Physical and Engineering Sciences in Medicine最新文献

Accurate segmentation of ovarian cancer (OC) lesions in PET/CT images is essential for effective disease management, yet manual segmentation for radiomics analysis is labor-intensive and time-consuming. This study introduces the application of a 3D U-Net deep learning model, leveraging advanced 3D networks, for multi-class semantic segmentation of OC in PET/CT images and assesses the stability of the extracted radiomics features. Utilizing a dataset of 3120 PET/CT images from 39 OC patients, the dataset was divided into training (70%), validation (15%), and test (15%) subsets to optimize and evaluate the model's performance. The 3D U-Net model, especially with a VGG16 backbone, achieved notable segmentation accuracy with a Dice score of 0.74, Precision of 0.76, and Recall of 0.78. Additionally, the study demonstrated high stability in radiomics features, with over 85% of PET and 84% of CT image features showing high intraclass correlation coefficients (ICCs > 0.8). These results underscore the potential of automated 3D U-Net-based segmentation to significantly enhance OC diagnosis and treatment planning. The reliability of the extracted radiomics features from automated segmentation supports its application in clinical decision-making and personalized medicine. This research marks a significant advancement in oncology diagnostics, providing a robust and efficient method for segmenting OC lesions in PET/CT images. By addressing the challenges of manual segmentation and demonstrating the effectiveness of 3D networks, this study contributes to the growing body of evidence supporting the application of artificial intelligence in improving diagnostic accuracy and patient outcomes in oncology.

Breathing conditions affect a wide range of people, including those with respiratory issues like asthma and sleep apnea. Smartwatches with photoplethysmogram (PPG) sensors can monitor breathing. However, current methods have limitations due to manual parameter tuning and pre-defined features. To address this challenge, we propose the PPG2RespNet deep-learning framework. It draws inspiration from the UNet and UNet + + models. It uses three publicly available PPG datasets (VORTAL, BIDMC, Capnobase) to autonomously and efficiently extract respiratory signals. The datasets contain PPG data from different groups, such as intensive care unit patients, pediatric patients, and healthy subjects. Unlike conventional U-Net architectures, PPG2RespNet introduces layered skip connections, establishing hierarchical and dense connections for robust signal extraction. The bottleneck layer of the model is also modified to enhance the extraction of latent features. To evaluate PPG2RespNet’s performance, we assessed its ability to reconstruct respiratory signals and estimate respiration rates. The model outperformed other models in signal-to-signal synthesis, achieving exceptional Pearson correlation coefficients (PCCs) with ground truth respiratory signals: 0.94 for BIDMC, 0.95 for VORTAL, and 0.96 for Capnobase. With mean absolute errors (MAE) of 0.69, 0.58, and 0.11 for the respective datasets, the model exhibited remarkable precision in estimating respiration rates. We used regression and Bland-Altman plots to analyze the predictions of the model in comparison to the ground truth. PPG2RespNet can thus obtain high-quality respiratory signals non-invasively, making it a valuable tool for calculating respiration rates.

Radiochromic film, evaluated with flatbed scanners, is used for practical radiotherapy QA dosimetry. Film and scanner component effects contribute to the Lateral Response Artefact (LRA), which is further enhanced by light polarisation from both. This study investigates the scanner bed’s contribution to LRA and also polarisation from the mirrors for widely used EPSON scanners, as part of broader investigations of this dosimetry method aiming to improve processes and uncertainties. Alternative scanner bed materials were compared on a modified EPSON V700 scanner. Polarisation effects were investigated for complete scanners (V700, V800, on- and off-axis, and V850 on-axis), for a removed V700 mirror system, and independently using retail-quality single mirror combinations simulating practical scanner arrangements, but with varying numbers (0–5) and angles. Some tests had no film present, whilst others included films (EBT3) irradiated to 6 MV doses of 0–11.3 Gy. For polarisation analysis, images were captured by a Canon 7D camera with 50 mm focal length lens. Different scanner bed materials showed only small effects, within a few percent, indicating that the normal glass bed is a good choice. Polarisation varied with scanner type (7–11%), increasing at 10 cm lateral off-axis distance by around a further 6%, and also with film dose. The V700 mirror system showed around 2% difference to the complete scanner. Polarization increased with number of mirrors in the single mirror combinations, to 14% for 4 and 5 mirrors, but specific values depend on angles and mirror quality. Novel film measurement methods could reduce LRA effect corrections and associated uncertainties.

This study introduces a novel watermarking technique for electrocardiogram (ECG) signals. Watermarking embeds critical information within the ECG signal, enabling data origin authentication, ownership verification, and ensuring the integrity of research data in domains like telemedicine, medical databases, insurance, and legal proceedings. Drawing inspiration from image watermarking, the proposed method transforms the ECG signal into a two-dimensional format for QR decomposition. The watermark is then embedded within the first row of the resulting R matrix. Three implementation scenarios are proposed: one in the spatial domain and two in the transform domain utilizing discrete wavelet transform (DWT) for improved watermark imperceptibility. Evaluation on real ECG signals from MIT-BIH Arrhythmia database and comparison to existing methods demonstrate that the proposed method achieves: (1) higher Peak Signal-to-Noise Ratio (PSNR) indicating minimal alterations to the watermarked signal, (2) lower bit error rates (BER) in robustness tests against external modifications such as AWGN noise (additive white Gaussian noise), line noise and down-sampling, and (3) lower computational complexity. These findings emphasize the effectiveness of the proposed QR decomposition-based watermarking method, achieving a balance between robustness and imperceptibility. The proposed approach has the potential to improve the security and authenticity of ECG data in healthcare and legal contexts, while its lower computational complexity enhances its practical applicability.

In this paper, we propose a new deep learning method based on Quaternion Wavelet Transform (QWT) phases of 2D echocardiographic sequences to estimate the motion and strain of myocardium. The proposed method considers intensity and phases gained from QWT as the inputs of customized PWC-Net structure, a high-performance deep network in motion estimation. We have trained and tested our proposed method performance using two realistic simulated B-mode echocardiographic sequences. We have evaluated our proposed method in terms of both geometrical and clinical indices. Our method achieved an average endpoint error of 0.06 mm per frame and 0.59 mm between End Diastole and End Systole on a simulated dataset. Correlation analysis between ground truth and the computed strain shows a correlation coefficient of 0.89, much better than the most efficient methods in the state-of-the-art 2D echocardiography motion estimation. The results show the superiority of our proposed method in both geometrical and clinical indices.

Theory and modelling suggest that detection of neuronal activity may be feasible using phase sensitive MRI methods. Successful detection of neuronal activity both in vitro and in vivo has been described while others have reported negative results. Magnetic resonance electrical properties tomography may be a route by which signal changes can be identified. Here, we report successful and repeatable detection at 3 Tesla of human brain activation in response to visual and somatosensory stimuli using a functional version of tissue conductivity imaging (funCI). This detects activation in both white and grey matter with apparent tissue conductivity changes of 0.1 S/m (17–20%, depending on the tissue baseline conductivity measure) allowing visualization of complete system circuitry. The degree of activation scales with the degree of the stimulus (duration or contrast). The conductivity response functions show a distinct timecourse from that of traditional fMRI haemodynamic (BOLD or Blood Oxygenation Level Dependent) response functions, peaking within milliseconds of stimulus cessation and returning to baseline within 3–4 s. We demonstrate the utility of the funCI approach by showing robust activation of the lateral somatosensory circuitry on stimulation of an index finger, on stimulation of a big toe or of noxious (heat) stimulation of the face as well as activation of visual circuitry on visual stimulation in up to five different individuals. The sensitivity and repeatability of this approach provides further evidence that magnetic resonance imaging approaches can detect brain activation beyond changes in blood supply.

In recent years, eye lens exposure among radiation workers has become a serious concern in medical X-ray fluoroscopy and interventional radiology (IVR), highlighting the need for radiation protection education and training. This study presents a method that can maintain high accuracy when calculating spatial dose distributions obtained via Monte Carlo simulation and establishes another method to three-dimensionally visualize radiation using the obtained calculation results for contributing to effective radiation-protection education in X-ray fluoroscopy and IVR. The Monte Carlo particle and heavy ion transport code system (PHITS, Ver. 3.24) was used for calculating the spatial dose distribution generated by an angiography device. We determined the peak X-ray tube voltage and half value layer using Raysafe X2 to define the X-ray spectrum from the source and calculated the X-ray spectrum from the measured results using an approximation formula developed by Tucker et al. Further, we performed measurements using the "jungle-gym" method under the same conditions as the Monte Carlo calculations for verifying the accuracy of the latter. An optically stimulated luminescence dosimeter (nanoDot dosimeter) was used as the measuring instrument. In addition, we attempted to visualize radiation using ParaView (version 5.12.0-RC2) using the spatial dose distribution confirmed by the above calculations. A comparison of the measured and Monte Carlo calculated spatial dose distributions revealed that some areas showed large errors (12.3 and 24.2%) between the two values. These errors could be attributed to the scattering and absorption of X-rays caused by the jungle gym method, which led to uncertain measurements, and (2) the angular and energy dependencies of the nanoDot dosimetry. These two causes explain the errors in the actual values, and thus, the Monte Carlo calculations proposed in this study can be considered to have high-quality X-ray spectra and high accuracy. We successfully visualized the three-dimensional spatial dose distribution for direct and scattered X-rays separately using the obtained spatial dose distribution. We established a method to verify the accuracy of Monte Carlo calculations performed through the procedures considered in this study. Various three-dimensional spatial dose distributions were obtained with assured accuracy by applying the Monte Carlo calculation (e.g., changing the irradiation angle and adding a protective plate). Effective radiation-protection education can be realized by combining the present method with highly reliable software to visualize dose distributions.

A novel phantom for measuring the 10% and 50% values of the modulation transfer function (MTF) for computed tomography scanners (CT) was investigated. The phantom was constructed by drilling rows of holes of different sizes and frequencies into a small block of polymethyl methacrylate (PMMA). The MTF at a given frequency was determined from the ratio of the range of Hounsfield units within the rows of holes at different frequencies, and the difference in Hounsfield units between air and PMMA. A MTF curve was plotted from measurements at different frequencies and the 10% and 50% MTF values were obtained from a cubic spline interpolation. The MTF results obtained with the drilled hole phantom method were compared to a conventional method - using a thin wire and Spice-CT ImageJ Plugin- and with identical acquisition and reconstruction parameters. The drilled hole phantom measured the 50% MTF with reasonable accuracy but underestimated the 10% MTF by 8.2% on average. MTF measurements were reproducible for repeated image acquisitions and with different users analysing the images, and the phantom was able to accurately measure the change in MTF when measured on images using different reconstruction kernels. The tool may find application as a cheap, easy to use method for routine QC testing of CT scanners.

To develop and assess an automated Sub-arc Collimator Angle Optimization (SACAO) algorithm and Cumulative Blocking Index Ratio (CBIR) metrics for single-isocenter coplanar volumetric modulated arc therapy (VMAT) to treat multiple brain metastases. This study included 31 patients with multiple brain metastases, each having 2 to 8 targets. Initially, for each control point, the MLC blocking index was calculated at different collimator angles, resulting in a two-dimensional heatmap. Optimal sub-arc segmentation and collimator angle optimization were achieved using an interval dynamic programming algorithm. Subsequently, VMAT plans were designed using two approaches: SACAO and the conventional Full-Arc Fixed Collimator Angle. CBIR was calculated as the ratio of the cumulative blocking index between the two plan approaches. Finally, dosimetric and planning parameters of both plans were compared. Normal brain tissue, brainstem, and eyes received better protection in the SACAO group (P < 0.05).Query Notable reductions in the SACAO group included 11.47% in gradient index (GI), 15.03% in monitor units (MU), 15.73% in mean control point Jaw area (A_Jaw,mean), and 19.14% in mean control point Jaw-X width (W_Jaw-X,mean), all statistically significant (P < 0.001). Furthermore, CBIR showed a strong negative correlation with the degree of plan improvement. The SACAO method enhanced protection of normal organs while improving transmission efficiency and optimization performance of VMAT. In particular, the CBIR metrics show promise in quantifying the differences specifically in the 'island blocking problem' between SACAO and conventional VMAT, and in guiding the enhanced application of the SACAO algorithm.