Physical and Engineering Sciences in Medicine最新文献

Positron emission tomography (PET) using $_{}^{18}$ F-FDG is a well-known modality for the diagnosis of various diseases in patients of different ages, sexes, and states of health, which implies that internal radiation dosimetry is highly desired for different phantom anatomies. In this study, we validate "DoseCalcs," a new Monte Carlo platform that combines personalized internal dosimetry calculations with Monte Carlo simulations. To achieve that, we used the specific absorbed fraction (SAF) calculated by DoseCalcs and those from ICRP publication 133 to estimate the absorbed dose per injected activity (AD/IA) and effective dose per injected activity (ED/IA) for $_{}^{18}$ F-FDG. The investigation focused on various voxelized phantoms representing different age groups, including adult male and female, and pediatric phantoms of various ages, from newborn to 15 years old. Using the DoseCalcs Monte Carlo platform, we have simulated the emission of $_{}^{18}$ F-FDG positrons based on the energy spectrum provided in ICRP publication 107. The results demonstrated the impact of anatomical differences and different organ/tissue compositions on radiation absorption, with significant variations in the AD/IA across different phantoms. Interestingly, organs/tissues near the emission source showed higher AD/IA, highlighting the anatomical dependence on the phantom. When our results were compared to established reference data, especially from ICRP128, most organs/tissues had good agreement. Still, some cases have shown differences. This shows how important it is to use accurate radionuclide data and biokinetic modeling in internal dosimetry calculations. Furthermore, we compared AD/IA and ED/IA values calculated in newborns by DoseCalcs with those derived from alternative codes, MCNP and EGSnrc. While the results generally exhibited consistency, subtle variations underscored the influence of biokinetics modeling choices and computational methodologies. Overall, this research contributes valuable insights into the precision of internal dosimetry calculations using "DoseCalcs-Gui" by providing one platform for Monte Carlo simulation and personalized internal dosimetry in nuclear medicine. The DoseCalcs platform is free for research and available for download at www.github.com/TarikEl/DoseCalcs-Gui .

Since its introduction in 2015, the U-Net architecture used in Deep Learning has played a crucial role in medical imaging. Recognized for its ability to accurately discriminate small structures, the U-Net has received more than 2600 citations in academic literature, which motivated continuous enhancements to its architecture. In hospitals, chest radiography is the primary diagnostic method for pulmonary disorders, however, accurate lung segmentation in chest X-ray images remains a challenging task, primarily due to the significant variations in lung shapes and the presence of intense opacities caused by various diseases. This article introduces a new approach for the segmentation of lung X-ray images. Traditional max-pooling operations, commonly employed in conventional U-Net++ models, were replaced with the discrete wavelet transform (DWT), offering a more accurate down-sampling technique that potentially captures detailed features of lung structures. Additionally, we used attention gate (AG) mechanisms that enable the model to focus on specific regions in the input image, which improves the accuracy of the segmentation process. When compared with current techniques like Atrous Convolutions, Improved FCN, Improved SegNet, U-Net, and U-Net++, our method (U-Net++-DWT) showed remarkable efficacy, particularly on the Japanese Society of Radiological Technology dataset, achieving an accuracy of 99.1%, specificity of 98.9%, sensitivity of 97.8%, Dice Coefficient of 97.2%, and Jaccard Index of 96.3%. Its performance on the Montgomery County dataset further demonstrated its consistent effectiveness. Moreover, when applied to additional datasets of Chest X-ray Masks and Labels and COVID-19, our method maintained high performance levels, achieving up to 99.3% accuracy, thereby underscoring its adaptability and potential for broad applications in medical imaging diagnostics.

Our lab has been developing a deuterium-deuterium (DD) neutron generator-based neutron activation analysis (NAA) system to quantify metals and elements in the human body in vivo. The system has been used to quantify metals such as manganese, aluminum, sodium in bones of a living human. The technology provides a useful way to assess metal exposure and to estimate elemental deposition, storage and biokinetics. It has great potential to be applied in the occupational and environmental health fields to study the association of metal exposure and various health outcomes, as well as in the nutrition field to study the intake of essential elements and human health. However, the relatively low sensitivity of the system has greatly limited its applications. Neutron moderation plays an important role in designing an IVNAA facility, as it affects thermal neutron flux in irradiation cave and radiation exposure to the human subject. This study aims to develop a novel thermal neutron enhancement method to improve the sensitivity of the in vivo neutron activation analysis (IVNAA) system for elemental measurement but still maintain radiation dose. Utilizing a compact DD neutron source, we propose a new and practical moderator design that combines high density polyethylene with heavy water to enhance thermal neutrons by reducing thermal neutron absorption. All material dimensions are calculated by PHITS, a general-purpose Monte Carlo simulation program. The improvement of the new design predicted by the Monte Carlo simulation for the quantification of one of the elements, manganese was verified by experimental irradiation of manganese-doped bone equivalent phantoms. For the same radiation dose, a 67.9% thermal neutron flux enhancement is reached. With only 4.2% increase of radiation dose, the simulated thermal neutron flux and activation can be further increased by 84.2%. A 100% thermal neutron enhancement ratio is also achievable with a 20% dose increase. The experimental results clearly show higher manganese activation gamma ray counts for each specific phantom, with a significantly reduced minimum detection limit. Additionally, the photon dose was suppressed. The thermal neutron enhancement method can increase the number of useful neutrons significantly but maintain the radiation dose. This greatly decreased the detection limit of the system for elemental quantification at an acceptable dose, which will broadly expand the application of the technology in research and clinical use. The method can also be applied to other neutron medical applications, including neutron imaging and radiotherapy.

Simulating the outcome of double eyelid surgery is a challenging task. Many existing approaches rely on complex and time-consuming 3D digital models to reconstruct facial features for simulating facial plastic surgery outcomes. Some recent research performed a simple affine transformation approach based on 2D images to simulate double eyelid surgery outcomes. However, these methods have faced challenges, such as generating unnatural simulation outcomes and requiring manual removal of masks from images. To address these issues, we have pioneered the use of an unsupervised generative model to generate post-operative double eyelid images. Firstly, we created a dataset involving pre- and post-operative 2D images of double eyelid surgery. Secondly, we proposed a novel attention-class activation map module, which was embedded in a generative adversarial model to facilitate translating a single eyelid image to a double eyelid image. This innovative module enables the generator to selectively focus on the eyelid region that differentiates between the source and target domain, while enhancing the discriminator's ability to discern differences between real and generated images. Finally, we have adjusted the adversarial consistency loss to guide the generator in preserving essential features from the source image and eliminating any masks when generating the double eyelid image. Experimental results have demonstrated the superiority of our approach over existing state-of-the-art techniques.

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.