Time series signal prediction device, radiation therapy device, time series signal prediction method, program and storage medium

Abstract

To predict at high speed and with accuracy, a time series signal related to biological information of a patient.SOLUTION: A time series signal prediction device has a time series signal acquisition part, a block conversion part, a feature vector acquisition part, and a feature vector conversion part. The time series signal acquisition part acquires a time series signal related to biological information of a patient. The block conversion part divides the time series signal into partial signals for each prescribed interval, for converting the time series signal into a block signal having a shorter time series than the time series of the time series signal. The feature vector acquisition part outputs a feature vector which indicates a time series signal in the future time relative to an acquisition time point of the time series signal, from the block signal. The feature vector conversion part converts the feature vector into a prediction signal related to the biological information of the patient.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] An embodiment of the present invention relates to a time series signal prediction device, a radiotherapy device, a time series signal prediction method, a program, and a storage medium. [Background technology]

[0002] Radiation therapy is a treatment method that irradiates a patient's lesion with radiation to destroy it, but if the location of the lesion is not irradiated accurately, normal tissue will also be destroyed. Therefore, CT scans are performed in advance to grasp the location of the lesion inside the patient's body in three dimensions, and the direction and intensity of irradiation are planned to minimize irradiation of normal tissue.

[0003] In radiation therapy, in order to irradiate radiation according to the plan, it is necessary to align the patient's position between the time of treatment planning and the time of treatment. In order to align the positions of the lesion and bones in the patient's body with those in the treatment plan, there is a method in which the patient's position is calculated by comparing a fluoroscopic image of the inside of the patient's body lying on a bed in the treatment room just before treatment with a digitally reconstructed radiograph (DRR), which is a virtual fluoroscopic image reconstructed from a 3D CT image taken during treatment planning, and the bed is moved based on the difference. The patient's position is calculated by searching for the position of the CT image that reconstructs the DRR that is most similar to the fluoroscopic image. Many methods have been proposed to automate this search using a computer, but the search results must be confirmed by a radiotherapy professional by comparing the fluoroscopic image with the DRR.

[0004] When a tumor, which is a lesion of a patient, is located in an organ such as the lung or liver that moves with breathing or heartbeat, a respiratory-synchronized irradiation method is known in which the position of the tumor during irradiation is identified and a treatment beam is irradiated when the identified position coincides with the position determined in the treatment plan. In this case, a method of estimating the position of the tumor is to sense the movement of the body surface of the thorax and abdomen, which expands and contracts with breathing, and estimate the position of the tumor according to the sensor value. Another method is to take a fluoroscopic video of the patient during irradiation and track the tumor position in the fluoroscopic image. Hereinafter, the signal that can be obtained from the movement of the body surface and the trajectory of the tumor position are called "time-series signals". In this way, controlling the irradiation timing of the treatment beam has the effect of reducing unnecessary irradiation of the treatment beam to the patient, but since there is a response time of the device, such as the calculation time required for estimating the tumor position, it is necessary to predict the tumor position.

[0005] For example, the method disclosed in Non-Patent Document 1 predicts a signal value at a predetermined time by learning a recurrent network, Long Short Term Memory (LSTM), using time series signals from multiple patients. The method disclosed in Non-Patent Document 2, unlike the method in Non-Patent Document 1, uses a feedforward network, Convolutional Neural Network (CNN). This disclosed technology is a method for predicting a signal value at a future time (T+M, M>0) from a time series signal up to a certain time (T). This method is expected to be more accurate as the input time series signal is made longer, but on the other hand, the calculation time increases accordingly, so it may not be suitable for prediction. In addition, the amount of information used for learning increases when a waveform signal from time T+1 to T+M is taught, and therefore prediction accuracy can be expected to improve. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] “Towards real-time respiratory motion prediction based on long short-term memory neural networks”, Phys Med Biol. 2019 Apr 10; 64(8): 085010. [Non-Patent Document 2] “Respiratory Prediction Based on Multi-Scale Temporal Convolutional Network for Tracking Thoracic Tumor Movement”, Front. Oncol., 27 May 2023 Summary of the Invention [Problem to be solved by the invention]

[0007] The problem to be solved by the present invention is to provide a time series signal prediction device, a radiation therapy device, a time series signal prediction method, a program, and a storage medium that are capable of quickly and accurately predicting time series signals related to a patient's biological information. [Means for solving the problem]

[0008] A time series signal prediction device according to an embodiment includes a time series signal acquisition unit, a block conversion unit, a feature vector acquisition unit, and a feature vector conversion unit. The time series signal acquisition unit acquires a time series signal related to a patient's biometric information. The block conversion unit divides the time series signal into partial signals at predetermined intervals and converts the time series signal into block signals having a shorter time series than the time series signal. The feature vector acquisition unit acquires, from the block signal, a feature vector representing a time series signal at a time in the future relative to the time of acquisition of the time series signal. The feature vector conversion unit converts the feature vector into a prediction signal related to the patient's biometric information. Effect of the Invention

[0009] According to the present invention, it is possible to provide a time series signal prediction device, a radiation therapy device, a time series signal prediction method, a program, and a storage medium that are capable of quickly and highly accurately predicting time series signals related to a patient's biological information. [Brief description of the drawings]

[0010] [Figure 1] 1 is a block diagram showing a schematic configuration of a treatment system including a time-series signal prediction device according to a first embodiment. [Diagram 2] 1 is a block diagram showing a schematic configuration of a time-series signal prediction device 100 according to a first embodiment. [Diagram 3] FIG. 1 is a diagram showing an example of a waveform signal acquired by a time-series signal acquisition unit 110. [Figure 4] 4 is a diagram showing an example of conversion from a time-series signal to a block signal by a block conversion unit 120. FIG. [Diagram 5] FIG. 13 is a diagram showing another example of conversion from a time-series signal to a block signal by the block conversion unit 120. [Figure 6] 4 is a diagram showing an example of acquisition of a feature vector by a feature vector acquisition unit 130. FIG. [Figure 7] 11 is a diagram showing another example of how the feature vector acquiring unit 130 acquires a feature vector. FIG. [Figure 8] 4 is a flowchart showing an example of the flow of processing executed by the time-series signal prediction device 100 of the first embodiment. [Figure 9] FIG. 1 is a block diagram showing a schematic configuration of a time-series signal prediction device 200 according to a second embodiment. [Figure 10] 10 is a flowchart showing an example of the flow of processing executed by a time-series signal prediction device 200 of the second embodiment. [Figure 11] FIG. 11 is a block diagram showing a schematic configuration of a time-series signal prediction device 300 according to a third embodiment. [Figure 12] 10 is a flowchart showing an example of the flow of processing executed by a time-series signal prediction device 300 according to the third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, a time series signal prediction device, a radiotherapy device, a time series signal prediction method, a program, and a storage medium according to embodiments will be described with reference to the drawings.

[0012] (First embodiment) (Overall composition) 1 is a block diagram showing a schematic configuration of a treatment system including a time series signal prediction device of the first embodiment. The treatment system 1 includes, for example, a treatment device 10, a sensor S, and a time series signal prediction device 100. The treatment device 10 includes, for example, a bed 12, a computed tomography (CT) device 14 (hereinafter referred to as the "CT imaging device 14"), and a treatment beam irradiation gate 16.

[0013] The bed 12 is a movable treatment bed on which a subject (patient) P to be treated with radiation is fixed in a lying state, for example, by a fixture. The bed 12 moves in a state in which the patient P is fixed, into a circular CT imaging device 14 having an opening, according to the control from a bed control unit (not shown). The bed control unit controls a translation mechanism and a rotation mechanism provided on the bed 12 in order to change the direction in which the treatment beam B is irradiated onto the patient P fixed on the bed 12, according to the movement amount signal. The translation mechanism can drive the bed 12 in three axial directions, and the rotation mechanism can drive the bed 12 around three axes. For this reason, the bed control unit controls, for example, the translation mechanism and the rotation mechanism of the bed 12 to move the bed 12 with six degrees of freedom. The degrees of freedom with which the bed control unit controls the bed 12 do not have to be six degrees of freedom, and may be less than six degrees of freedom (for example, four degrees of freedom) or more than six degrees of freedom (for example, eight degrees of freedom).

[0014] The CT imaging device 14 is an imaging device for performing three-dimensional computed tomography. The CT imaging device 14 has a plurality of radiation sources arranged inside an annular opening, and each radiation source irradiates radiation for viewing the inside of the patient P. That is, the CT imaging device 14 irradiates radiation from a plurality of positions around the patient P. The radiation irradiated from each radiation source in the CT imaging device 14 is, for example, X-rays. The CT imaging device 14 detects radiation irradiated from the corresponding radiation source and passing through the inside of the patient P by a plurality of radiation detectors arranged inside the annular opening. The CT imaging device 14 generates a CT image of the inside of the patient P based on the magnitude of the energy of the radiation detected by each radiation detector. The CT image of the patient P generated by the CT imaging device 14 is a three-dimensional digital image in which the magnitude of the energy of the radiation is expressed as a digital value. Three-dimensional imaging of the inside of the body of patient P in the CT imaging device 14, that is, the irradiation of radiation from each radiation source and the generation of CT images based on the radiation detected by each radiation detector, are controlled, for example, by an imaging control unit (not shown).

[0015] The treatment beam irradiation gate 16 irradiates radiation as a treatment beam B to destroy a tumor (lesion) that is a treatment target site present inside the body of the patient P. The treatment beam B is, for example, an X-ray, a gamma ray, an electron beam, a proton beam, a neutron beam, or a heavy particle beam. The treatment beam B is linearly irradiated from the treatment beam irradiation gate 16 to the patient P (more specifically, the tumor inside the body of the patient P). The irradiation of the treatment beam B at the treatment beam irradiation gate 16 is controlled, for example, by a treatment beam irradiation control unit (not shown).

[0016] In this way, radiation therapy is performed by irradiating the tumor in the body of the patient P with the treatment beam B from the treatment beam irradiation port 16. However, if the tumor, which is the focus of the patient P, is in an organ that moves with the movement of breathing or heartbeat, such as the lungs or liver, it is necessary to irradiate the tumor with X-rays by the CT imaging device 14 or irradiate the tumor with the treatment beam B from the treatment beam irradiation port 16 at a timing when the tumor is within the irradiation range of the treatment beam B. However, in the conventional technology, for example, due to a gap in the machine response, the tumor may be irradiated with X-rays or the treatment beam B even if the tumor is outside the irradiation range of the treatment beam B, resulting in unnecessary exposure to radiation for the patient P. In light of this situation, the present invention utilizes the sensor S and the time series signal prediction device 100 to quickly and accurately predict the timing when the tumor is within the irradiation range of the treatment beam B.

[0017] The sensor S intermittently senses, for example, the movement of the surface of the thorax and abdomen that expands and contracts in association with the breathing of the patient P, and outputs a one-dimensional waveform signal with the obtained sensor value as the amplitude to the time series signal prediction device 100. The sensor S may be a contact type sensor placed on the upper surface of the abdomen of the patient P lying on the bed 12, or a non-contact type sensor that measures the distance to the thorax and abdomen by a laser.

[0018] FIG. 2 is a block diagram showing a schematic configuration of a time series signal prediction device 100 according to the first embodiment. The time series signal prediction device 100 includes, for example, a time series signal acquisition unit 110, a block conversion unit 120, a feature vector acquisition unit 130, and a feature vector conversion unit 140. Some or all of the components included in the time series signal prediction device 100 are realized by, for example, a hardware processor such as a CPU (Central Processing Unit) executing a program (software). Some or all of these components may be realized by hardware (including circuitry) such as an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a GPU (Graphics Processing Unit), or may be realized by cooperation between software and hardware. Some or all of the functions of these components may be realized by a dedicated LSI. The program may be stored in advance in a storage device (storage device equipped with a non-transient storage medium) such as a Read Only Memory (ROM), a Random Access Memory (RAM), a Hard Disk Drive (HDD), or a flash memory equipped in the time series signal prediction device 100, or may be stored in a removable storage medium (non-transient storage medium) such as a DVD or a CD-ROM, and installed in the HDD or flash memory equipped in the time series signal prediction device 100 by mounting the storage medium in a drive device equipped in the time series signal prediction device 100. The program may be downloaded from another computer device via a network and installed in the HDD or flash memory equipped in the time series signal prediction device 100.

[0019] The time series signal acquiring unit 110 acquires a time series signal representing a waveform from the sensor S, for example, by wireless communication. FIG. 3 is a diagram showing an example of a waveform signal acquired by the time series signal acquiring unit 110. In the graph of FIG. 3, the horizontal axis indicates time and the vertical axis indicates amplitude. In the waveform shown in FIG. 3, peak P1, which is a mountain, indicates a point at which the inhalation by the patient P is maximum, and peak P2, which is a valley, indicates a point at which the exhalation by the patient P is maximum. The time series signal acquiring unit 110 outputs the acquired time series signal to the block conversion unit 120.

[0020] As another example, the time series signal acquired by the time series signal acquiring unit 110 may be a trajectory representing the position of a tumor that moves with the breathing of the patient P. Here, the trajectory representing the position of the tumor means a time series signal representing a change over time in the center of gravity position of the tumor in a fluoroscopic moving image of the patient P. In this case, the time series signal acquiring unit 110 may acquire the time series signal not from the sensor S but from the CT imaging device 14.

[0021] As another example, the time series signal acquired by the time series signal acquisition unit 110 may be a time series signal representing a time change in the diaphragm of the patient P, or may be a trajectory of a marker placed near a tumor in the patient P's body that moves with breathing. The trajectory of the marker is a time series signal representing a time change in the marker position in the fluoroscopic moving image of the patient. The position of the tumor, diaphragm, and marker (hereinafter, the tracking target) in the fluoroscopic moving image at each time is estimated by, for example, template matching using an image in which the tracking target appears before tracking as a template. Alternatively, for example, the position of the tracking target at each time is estimated based on a feature amount obtained by converting an image in which the tracking target appears by a model prepared in advance. The model at this time can be constructed by machine learning that learns multiple images in which the tracking target appears before tracking.

[0022] The block conversion unit 120 divides the time series signal acquired by the time series signal acquisition unit 110 into partial signals at a predetermined interval, and converts the partial signals into block signals having a time series shorter than the time series of the time series signal. FIG. 4 is a diagram showing an example of conversion from a time series signal to a block signal by the block conversion unit 120. FIG. 4 shows, as an example, a state in which a 12-sequence time series signal consisting of 12 signal values ​​y(t-11) to y(t) from time t-11 to time t (i.e., the current time) is converted into a 3-sequence block signal. The partial signals at this time are vector data consisting of signal values ​​y(t-3) to y(t) from time t-3 to time t, signal values ​​y(t-7) to y(t-4) from time t-7 to time t-4, and signal values ​​y(t-11) to y(t-8) from time t-11 to time t-8.

[0023] Fig. 5 is a diagram showing another example of conversion from a time-series signal to a block signal by the block conversion unit 120. Fig. 5 shows, as an example, a state in which 8 sequences of signal values ​​y(t-7) to y(t) consisting of 8 values ​​from time t-7 to time t are converted into 3 sequences of block signals. The partial signals at this time are vector data consisting of signal values ​​y(t-3) to y(t) from time t-3 to time t, signal values ​​y(t-5) to y(t-2) from time t-5 to time t-2, and signal values ​​y(t-7) to y(t-4) from time t-7 to time t-4, and thus overlapping of signal values ​​between sequences may be allowed without changing the order of the time series.

[0024] The feature vector acquisition unit 130 acquires a feature vector representing a time-series signal at a time in the future from the time of acquisition of the time-series signal, from the block signal converted by the block conversion unit 120. More specifically, in order to predict a time-series signal at a future time from the input time-series signal, the feature vector acquisition unit 130 is configured in advance as a predictor obtained by statistically modeling the signal fluctuation from time-series signals related to the collected biological information of a large number of patients.

[0025] As a method for modeling the feature vector acquisition unit 130, for example, there is a method of constructing a state space model. Specifically, when a pair of two waveform values ​​y(t) and y(t-δ) at time t and time t-δ are plotted on a two-dimensional coordinate system, the plotted points move elliptically with time because the respiratory waveform is periodic. This characteristic is expressed by a state space model. Since this model is a nonlinear model, an extended Kalman filter (EKF) that approximates the Kalman filter to correspond to a nonlinear state space model is used. The feature vector acquisition unit 130 is configured by optimizing the parameters of this state space model using the collected time series signals.

[0026] Another method for modeling the feature vector acquisition unit 130 is, for example, to use a recurrent neural network. Here, the recurrent neural network is a neural network model that receives input data of multiple sequences in order, transitions the state inside the model, and obtains an output. A representative example of this is LSTM (Long Short Term Memory).

[0027] As another method for modeling the feature vector acquisition unit 130, for example, a feedforward neural network is used. Here, the feedforward neural network is a neural network model that inputs input data all at once without dividing it into sequences and obtains an output. For example, when the feedforward network is a convolutional neural network (CNN), when a block signal is input, the block signal is processed as two-dimensional data. Alternatively, the sequence of block signals may be input to the network as data of separate channels.

[0028] When using these neural network models, the feature vector acquisition unit 130 is configured by learning the internal parameters of the model through deep learning using the collected time-series signals. At this time, the collected time-series signals are divided into a plurality of partial signals, allowing overlaps, and each partial signal is divided into an input teaching block signal and a target feature vector. The data contained in the target feature vector is composed only of signals at a time later than the teaching block signal.

[0029] The learning mentioned above refers to adjusting the internal parameters so that the feature vector obtained as the output when the teaching block signal is input to the model approaches the target feature vector. A function that measures the distance between the target feature vector and the feature vector is used as the error function. In order to minimize the error function, an optimization method such as the error backpropagation method is used.

[0030] Fig. 6 is a diagram showing an example of acquisition of a feature vector by the feature vector acquisition unit 130. As shown in Fig. 6, the feature vector is, for example, a vector having the amplitude values ​​of the partial signals as elements. That is, when predicting a signal value at time t+M (M>0, M is a natural number) for a teaching block signal up to time t, the target feature vector becomes a vector having signal values ​​y(t+1) to y(t+M) from time t+1 to time t+M as elements.

[0031] Fig. 7 is a diagram showing another example of acquisition of a feature vector by the feature vector acquisition unit 130. As shown in Fig. 7, the feature vector may be, for example, a vector having the difference in amplitude value of the partial signal as an element. That is, when predicting a signal value y(t+M) at time t+M (M>0, M is a natural number) for a teaching block signal up to time t, the target feature vector becomes a vector having the signal value differences d(t+1) to d(t+M) from time t+1 to time t+M as elements, starting from the teaching block signal at time t.

[0032] The feature vector conversion unit 140 converts the feature vector acquired by the feature vector acquisition unit 130 into a predicted signal related to the biological information of the patient P. More specifically, for example, when the feature vector shown in FIG. 6 is acquired by the feature vector acquisition unit 130, the feature vector conversion unit 140 acquires a signal value y(t+M) at time t+M from the feature vector as a predicted signal. Also, for example, when the feature vector shown in FIG. 7 is acquired by the feature vector acquisition unit 130, the feature vector conversion unit 140 acquires a signal value y(t)+d(t+1)+···d(t+M) obtained by adding the sum of the elements d(t+1) to d(t+M) of the feature vector from time t+1 to time t+M to the signal value y(t) at time t, as the signal value y(t+M) at time t+M. The feature vector conversion unit 140 outputs the calculated prediction signal to a monitor or the like of the time series signal prediction device 100, and the operator of the treatment device 10, while checking the prediction signal output to the monitor or the like, determines the timing of capturing the X-ray video image and the timing of irradiating the treatment beam B, and performs capturing the X-ray video image and irradiating the treatment beam B.

[0033] Furthermore, the feature vector conversion unit 140 calculates the reliability of the time series signal based on the feature vector acquired by the feature vector acquisition unit 130. For example, the feature vector conversion unit 140 calculates the reliability as a smaller value as the element of the feature vector is discontinuous in the time series signal. Here, the discontinuity can be determined, for example, by an approximation error between an approximate curve acquired from a plurality of elements included in the feature vector using a method such as spline interpolation and these plurality of elements. At this time, the feature vector conversion unit 140 may output the predicted signal and the calculated reliability together to a monitor of the time series signal prediction device 100. In that case, the operator of the treatment device 10 can utilize the output predicted signal for taking X-ray moving images and irradiating the treatment beam B, taking into account the reliability of the predicted signal.

[0034] [Processing flow] Next, a flow of processing executed by the time series signal prediction device of the first embodiment will be described with reference to Fig. 8. Fig. 8 is a flowchart showing an example of a flow of processing executed by the time series signal prediction device of the first embodiment.

[0035] First, the time-series signal acquisition unit 110 acquires a time-series signal related to the biological information of the patient P from the sensor S (step S100). Next, the block conversion unit 120 divides the acquired time-series signal into partial signals and acquires them as block signals (step S102). Next, the feature vector acquisition unit 130 acquires a feature vector representing a time-series signal at a future time from the acquired block signal (step S104). Next, the feature vector conversion unit converts the acquired feature vector into a predicted signal related to the biological information of the patient P (step S106). This ends the processing of this flowchart.

[0036] According to the first embodiment described above, the input time series signal is converted into a block signal, thereby shortening the input length while maintaining the amount of information of the input data. As a result, compared to the conventional technology in which the time series signal is input sequentially, it is possible to reduce the time required for learning a recurrent neural network model and calculating a feature vector. Furthermore, according to the first embodiment, compared to the technology described in Non-Patent Document 1 in which only the signal value to be predicted is taught in model learning, in the first embodiment, the amount of information to be learned is increased by learning multiple signal values ​​up to the time to be predicted. As a result, it is possible to improve the accuracy of the feature vector output by the predictor.

[0037] Second Embodiment (Overall composition) In the first embodiment, a method for predicting a time series signal at a future time from a time series signal inputted regarding the biological information of the patient P has been described. As an example of the biological information of the patient P, the respiratory signal has a tendency to be irregular when the patient P inhales completely because the amount of air taken in during inhalation changes even for the same patient P. On the other hand, the amplitude when the patient P exhales completely tends to be stable and regular. By utilizing this tendency, in radiation therapy, it is preferable to avoid taking X-ray moving images and irradiating the treatment beam B while the patient P is inhaling or exhaling, and to set the position of the tumor when the patient P exhales completely as the irradiation spot. In light of this situation, the time series signal prediction device 200 according to the second embodiment is capable of predicting the time when the position of the tumor will enter the irradiation spot, while avoiding taking X-ray moving images and irradiating the treatment beam B when an abnormal state occurs during inhalation, exhalation, or the respiratory state of the patient P.

[0038] FIG. 9 is a block diagram showing a schematic configuration of a time series signal prediction device 200 of the second embodiment. The time series signal prediction device 200 includes, for example, a time series signal acquisition unit 210, a block conversion unit 220, a feature vector acquisition unit 230, a feature vector conversion unit 240, and a state estimation unit 250. The configurations of the time series signal acquisition unit 210, the block conversion unit 220, the feature vector acquisition unit 230, and the feature vector conversion unit 240 are similar to the configurations of the time series signal acquisition unit 110, the block conversion unit 120, the feature vector acquisition unit 130, and the feature vector conversion unit 140 of the time series signal prediction device 100 of the first embodiment, and therefore will not be described. Furthermore, in the second embodiment, the configuration of the treatment system 1 shown in FIG. 1 is also applied.

[0039] The state estimation unit 250 estimates the state of the patient P based on the prediction signal acquired from the feature vector conversion unit 240, and outputs the state to a monitor of the time series signal prediction device 200 or the like. Here, the estimated state of the patient refers to a state (abnormal state) that deviates from normal breathing due to, for example, coughing. Before and after coughing or sneezing, breathing becomes disordered and differs from the normal state, so the difference between the predicted value and the actual measured value becomes large. The state estimation unit 250 may output the estimated state of the patient to a monitor of the treatment device 10 or the like.

[0040] For example, the state estimation unit 250 may calculate the absolute value of the difference between a predicted value obtained a predetermined time ago (for example, 100 milliseconds ago) and an actual measurement value at that time, and if the absolute value is greater than a predetermined value, estimate and output that the patient's state is abnormal. Also, for example, the state estimation unit 250 may calculate the absolute value of the difference between a predicted value obtained a predetermined time ago and an actual measurement value at that time, and if a state where the value is greater than a predetermined value continues for a predetermined period, estimate and output that the patient's state is abnormal. Also, for example, the state estimation unit 250 may calculate the absolute value of the difference between a predicted value obtained a predetermined time ago and an actual measurement value at that time in multiple time windows, and if the number of time windows in which the calculated absolute value is greater than a predetermined value is half or more of the multiple time windows, estimate and output that the patient's state is abnormal.

[0041] As another example, the state of the patient may be, for example, a state of a respiratory phase around the time when the patient's breathing changes from expiration to inspiration, that is, around the time when the patient completely exhales. When the amplitude of the respiratory waveform becomes large during inspiration and small during expiration, the above-mentioned respiratory phase occurs at the time when the amplitude value becomes smaller than a certain threshold. This threshold value is set in advance from the respiratory waveform of the same patient for several past breaths by, for example, a user monitoring the respiratory waveform displayed on a monitor of the time series signal prediction device 200. For example, the user may set the threshold value to a predetermined level (for example, a level of 20%) of the maximum amplitude of the time series signal acquired by the time series signal acquisition unit 110. The past refers to, for example, the period from the end of setup such as patient positioning to the start of treatment. Alternatively, the state estimation unit 250 may automatically identify the maximum amplitude of the time series signal acquired by the time series signal acquisition unit 110 without requiring the user to set the threshold value, and set the threshold value to a predetermined level of the identified maximum amplitude. In this case, the state estimation unit 250 has a function as a "parameter setting unit".

[0042] When the predicted signal is a trajectory of a tracked object such as a tumor, a diaphragm, or a marker in a fluoroscopic moving image, the state estimation unit 250 may estimate and output the state of the tracked object in the fluoroscopic moving image. The image quality of the tracked object such as a tumor, a diaphragm, or a marker in the fluoroscopic moving image may deteriorate due to increased noise in the fluoroscopic image, making it difficult to track the object. In addition, the patient P may have some abnormality (e.g., coughing or sneezing), causing the tracked object to move irregularly. Therefore, when the difference between the predicted value and the actual measurement value of the predicted signal becomes large, the state estimation unit 250 may estimate and output that the state of the tracked object is abnormal. In addition, for example, in the same way as in the state estimation regarding the respiratory waveform, when the time around the time when the change in the amplitude of the time-series signal consisting of the trajectory of the tracked object changes from downward to upward is planned so that the tumor will enter the irradiation spot, the state estimation unit 250 may set a threshold value in the same way as above, and estimate and output that the tumor is in the irradiation spot when the amplitude of the time-series signal is equal to or less than the threshold value.

[0043] In the above description, the state estimation unit 250 is described as being included in the time series signal prediction device 200, but the state estimation unit 250 may be included in the treatment device 10. In this case, the time series signal prediction device 200 may transmit the prediction signal acquired from the feature vector conversion unit 240 to the treatment device 10, and the state estimation unit 250 included in the treatment device 10 may estimate the state of the patient P based on the received prediction signal and display it on a monitor of the time series signal prediction device 200, for example.

[0044] [Processing flow] Next, a flow of processing executed by the time series signal prediction device of the second embodiment will be described with reference to Fig. 10. Fig. 10 is a flowchart showing an example of a flow of processing executed by the time series signal prediction device of the second embodiment.

[0045] First, the time-series signal acquisition unit 210 acquires a time-series signal related to the biological information of the patient P from the sensor S (step S200). Next, the block conversion unit 220 divides the acquired time-series signal into partial signals and acquires them as block signals (step S202). Next, the feature vector acquisition unit 230 acquires a feature vector representing a time-series signal at a future time from the acquired block signal (step S204). Next, the feature vector conversion unit 240 converts the acquired feature vector into a prediction signal related to the biological information of the patient P (step S206). Next, the state estimation unit 250 estimates the state of the patient P from the acquired prediction signal and outputs it to a monitor of the time-series signal prediction device 200 or the like (step S208). This ends the processing of this flowchart.

[0046] According to the second embodiment described above, the condition of the patient P is estimated from the prediction signal acquired regarding the biological information of the patient P and output to a monitor or the like of the time series signal prediction device 200, and for example, the operator of the treatment device 10 judges the timing of capturing the X-ray video sequence and the timing of irradiating the treatment beam while checking the condition of the patient P output to the monitor or the like, and executes capturing the X-ray video sequence and irradiating the treatment beam. This makes it possible to reduce exposure to radiation associated with unnecessary capturing of the X-ray video sequence and irradiation of the treatment beam.

[0047] (Third embodiment) In the second embodiment, a method for estimating the condition of the patient P from a prediction signal acquired regarding the biological information of the patient P and outputting the condition to a monitor of the time series signal prediction device 200 or the like has been described. In this case, the operator of the treatment device 10 manually captures X-ray video images and irradiates the treatment beam B while checking the condition of the patient P output to a monitor or the like, but this may result in human operation errors or deviations in execution timing. In light of this, the time series signal prediction device 300 according to the third embodiment automatically controls the treatment device 10 based on the estimated condition of the patient P.

[0048] FIG. 11 is a block diagram showing a schematic configuration of a time series signal prediction device 300 of the third embodiment. The time series signal prediction device 300 includes, for example, a time series signal acquisition unit 310, a block conversion unit 320, a feature vector acquisition unit 330, a feature vector conversion unit 340, a state estimation unit 350, and a treatment device control unit 360. The configurations of the time series signal acquisition unit 310, the block conversion unit 320, the feature vector acquisition unit 330, the feature vector conversion unit 340, and the state estimation unit 350 are similar to the configurations of the time series signal acquisition unit 210, the block conversion unit 220, the feature vector acquisition unit 230, the feature vector conversion unit 240, and the state estimation unit 250 of the time series signal prediction device 200 of the second embodiment, so that the description will be omitted. Furthermore, in the third embodiment, the configuration of the treatment system 1 shown in FIG. 1 is also applied.

[0049] The treatment device control unit 360 controls the treatment device 10 based on the state of the patient P estimated by the state estimation unit 350. More specifically, when the state estimation unit 350 determines that the state of the patient P is in a respiratory phase near the time when the patient P completely exhales (in other words, when the amplitude value becomes smaller than a certain threshold), the treatment device control unit 360 starts capturing X-ray moving images of the patient P and / or preparing to irradiate the treatment beam B from the treatment device 10. Also, for example, when the state estimation unit 350 estimates that a tumor in a fluoroscopic moving image is within an irradiation spot, the treatment device control unit 360 may shorten the capturing interval of the X-ray moving images and start preparing to irradiate the treatment beam B from the treatment device 10.

[0050] On the other hand, when the state estimation unit 350 judges that the state of the patient P is abnormal, the treatment device control unit 360 stops capturing X-ray moving images of the patient P and / or irradiating the treatment beam B from the treatment device 10, or shortens the interval. Also, for example, when the state estimation unit 350 estimates that the patient P has exhaled completely and then transitioned to inhalation again, the treatment device control unit 360 may stop capturing X-ray moving images of the patient P and / or irradiating the treatment beam B from the treatment device 10. Also, for example, when the state estimation unit 350 estimates that the trajectories of the tracking targets such as the tumor, diaphragm, and markers in the fluoroscopic moving images are abnormal, the treatment device control unit 360 may stop capturing X-ray moving images of the patient P and / or irradiating the treatment beam B from the treatment device 10.

[0051] In the above description, the treatment device control unit 360 is described as being included in the time series signal prediction device 300, but the treatment device control unit 360 may be included in the treatment device 10. In this case, the time series signal prediction device 300 may transmit the state of the patient P estimated by the state estimation unit 350 to the treatment device 10, and the treatment device control unit 360 included in the treatment device 10 may control the treatment device 10 based on the received state of the patient P.

[0052] [Processing flow] Next, a flow of processing executed by the time series signal prediction device of the third embodiment will be described with reference to Fig. 12. Fig. 12 is a flowchart showing an example of the flow of processing executed by the time series signal prediction device of the third embodiment.

[0053] First, the time-series signal acquisition unit 310 acquires a time-series signal related to the biological information of the patient P from the sensor S (step S300). Next, the block conversion unit 320 divides the acquired time-series signal into partial signals and acquires them as block signals (step S302). Next, the feature vector acquisition unit 330 acquires a feature vector representing a time-series signal at a future time from the acquired block signal (step S304). Next, the feature vector conversion unit 340 converts the acquired feature vector into a predicted signal related to the biological information of the patient P (step S306). Next, the state estimation unit 350 estimates the state of the patient P from the acquired predicted signal (step S308). Next, the treatment device control unit 360 controls the treatment device 10 based on the estimated state of the patient P. This ends the processing of this flowchart.

[0054] According to the third embodiment described above, the treatment device 10 is controlled based on the estimated state of the patient P to perform or stop capturing X-ray video images of the patient P and / or irradiating the treatment beam B from the treatment device 10. This makes it possible to further reduce exposure to radiation associated with unnecessary capturing of X-ray video images and irradiation of the treatment beam.

[0055] Although several embodiments of the present invention have been described above, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and spirit of the invention, and are included in the scope of the invention and its equivalents described in the claims.

[0056] The time series signal prediction device, radiotherapy device, time series signal prediction method, program, and storage medium according to the present invention employ the following configuration. (1): A time series signal prediction device according to one embodiment of the present invention includes a time series signal acquisition unit that acquires a time series signal related to a patient's bioinformation; a block conversion unit that divides the time series signal into partial signals at predetermined intervals and converts the partial signals into block signals of a time series shorter than the time series of the time series signal; a feature vector acquisition unit that acquires, from the block signals, a feature vector representing a time series signal at a time in the future relative to the time of acquisition of the time series signal; and a feature vector conversion unit that converts the feature vector into a prediction signal related to the patient's bioinformation.

[0057] (2): In the above aspect (1), the time-series signal prediction device further includes a state estimation unit that predicts a state of the patient from the prediction signal.

[0058] (3): In the above aspect (2), the time series signal prediction device further includes a parameter setting unit that adjusts parameters used for prediction in the state estimation unit based on the time series signal and the prediction signal.

[0059] (4) In the above aspects (1) to (3), the plurality of block signals include overlapping signals at a certain time.

[0060] (5) In the above aspects (1) to (4), the feature vector conversion unit calculates the reliability of the prediction signal from the feature vector.

[0061] (6): In the above aspects (1) to (5), the time series signal is a signal that changes according to the respiration of the patient, and the state is an abnormal state of the patient's respiratory movement.

[0062] (7): In the above aspects (1) to (5), the time series signal is a signal that changes in response to the patient's breathing, and the state is the timing at which the patient's breathing changes from exhalation to inhalation.

[0063] (8): In the above aspects (1) to (5), the time series signal is a signal that changes depending on the position of a tumor in the patient, and the predicted signal is a predicted position of a tumor in the patient.

[0064] (9): A radiation therapy device according to one aspect of the present invention controls the timing of taking fluoroscopic images of the patient based on the state predicted by the time series signal prediction device according to aspect (2) above.

[0065] (10): A radiation therapy device according to one aspect of the present invention controls the timing of irradiation of a therapeutic beam to the patient based on the state predicted by the time series signal prediction device according to aspect (2) above.

[0066] (11): A time series signal prediction method according to one embodiment of the present invention includes a computer that acquires a time series signal relating to a patient's bioinformation, divides the time series signal into partial signals at predetermined intervals, converts the time series signal into a block signal having a shorter time series than the time series of the time series signal, acquires from the block signal a feature vector representing a time series signal at a time in the future relative to the time when the time series signal was acquired, and converts the feature vector into a prediction signal relating to the patient's bioinformation.

[0067] (12): A program according to one embodiment of the present invention causes a computer to acquire a time series signal relating to a patient's bioinformation, divide the time series signal into partial signals at predetermined intervals, convert the time series signal into a block signal having a shorter time series than the time series of the time series signal, acquire from the block signal a feature vector representing a time series signal at a time in the future relative to the time of acquisition of the time series signal, and convert the feature vector into a predictive signal relating to the patient's bioinformation.

[0068] (13) A storage medium according to one embodiment of the present invention stores a program that causes a computer to acquire a time series signal relating to a patient's bioinformation, divide the time series signal into partial signals at predetermined intervals, convert the time series signal into a block signal having a shorter time series than the time series of the time series signal, acquire from the block signal a feature vector representing a time series signal at a time in the future relative to the time of acquisition of the time series signal, and convert the feature vector into a predictive signal relating to the patient's bioinformation. [Explanation of symbols]

[0069] 1 Treatment system, 10 Treatment device, 12 Bed, 14 CT imaging device, 16 Treatment beam irradiation port, 100, 200, 300 Time series signal prediction device, 110, 210, 310 Time series signal acquisition unit, 120, 220, 320 Block conversion unit, 130, 230, 330 Feature vector acquisition unit, 140, 240, 340 Feature vector conversion unit, 250, 350 State estimation unit, 360 Treatment device control unit

Claims

1. a time series signal acquiring unit for acquiring a time series signal related to biological information of a patient; a block conversion unit that divides the time series signal into partial signals at predetermined intervals and converts the partial signals into block signals of a time series shorter than the time series of the time series signal; a feature vector acquisition unit that acquires, from the block signal, a feature vector representing a time series signal at a time later than the time when the time series signal is acquired; and a feature vector conversion unit that converts the feature vector into a prediction signal related to the biological information of the patient. Time series signal forecasting device.

2. A state estimation unit that predicts a state of the patient from the prediction signal, The time series signal prediction device according to claim 1 .

3. a parameter setting unit that adjusts parameters used for prediction by the state estimation unit based on the time series signal and the prediction signal; The time series signal prediction device according to claim 2 .

4. The plurality of block signals include overlapping signals at a certain time. The time series signal prediction device according to claim 1 .

5. The feature vector conversion unit calculates a reliability of the previous prediction signal from the feature vector. The time series signal prediction device according to claim 1 .

6. The time series signal is a signal that changes according to the respiration of the patient, and the state is an abnormal state of the respiration of the patient. The time-series signal prediction device according to claim 2 or 3.

7. the time series signal is a signal that changes in response to the breathing of the patient, and the state is a timing at which the breathing motion of the patient changes from exhalation to inhalation; The time-series signal prediction device according to claim 2 or 3.

8. The time series signal is a signal that changes depending on a tumor position of the patient, and the predicted signal is a predicted position of the tumor of the patient. The time-series signal prediction device according to any one of claims 1 to 5.

9. and controlling a timing of capturing a fluoroscopic image of the patient based on the state predicted by the time-series signal prediction device according to claim 2. Radiation therapy equipment.

10. and controlling a timing of irradiating the patient with a treatment beam based on the state predicted by the time-series signal prediction device according to claim 2. Radiation therapy equipment.

11. The computer Acquire a time series signal relating to the patient's vital signs; Dividing the time series signal into partial signals at predetermined intervals and converting them into block signals of a time series shorter than the time series of the time series signal; A feature vector representing a time series signal at a time later than the time of acquisition of the time series signal is obtained from the block signal; converting the feature vector into a predictive signal related to the patient's biometric information; Time series signal forecasting methods.

12. On the computer, Acquiring a time series signal relating to a patient's vital signs; Dividing the time series signal into partial signals at a predetermined interval and converting the partial signals into block signals of a time series shorter than the time series of the time series signal; acquiring, from the block signal, a feature vector representing a time series signal at a time later than the time of acquisition of the time series signal; converting the feature vector into a predictive signal related to the patient's biometric information; program.

13. On the computer, Acquiring a time series signal relating to a patient's vital signs; Dividing the time series signal into partial signals at a predetermined interval and converting the partial signals into block signals of a time series shorter than the time series of the time series signal; acquiring, from the block signal, a feature vector representing a time series signal at a time later than the time of acquisition of the time series signal; converting the feature vector into a predictive signal related to the patient's biometric information; A storage medium for storing a program.

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