Medical & Biological Engineering & Computing最新文献

Nowadays, the research of image fusion methods focuses on two-dimensional medical images, and almost no three-dimensional medical image fusion methods based on deep learning have been proposed. However, 3D image fusion is significant in clinical diagnosis. Therefore, this paper proposed a 3D medical image fusion enhancement network (3DFE-Net) for the gap in deep learning. 3DFE-Net included a feature extraction module, a multi-attention fusion module, and a feature reconstruction module. Firstly, multi-receptive field convolution blocks (MRFC) and multi-receptive field bottleneck blocks (MRFB) were devised instead of the traditional convolutional blocks to extract features of multiple receptive fields. Then, the multi-attention fusion module was designed using channel attention, self-attention, and spatial attention to make the network focus on the critical information in source images. Finally, the 3D fused image was obtained by the feature reconstruction module. In addition, a multivariate loss function was proposed for network training so that the fused image retains more edge structural information and texture details. MR-T1ce/MR-T2 fusion experiments show that, compared with the traditional method, 3DFE-Net improved the evaluation metrics EN (Information Entropy), MI (Mutual Information), SD (Standard Deviation), Qabf (Quality assessment of binary), and VIF (Visual Information Fidelity) by 0.0501, 0.1003, 5.2682, 0.1874, and 0.2129, respectively. 3DFE-Net can focus on the glioma lesion region in glioma slice fusion to achieve outstanding results and keep the structural information in MR-T1ce and the brightness information in MR-T2 well in normal slices. In qualitative and quantitative evaluations, 3DFE-Net performs better than conventional methods.

This paper introduces a novel densely connected neural network architecture designed for the pervasive visualisation of musculoskeletal system simulations. These simulations are built upon continuum-mechanical frameworks, which effectively integrate the diverse structural and physiological properties of the musculoskeletal system. A significant drawback of continuum-mechanical musculoskeletal models is their substantial computational resource requirement, making them difficult to transfer to/visualise the results on resource-poor systems like augmented reality or mobile devices. Such technologies, however, will be crucial for future advancements in human-machine interaction, surgical support tools, or physiotherapy. We use an activation-driven five-muscle continuum-mechanical upper limb model to obtain the activation-induced deformations of the respective muscles. Exemplified on the m. biceps brachii, we fit a sparse grid surrogate to capture the surface deformation and train a deep learning model that is subsequently used in our real-time visualisation. Based on the activation levels of the five muscles, the result of our trained neural network leads to an average positional error of 0.97±0.16 mm, or 0.57±0.10% for the 2809 mesh nodes of the m. biceps brachii's surface. With the novel deep neural network model, we achieved evaluation times for the m. biceps brachii's surface deformation of 9.88 ms on CPU-only architectures and 3.48 ms on architectures with GPU support. This leads to theoretical frame rates of 101 fps and 287 fps, respectively. The combination of surrogates and deep neural networks presented here succeeds as a proof-of-concept for real-time visualisation of a complex musculoskeletal system model, and does not rely on the inherent characteristics of the musculoskeletal system, and, hence, is also applicable to other real-time visualisations of complex meshed models in other applications.

In this paper, we extend the SonoNet architecture to capture spatio-temporal information from ultra-sound (US) sequences. More specifically, we propose 3D-SonoNet32 - which lifts 2D convolutions to 3D - and to an efficient (2+1)D variant - to keep the computational cost under control while preserving the benefits of the spatio-temporal model. We investigate the potential of these architectures on a scan-plane detection problem and discuss how these methodologies can be beneficial for AI-driven online "scan assistants", to enhance the quality and reproducibility of the evaluation and ultimately support the clinicians in the US examination. Our main contributions are (i) the design of novel Space-Time SonoNet architectures for analysing US video sequences, (ii) an in depth experimental analysis to show the benefit of using space-time models with respect to purely spatial ones, and to discuss the potential improvements gained by exploiting domain-specific properties like temporal coherence and prior knowledge of the ongoing scan. Overall, we show that the proposed models are specifically designed to be computationally lightweight, but also competitive in performance, making them suitable for real-time deployment on portable US devices.

Vaginal deliveries are frequently associated with perineal trauma, including severe tearing in some cases. Understanding of pelvic floor muscle damage and perineal tearing during childbirth is of great clinical relevance. However, the knowledge of these complex phenomena is incomplete. The objective of the present study is to explore the multifactorial view of pelvic floor muscle damage and perineal tearing during childbirth. Using nonlinear finite element modeling coupled to statistical surrogate modeling, we modeled fetal descent with imposed displacement and used active maternal for muscle contraction to estimate the pelvic floor muscle damage and perineal tearing indicators under different influencing factors such as fetal head deformability and biometry, as well as constitutive behaviors. The obtained results show that fetal head deformability reduces stress and strain concentrations in the pelvic floor muscles (PFM) and perineal region, while increasing fetal head size leads to heightened internal tissue responses. Linear regression analysis demonstrated strong model performance (R² = 0.782-0.981) and statistically predictive relationships between fetal biometric parameters, soft tissue constitutive behaviors, and associated mechanical responses. By integrating advanced finite element modeling with statistical modeling and regression, this work provides new quantitative insights into the biomechanical factors, highlighting tissue deformation patterns and indicating potential risk of tissue damage in highly strained areas due to localized mechanical stress. This approach offers a predictive and non-invasive strategy for assessing maternal tissue vulnerability during childbirth.

A spinal cord injury (SCI) is a neurological disorder that impairs motor and physiological functions and leads to a reduced quality of life and autonomy for the person affected. In this scenario, human-machine interfaces (HMIs) have emerged as an effective tool to leverage residual motor capabilities and benefit injured persons. This work aims to develop a closed-loop HMI system for lower-limb rehabilitation composed of an in-house multi-channel Functional Electrical Stimulation (FES), which is activated by considering gait and pedaling cycles measured by an Inertial Measurement Unit. Two experiments were conducted with individuals suffering partial SCI who performed cycling and walking activities by using our proposed HMI, while inertial and electroencephalography signals were collected for further analysis and validation. Relative power changes were observed in mu (8-13 Hz) and high beta (20-30 Hz) bands over the foot area (Cz location), comparing both FES and non-FES conditions during gait and pedaling. This comparison also showed that the volunteers performed physical activities with greater speed and cadence by using the proposed HMI system, which correctly identified the movement phases.

Three-dimensional models have been widely used to study knee joint biomechanics in both healthy and pathological conditions. However, the lack of data on pediatric knee models affected by a discoid lateral meniscus necessitates further investigation. This study analyzed the biomechanical behavior of a pediatric knee joint with a discoid lateral meniscus malformation and the effects of partial meniscectomy on restoring its normal configuration. The three-dimensional geometry was reconstructed from computed tomography and magnetic resonance imaging data to develop a finite element model of the pediatric knee. The finite element method was used to simulate the joint in an upright position, and contact, compressive, and shear stresses were analyzed across seven lateral meniscus configurations with varying residual tissue widths to simulate progressive degrees of partial meniscectomy. A discoid lateral meniscus altered knee biomechanics, increasing medial-compartment stress, associated with femoral cartilage damage. Under body weight loading, the pediatric model showed a significant rise in stress when the meniscal width fell below 12 mm. A residual meniscal width of 12 mm provided a more favorable biomechanical response in this pediatric knee model, potentially reducing cartilage damage and the risk of early degeneration after partial meniscectomy.