With the extension of ion species in ion-beam radiotherapy, the sole dependence of relative biological effectiveness (RBE) on linear energy transfer (LET) is insufficient when comparing RBE for ion beams with the same LET value. The aim of the present study was to provide a systematic study of the nanodosimetry for ion beams with the same LET value. Based on the calculated LET profiles of ion beams with range about 130 mm, lineal energy spectra and dose-averaged lineal energy [Formula: see text] on 4 nm site for various clinical ion beams were obtained. Then, the lineal energy spectra and [Formula: see text] values were compared for ion beams with the same LET values. The results showed that the relationships between [Formula: see text] and LET for various ion beams present an dependence on ion species. For ion beams with the same LET value, the ion beams with smaller nucleon number yielded greater [Formula: see text] values. The probability of the small-nucleon-number ion beams to generate large energy deposition events on nanoscale was higher than that of the large-nucleon-number ion beams. The dependence of the relationship between RBE and LET on ion species might be attributed to the fluctuation of energy depositions on nanometer scale.

The development of brain-computer interface (BCI) systems is an important approach in brain studies. Control of communication devices and prostheses in real-world scenarios requires complex movement parameters. Decoding a variety of neural signals captured by micro-wire arrays is a potential applicant for extracting movement-related information. The present work was conducted to compare the functionality of partial least square (PLS) regression and Kalman filter to predict the force parameter from local field potential (LFP) signals of the primary motor cortex (M1). The signals were recorded using a 16-channel micro-wire array from the forelimb-related area of the M1 of three rats performing a behavioral task in which the force signal of the rat's forelimb paw was generated. Our results show that PLS regression and Kalman filters with the mean performance of 0.75 and 0.72 in terms of the correlation coefficient (CC) and 0.37 and 0.48 in terms of normalized mean square error (NMSE), respectively, are effective methods for decoding the force parameter from LFPs. Kalman filter underperforms PLS both in performance and speed. Although adding nonlinearity to the Kalman filter results in equally accurate CC performance as PLS, it has even more computational cost. Therefore, it is inferred that nonlinear methods do not necessarily have better functionality than linear ones and PLS, as a simple fast linear method could be an effectively applicable regression technique for BCIs.

Electroencephalogram (EEG) has become a practical tool for monitoring and diagnosing pathological/psychological brain states. To date, an increasing number of investigations considered differences between brain dynamic of patients with schizophrenia and healthy controls. However, insufficient studies have been performed to provide an intelligent and accurate system that detects the schizophrenia using EEG signals. This paper concerns this issue by providing new feature-level fusion algorithms. Firstly, we analyze EEG dynamics using three well-known nonlinear measures, including complexity (Cx), Higuchi fractal dimension (HFD), and Lyapunov exponents (Lya). Next, we propose some innovative feature-level fusion strategies to combine the information of these indices. We evaluate the effect of the classifier parameter (σ) adjustment and the cross-validation partitioning criteria on classification accuracy. The performance of EEG classification using combined features was compared with the non-combined attributes. Experimental results showed higher classification accuracy when feature-level features were utilized, compared to when each feature was used individually or all fed to the classifier simultaneously. Using the proposed algorithm, the classification accuracy increased up to 100%. These results establish the suggested framework as a superior scheme compared to the state-of-the-art EEG schizophrenia diagnosis tool.

Radiochromic film is a good dosimeter choice for patient QA for complex treatment techniques (IMRT, VMAT, SABR, SBRT) because of its near tissue equivalency, very high spatial resolution and established method of use. Commercial scanners are usually used for film dosimetry, among which EPSON scanners are the most common. NCCI have used an EPSON V700 scanner, but recently acquired a new model EPSON V800 scanner. The purpose of this work was to evaluate any differences between these two scanners to consider whether they can be used interchangeably or not. Different aspects of film dosimetry, e.g. lateral response artefact (LRA) effect, orientation effect, scanner response etc., were compared. EBT3 films were irradiated with 40 × 40 cm field size 6 MV beams and scanned in both the scanners. The scanned images were read in ImageJ V1.49 software. The data obtained was then copied in MS Excel to compare the scanners. The V800 scanner causes more polarisation, which results in more LRA effect than for the V700 scanner. The responses of the scanners in all three colour channels are not the same for the same film and irradiation. The V800 scanner shows an increase of response of up to 1.6% compared to 3.7% increase in the V700 scanner after scanning a piece of irradiated film 20 times. The scanners cannot be used interchangeably. The correction factors for LRA effect and the calibration curves are different. Further characterisation, evaluation and commissioning is required before clinical use.

To explore the parotid normal tissue complication probability (NTCP) modeling with percolation-based dose clusters for head-and-neck patients receiving concomitant chemotherapy and radiation therapy. Cluster models incorporating the spatial dose distribution in the parotid gland were developed to evaluate the radiation induced complication. Cluster metrics including the mean cluster size (NMCS) and the largest cluster size both normalized by the gland volume (NSLC) were evaluated and scrutinized against the benchmark NTCP. Two fitting strategies to the Lyman-Kutcher-Burman (LKB) model using the maximum likelihood method were devised: the volume parameter n fixed at 1.0 (mean dose model) and unrestricted (full LKB model). The fitted parameters TD and m were assessed with the LKB NTCP models with the available xerostomia data. NSLC was a better metric than NMCS with reference to the LKB model and strong correlation (r ~ 0.95) was observed between NTCP and NSLC. The mean dose model returned the parameter TD (39.9 Gy) and m (0.4) from the NSLC of threshold dose at around 40 Gy. Drastically different TD and m values were obtained from the fittings via the full LKB model, where the threshold dose would be near 27 Gy. Bootstrapping analyses further confirmed the fitting outcomes. Strong correlation with the traditional NTCP models revealed that the cluster model could achieve what NTCP models attain and may offer additional information. Parameterization of the model indicated that the model could have different predictions from current clinical recommendations. Further investigation using toxicity data is under way to validate the cluster model.

The effectiveness of radiotherapy treatments depends on the accuracy of the dose delivery process. The majority of radiotherapy courses are delivered on linear accelerators with a Multi Leaf Collimator (MLC) in 3D conformal Radiation Therapy, Intensity Modulated Radiation Therapy (IMRT) or Volumetric Modulated Arc Therapy (VMAT) modes that require accurate MLC positioning. This study investigates the MLC calibration accuracy, following manufacturer procedures for an Elekta Synergy linac with the Agility head, against the radiation focal spot offset (alignment with the collimator axis of rotation). If the radiation focal spot is not aligned ideally with the collimator axis of rotation then a systematic error can be introduced into the calibration procedure affecting absolute MLC leaf positions. Calibration of diaphrams is equally affected; however they are not investigated here. The results indicate that an estimated 0.15 mm MLC uncertainty in all MLC leaves positions can be introduced due to uncertainty of the radiation focal spot position of 0.21 mm.

Total hip arthroplasty (THA) is the appropriate treatment for hip pain, dislocation, and dysfunction. THA refers to surgery to replace a hip implant, which is an effective way to recover normal hip function. The design of an implant imitates hip functions and allows bone growth in the implant area. However, it should be noted that the implant can dislocate after surgery. The main factor that should be considered during surgery is the correct position of the implant component. The acetabular cup of the hip implant should be positioned at [Formula: see text] anteversion and [Formula: see text] inclination. The evaluation of the implant inclination and anteversion during the operation decrease the risk of the implant dislocation after surgery. Developing a new innovative Force-PRO device can aid the doctor in evaluating the force on the surface of the acetabular liner and the angle of the acetabular liner during the hip implant operation. This device consists of two main sensors-force sensors and inertial measurement unit sensors. Furthermore, the 3D printings of an implant's parts should be specifically designed to integrate with these sensors. To develop the graphical user interface application, C[Formula: see text] should be the programming language of use. The graphical user interface application communicates between the device and user via a wireless communication system. CT-based imaging and force gauge measurement are the methods to evaluate the efficiency of this device. For this purpose, the sterile method is considered.

Size-specific dose estimate (SSDE) was proposed by the American Association of Physicists in Medicine (AAPM) Task Group 204 to consider the effect of patient size in the x-ray CT dose estimation. Size correction factors to calculate SSDE were derived based on the conventional weighted CT dose index (CTDI) equation. This study aims to investigate the influence of Bakalyar's and the authors' own CTDI equations on the size correction factors described by the AAPM Task Group 204, using Monte Carlo simulations. The simulations were performed by modeling four types of x-ray CT scanner designs, to compute the dose values in water for cylindrical phantoms with 8-40 cm diameters. CTDI method and the AAPM Task Group 111's proposed method were employed as the CT dosimetry models. Size correction factors were obtained for the computed dose values of various phantom diameters for the conventional, Bakalyar's, and the authors' weighting factors. Maximum difference between the size correction factors for the Bakalyar's weighting factor and those of the AAPM Task Group 204 was 27% for a phantom diameter of 11.2 cm. On the other hand, the size correction factors calculated for the authors' weighting factor were in good agreement with those from the AAPM Task Group 204 report with a maximum difference of 17%. The results indicate that the SSDE values obtained with the authors' weighting factor can be evaluated by using the size correction factors reported by the AAPM Task Group 204, which is currently accepted as a standard.

Posterior lumbar interbody fusion (PLIF) with a spacer and posterior instrument (PI) via minimally invasive surgery (MIS) restores intervertebral height in degenerated disks. To align with MIS, the spacer has to be shaped with a slim geometry. However, the thin spacer increases the subsidence and migration after PLIF. This study aimed to propose a new lumbar fusion approach using bone cement to achieve a larger supporting area than that achieved by the currently used poly aryl-ether-ether-ketone (PEEK) spacer and assess the feasibility of this approach using a sawbone model. Furthermore, the mechanical responses, including the range of motion (ROM) and bone stress with the bone cement spacer were compared to those noted with the PEEK spacer by finite element (FE) simulation. An FE lumbar L3-L4 model with PEEK and bone cement spacers and PI was developed. Four fixing conditions were considered: intact lumbar L3-L4 segment, lumbar L3-L4 segment with PI, PEEK spacer plus PI, and bone cement spacer plus PI. Four kinds of 10-NM moments (flexion, extension, lateral bending, and rotation) and two different bone qualities (normal and osteoporotic) were considered. The bone cement spacer yielded smaller ROMs in extension and rotation than the PEEK spacer, while the ROMs of the bone cement spacer in flexion and lateral bending were slightly greater than with the PEEK spacer. Compared with the PEEK spacer, peak contact pressure on the superior surface of L4 with the bone cement spacer in rotation decreased by 74% (from 8.68 to 2.25 MPa) and 69.1% (from 9.1 to 2.82 MPa), respectively, in the normal and osteoporotic bone. Use of bone cement as a spacer with PI is a potential approach to decrease the bone stress in lumbar fusion and warrants further research.

There have been increasing cases of people seeking treatment for neck and back pain. The most common cause of neck and back pain is due to long-term poor sitting posture. The most common poor sitting posture cases are humpback, and head and neck being too far forward. It is easy to cause neck and back pain and other symptoms. Therefore, the development of wearable posture monitoring system for dynamic assessment of sitting posture becomes both helpful and necessary. In addition to recording the wearer's posture when sitting with quantitative assessment, it is needed to execute real-time action feedback for correctness of posture, in order to reduce neck and back pain due to long-term poor sitting posture. This study completed an instant recording and dynamic assessment of position measurement and feedback system. The system consists of a number of dynamic measurement units that can describe the posture trajectory, which integrates three-axis gyro meter, three-axis accelerometer, and magnetometer in order to measure the dynamic tracking. In the reliability analysis experiment, angle measurement error is less than 2%. The correlation coefficient between correlation analysis and Motion Analysis (MA) is 0.97. It is shown that the motion trajectory of this system is highly correlated with MA. In the feasibility test of sitting position detection, it is possible to detect the sitting position from the basic action of the walking, standing, sitting and lying down, and the sensitivity reaches 95.84%. In the assessment of the sitting position, the information published by the Canadian Centre for Occupational Health and Safety was used, as well as the recommendations of professional physicians as a basis for evaluating the threshold of the sitting measurement parameters and immediately feedback to the subjects. The system developed in this study can be helpful to reduce neck and back pain due to long-term poor sitting posture.

The aim of this study was to improve reinforcement learning algorithm by combining artificial bee colony algorithm. The traditional method of reinforcement learning algorithm has a very low convergence rate due to random choices. An ant algorithm will help to make random choices in reinforcement learning more appropriate. This hybrid algorithm called the bee colony reinforcement (BCR) algorithm. The tip of the arm must reach a predetermined purpose by BCR algorithm. The results show that the BCR algorithm in the model has been able to reduce the time to reach the goal than the reinforcement learning algorithm (In average 12 steps faster). Also, the path for reaching the goal in the BCR algorithm was far more direct and shorter than the reinforcement learning algorithm. This method also detects the optimal path towards the goal.

The proliferation of digital radiography (DR) has led to a re-evaluation of exposure parameters and image quality. Currently, there is a move towards reducing X-ray tube voltage (kVp) in paediatric exposures down to 40 kVp to achieve better images. However, the effect on patient dose of these modifications is uncertain. The main aims of this phantom study were to evaluate the effect of reducing the kVp in paediatric limb DR exposures on contrast-to-noise ratio (CNR) and patient dose. For this purpose, Monte Carlo simulations of radiographic exposures on a paediatric limb phantom were performed. The phantom included muscle tissue and bone segments of five different densities in the range of 1.12 to 1.48 g/cm. The overall thickness of the phantom varied between 1 and 12 cm. Dependence of the CNR at constant limb phantom muscle and bone doses and dependence of the CNR per unit of muscle and bone dose at constant detector dose on radiographic exposure factors and limb thickness were calculated. X-ray tube current-time product (mAs) values required to achieve equal detector dose versus limb thickness for different kVp were calculated, as well as muscle and bone doses for the limb phantom of varying thickness. Present work has shown that reducing the kVp in paediatric radiography of the extremities can result in a significant increase in radiation dose, particularly for thicker limbs. Low kVp radiography requires justification for use on the extremities.

A computed tomography (CT) image is generally reconstructed by a filtered back projection (FBP) algorithm. In an FBP algorithm, the image quality primarily depends on a reconstruction filter kernel. Although the details of the filter kernel are not disclosed to users, the frequency response of the filter kernel can theoretically be calculated using the relational formula of the filter kernel and the modulation transfer function (MTF) of the reconstruction algorithm (MTF). In this study, we proposed a method to determine the frequency response of a filter kernel and verify its validity. Two clinical CT scanners were used to derive the filter kernel. The MTF was obtained and subsequently separated to the MTF of the scanner system and MTF. Using the relational formula of the filter kernel and MTF, we calculated the frequency response of the filter kernel. To verify the calculated result, we measured the noise power spectrum (NPS). Additionally, the filter kernel was calculated using the relational formula of the filter kernel and NPS. In both CT scanners, the filter kernels calculated by the two methods showed good agreement, and we confirmed the validity of the results and the effectiveness of the proposed method. Furthermore, the inherent image quality performance of the CT scanner could be clarified by the reconstruction filter kernel.

Chest X-rays are used for mass screening for the early detection of lung cancer. However, lung nodules are often overlooked because of bones overlapping the lung fields. Bone suppression techniques based on artificial intelligence have been developed to solve this problem. However, bone suppression accuracy needs improvement. In this study, we propose a convolutional neural filter (CNF) for bone suppression based on a convolutional neural network which is frequently used in the medical field and has excellent performance in image processing. CNF outputs a value for the bone component of the target pixel by inputting pixel values in the neighborhood of the target pixel. By processing all positions in the input image, a bone-extracted image is generated. Finally, bone-suppressed image is obtained by subtracting the bone-extracted image from the original chest X-ray image. Bone suppression was most accurate when using CNF with six convolutional layers, yielding bone suppression of 89.2%. In addition, abnormalities, if present, were effectively imaged by suppressing only bone components and maintaining soft-tissue. These results suggest that the chances of missing abnormalities may be reduced by using the proposed method. The proposed method is useful for bone suppression in chest X-ray images.

Arrhythmia is slow, fast or irregular heartbeat. Manual ECG assessment and disease classification is an error-prone task because of vast differences in ECG morphology and difficulty in accurate identifying ECG components. Moreover, proposing a computer-aided diagnosis system for heartbeat classification can be useful when access to medical care centers is difficult or impossible. Therefore, the main aim of this study is classifying ECG beats for arrhythmia detection (four beat classes are considered). Previous studies have proposed different methods based on traditional machine learning and/or deep learning. In this paper, a novel feature engineering method is proposed based on deep learning and K-NNs. The features extracted by our proposed method are classified with different classifiers such as decision trees, SVMs with different kernels and random forests. Our proposed method has reasonably good performance for beat classification and achieves the average Accuracy of 99.77%, AUC of 99.99%, Precision of 99.75% and Recall of 99.30% using fivefold Cross Validation strategy. The main advantage of the proposed method is its low computational time compared to training deep learning models from scratch and its high accuracy compared to the traditional machine learning models. The strength and suitability of the proposed method for feature extraction is shown by the high balance between sensitivity and specificity.

Recently, developing an accurate automatic emotion recognition system using a minimum number of bio-signals has become a challenging issue in "affective computing." This study aimed to propose a reliable system by examining nonlinear dynamics of photoplethysmogram (PPG) and galvanic skin response (GSR). To address this goal, two strategies were adopted. First, the efficiency of each signal in valence/arousal based emotion categorization was examined. Then, the proficiency of a hybrid feature, by combining both GSR and PPG features was studied. Lyapunov exponents, lagged Poincare's measures, and approximate entropy were extracted to characterize the irregularity and chaotic behavior of the phase space. To discriminate two levels of arousal and two levels of the valence, a probabilistic neural network (PNN) with different sigma adjustment parameter was examined. The results showed that the phase space geometry and consequently, the signal dynamics are influenced by the emotional music video. Additionally, distinctive patterns of the phase space behavior were observed under the influence of different lags. For both signals, the most irregularity was observed during the high valence, and the least irregularity was seen during the low valence. Consequently, signals' irregularity is affected by the valence dimension. The results showed that the fusion has more potential for emotion recognition than that of using each signal separately. For sigma = 0.1, the highest recognition rate was 100% in a subject-dependent mode. In a subject-independent mode, the maximum accuracies of 88.57 and 86.8% were obtained for arousal and valence dimensions, respectively.