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The purpose of this study was to develop a wearable platform system that can detect and acquire a soldier’s biosignals (i.e., heart activity signal, respiration rate, etc.) in a nonrestrained, unconscious manner. These detected biosignals are transmitted to a processing device to analyze and monitor the soldier’s physical status. To achieve this, textile-based heart activity electrodes and a strain gauge sensor for the respiration signal measurement were developed, and their performances in detecting each signal were verified. These sensors were embedded in a chest belt to design a wearable platform that can simultaneously measure heart activity and respiration signals. The sensor part of the chest belt has a dual layer structure to detect high-quality signals. Stretch fabric was used on the outer layer and a respiration sensor was attached to the belt. On the inside layer, a non-stretch fabric was used as the base fabric and a heart activity-sensing electrode, that is capable of taking measurements using a modified lead-II heart activity signal induction method, was embroidered onto the fabric. Subjects were asked to wear the chest belt, and a biosignal processor module was attached to verify the system’s performance while simultaneously acquiring the heart activity and respiration signals. More specifically, it was confirmed that the two signals were detected in a stable. It is expected that the biosignal-monitoring wearable platform system developed in this study will be able to effectively analyze and monitor soldiers’ biosignals.

The strain-gauge type textile sensors adopted in many studies on respiration-sensing wearable systems have been reported to have two major limitations that result in reduced sensing accuracy and insufficient durability of the sensor. The two limitations are the inability to accurately monitor the changes in the three-dimensional (3D) body contour during changes in the respiration cycle and the frequent occurrence of baseline drifts. To solve these issues, this study proposes new types of textile respiration rate sensors with a 3D structure, which measure the respiration rate based on the variation in the size of the contacting section’s surface during respiration, rather than the changes in the length of the sensor, as in existing strain-gauge type sensors. Firstly, the sensing signals were analyzed based on morphology and size measurements. Then, the sensing reliability of three respiration rate sensor types, namely the no-filler, 3D hard, and 3D soft types, was analyzed by comparing their measurements with those of the SS5LB. Finally, the reproducibility and baseline drifts of the sensors’ measurements were evaluated by taking and comparing repeated measurements. As a result, the consistency of the sensing signals of the SS5LB and those of the two types of 3D sensors was higher than those of the no-filler type sensor, and the 3D soft type sensor had the highest reliability and reproducibility among the three new types of sensors. The result showed relatively reduced baseline drifts in the two types of 3D sensors.

This study investigated a dictionary-learning (DL)-based image deblurring method for improving image performance in x-ray nondestructive testing. DL is a representation learning theory that aims to find a sparse representation of the input signal in the form of a linear combination of basic elements as well as those basic elements themselves. In this study, a DL-based algorithm was implemented, and a computational simulation and experiment were then performed to evaluate the algorithm’s effectiveness for image deblurring. The hardware system used in the experiment consisted of an x-ray tube with a focal spot size of 0.6 mm and a flat-panel detector with a pixel size of 100 μ m2. X-ray images of several electronic components were acquired at x-ray tube conditions of 80 kV p and 1.25 mAs. The image characteristics of the deblurred images generated by the DL-based algorithm were quantitatively evaluated in terms of intensity profile, universal-quality index, and noise power spectrum. Our results indicate that our DL-based image deblurring method effectively improves image performance in x-ray nondestructive testing.

Dual-energy material decomposition (DEMD) is a well-established theoretical x-ray technique that uses low- and high-kilovoltage radiographs to separate soft tissue and bone in radiography and compu...

Background and objective Digital breast tomosynthesis (DBT) is a well-established multiplanar imaging modality in breast examinations designed to overcome the limitations of conventional mammography. However, reconstructed DBT images from the acquired projection data are often limited in image performance due mainly to blur artifacts resulting from inherent aspects of imaging systems, including detector resolution and the finite focal spot of the x-ray tube. Methods We investigated an effective blind-deblurring method based on a compressed-sensing scheme in an attempt to solve the blurring problem in DBT. We implemented the proposed algorithm and performed a systematic simulation and an experiment to demonstrate its viability. In both simulation and experiment, all of the projection data were taken with a tomographic angle of θ = 32° and an angle step of Δθ = 2°. The proposed deblurring algorithm was then applied to the projection data before performing the common filtered-backprojection-based DBT reconstruction process. Results The deblurred projection images showed much better image performance compared with the blurred projection images, demonstrating the viability of the proposed blind-deblurring scheme in conventional radiography. The PSNR and RMSE characteristics of the deblurred DBT image improved by factors of approximately 1.63 and 0.37, respectively, compared with those of the blurred DBT image. Conclusions Our results indicate that the proposed blind-deblurring method was effective in reducing the blurring problem in both DBT and in conventional radiography, excluding additional measurement of the system response function.

In conventional radiography, image visibility is often limited mainly due to the superimposition of the object’s structure under investigation and scattered x-rays. Several methods, including the antiscatter grid technique, the air-gap technique, and scatter correction methods using measurements, mathematical-physical modeling, or a combination of both, have been extensively investigated in an attempt to overcome these difficulties. However, these methods require special equipment, geometry, and extra work to measure the scatter characteristics. In this study, we propose a new image restoration method based on a simple radiographic scattering model in which the intensity of the scattered x-rays and the direct transmission function of a given object are estimated from a single x-ray image by using the dark-channel prior. We implemented the proposed algorithm and performed a systematic experiment by using a 450-kV industrial x-ray inspection system to demonstrate its viability for nondestructive testing. Our results indicated that the structure of the examined object was much more clearly visible in the restored image, considerably improving the radiographic visibility.

In this study, we investigated an effective low-dose digital tomosynthesis (DTS) where a multislit collimator placed between the X-ray tube and the patient oscillates during projection data acquisition, partially blocking the X-ray beam to the patient thereby reducing the radiation dosage. We performed a simulation using the proposed DTS with two sets of multislit collimators both having a 50% duty cycle and investigated the image characteristics to demonstrate the feasibility of this proposed approach. In the simulation, all projections were taken at a tomographic angle of θ = ± 5 0 ° and an angle step of Δ θ = 2 ° . We utilized an iterative algorithm based on a compressed-sensing (CS) scheme for more accurate DTS reconstruction. Using the proposed DTS, we successfully obtained CS-reconstructed DTS images with no bright-band artifacts around the multislit edges of the collimator, thus maintaining the image quality. Therefore, the use of multislit collimation in current real-world DTS systems can reduce the radiation dosage to patients.

In conventional planar radiography, image visibility is often limited mainly due to the superimposition of the object structure under investigation and the artifacts caused by scattered x-rays and noise. Several methods, including computed tomography (CT) as a multiplanar imaging modality, air-gap and grid techniques for the reduction of scatters, phase-contrast imaging as another image-contrast modality, etc., have extensively been investigated in attempt to overcome these difficulties. However, those methods typically require higher x-ray doses or special equipment. In this work, as another approach, we propose a new model-based radiography restoration method based on simple scatter-degradation scheme where the intensity of scattered x-rays and the transmission function of a given object are estimated from a single x-ray image to restore the original degraded image. We implemented the proposed algorithm and performed an experiment to demonstrate its viability. Our results indicate that the degradation of image characteristics by scattered x-rays and noise was effectively recovered by using the proposed method, which improves the image visibility in radiography considerably.

In recent digital tomosynthesis (DTS), iterative reconstruction methods are often used owing to the potential to provide multiplanar images of superior image quality to conventional filtered-backprojection (FBP)-based methods. However, they require enormous computational cost in the iterative process, which has still been an obstacle to put them to practical use. In this work, we propose a new DTS reconstruction method incorporated with a dual-resolution voxelization scheme in attempt to overcome these difficulties, in which the voxels outside a small region-of-interest (ROI) containing target diagnosis are binned by 2 × 2 × 2 while the voxels inside the ROI remain unbinned. We considered a compressed-sensing (CS)-based iterative algorithm with a dual-constraint strategy for more accurate DTS reconstruction. We implemented the proposed algorithm and performed a systematic simulation and experiment to demonstrate its viability. Our results indicate that the proposed method seems to be effective for reducing computational cost considerably in iterative DTS reconstruction, keeping the image quality inside the ROI not much degraded. A binning size of 2 × 2 × 2 required only about 31.9% computational memory and about 2.6% reconstruction time, compared to those for no binning case. The reconstruction quality was evaluated in terms of the root-mean-square error (RMSE), the contrast-to-noise ratio (CNR), and the universal-quality index (UQI).

Conventional digital tomosynthesis (DTS) based on the filtered-backprojection (FBP) reconstruction requires full field-of-view scan and also relatively dense projections, which results in still high dose for medical imaging purposes. In this work, to overcome these difficulties, we propose a new type of DTS examinations, the so-called scout-view assisted interior DTS (iDTS), in which the x-ray beam span covers only a small region-of-interest (ROI) containing target diagnosis with the help of some scout views and they are used in the reconstruction to add additional information to interior ROI otherwise absent with conventional iDTS reconstruction methods. We considered an effective iterative algorithm based on compressed-sensing theory, rather than the FBP-based algorithm, for more accurate iDTS reconstruction. We implemented the proposed algorithm, performed a systematic simulation and experiment, and investigated the image characteristics. We successfully reconstructed iDTS images of substantially high accuracy and no truncation artifacts by using the proposed method, preserving superior image homogeneity, edge sharpening, and in-plane spatial resolution.

We have recently developed precise x-ray grids having strip densities in the range of 100 – 250 lines/inch by adopting the precision sawing process and carbon interspace material for the demands of specific x-ray imaging techniques. However, quality assurance in the grid manufacturing has not yet satisfactorily conducted because grid strips of a high strip density are often invisible through an x-ray nondestructive testing with a flat-panel detector of an ordinary pixel resolution (>100 μ m). In this work, we propose a useful method to evaluate actual grid strip densities over the Nyquist sampling rate based on the moire artifact analysis. We performed a systematic simulation and experiment with several sample grids and a detector having a 143- μ m pixel resolution to verify the proposed quality assurance method. According to our results, the relative differences between the nominal and the evaluated grid strip densities were within 0.2% and 1.8% in the simulation and experiment, respectively, which demonstrates that the proposed method is viable with an ordinary detector having a moderate pixel resolution for quality assurance in grid manufacturing.

Single grid-based phase-contrast x-ray imaging (g-PCXI) technique, which was recently proposed by Wen et al. to retrieve absorption, scattering, and phase-gradient images from the raw image of the examined object, seems a practical method for phase-contrast imaging with great simplicity and minimal requirements on the setup alignment. In this work, we developed a useful simulation platform for g-PCXI and performed a simulation to demonstrate its viability. We also established a table-top setup for g-PCXI which consists of a focused-linear grid (200-lines/in strip density), an x-ray tube (100-μm focal spot size), and a flat-panel detector (48-μm pixel size) and performed a preliminary experiment with some samples to show the performance of the simulation platform. We successfully obtained phase-contrast x-ray images of much enhanced contrast from both the simulation and experiment and the simulated contract seemed similar to the experimental contrast, which shows the performance of the developed simulation platform. We expect that the simulation platform will be useful for designing an optimal g-PCXI system.

In this work, we investigated a compressed-sensing (CS)-based deblurring scheme for image deblurring of high accuracy in digital tomosynthesis (DTS). We implemented the proposed deblurring scheme and performed a systematic simulation to demonstrate its viability for improved x-ray nondestructive testing. We also performed an experiment by using a table-top setup which consists of an x-ray tube (90 kVp, 6 mAs) and a CMOS-type flat-panel detector having a 198-μm pixel resolution. In both the simulation and the experiment, 51 projection images were taken with a tomographic angle range of θ=60° and an angle step of Δθ=1.2° and then deblurred by using the proposed algorithm before performing the common filtered-backprojection (FBP)-based DTS reconstruction. Our results indicate that the proposed deblurring scheme appears to be effective for the blurring problems in DTS and is applicable to improve the image characteristics in the present x-ray nondestructive testing.

Objective: This study aimed to examine the characteristics of joint kinematics and synchronicity of rowing motion between elite and non-elite rowers. Methods: Two elite and two non-elite rowers performed rowing strokes (3 trials, 20 strokes in each trial) at three different stroke rates (20, 30, 40 stroke/min) on two stationary rowing ergometers. The rowing motions of the rowers were captured using a 3-dimensional motion analysis system (8-infrared camera VICON system, Oxford, UK). The range of motion (RoM) of the knee, hip, and elbow joints on the sagittal plane, the lead time (TLead) and the drive time TDrive) for each joint, and the elapsed time for the knee joint to maintain a fully extended position (TKnee) during the stroke were analyzed and compared between elite and non-elite rowers. Synchronicity of the rowing motion within and between groups was examined using coefficients of variation (CV) of the TDrive for each joint. Results: Regardless of the stroke rate, the RoM of all joints were greater for the elite than for non-elite rowers, except for the RoMs of the knee joint at 30 stroke/min and the elbow joint at 40 stroke/min (p < .05). Although the TLead at all stroke rates were the same between the groups, the TDrive for each joint was shorter for the elite than for the nonelite rowers. During the drive phase, elite rowers kept the fully extended knee joint angle longer than the non-elite rowers (p < .05). The CV values of the TDrive within each group were smaller for the elite compared with non-elite rowers, except for the CV values of the hip at all stroke/min and elbow at 40 stroke/min. Conclusion: The elite, compared with non-elite, rowers seem to be able to perform more powerful and efficient rowing strokes with large RoM and a short TDrive with the same TLead.

In this paper, we propose a new/next-generation type of CT examinations, the so-called Interior Computed Tomography (ICT), which may presumably lead to dose reduction to the patient outside the target region-of-interest (ROI), in dental x-ray imaging. Here an x-ray beam from each projection position covers only a relatively small ROI containing a target of diagnosis from the examined structure, leading to imaging benefits such as decreasing scatters and system cost as well as reducing imaging dose. We considered the compressed-sensing (CS) framework, rather than common filtered-backprojection (FBP)-based algorithms, for more accurate ICT reconstruction. We implemented a CS-based ICT algorithm and performed a systematic simulation to investigate the imaging characteristics. Simulation conditions of two ROI ratios of 0.28 and 0.14 between the target and the whole phantom sizes and four projection numbers of 360, 180, 90, and 45 were tested. We successfully reconstructed ICT images of substantially high image quality by using the CS framework even with few-view projection data, still preserving sharp edges in the images.

In this work, we propose a practical method that can combine the two functionalities of dental panoramic and cone-beam CT (CBCT) features in one by using a single panoramic detector. We implemented a CS-based reconstruction algorithm for the proposed method and performed a systematic simulation to demonstrate its viability for 3D dental X-ray imaging. We successfully reconstructed volumetric images of considerably high accuracy by using a panoramic detector having an active area of 198.4 mm × 6.4 mm and evaluated the reconstruction quality as a function of the pitch (p) and the angle step (Δθ). Our simulation results indicate that the CS-based reconstruction almost completely recovered the phantom structures, as in CBCT, for p≤2.0 and θ≤6°, indicating that it seems very promising for accurate image reconstruction even for large-pitch and few-view data. We expect the proposed method to be applicable to developing a cost-effective, volumetric dental X-ray imaging system.

Dual-energy computed tomography (DECT) is a well-known imaging technique that can be used to differentiate and classify material composition by using projection data acquired at two different x-ray tube voltages. Dual-energy projection data can be also used to create virtual monochromatic images as the potential to reduce beam-hardening artifacts that are usually observed in conventional polychromatic images. Despite DECT’s merits, main concerns in the use of DECT in clinics may be high radiation dose imposed to patients during the examinations. In this study, we investigated sparse-view virtual monochromatic CT reconstruction using a dictionary-learning (DL)-based algorithm to provide quantitative measurements at reduced radiation dose. DL is an advanced representation learning theory that aims to find a sparse representation of the input signal in the form of a linear combination of basis elements. To validate the proposed method, we performed a systematic simulation and also we made an experiment on a skull phantom using a commercially-available dental cone-beam CT system. Two data sets of 60 projections were acquired at 80 kV p and 120 kV p separately from the system and used to create virtual monochromatic images at 90 keV and 130 keV. The image qualities were evaluated in terms of the image intensity and the peak signal-to-noise ratio.

We investigated the compressed-sensing (CS)-based deblurring framework incorporated with the total-variation (TV) regularization penalty for effective image deblurring of high accuracy in x-ray imaging. We implemented the proposed algorithm and performed a systematic experiment to demonstrate its viability for image deblurring in x-ray nondestructive testing. We obtained x-ray images of several selected electronic components at an x-ray tube condition of 80 kVp and 1.25 mAs and investigated the imaging characteristics in terms of the noise power spectrum and the modulation. We expect the proposed deblurring method to be applicable to improve the image characteristics considerably in x-ray nondestructive testing.

In practical applications of three-dimensional (3D) tomographic imaging, there are often challenges for image reconstruction from insufficient sampling data. In computed tomography (CT), for example, image reconstruction from sparse views and/or limited-angle (

Digital tomosynthesis (DTS) is a geometric tomography technique using a limited-angle scan. It has been popularly used in both medical and industrial x-ray imaging applications. DTS provides the tomographic benefits of computed tomography with reduced dose and time. However, conventional DTS reconstruction based on the computationally cheap filtered back-projection (FBP) method typically produces poor image quality due to limited angular samplings. To overcome these difficulties, iterative reconstruction methods are often used in DTS reconstruction as they have the potential to provide multiplanar images of higher quality than conventional FBP-based methods. However, they require enormous computational cost in the iterative process, which remains an obstacle to practical applications. In this study, we propose a method for effectively reducing limited-angle artifacts in conventional FBP reconstruction, using a state-of-the-art deep learning scheme with a convolutional neural network. Our results indicate that the proposed DTS reconstruction method effectively minimized limited-angle artifacts, thus improving image performance in DTS, and that further it provided good image quality in both sagittal and coronal views (as in computed tomography) as well as in axial view.

Computed tomography (CT) images obtained at different monochromatic X-ray beam energies can be synthesized from conventional dual-energy CT scans. This approach to synthesizing monochromatic CT images is based on basis material decomposition and the knowledge of attenuation of basis materials. The main benefits of virtual monochromatic CT (VMCT) images include reduction of beam-hardening artifacts and provision of accurate atteuation measurements. Despite the VMCT's benefits, main concerns in the use of VMCT in clinics may be high radiation dose the patient is exposed to. In this study, we investigated a projection-based sparse-view VMCT method in an attempt to overcome these difficulties. We performed a computational simulation and evaluated the feasibility of using the VMCT method in sparse-view CT. Two polychromatic data sets of 90 projections, far less than what is required by the Nyquist sampling theory, were simulated at 80 kVp and 140 kVp and used to synthesize VMCT images at a monochromatic energy range of 40–140 keV . VMCT image characteristics were quantitatively evaluated in terms of intensity profile, the contrast-to-noise ratio, and the signal-to-noise ratio. Our results indicate that the CS-based algorithm produced high-quality sparse-view CT images, and thereby the proposed VMCT method yielded CT image results of improved beam-hardening artifacts and quantitative measurements.

This study investigated analytic digital tomosynthesis (DTS) reconstruction in partial sampling with a multislit collimator that partially blocks the x-ray beam to the patient during projection data acquisition, thereby reducing excessive radiation dose to patients. Partially-sampled DTS images reconstructed using the analytic filtered-backprojection (FBP) algorithm usually suffer from severe bright-band artifacts around multislit edges of the collimator due to incomplete spatial sampling. In this study, a new prior sinogram interpolation method was introduced to the analytic DTS reconstruction in partial sampling to alleviate such artifacts. To verify the proposed DTS method, we conducted a systematic simulation and investigated image characteristics. Three multislit collimator layouts of C(2/2), C(3/3), and C(4/4) with a 50% duty cycle were designed and used in the simulation. Here C(n/n) denotes a collimator layout that blocks the x-ray beam over n detector pixels vertically with a n-pixel interval. All projections were obtained at a tomographic angle of θ = ±40o and an angle step of Δθ = 2o and used to reconstruct DTS images using the FBP algorithm. Our results indicate that the proposed sinogram interpolation method effectively minimized bright-band artifacts in analytic DTS reconstruction in partial sampling, thus maintaining the image quality.

The most popular reconstruction algorithm for cone-beam computed tomography (CBCT) is based on the computationally-inexpensive filtered-backprojection (FBP) method. However, that method usually requires dense projections over the Nyquist samplings, which imposes severe restrictions on the imaging doses. Moreover, the algorithm tends to produce cone-beam artifacts as the cone angle is increased. Several variants of the FBP-based algorithm have been developed to overcome these difficulties, but problems with the cone-beam reconstruction still remain. In this study, we considered a compressed-sensing (CS)-based reconstruction algorithm for low-dose, high-quality dental CBCT images that exploited the sparsity of images with substantially high accuracy. We implemented the algorithm and performed systematic simulation works to investigate the imaging characteristics. CBCT images of high quality were successfully reconstructed by using the built-in CS-based algorithm, and the image qualities were evaluated quantitatively in terms of the universal-quality index (UQI) and the slice-profile quality index (SPQI).We expect the reconstruction algorithm developed in the work to be applicable to current dental CBCT systems, to reduce imaging doses, and to improve the image quality further.

As a new direction for computed tomography (CT) imaging, inverse-geometry CT (IGCT) has been recently introduced and is intended to overcome limitations in conventional cone-beam CT (CBCT) such as the cone-beam artifacts, imaging dose, temporal resolution, scatter, cost, and so on. While the CBCT geometry consists of X-rays emanating from a small focal spot and collimated toward a larger detector, the IGCT geometry employs a large-area scanned source array with the Xray beams collimated toward a smaller-area detector. In this research, we explored an effective IGCT reconstruction algorithm based on the total-variation (TV) minimization method and studied the feasibility of the IGCT geometry for potential applications to fast, low-dose volumetric dental X-ray imaging. We implemented the algorithm, performed systematic simulation works, and evaluated the imaging characteristics quantitatively. Although much engineering and validation works are required to achieve clinical implementation, our preliminary results have demonstrated a potential for improved volumetric imaging with reduced dose.

In this paper, we introduce limited-angle tomography in which the object being imaged is rotated around the center of an inclined X-ray beam, the so-called micro-DTS (digital tomosynthesis), with a few-view image reconstruction based on the compressed-sensing (CS) theory for applications of fast, low-dose X-ray imaging. We implemented an effective CS-based reconstruction algorithm for micro-DTS and performed systematic simulation works. The assessment of the image characteristics was performed by using several figures of merit such as the root-mean-square error (RMSE), the contrast-to-noise ratio (CNR), and the universal image-quality index (UQI) to compare the reconstructed images to the simulated phantoms. According to our results, compared to the FBP-based method, the CS-based reconstruction method substantially enhanced image accuracy against image artifacts from few-view and limited-angle projections. In the simulation, 41 projections were used for the half-tomographic angles of 30°, 45°, and 60°, giving UQI values of 0.92 ∼ 0.97, which seems promising for potential applications of fast, low-dose X-ray imaging.

Digital tomosynthesis (DTS) is a limited-angle tomographic technique that provides some of the tomographic benefits of computed tomography (CT) but at reduced dose and cost. Thus, the potential for application of DTS to dental X-ray imaging seems promising. As a continuation of our dental radiography R&D, we developed an effective DTS reconstruction algorithm and implemented it in conjunction with a commercial dental CT system for potential use in dental implant placement. The reconstruction algorithm employed a backprojection filtering (BPF) method based upon optimal deblurring filters to suppress effectively both the blur artifacts originating from the out-focus planes and the high-frequency noise. To verify the usefulness of the reconstruction algorithm, we performed systematic simulation works and evaluated the image characteristics. We also performed experimental works in which DTS images of enhanced anatomical resolution were successfully obtained by using the algorithm and were promising to our ongoing applications to dental X-ray imaging. In this paper, our approach to the development of the DTS reconstruction algorithm and the results are described in detail.

Panoramic radiography with which only structures within a certain image layer are in focus and others out of focus on the panoramic image has become a popular imaging technique especially in dentistry. However, the major drawback to the technique is a mismatch between the structures to be focused and the predefined image layer mainly due to the various shapes and sizes of dental arches and/or to malpositioning of the patient. These result in image quality typically inferior to that obtained using intraoral radiographic techniques. In this paper, to overcome these difficulties, we suggest a new panoramic reconstruction algorithm, the so-called adaptive panoramic tomography (APT), capable of reconstructing multifocal image layers with no additional exposure. In order to verify the effectiveness of the proposed algorithm, we performed systematic simulation studies with a circular rotational movement and investigated the image performance.

The behavior of charged particles is investigated for nano-scale ion beam therapy using a medical accelerator. Computational work is performed for the Bragg-peak simulation, which is focused on human organ material of pancreas and thyroid. The Results show that the trends of the dose have several different kinds of distributions. Before constructing a heavy ion collider, this study can give us the reliability of the therapeutic effect. Realistic treatment using human organs is calculated in a simple and cost effective manner using the computational code, the Stopping and Range of Ions in Matter 2008 (SRIM 2008). Considering the safety of the therapy, it is suggested to give a patient orient planning of the cancer therapy. The energy losses in ionization and phonon are analyzed, which are the behaviors in the molecular level nano-scopic investigation. The different fluctuations are shown at 150 MeV, where the lowest temperature is found in proton and pancreas case. Finally, the protocol for the radiation therapy is constructed by the simulation in which the procedure for a better therapy is selected. An experimental measurement incorporated with the simulations could be programmed by this protocol.

The irradiation-induced nuclear waste material has been investigated. Alpha particles are considered as the waste radiation. The material aspects are considered for four kinds of materials, which are used for the nuclear waste forms. These materials are compared for the possibility of the irradiation-induced amorphization. Several variables are investigated for the ion radiation interactions. We have used the Stopping and Range of Ion in Matter 2008 (SRIM 2008) code system to show that the ion dose is changed to the displacement per atom (dpa) completely and the kinetic energy is transferred to each target atom through nuclear collision. The necessary thickness of the waste form has been investigated. More reasonable study has been done for the nuclear waste material container. The thickness of 204 nm is considered for the optimized structure of waste drum by crystalline silicotitanate.

In this study, we fabricated a fiber-optic radiation sensor for proton therapy dosimetry and measured the output and the peak-to-plateau ratio of scintillation light with various kinds of organic scintillators in order to select an organic scintillator appropriate for measuring the dose of a proton beam. For the optimization of an organic scintillator, the linearity between the light output and the stopping power of a proton beam was evaluated for two different diameters of the scintillator, and the angular dependency and standard deviation of the light pulses were investigated for four different scintillator lengths. We also evaluated the linearity between the light output and the dose rate and monitor units of a proton generator, respectively. The relative depth–dose curve of the proton beam was obtained and corrected using Birk’s theory.