Biomedical Physics & Engineering Express最新文献

Objective. To accurately model and validate the 6 MV Elekta Compact linear accelerator using the Geant4 Application for Tomographic Emission (GATE). In particular, this study focuses on the precise calibration and validation of critical parameters, including jaw collimator positioning, electron source nominal energy, flattening filter geometry, and electron source spot size, which are often not provided in technical documentation.Methods. Simulation of the Elekta Compact^TM6 MV linear accelerator was performed using the Geant4 Application for Tomographic Emission (GATE) v.9.1. A 50 cm × 50 cm × 50 cm water phantom was irradiated with a source-to-surface distance of 100 cm. Percentage Depth Dose Profile (PDD) and Lateral Dose Profile (Crossplane and Inplane) were assessed as reference dose measurements. The half-length field difference (FHLD) method was introduced to optimize the jaw collimator setup. Gamma index analysis was used to quantitatively assess the accuracy of the simulated dosimetry data in relation to the actual dose measurements.Results. Crucial parameters of the Linac Head have been successfully optimized. The validation achieved Gamma-Index acceptance rates of 97.93% for the Depth Dose profile, 100% for the Crossplane (X) Dose Profile, and 93.98% for the Inplane (Y) Dose Profile, all meeting the 1%/1 mm Gamma-Index criteria.Conclusion. The simulation and calibration of the Elekta Compact Linac have achieved a reliable model with high fidelity in dosimetry calculations which could pave the way for the future development and application of new techniques using Elekta Compact^TMLinear Accelerator.

Deep learning has emerged as a powerful tool in medical imaging, particularly for corneal topographic map classification. However, the scarcity of labeled data poses a significant challenge to achieving robust performance. This study investigates the impact of various data augmentation strategies on enhancing the performance of a customized convolutional neural network model for corneal topographic map classification. We propose a hybrid data augmentation approach that combines traditional transformations, generative adversarial networks, and specific generative models. Experimental results demonstrate that the hybrid data augmentation method, achieves the highest accuracy of 99.54%, significantly outperforming individual data augmentation techniques. This hybrid approach not only improves model accuracy but also mitigates overfitting issues, making it a promising solution for medical image classification tasks with limited data availability.

Medical imaging is pivotal in early disease diagnosis, providing essential insights that enable timely and accurate detection of health anomalies. Traditional imaging techniques, such as Magnetic Resonance Imaging (MRI), Computer Tomography (CT), ultrasound, and Positron Emission Tomography (PET), offer vital insights into three-dimensional structures but frequently fall short of delivering a comprehensive and detailed anatomical analysis, capturing only amplitude details. Three-dimensional holography microscopic medical imaging provides a promising solution by capturing the amplitude (brightness) and phase (structural information) details of biological structures. In this study, we investigate the novel collaborative potential of Deep Learning (DL) and holography microscopic phase imaging for cancer diagnosis. The study comprehensively examines existing literature, analyzes advancements, identifies research gaps, and proposes future research directions in cancer diagnosis through the integrated Quantitative Phase Imaging (QPI) and DL methodology. This novel approach addresses a critical limitation of traditional imaging by capturing detailed structural information, paving the way for more accurate diagnostics. The proposed approach comprises tissue sample collection, holographic image scanning, preprocessing in case of imbalanced datasets, and training on annotated datasets using DL architectures like U-Net and Vision Transformer(ViT's). Furthermore, sophisticated concepts in DL, like the incorporation of Explainable AI (XAI) techniques, are suggested for comprehensive disease diagnosis and identification. The study thoroughly investigates the advantages of integrating holography imaging and DL for precise cancer diagnosis. Additionally, meticulous insights are presented by identifying the challenges associated with this integration methodology.

Biological cells, characterized by complex and dynamic structures, demand precise models for comprehensive understanding, especially when subjected to external factors such as electric fields (EF) for manipulation or treatment. This interaction is integral to technologies like pulsed electric fields (PEF), inducing reversible and irreversible structural variations. Our study explores both simplified and sophisticated equivalent circuit models for biological cells under the influence of an external EF, covering diverse cell structures from single- to double-shell configurations. The paper highlights challenges in circuit modeling, specifically addressing the incorporation of reversible or irreversible pores in the membrane during external EF interactions, emphasizing the need for further research to refine technical aspects in this field. Additionally, we review a comparative analysis of the performance and applicability of the proposed circuit models, providing insights into their strengths and limitations. This contributes to a deeper insight of the complexities associated with modeling biological cells under external EF influences, paving the way for enhanced applications in medical and technological domains in future.

Multiple sclerosis (MS) is a disorder in which the body's immune system attacks structures of the central nervous system, resulting in lesions that can occur throughout the brain and spinal cord. Cortical lesions, in particular, can contribute to motor dysfunction. Walking disability is reported as the main impairment by people with MS (pwMS), often due to limited ankle movement. This study explored the event-related desynchronization (ERD) onset latency of the sensorimotor rhythms during foot dorsiflexion in pwMS computed using an objective and independent of human criterion method, as an electroencephalogram (EEG) based biomarker. EEG signals were recorded in eight persons with neither neurological condition nor motor dysfunction and eight pwMS with relapsing-remitting, primary progressive or secondary progressive MS. Recordings were divided into three groups: control, more affected lower limb and less affected lower limb. The ERD-onset latency was determined using a method based on the percent of ERD time course and the cluster depth tests. The median and interquartile range of the ERD-onset latency were 1186.0 (1100.0, 1250.0) ms; 1064.0 (1031.0, 1127.0) ms for the more and less affected groups respectively, whereas the median and interquartile range for the control group was 656.0 (472.2, 950.0) ms. There was a significant delay in the ERD-onset latencies of the pwMS groups compared to the control group (p<0.001 for both comparisons). These findings suggest that the ERD-onset latency computed using the proposed method could be used as an EEG biomarker to evaluate disease progression or therapeutic interventions in pwMS.

This article proposes a novel biosensor based on a five-semi-circular cladding tube hollow core antiresonant fiber (HC-ARF) with a frequency range of 0.5-2.8 THz, using Zeonex as the background material. The HC-ARF biosensor analyses various blood components, namely water, plasma, white blood cells (WBC), hemoglobin (HB), and red blood cells (RBC). We utilized COMSOL Multiphysics to perform the numerical analysis of the sensor model. For water, plasma, WBC, HB, and RBC, the proposed HC-ARF biosensor exhibits the highest sensitivity levels of 99.50%, 99.58%, 99.43%, 99.58%, and 99.46%, respectively. Furthermore, it demonstrates confinement loss (CL) and effective material loss (EML) of 1.3  × 10^-3dBcm^-1and 5.3  × 10^-5dBcm^-1, respectively.

Conventional x-ray radiography relies on attenuation differences in the object, which often results in poor contrast in soft tissues. X-ray phase imaging has the potential to produce higher contrast but can be difficult to utilize. Instead of grating-based techniques, analyzer-based imaging, also known as diffraction enhanced imaging (DEI), uses a monochromator crystal with an analyzer crystal after the object. Analyzer-based systems most commonly employ synchrotron sources to provide adequate intensity, and typically use higher photon energies. In this work, a simulation has been devised to assess the potential for a polycapillary-based system. A polycapillary collimating optic has previously been shown to greatly enhance the intensity of the beam diffracted from the monochromatizing crystal. Detailed simulation of the optic is computationally intensive and requires comprehensive knowledge of the internal shape of the optic, so a simple geometric model using easier to obtain optic output data was developed and compared to the more detailed simulation. After verification, refraction band visibility was used as a quality parameter to address the effectiveness of the polycapillary-based DEI system at x-ray photon energies of 8 and 17.5 keV. The result shows promise for a polycapillary-coupled analyzer-based system even at low x-ray photon energy.

Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease (AD) and can serve as an important indicator of disease progression. However, many existing methods focus mainly on the image when processing brain imaging data, ignoring other non-imaging data (e.g., genetic, clinical information, etc.) that may have underlying disease information. In addition, imaging data acquired from different devices may exhibit varying degrees of heterogeneity, potentially resulting in numerous noisy connections during network construction. To address these challenges, this study proposes a Multimodal Multiview Bilinear Graph Convolution (MMBGCN) framework for disease risk prediction. Firstly, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) features are extracted from magnetic resonance imaging (MRI), and non-imaging information is combined with the features extracted from MRI to construct a multimodal shared adjacency matrix. The shared adjacency matrix is then used to construct the multiview network so that the effect of potential disease information in the non-imaging information on the model can be considered. Finally, the MRI features extracted by the multiview network are weighted to reduce noise, and then the spatial pattern is restored by bilinear convolution. The features of the recovered spatial patterns are then combined with the genetic information for disease prediction. The proposed method is tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Extensive experiments demonstrate the superior performance of the proposed framework and its ability to outperform other related algorithms. The average classification accuracy in the binary classification task in this study is 89.6%. The experimental results demonstrate that the method proposed in this study facilitates research on MCI diagnosis using multimodal data.

The optimal method for three-dimensional thermal imaging within cells involves collecting intracellular temperature responses while simultaneously obtaining corresponding 3D positional information. Current temperature measurement techniques based on the photothermal properties of quantum dots face several limitations, including high cytotoxicity and low fluorescence quantum yields. These issues affect the normal metabolic processes of tumor cells. This study synthesizes a low-toxicity cell membrane-targeted quantum dot temperature sensor by optimizing the synthesis method of CdTe/CdS/ZnS core-shell structured quantum dots. Compared to CdTe-targeted quantum dot temperature sensors, the cytotoxicity of CdTe/CdS/ZnS-targeted quantum dot temperature sensors is reduced by 40.79%. Additionally, a novel cepstrum-based spatial localization algorithm is proposed to achieve rapidly compute the three-dimensional positions of densely distributed quantum dot temperature sensors. Ultimately, both targeted and non-targeted CdTe/CdS/ZnS quantum dot temperature sensors were used simultaneously to label the internal and external regions of human osteosarcoma cells to obtain temperature data at these labeling positions. By combining this with the cepstrum-based spatial localization algorithm, the spatial coordinates of the quantum dot temperature sensors were obtained. Three-dimensional temperature field reconstruction of three local regions was achieved within a 12 μm axial range in living cells. The method described in this paper can be widely applied to the quantitative study of intracellular thermal responses.

Cancer grade classification is a challenging task identified from the cell structure of healthy and abnormal tissues. The practitioners learns about the malignant cell through the grading and plans the treatment strategy accordingly. A major portion of researchers used DL models for grade classification. However, the behavior of DL models is hidden type, it is unknown which features contribute to the accuracy and how the features are chosen for grading. To address the issue the study proposes a Grade Differentiation Integrated Model (GradeDiff-IM) to classify the grades G1, G2, and G3. In GradeDiff-IM, different ML models, are used for grade classification from clinical and pathological reports. The biological-significant features with ranking technique prioritize influential features are used to identify grades G. Subsequently, histopathological images are used by DL models for grade classification and compared with ML models. Instead of employing a single ML model, the GradeDiff-IM model uses the stack-ensembled approach to improve the grade G classification performance. The maximum accuracy is attained by stacking G1-98.2, G2-97.6, and G3-97.5. The proposed study shows that the ML ensemble model is more accurate than the DL models. As a result, the proposed model achieved higher accuracy for G by implementing the stacking technique than the other state-of-the-art models.