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Purpose The purpose of this study is to propose algorithms and methods for achieving high accuracy in tracking and interception irradiation technology for tumors that move by respiration using MR-linac (MRIdian®, ViewRay Inc.) and to use deep learning to predict the movement of moving tumors in real time during radiation therapy and reconstruct cine magnetic resonance imaging (cine-MRI) into four-dimensional (4D) movies. Methods In this study, we propose a reconstruction algorithm using 4DCT for treatment planning taken before irradiation as training data in consideration of the actual treatment flow. In the algorithm, two neural networks made before treatment are used to reconstruct 4D movies that predict tumor movement in real time during treatment. Cycle GAN (generative adversarial network) was used to convert MR images to CT images, and long short-term memory was used to convert cine-MRI to 4D movies and predict tumor movement. Results We succeeded in predicting the time including the imaging time of the MR images, the lag until irradiation, and the calculation time in the algorithm. In addition, the conversion and prediction results at each phase of reconstruction were generally good so that they could be clinically applied. Conclusions The reconstruction algorithm proposed in this study enables high-precision radiotherapy while predicting the volume information of the tumor and the actual tumor position, which could not be obtained during radiotherapy.

To identify functions and facilities that are critical for ensuring safety in advanced reactors, for which only a limited usage history is available, it is necessary to consider the characteristics of each reactor type by utilizing more risk-related information. In this study, we compare the response capability during an accident for light water reactors and fast reactors to quantitatively evaluate the impact of the reactor type on resilience. A resilience evaluation procedure is developed for assessing the response margin and response reliability of a nuclear power plant during an accident; this procedure was then applied to a simple pressurized-water reactor and a sodium-cooled fast reactor plant model using an accident scenario involving an earthquake that generated a leakage of coolant from the primary heat-transport system. Our research indicated a relatively large difference between the response reliability with respect to time for the two reactor types.

Installation of surveillance cameras in nuclear power plants is critical to protecting the facilities against terrorist attacks or monitoring the reactor operator. This has led to large amounts of video surveillance data, creating a demand for automatic detection of anomalies or suspicious movements. Tracking human motion from video sequences is a notable technique used for detecting anomalies in human behavior and is currently achieved with the use of a depth camera. However, depth cameras require a complicated camera system and their field of view is limited. To overcome this problem, there is a need for recognizing human motion in wide-angle images – a view that often causes distortion. In this study, we devised a method for tracking human motion through wideangle image distortion. The main contribution of this study is a methodology that automatically estimates the transformation parameters needed to improve the accuracy of motion recognition; these parameters are applied to a distorted wide-angle image in every frame. We propose a new multi-layered convolutional neural architecture for estimating the locations of human joints in images and transformation parameters simultaneously. When applied to distorted wide-angle images, the robustness of our method is demonstrated through a quantitative evaluation of human joint location prediction. In addition, we compare our method with a motion tracking system and an infrared-camera-based motion capture system to demonstrate its ability to handle wide-angle and close-range images.

During lung radiotherapy, the position of infrared reflective objects on the chest can be recorded to estimate the tumor location. However, radiotherapy systems have a latency inherent to robot control limitations that impedes the radiation delivery precision. Prediction with online learning of recurrent neural networks (RNN) allows for adaptation to non-stationary respiratory signals, but classical methods such as RTRL and truncated BPTT are respectively slow and biased. This study investigates the capabilities of unbiased online recurrent optimization (UORO) to forecast respiratory motion and enhance safety in lung radiotherapy. We used 9 observation records of the 3D position of 3 external markers on the chest and abdomen of healthy individuals breathing during intervals from 73s to 222s. The sampling frequency was 10Hz, and the amplitudes of the recorded trajectories range from 6mm to 40mm in the superior-inferior direction. We forecast the 3D location of each marker simultaneously with a horizon value between 0.1s and 2.0s, using an RNN trained with UORO. We compare its performance with an RNN trained with RTRL, LMS, and offline linear regression. We provide closed-form expressions for quantities involved in the loss gradient calculation in UORO, thereby making its implementation efficient. Training and cross-validation were performed during the first minute of each sequence. On average over the horizon values considered and the 9 sequences, UORO achieves the lowest root-mean-square (RMS) error and maximum error among the compared algorithms. These errors are respectively equal to 1.3mm and 8.8mm, and the prediction time per time step was lower than 2.8ms (Dell Intel core i9-9900K 3.60 GHz). Linear regression has the lowest RMS error for the horizon values 0.1s and 0.2s, followed by LMS for horizon values between 0.3s and 0.5s, and UORO for horizon values greater than 0.6s., Comment: 24 pages, 16 figures, corrected mistakes and improved notations in Eqs. 1, 3, 7, 29-35, footnote 11, line 18 in Algorithm 1, and Appendix A.2

During the radiotherapy treatment of patients with lung cancer, the radiation delivered to healthy tissue around the tumor needs to be minimized, which is difficult because of respiratory motion and the latency of linear accelerator systems. In the proposed study, we first use the Lucas-Kanade pyramidal optical flow algorithm to perform deformable image registration of chest computed tomography scan images of four patients with lung cancer. We then track three internal points close to the lung tumor based on the previously computed deformation field and predict their position with a recurrent neural network (RNN) trained using real-time recurrent learning (RTRL) and gradient clipping. The breathing data is quite regular, sampled at approximately 2.5Hz, and includes artificial drift in the spine direction. The amplitude of the motion of the tracked points ranged from 12.0mm to 22.7mm. Finally, we propose a simple method for recovering and predicting 3D tumor images from the tracked points and the initial tumor image based on a linear correspondence model and Nadaraya-Watson non-linear regression. The root-mean-square error, maximum error, and jitter corresponding to the RNN prediction on the test set were smaller than the same performance measures obtained with linear prediction and least mean squares (LMS). In particular, the maximum prediction error associated with the RNN, equal to 1.51mm, is respectively 16.1% and 5.0% lower than the maximum error associated with linear prediction and LMS. The average prediction time per time step with RTRL is equal to 119ms, which is less than the 400ms marker position sampling time. The tumor position in the predicted images appears visually correct, which is confirmed by the high mean cross-correlation between the original and predicted images, equal to 0.955., Comment: 21 pages, 24 figures; accepted manuscript version

Respiratory motion management is a huge challenge in radiation therapy. Respiratory motion induces temporal anatomic changes that distort the tumor volume and its position. In this study, a markerless tumor-tracking algorithm was investigated by performing phase recognition during stereotactic body radiation therapy (SBRT) using four-dimensional cone-beam computer tomography (4D-CBCT) obtained at patient registration, and in-treatment cone-beam projection images. The data for 20 treatment sessions (five lung cancer patients) were selected for this study. Three of the patients were treated with conventional flattening filter (FF) beams, and the other two were treated with flattening filter-free (FFF) beams. Prior to treatment, 4D-CBCT was acquired to create the template projection images for 10 phases. In-treatment images were obtained at near real time during treatment. Template-based phase recognition was performed for 4D-CBCT re-projected templates using prior 4D-CBCT based phase recognition algorithm and was compared with the results generated by the Amsterdam Shroud (AS) technique. Visual verification technique was used for the verification of the phase recognition and AS technique at certain tumor-visible angles. Offline template matching analysis using the cross-correlation indicated that phase recognition performed using the prior 4D-CBCT and visual verification matched up to 97.5% in the case of FFF, and 95% in the case of FF, whereas the AS technique matched 83.5% with visual verification for FFF and 93% for FF. Markerless tumor tracking based on phase recognition using prior 4D-CBCT has been developed successfully. This is the first study that reports on the use of prior 4D-CBCT based on normalized cross-correlation technique for phase recognition.

The detection of the presence of a structural abnormality in rotating machines with parts without gap, such as rigid couplings, by using the conventional method of vibration RMS, is difficult. This work investigates the application of Singular Spectral Analysis (SSA) to change detection in rotating machines. The performance of this technique is quantitatively evaluated in the scenario of misalignment in a turbopump assembly with rigid coupling. It is shown that a statistically significant change appears when using the Change Score calculated using the SSA method. Comparison with special indicators of a structural abnormality is also performed. Since the SSA detects changes in the shape of the signal, it is much more sensitive to changes related to abnormality than conventional methods or the use of special indicators.

Decommissioning of the Fukushima Daiichi nuclear power station (NPS) is challenging due to industrial and chemical hazards as well as radiological ones. The decommissioning workers in these sites are instructed to wear proper Personal Protective Equipment (PPE) for radiation protection. However, workers may not be able to accurately comply with safety regulations at decommissioning sites, even with prior education and training. In response to the difficulties of on-site PPE management, this paper presents a vision-based automated monitoring approach to help to facilitate the occupational safety monitoring task of decommissioning workers to ensure proper use of PPE by the combination of deep learning-based individual detection and object detection using geometric relationships analysis. The performance of the proposed approach was experimentally evaluated, and the experimental results demonstrate that the proposed approach is capable of identifying decommissioning workers&rsquo, improper use of PPE with high precision and recall rate while ensuring real-time performance to meet the industrial requirements.

Respiratory motion induces the limit in delivery accuracy due to the lack of the consideration of the anatomy motion in the treatment planning. Therefore, image-guided radiation therapy (IGRT) system plays an essential role in respiratory motion management and real-time tumor tracking in external beam radiation therapy. The objective of this research is the prediction of dynamic time-series images considering the motion and the deformation of the tumor and to compensate the delay that occurs between the motion of the tumor and the beam delivery. For this, we propose a prediction algorithm for dynamic time-series images. Prediction is performed using principal component analysis (PCA) and multi-channel singular spectral analysis (MSSA). Using PCA, the motion can be denoted as a vector function and it can be estimated by its principal component which is the linear combination of eigen vectors corresponding to the largest eigen values. Time-series set of 320-detector-row CT images from lung cancer patient and kilovolt (kV) fluoroscopic images from a moving phantom were used for the evaluation of the algorithm, and both image sets were successfully predicted by the proposed algorithm. The accuracy of prediction was quite high, more than 0.999 for CT images, whereas 0.995 for kV fluoroscopic images in cross-correlation coefficient value. This algorithm for image prediction makes it possible to predict the tumor images over the next breathing period with significant accuracy.

Our group is engaged in creating an innovative system in which an X-band linac X-ray source and 12TW50fs laser technologies are applied to medical and nano-technical uses. As pioneers in medical physics for reliable and safe medical radiology, we have endeavored to develop advanced laser beam technologies for cross-sectional and fused applications in the humanities and natural sciences to yield new synergies. This paper describes the developmental outcomes we have achieved to date.

In superconducting magnet bearings there are three major problems, AC Loss, non-linear motion and irregular trap field in the superconductor. The effect of AC Loss and non-linear motion is reduced through the manufacturing techniques and the control systems. However the rotation loss increases as the rotation velocity increases or the irregular trap field is enhanced. We have considered that the angular velocity loss come from the Lorentz force between the irregular magnetization of the superconductor and the current induced by the irregular field. Thus far the loss has been evaluated in our computer simulation method. In this study, the model of the rotation loss is demonstrated by the computer simulation and compared with the experiment of 10kWh flywheel and then the validity of the time variation of our calculation model was verified

In constructing the 100 kWh flywheel energy storage systems, the performance of the Superconducting Magnet Bearing (SMB) needs to be calculated to get better technical and practical results. The SMB system mainly consists of the Superconducting (SC) stator and the Permanent Magnet (PM) rotor. The SMB has the advantage that the rotational dragging force between the PM rotor and the SC stator is greatly smaller compared with the usual bearing systems. However, the magnetic friction becomes large enough to cause significant rotational speed degradation. The rotational speed degradation, which cuts down the stored energy, occurring in the PM rotors is one of the most significant problems for the practical use of the SMBs. It is caused mainly by the Lorentz force due to the inhomogeneous magnetic field of the SC stator and the induced eddy current in the PM rotor. In this study, we tried up various shapes of the SC stator to smooth the magnetic field and decrease the energy loss and then evaluated the levitation force, the magnetic field of the SC stator and the energy loss.

The suppression of the rotation speed degradation of the Superconducting Magnetic Bearing (SMB) with the Permanent Magnet (PM) rotor including the layered insulator thin films is studied. The SMB system consists of the superconductor stator and the PM rotor, and is a no-contact bearing system using the levitation force between them. The SMB was expected to be applied to the electric power storage flywheel system because friction causes the rotation speed degradation and decreases the energy storage capability. However, it was found that the electromagnetic friction of the SMB was not zero, due to electromagnetic forces; 1) the magnetic force interaction between the inhomogeneous magnetic field of the PM rotor and the eddy current it induces in the cryostat, 2) the magnetic force between the inhomogeneous magnetic field of the PM rotor and the shielding current it induces in the superconductor, 3) the magnetic force between the magnetic field of superconductors yielding the levitation force and the eddy current induced in the PM rotor. This magnetic field of superconductor is inhomogeneous too, because the superconductors consist of several HTSC bulks. In this research, it was found that the 3rd phenomenon is most significant. So, we use the advanced PM rotor including the layered insulator thin films to suppress its eddy current and the degradation of the rotation speed. Using our SMB equipment, the rotation speed degradation was measured using the PM rotors with/without the insulator thin films and the electromagnetic frictions were compared to each other.

The degradation of levitation force and rotation speed is one of the most significant problems for the practical use of superconducting magnetic bearings (SMB) for flywheel energy storage systems. The rotation loss is caused by the AC magnetic field due to the inhomogeneous distribution of magnetic flux density of the permanent magnet (PM) rotor. A simulation method has been developed by the authors' group to simulate the rotation losses. In this research, this method was improved so that the eddy current flowing in the cryostat around the superconductor was taken into account. The dependency of the rotation losses of radial type SMB with cryostat were evaluated by this new simulation method with respect to the levitating height, the initial rotation speed and the amplitude of inhomogeneous component of PM rotor's field.

The new simulation method was developed based on the T -method to simulate the AC loss of high temperature superconducting magnetic flywheel when the magnetic rotor rotates with the inhomogeneous component of the magnetic field. The dependencies of AC loss upon some conditions such as the wave number, the amplitude of inhomogeneous component and so on were analyzed.

For realization of the flywheel system using high T c superconductors (HTSCs), AC loss is one of the serious problems to be solved. An un-uniformity of magnetization of permanent magnets rotating at high speed generates AC field around HTSC. This causes AC losses. In this study we assumed that AC field generated by a rotating permanent magnet is equivalent to AC field generated by coil current. Based on this model the experiments were done. These results were compared with those of numerical simulation. Furthermore, to suppress AC loss, a pre-loading method was suggested.

This paper studies the ultrasonic detection and evaluation of internal volume defects in metals using laser generation and electromagnetic acoustic transducer (EMAT) detection. A finite element model is developed to simulate the interaction of laser-generated ultrasonic waves with the defect in the material. Not only have the directly scattered shear waves been observed, but also the mode-converted creeping waves on the defect surface. A noncontact laser-EMAT ultrasonic testing experimental system was successfully applied to validate the observed phenomena in the simulation results. The defect can not only be detected and located by the directly scattered shear waves, but can also be quickly evaluated with a new method based on quantitative time-of-flight analysis of the directly scattered waves and the mode-converted waves on the defect surface.

Magnetoelastically coupled vibration of the fusion reactor first wall was investigated in light of the magnetic damping effect. A law of similarity for the coupled vibration is proposed by non-dimensionalizing a set of equations describing the coupled problem between the elastic vibration and the eddy current field. The law was verified through numerical experiments. In addition, the dynamic behavior of the ITER first wall during plasma disruption was simulated, taking into account the magnetic damping effect. It was found that the dynamic stress is overestimated by neglecting the magnetic damping. This effect significantly reduces the dynamic stress in the case of a thin first wall, so that the flexible thin shell structure is found to be applicable to fusion reactor components.

This paper describes a numerical analysis of the buckling of fusion reactor components, resulting from electromagnetic loads. An analysis method for the calculation of the dynamic electromagnetically coupled buckling is proposed, where the geometrical non-linearity, the magnetic damping and the magnetic stiffness effects are taken into consideration. In the cases of the analysis of a shallow arch, the numerical results agreed well with the experimental results. The comparison showed that the inclusion of the geometrical non-linearity and the magnetic stiffness in the analysis is evidently necessary for good agreement. The analysis of the ITER vacuum vessel showed that, in the case of plasma disruption, the possiblity of electromagnetic buckling of the ITER vessel is very small under the conceptual design activity condition.

Neutron capture therapy (NCT) is a promising non-invasive cancer therapy approach and some recent NCT research has focused on using compounds containing gadolinium as an alternative to currently used boron-10 considering several advantages that gadolinium offers compared to those of boron. In this study, we evaluated gadolinium-entrapped liposome compound as neutron capture therapy agent by in vivo experiment on colon-26 tumor-bearing mice. Gadolinium compound were injected intravenously via tail vein and allowed to accumulate into tumor site. Tumor samples were taken for quantitative analysis by ICP-MS at 2, 12, and 24 h after gadolinium compound injection. Highest gadolinium concentration was observed at about 2 h after gadolinium compound injection with an average of 40.3 μg/g of wet tumor tissue. We performed neutron irradiation at JRR-4 reactor facility of Japan Atomic Energy Research Institute in Tokaimura with average neutron fluence of 2 × 10 12 n/cm 2 . The experimental results showed that the tumor growth suppression of gadolinium-injected irradiated group was revealed until about four times higher compared to the control group, and no significant weight loss were observed after treatment suggesting low systemic toxicity of this compound. The gadolinium-entrapped liposome will become one of the candidates for Gd delivery system on NCT.

We are developing compact electron linear accelerators (hereafter linac) with high RF (Radio Frequency) frequency (9.3 GHz, wavelength 32.3 mm) of X-band and applying to medicine and non-destructive testing. Especially, potable 950 keV and 3.95 MeV linac X-ray sources have been developed for on-site transmission testing at several industrial plants and civil infrastructures including bridges. 6 MeV linac have been made for pinpoint X-ray dynamic tracking cancer therapy. The length of the accelerating tube is ~600 mm. The electron beam size at the X-ray target is less than 1 mm and X-ray spot size at the cancer is less than 3 mm. Several hardware and software are under construction for dynamic tracking therapy for moving lung cancer. Moreover, as an ultimate compact linac, we are designing and manufacturing a laser dielectric linac of ~1 MeV with Yr fiber laser (283 THz, wavelength 1.06 pm). Since the wavelength is 1.06 μm, the length of one accelerating strcture is tens pm and the electron beam size is in sub-micro meter. Since the sizes of cell and nuclear are about 10 and 1 μm, respectively, we plan to use this "On-chip" linac for radiation-induced DNA damage/repair analysis. We are thinking a system where DNA in a nucleus of cell is hit by ~1 μm electron or X-ray beam and observe its repair by proteins and enzymes in live cells in-situ.

In this work, a phased array laser ultrasound system with using fibre optic delivery and a custom-designed focusing objective lens has been developed for enhancing the ultrasound generation. The fibre-phased array method is applied to improve the sensitivity and detecting ability of the laser-EMAT system for defect inspection.