includes FIG. 20(a) and FIG. 20(b).
[0278] FIG. 20(a)(2010) is a drawing illustrating an example of a beam angle.
[0279] FIG. 20(b) (2020) is a drawing illustrating an example of an energy layer.
[0280] FIG. 21 is a drawing illustrating the technical features of the present invention according to the present disclosure.
[0281] Referring to FIG. 21 (2110), in the case of the prior art, the optimized dose is expressed by the following mathematical formula 12.
[0282] [Mathematical Formula 12]
[0283]
[0284] In the case of conventional technology, there was a problem in that it only reflected physical dose and did not reflect RBE weighted dose.
[0285] The processor (130) converts the physical dose into an RBE weighted dose to calculate the optimized dose.
[0286] The optimal dose is expressed by the following mathematical formula 13.
[0287] [Mathematical Formula 13]
[0288]
[0289] According to the present invention, beam angle optimization and energy layer optimization can be reflected in terms of medical treatment, thereby increasing the effect of radiation therapy.
[0290] RBE (Relative Biological Effectiveness) is explained further.
[0291] RBE refers to an indicator used to evaluate the effects of specific radiation on biological tissues (particularly cancer cells). In other words, it is a concept that corrects for the fact that even with the same physical dose (Gray, Gy), the biological effects vary depending on the type of radiation.
[0292] Explains the importance of RBE in heavy ion therapy.
[0293] The carbon-ion beam used in heavy ion therapy exhibits much more powerful biological effects than X-rays or proton beams.
[0294] For example, if the RBE of X-rays = 1 (reference value) and the RBE of carbon ion rays In cases where it is 2 to 5, carbon ion rays can kill cancer cells 2 to 5 times more strongly than X-rays even with the same dose (Gy).
[0295] This is because carbon ion beams have the effect of completely severing double-stranded DNA (complex double-strand breaks, DSBs), making it difficult for cancer cells to repair themselves. In particular, they can effectively eliminate even cancer cells that are resistant to conventional radiation (X-ray) therapy.
[0296] FIG. 22 is a drawing illustrating the effects of the present invention according to the present disclosure.
[0297] Referring to Fig. 22 (2210), the effect of prostate cancer treatment is explained.
[0298] The figure above shows the case of unconstrained optimization.
[0299] The figure below is the case of the present invention (Group sparse optimization).
[0300] The processor (130) reduces the beam angle through group sparse optimization.
[0301] According to the present invention, the beam angle can be reduced so that precise treatment can be performed, thereby increasing the effect of radiation therapy.
[0302] FIG. 23 is a diagram illustrating the effect of a group sparse optimization algorithm according to the present disclosure.
[0303] Referring to Fig. 23 (2310), the role of the group sparse optimization algorithm in carbon heavy particle therapy planning is explained.
[0304] (a) This is the case where the beam angle is selected.
[0305] When planning treatment, it may be difficult to manually select the optimal beam angle to calculate the optimal RBE dose distribution.
[0306] When the processor (130) selects a beam angle, it calculates an optimized dose by applying an optimal RBE weighted dose corresponding to the selected beam angle.
[0307] According to the present invention, beam angle selection and RBE dose-based treatment planning optimization can be combined through a group sparse algorithm.
[0308] (b) This is the case of energy layer optimization (minimization).
[0309] Energy layer switching, one of the characteristics of PBS, generally takes the most time. During respiratory peristalsis therapy, treatment time delays and interplay effects are increased.
[0310] According to the present invention, treatment efficiency can be increased and the interplay effect improved by optimizing (minimizing) the energy layer.
[0311] The aforementioned program may include code encoded in a computer language such as C, C++, JAVA, or machine language, which can be read by the computer's processor (CPU) through the computer's device interface, in order for the computer to read the program and execute the methods implemented in the program. Such code may include functional code related to functions that define the necessary functions for executing the methods, and may include control code related to execution procedures necessary for the computer's processor to execute the functions according to a predetermined procedure. Additionally, such code may further include memory reference code regarding where (address) additional information or media necessary for the computer's processor to execute the functions should be referenced in the computer's internal or external memory. In addition, if the processor of the computer needs to communicate with any other computer or server located remotely in order to execute the above functions, the code may further include communication-related code regarding how to communicate with any other computer or server located remotely using the communication module of the computer, and what information or media to transmit or receive during communication.
[0312] The above-mentioned storage medium refers to a medium that stores data semi-permanently and is readable by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory. Specifically, examples of the above-mentioned storage medium include, but are not limited to, ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage device. That is, the above-mentioned program may be stored on various recording media on various servers that the computer can access, or on various recording media on the user's computer. Additionally, the above-mentioned medium may be distributed across networked computer systems, and computer-readable code may be stored in a distributed manner.
[0313] The steps of the method or algorithm described in connection with the embodiments of the present disclosure may be implemented directly in hardware, implemented as a software module executed by hardware, or implemented by a combination thereof. The software module may reside in RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), Flash Memory, a hard disk, a removable disk, a CD-ROM, or any form of computer-readable recording medium well known in the art to which the present disclosure belongs.
[0314] Although embodiments of the present disclosure have been described above with reference to the attached drawings, those skilled in the art will understand that the present disclosure may be implemented in other specific forms without altering its technical concept or essential features. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive.
[0315] The various embodiments of the present disclosure are not intended to list all possible combinations but to describe representative aspects of the present disclosure, and the matters described in the various embodiments may be applied independently or in combination of two or more.
Claims
1. In a heavy ion radiotherapy RBE weighted dose optimization device utilizing a group sparsity algorithm, Memory for storing at least one process for performing heavy ion radiation therapy on a patient; and It includes a processor that performs a control operation according to the above process, The above processor is, Receives an image including a patient's CT image as input, and Analyze the input video image above, and Based on the analysis results of the above video images, the treatment site and normal tissue are modeled in 3D, and Set the path of the heavy ion radiation beam to be irradiated onto the modeled treatment site, and Calculate an optimized dose to determine how much of the heavy ion radiation beam to be irradiated to the treatment site according to the set path based on the RBE weighted dose, and Predicting the effect that occurs by irradiating the treatment site with the heavy ion radiation beam by the calculated optimized dose prior to the actual treatment of the patient, and Outputting the predicted above result, Heavy ion radiation therapy RBE weighted dose optimization device.
2. In Paragraph 1, The above processor calculates the optimized dose using groupable parameters of the group-wise sparsity technique, and The above groupable parameters include a beam angle and an energy layer, Heavy ion radiation therapy RBE weighted dose optimization device.
3. In claim 1, the processor, The above optimized dose is calculated as the sum of physical dose (Dose constraints), element sparsity, and group sparsity, and The above physical dose is converted into the above RBE weighted dose to calculate the above optimized dose, and When calculating the above optimized dose, the L2,p-norm is used to remove unnecessary groups and retain only meaningful groups. Heavy ion radiation therapy RBE weighted dose optimization device.
4. In claim 1, the processor, To calculate the above-mentioned optimized dose, a dose effect matrix is generated, The above dose effect matrix is expressed by the following mathematical formula, and Here, the above Dij refers to the dose effect matrix, and The above w represents the spot weight to be optimized, and The above d represents an ideal dose distribution, Heavy ion radiation therapy RBE weighted dose optimization device.
5. In claim 1, the processor, Calculating the optimized dose by applying a non-linear operation that converts the physical dose into the RBE weighted dose when optimizing the weight of the beam spot, Heavy ion radiation therapy RBE weighted dose optimization device.
6. In claim 5, the processor, Reducing the beam angle through group sparse optimization, Heavy ion radiation therapy RBE weighted dose optimization device.
7. In claim 6, the processor, When selecting the above beam angle, the optimal dose is calculated by applying the optimal RBE weighted dose corresponding to the selected beam angle, Heavy ion radiation therapy RBE weighted dose optimization device.
8. In a heavy ion radiotherapy RBE weighted dose optimization method utilizing a group sparsity algorithm performed by the device processor, A step of receiving an image including a patient's CT image; A step of analyzing the input image above; A step of modeling the treatment site and normal tissue in three dimensions based on the analysis results of the above video images; A step of setting the path of a heavy ion radiation beam to be irradiated onto the modeled treatment site; A step of calculating an optimized dose to determine how much of the heavy ion radiation beam to irradiate the treatment site according to the set path based on the RBE weighted dose; A step of predicting the effect occurring by irradiating the treatment site with the heavy ion radiation beam by the calculated optimized dose before the actual treatment of the patient; and A step comprising outputting the predicted result above, Heavy ion radiation therapy RBE weighted dose optimization method.
9. In Paragraph 8, The method further includes the step of calculating the optimized dose using groupable parameters of the group-wise sparsity technique, The above groupable parameters include a beam angle and an energy layer, Heavy ion radiation therapy RBE weighted dose optimization method.
10. In Paragraph 8, A step of calculating the above-mentioned optimized dose as the sum of physical doses (Dose constraints), element sparsity, and group sparsity; A step of converting the physical dose into the RBE weighted dose to calculate the optimized dose; and The method further includes the step of using L2,p-norm to remove unnecessary groups and retain only meaningful groups when calculating the above-mentioned optimized dose. Heavy ion radiation therapy RBE weighted dose optimization method.
11. In Paragraph 8, The method further includes the step of generating a dose effect matrix j to calculate the above-mentioned optimized dose, and The above dose effect matrix is expressed by the following mathematical formula, and Here, the above Dij refers to the dose effect matrix, and The above w represents the spot weight to be optimized, and The above d represents an ideal dose distribution, Heavy ion radiation therapy RBE weighted dose optimization method.
12. In Paragraph 8, A method further comprising the step of calculating the optimized dose by applying a non-linear operation that converts the physical dose into the RBE weighted dose when optimizing the weight of the beam spot. Heavy ion radiation therapy RBE weighted dose optimization method.
13. In Paragraph 12, A method further comprising a step of reducing the beam angle through group sparse optimization, Heavy ion radiation therapy RBE weighted dose optimization method.
14. In Paragraph 13, When selecting the beam angle, the method further includes the step of calculating the optimized dose by applying the optimal RBE weighted dose corresponding to the selected beam angle. Heavy ion radiation therapy RBE weighted dose optimization method.