Physical and Engineering Sciences in Medicine

Ultrasound guidance has become the gold standard for obtaining vascular access. Angle information, which indicates the entry angle of the needle into the vein, is required to ensure puncture success. Although various image processing-based methods, such as deep learning, have recently been applied to improve needle visibility, these methods have limitations, in that the puncture angle to the target organ is not measured. We aim to detect the target vessel and puncture needle and to derive the puncture angle by combining deep learning and conventional image processing methods such as the Hough transform. Median cubital vein US images were obtained from 20 healthy volunteers, and images of simulated blood vessels and needles were obtained during the puncture of a simulated blood vessel in four phantoms. The U-Net architecture was used to segment images of blood vessels and needles, and various image processing methods were employed to automatically measure angles. The experimental results indicated that the mean dice coefficients of median cubital veins, simulated blood vessels, and needles were 0.826, 0.931, and 0.773, respectively. The quantitative results of angular measurement showed good agreement between the expert and automatic measurements of the puncture angle with 0.847 correlations. Our findings indicate that the proposed method achieves extremely high segmentation accuracy and automated angular measurements. The proposed method reduces the variability and time required in manual angle measurements and presents the possibility where the operator can concentrate on delicate techniques related to the direction of the needle.

The purpose of this study was to evaluate the effect of single-energy metal artifact reduction (SEMAR) for metal artifacts using CT images reconstructed with adaptive iterative dose reduction three dimensional (AIDR3D) and advanced intelligent clear-IQ engine (AiCE) in calibration-field of view of various sizes. A prosthetic hip joint was arranged at the center of the phantom. The phantom images were scanned by changing calibration-field of view of 320 mm and 500 mm, and were reconstructed using filtered back-projection (FBP), AIDR3D, and AiCE with and without SEMAR, respectively. The metal artifact reduction with SEMAR was evaluated by calculated the relative artifact index value and visual scores in degree of artifact by seven radiology technologists. Relative artifact index of FBP, AIDR3D, and AiCE with 320 mm/500 mm calibration-field of views were 10.2/10.0, 16.3/16.4, and 17.8/17.9 without SEMAR, 3.3/3.1, 2.6/2.5, and 2.3/2.0 with SEMAR, respectively. Visual scores were not significantly different between 320 and 500 mm calibration-field of views in all reconstruction methods. The effect of metal artifact reduction was not affected by calibration-field of view sizes in the SEMAR combined with AIDR3D or AiCE.

In this study, we aim to clarify the dosimetric characteristics of a real time variable shape rubber containing tungsten (STR) as a thin bolus in 6-MV photon radiotherapy. The percentage depth doses (PDDs) and lateral dose profiles (irradiation field = 10 × 10  cm2) in the water-equivalent phantom were measured and compared between no bolus, a commercial 5-mm gel bolus, and 0.5-, 1-, 2-, and 3-mm STR boluses. The characteristics of the PDDs were evaluated according to relative doses at 1 mm depth (D1mm) and depth of maximum dose (dmax). To determine the distance of the shift caused by the STR bolus, the PDD value at a depth of 100 mm without a bolus was obtained. For each STR thickness, the difference between the depth corresponding to this PDD value and 100 mm was calculated. The penumbra size and width of the 50% dose were evaluated using lateral dose profiles. The D1mm with no bolus, 5-mm gel bolus, and 0.5-, 1-, 2-, and 3-mm STR boluses were 47.6%, 91.5%, 78.2%, 86.6%, 89.3%, and 89.4%, respectively, and the respective dmax values were 15, 10, 13, 12, 11, and 10 mm. The shifting distance of the 0.5-, 1-, 2-, and 3-mm STR boluses were 2.7, 4.4, 4.8, and 4.9 mm, respectively. There were no differences for those in lateral dose profiles. The 1-mm-thick STR thin bolus shifted the depth dose profile by 4.4 mm and could be used as a customized bolus for photon radiotherapy.

Preterm birth anticipation is a crucial task that can reduce both the rate and the complications of preterm birth. Electrohysterogram (EHG) or uterine electromyogram (EMG) data have shown that they can provide useful information for preterm birth anticipation. Four distinct time-domain features (mean absolute value, average amplitude change, difference in absolute standard deviation value, and log detector) that are commonly applied to EMG signal processing were utilized and investigated in this study. A single channel of EHG data was decomposed into its constituent components (i.e., into intrinsic mode functions) by using empirical mode decomposition (EMD) before their time-domain features were extracted. The time-domain features of the intrinsic mode functions of the EHG data associated with preterm and term births were applied for preterm-term birth classification by using a support vector machine with a radial basis function. The preterm-term birth classifications were validated by using 10-fold cross validation. From the computational results, it was shown that excellent preterm-term birth classification can be achieved by using single-channel EHG data. The computational results further suggested that the best overall performance concerning preterm-term birth classification was obtained when thirteen (out of sixteen) EMD-based time-domain features were applied. The best accuracy, sensitivity, specificity, and $$F_1$$ -score achieved were 0.9382, 0.9130, 0.9634, and 0.9366, respectively.

Modern human populations are exposed to anthropogenic sources of radiofrequency-electromagnetic fields (RF-EMFs), primarily to telecommunication and broadcasting technologies. As a result, ongoing concerns from some members of the public have arisen regarding potential health effects following RF-EMF exposures. In order to monitor human RF-EMF exposures and investigate potential health effects, an objective assessment of RF-EMF exposures is necessary. Accurate dosimetry is essential for any investigation of potential associations between RF-EMF exposure and health effects in human populations. This review updates state-of-the-art knowledge of currently available RF-EMF exposure assessment tools applicable in human epidemiological studies. These tools cater for assessing RF-EMF exposures in human environments; through mobile phone-based tools or other standalone tools. RF-EMF exposure assessment has been significantly improved through the application of some of these tools in recent years.

This paper proposes a novel and compact monopole microstrip antenna design with a three-dimensional (3D) printed curved substrate for biomedical applications. A curved substrate was formed by inserting a semi-cylinder structure in the middle of the planar substrate consisting of polylactic acid. The antenna was fed with a microstrip line, and a partial ground plane was formed at the bottom side of the substrate. The copper plane with two triangular slots is arranged on the curved semi-cylinder structure of the substrate. The physical dimensions of the radiating plane and ground plane were optimally determined with the use of the sparrow search algorithm to provide a wide—10 dB bandwidth between 3 and 12 GHz. A total of six microstrip antennas having different parameters related to physical dimensions were designed and simulated to compare the performance of the proposed antenna with the help of full-wave electromagnetic simulation software called CST Microwave Studio. The proposed curved antenna was fabricated, and a PNA network analyzer was used to measure the S11 of the proposed antenna. It was demonstrated that the measured S11 covers the desired frequency range.

Schizophrenia is a severe mental illness which can cause lifelong disability. Most recent studies on the Electroencephalogram (EEG)-based diagnosis of schizophrenia rely on bespoke/hand-crafted feature extraction techniques. Traditional manual feature extraction methods are time-consuming, imprecise, and have a limited ability to balance accuracy and efficiency. Addressing this issue, this study introduces a deep residual network (deep ResNet) based feature extraction design that can automatically extract representative features from EEG signal data for identifying schizophrenia. This proposed method consists of three stages: signal pre-processing by average filtering method, extraction of hidden patterns of EEG signals by deep ResNet, and classification of schizophrenia by softmax layer. To assess the performance of the obtained deep features, ResNet softmax classifier and also several machine learning (ML) techniques are applied on the same feature set. The experimental results for a Kaggle schizophrenia EEG dataset show that the deep features with support vector machine classifier could achieve the highest performances (99.23% accuracy) compared to the ResNet classifier. Furthermore, the proposed model performs better than the existing approaches. The findings suggest that our proposed strategy has capability to discover important biomarkers for automatic diagnosis of schizophrenia from EEG, which will aid in the development of a computer assisted diagnostic system by specialists.