Physical and Engineering Sciences in Medicine最新文献

The increasing demand for secure, high-quality medical image transmission across healthcare institutions has posed a significant challenge to modern telemedicine systems. Traditional network infrastructures often fail to provide sufficient bandwidth and low latency required for transferring large volumes of high-resolution medical images, such as MRI and CT scans, over long distances. To address this limitation, a fiber-optic transmission framework was designed and evaluated with the objective of enhancing the speed, reliability, and accuracy of inter-hospital medical image sharing. In this study, a simulation-based approach was employed using OPTISYSTEM and MATLAB to model the optical transmission chain, including stages of image digitization, modulation, fiber propagation, and optical-to-electrical conversion at the receiving end. Various performance parameters such as Bit Error Rate (BER), Quality Factor (Q), transmission power, and noise levels were analyzed for different image resolutions and transmission distances. The results showed that Q-Factor values between 8.5 and 9.5 were obtained, with BER reaching values as low as 10⁻²⁰, even for high-resolution images transmitted over distances up to 90 km. These results were compared to existing benchmarks in the literature and demonstrated superior performance. The proposed system exhibited strong robustness in handling large image datasets, with minimal signal distortion and negligible transmission errors. It was concluded that the adoption of this fiber-optic architecture could significantly improve the efficiency of telemedicine applications, offering a reliable and high-capacity solution for real-time diagnostic collaboration and patient monitoring between geographically distributed medical facilities.

This study calculated the radiation dose to young patients with high-risk abdominal neuroblastoma from therapeutic and imaging procedures. Computational XCAT phantoms representing typical patients aged 5-15 years were used. Intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) plans were generated with 6 MV photons for a planning target volume (PTV) on the left and right abdominal side. Dose-volume-histograms from the plans were used to find the average dose (D_av) to critical normal abdominal and thoracic organs. The imaging dose to these organs and PTV was calculated by simulating kV cone-beam computed tomography (CBCT) scanning for patient setup before radiotherapy. Different CBCT protocols were simulated with Monte Carlo methods. The IMRT and VMAT plans provided similar PTV coverage and organ sparing. For a 21.6 Gy target dose, the D_av of the abdominal organs from the treatment plans was 3.6-19.6 Gy and that of thoracic organs was 0.1-2.3 Gy. Daily CBCT scans on 15-year-old patients with the standard adult protocol gave total PTV and organ doses of 95.3-485.3 mGy. The doses from the modified standard protocol for 5- and 10-year-old patients were 74.2-159.6 mGy. The dose calculations with a specially designed CBCT protocol for patients up to 10 years were 6.0-27.8 mGy. The total imaging dose to the PTV was up to 2.2% of the delivered therapeutic dose. The replacement of the modified adult CBCT protocol with a special protocol solely defined for children reduced the radiation dose to target and normal organs by more than five times.

According to the World Health Organization (WHO), 17.9 million people die yearly from cardiovascular Diseases (CVDs), including heart attacks. Cardiovascular diseases, including heart attack, kill 32% of people globally. Current approaches struggle with electrocardiogram (ECG) signal variability, causing diagnosing errors. The adoption of automated and accurate models for heart disease detection is lacking since conventional methods rely on human analysis, which is time-consuming and error-prone. This work covers the crucial topic of heart disease diagnosis, especially ECG data analysis for cardiovascular disease detection. The integration of the Deep-Convolutional Neural Network (Deep-CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) model with an Attention Mechanism enhances the accuracy and reliability of heart disease categorisation. The Deep-CNN component efficiently extracts features from capture spatial linkages, while the Bi-LSTM layers handle temporal dependencies to identify patient health patterns over time. The model is evaluated on 303 patient records with 14 clinical characteristics from the University of California, Irvine (UCI) Cleveland Heart Disease dataset. The suggested technique has 97.23% accuracy, 97.72% recall, precision, and 96.90% F1 score. These findings show that the proposed architecture improves diagnostic performance more than boosting ensemble approaches and hybrid models.

Radiotherapy is a crucial treatment for brain tumor malignancies. To address the limitations of CT-based treatment planning, recent research has explored MR-only radiotherapy, requiring precise MR-to-CT synthesis. This study compares two deep learning approaches, supervised (Pix2Pix) and unsupervised (CycleGAN), for generating pseudo-CT (pCT) images from T1- and T2-weighted MR sequences. 3270 paired T1- and T2-weighted MRI images were collected and registered with corresponding CT images. After preprocessing, a supervised pCT generative model was trained using the Pix2Pix framework, and an unsupervised generative network (CycleGAN) was also trained to enable a comparative assessment of pCT quality relative to the Pix2Pix model. To assess differences between pCT and reference CT images, three key metrics (SSIM, PSNR, and MAE) were used. Additionally, a dosimetric evaluation was performed on selected cases to assess clinical relevance. The average SSIM, PSNR, and MAE for Pix2Pix on T1 images were 0.964 ± 0.03, 32.812 ± 5.21, and 79.681 ± 9.52 HU, respectively. Statistical analysis revealed that Pix2Pix significantly outperformed CycleGAN in generating high-fidelity pCT images (p < 0.05). There was no notable difference in the effectiveness of T1-weighted versus T2-weighted MR images for generating pCT (p > 0.05). Dosimetric evaluation confirmed comparable dose distributions between pCT and reference CT, supporting clinical feasibility. Both supervised and unsupervised methods demonstrated the capability to generate accurate pCT images from conventional T1- and T2-weighted MR sequences. While supervised methods like Pix2Pix achieve higher accuracy, unsupervised approaches such as CycleGAN offer greater flexibility by eliminating the need for paired training data, making them suitable for applications where paired data is unavailable.

Radiomic biomarkers have demonstrated significant potential in non-invasively assessing tumor biology and providing essential insights for precision medicine. However, the clinical translation is often hindered by challenges in multicenter studies, primarily due to a lack of standardization, such as variations in scanner models, acquisition protocols, reconstruction techniques, etc. This study aims to assess the impact of various harmonization methods in multicenter, 18 F-FDG PET-based radiomics for the classification of lung cancer histological subtypes using a machine learning model. Retrospective data included 178 lung cancer cohorts, comprising 117 adenocarcinomas and 61 squamous cell carcinomas from three different centers. PET DICOM image data was preprocessed with 3D ROI segmentation of the lung tumor and healthy liver, followed by the extraction of 111 radiomic features. Subsequently, Z-Score, Quantile, and ComBat were applied to generate three different harmonized datasets. Feature distribution was analyzed, and the top ten features were selected using recursive feature elimination. An eXtreme gradient boosting model was built on each dataset, and performance was assessed using accuracy, precision, sensitivity, specificity, and AUC with a 95% confidence interval. Variations in radiomic feature distribution and feature selection were observed after applying different harmonization methods. During validation of the trained model, AUC improved from 0.556 [95% CI 0.551-0.563] in the unharmonized data to 0.719 [95% CI 0.710-0.720], 0.952 [95% CI 0.951-0.954], and 0.996 [95% CI 0.995-0.996] in Z-Score, Quantile, and ComBat harmonized data, respectively, for classifying adenocarcinoma and squamous cell carcinoma subtypes. The study indicates that feature selection was affected by the different harmonization methods. The ComBat method was shown to significantly enhance the performance of AI-assisted PET radiomics.

A naevus is a benign melanocytic skin tumour made up of naevus cells, characterised by variations in size, shape, and colour. Understanding naevi is essential due to their significant role as markers for the risk of developing melanoma. This study focused on creating a visual representation called a manifold that illustrates the distribution of two types of naevi: suspicious and non-suspicious. The research aimed to classify real naevi using generative adversarial networks (GANs), while also generating realistic synthetic samples and interpreting their distribution through a variational manifold. This inquiry holds promise for applying data-driven methods for early melanoma detection by identifying distinct features linked with suspicious naevi. Our variational autoencoder auxiliary classifier generative adversarial network (VAE-ACGAN) for suspicious naevi revealed a manifold with outstanding performance, including specificity, sensitivity, and area under the curve (AUC) scores, particularly representing suspicious naevi. These models surpassed various deep learning frameworks in key performance metrics while producing a manifold that indicated a significant distinction between the two categories in the resultant image, yielding high-quality and life-like representations of naevi. The results highlight the potential application of GANs in expanding data sets and enhancing the effectiveness of deep learning algorithms in dermatology. Accurate identification and categorisation of naevi could facilitate early melanoma detection and deepen our understanding of these skin lesions through an interpretable clustering method based on visual similarities.

Alzheimer's Disease (AD) is a chronic neurological disorder that impairs the cognitive and behavioral abilities of older people. Early detection and treatment are crucial for minimizing the progression of the disease. Electroencephalogram (EEG) makes it possible to investigate the brain activities linked to various forms of disabilities experienced by individuals with AD. Nevertheless, the EEG signals are non-linear and non-stationary in nature making it difficult to retrieve the concealed information from the EEG signals. Therefore, a Fourier Decomposition Method (FDM) and Hilbert Transform (HT) based EEG signals analysis (FHESA) method is developed in this paper for the automated detection of AD. The FHESA method aims to efficiently analyze the EEG data to identify the important brain regions vulnerable to AD, and to assess the impact of various EEG channels for the timely and early detection of AD. The proposed FHESA method is divided into three primary stages. The first stage deals with the decomposition of the EEG signals into a finite number of Fourier Intrinsic Band Functions (FIBFs). In the second stage, HT is applied to all FIBFs to obtain instantaneous amplitude, frequency, and phase, that are then used to construct feature vectors. In the last stage, various Machine Learning (ML) algorithms are used to classify these feature vectors for efficient AD detection. Two distinct data sets are employed to assess the effectiveness of the proposed FHESA method. The outcome demonstrates that with dataset-I and dataset-II, the proposed methodology can detect AD with 98% and 99% accuracy, respectively. The performance of the proposed FHESA method is compared to other state-of-the-art methods used for AD detection. The promising results show that the proposed FHESA method can assist neurological experts in identifying and utilizing EEG signals for AD detection.

To assess the clinical outcome of single-arc coplanar dynamic conformal arc (C-DCA) in three-fraction hypofractionated stereotactic radiotherapy (3F-HSRT) for single very small brain metastasis (BM; gross tumor volume [GTV] ≤ 1 cm³) from non-small cell lung cancer (NSCLC) and to compare treatment planning with single-arc coplanar volumetric modulated arc therapy (C-VMAT). From December 2019 to May 2023, twenty NSCLC patients with single BM treated with 3F-HSRT (24-39 Gy/3f) using C-DCA were enrolled in this study. Each plan was replanned using C-VMAT, and relevant planning indices were compared. Clinical outcome was evaluated following C-DCA treatment. C-VMAT yielded a higher homogeneity index (1.41 vs. 1.16, p < 0.001) and GTV D_98% (38.10 Gy vs. 32.72 Gy, p = 0.008), with slightly smaller normal brain tissue (NBT) V_23Gy, and V_21Gy. However, C-DCA offered 31.37% lower monitor units (p = 0.008) and 36.55% shorter beam on time (p = 0.007) while achieving a significantly higher gamma passing rate for the 2%/1 mm criterion (p = 0.001). As of October 2023, the median follow-up time was 9.2 months. The intracranial disease control rate was 70%, with a median intracranial progression-free survival of 11.4 (95% CI 4.5-18.3) months and a 1-year intracranial control rate of 45.4%. The intracranial local disease control rate was 95%. Only two irradiated lesions progressed at the end of follow-up. The cerebral radiation necrosis rate of all patients was 5%. For small BM, C-DCA provided nearly equivalent target coverage and NBT sparing to the C-VMAT while maintaining higher delivery efficiency and accuracy. C-DCA HSRT also provided good local control and limited toxicity.