Medical & Biological Engineering & Computing最新文献

Finite element analysis has become indispensable for biomechanical research on clavicle fractures. This review summarized evidence regarding configurations and applications of finite element analysis in clavicle fracture fixation. Seventeen articles involving 22 clavicles were synthesized from CINAHL, Embase, IEEE Xplore, PubMed, Scopus, and Web of Science databases. Most studies investigated midshaft transverse closed fractures by reconstructing intact models from CT scans and simulating fractures through gap creation. Common loading schemes included axial compression, distal torsion, and inferior bending. The primary objective was comparing different implant designs/placements on construct stiffness, von Mises stress, and fracture site micro-motion. Our review suggested a preference for plate fixation, particularly with anterior placement, for midshaft transverse fractures. However, limited fracture types studied constrain comprehensive recommendations. Additionally, the review highlighted discrepancies between finite element and clinical studies, emphasizing the need for improved modeling of physiological conditions. Future research should focus on developing a comprehensive database of finite element models to test various implant options and placements under common loading schemes, bridging the gap between biomechanical simulations and clinical outcomes.

Previous studies reported baseline state-dependent effects on neural and hemodynamic responses to transcranial ultrasound stimulation. However, due to neurovascular coupling, neither neural nor hemodynamic baseline alone can fully explain the ultrasound-induced responses. In this study, using a general linear model, we aimed to investigate the roles of both neural and hemodynamic baseline status as well as their interactions in ultrasound-induced responses. Thirty Sprague-Dawley rats were randomly assigned to Hypoxia, Hyperoxia, and Normoxia groups. The baseline states were altered by changing the oxygen concentrations. Micro-electrode and laser speckle contrast imaging were used to record local field potentials and cerebral blood flow during resting, before, and after ultrasound stimulation, respectively. We found that baseline neural activity played a positive role in neural response (Coefficient = 0.634, t = 1.748, p = 0.096, ${\eta }_p^2$ = 0.133), but a negative role in hemodynamic response (Coefficient = $-$ 0.060, t = $-$ 1.996, p = 0.060, ${\eta }_p^2$ = 0.166). Baseline hemodynamic activity also had a significantly negative correlation with the hemodynamic response (Coefficient = $-$ 0.760, t = $-$ 3.947, p $<$ 0.001, ${\eta }_p^2$ = 0.438). This study enriched our understanding of state-dependent effects underlying the neurovascular activation by ultrasound stimulation.

Photoacoustic tomography (PAT) has emerged as a promising imaging modality for breast cancer detection, offering unique advantages in visualizing tissue composition without ionizing radiation. However, limited-view scenarios in clinical settings present significant challenges for image reconstruction quality and computational efficiency. This paper introduces novel unrolled deep learning networks based on split Bregman total variation (SBTV) and relaxed basis pursuit alternating direction method of multipliers (rBP-ADMM) algorithms to address these challenges. Our approach combines transfer learning from full-view to limited-view scenarios with U-Net denoiser integration, achieving state-of-the-art reconstruction quality (MS-SSIM> 0.95) while reducing reconstruction time by 92% compared to traditional methods. The effectiveness of different sensor configurations is analyzed through restricted isometry property (RIP) analysis and coherence values, demonstrating that semicircular arrays achieve a RIP constant of 0.76 and coherence of 0.77, closely approximating full-view performance (RIP: 0.75, coherence: 0.78). These metrics validate the theoretical foundation for accurate sparse signal recovery in limited-view scenarios. Comprehensive evaluations across semicircular, concave, and convex sensor arrangements show that the proposed U-SBTV network consistently outperforms existing methods, particularly when combined with the U-Net denoiser. This advancement in limited-view PAT reconstruction brings the technology closer to practical clinical application, potentially improving early breast cancer detection capabilities.

Accurately classifying optical coherence tomography (OCT) images is essential for diagnosing and treating ophthalmic diseases. This paper introduces a novel generative adversarial network framework called MGR-GAN. The masked image modeling (MIM) method is integrated into the GAN model's generator, enhancing its ability to synthesize more realistic images by reconstructing them based on unmasked patches. A ResNet-structured discriminator is employed to determine whether the image is generated by the generator. Through the unique game process of the generative adversarial network (GAN) model, the discriminator acquires high-level discriminant features, essential for precise OCT classification. Experimental results demonstrate that MGR-GAN achieves a classification accuracy of 98.4% on the original UCSD dataset. As the trained generator can synthesize OCT images with higher precision, and owing to category imbalances in the UCSD dataset, the generated OCT images are leveraged to address this imbalance. After balancing the UCSD dataset, the classification accuracy further improves to 99%.

Selecting channels for motor imagery (MI)-based brain-computer interface (BCI) systems can not only enhance the portability of the systems, but also improve the decoding performance. Hence, we propose a cross-domain-based channel selection (CDCS) approach, which effectively minimizes the number of EEG channels used while maintaining high accuracy in MI recognition. The EEG source imaging (ESI) technique is employed to map scalp EEG into the cortical source domain. We divide the equivalent dipoles in the source domain into different regions by k-means clustering. Then, we calculate the band energy (5-40 Hz) of time series of dipoles in these regions by power spectral density (PSD), and the regions with the highest and lowest band energy are selected as the region of interests (ROIs) in the source domain. Subsequently, Pearson correlation coefficients between the dipole time series in ROIs and scalp EEG are used as the criterion for channel selection and a multi-trial-sorting-based channel selection strategy is proposed. Finally, we propose the CDCS-based MI classification framework, where common spatial pattern is applied to extract features and linear discriminant analysis is used to identify MI tasks. The CDCS method demonstrated significant improvement in decoding accuracy on two public datasets, achieving increases of 18.51% and 13.37% compared to all-channel method, and 10.74% and 3.43% compared to the three-channel method. The experimental results validated that CDCS is effective in selecting important channels.

Major depressive disorder (MDD) is a significant neurological disorder that imposes a substantial burden on society, characterized by its high recurrence rate and associated suicide risk. Clinical diagnosis, which relies on interviews with psychiatrists and questionnaires used as auxiliary diagnostic tools, lacks precision and objectivity in diagnosing MDD. To address these challenges, this study proposes an assessment method based on EEG. It involves calculating the phase lag index (PLI) in alpha and gamma bands to construct functional brain connectivity. This method aims to find biomarkers to assess the severity of MDD and suicidal ideation. The convolutional inception with shuffled attention network (CISANET) was introduced for this purpose. The study included 61 patients with MDD, who were classified into mild, moderate, and severe levels based on depression scales, and the presence of suicidal ideation was evaluated. Two paradigms were designed for the study, with EEG analysis focusing on 32 selected electrodes to extract alpha and gamma bands. In the gamma band, the classification accuracy reached 77.37% in the visual paradigm and 80.12% in the auditory paradigm. The average accuracy in classifying suicidal ideation was 93.60%. The findings suggest that gamma bands can be used as potential biomarkers differentiating illness severity and identifying suicidal ideation of MDD, and that objective assessment methods can effectively assess MDD The objective assessment method can effectively assess the severity of MDD and identify suicidal ideation of MDD patients, which provides a valuable theoretical basis for understanding the biological characteristics of MDD.

The generalization of deep learning (DL) models is critical for accurate lesion segmentation in breast ultrasound (BUS) images. Traditional DL models often struggle to generalize well due to the high frequency and scale variations inherent in BUS images. Moreover, conventional loss functions used in these models frequently result in imbalanced optimization, either prioritizing region overlap or boundary accuracy, which leads to suboptimal segmentation performance. To address these issues, we propose UMA-Net, an enhanced UNet architecture specifically designed for BUS image segmentation. UMA-Net integrates residual connections, attention mechanisms, and a bottleneck with atrous convolutions to effectively capture multi-scale contextual information without compromising spatial resolution. Additionally, we introduce an adaptive ensemble loss function that dynamically balances the contributions of different loss components during training, ensuring optimization across key segmentation metrics. This novel approach mitigates the imbalances found in conventional loss functions. We validate UMA-Net on five diverse BUS datasets-BUET, BUSI, Mendeley, OMI, and UDIAT-demonstrating superior performance. Our findings highlight the importance of addressing frequency and scale variations, confirming UMA-Net as a robust and generalizable solution for BUS image segmentation.

Positron emission tomography (PET) imaging plays a pivotal role in oncology for the early detection of metastatic tumors and response to therapy assessment due to its high sensitivity compared to anatomical imaging modalities. The balance between image quality and radiation exposure is critical, as reducing the administered dose results in a lower signal-to-noise ratio (SNR) and information loss, which may significantly affect clinical diagnosis. Deep learning (DL) algorithms have recently made significant progress in low-dose (LD) PET reconstruction. Nevertheless, a successful clinical application requires a thorough evaluation of uncertainty to ensure informed clinical judgment. We propose NPB-LDPET, a DL-based non-parametric Bayesian framework for LD PET reconstruction and uncertainty assessment. Our framework utilizes an Adam optimizer with stochastic gradient Langevin dynamics (SGLD) to sample from the underlying posterior distribution. We employed the Ultra-low-dose PET Challenge dataset to assess our framework's performance relative to the Monte Carlo dropout benchmark. We evaluated global reconstruction accuracy utilizing SSIM, PSNR, and NRMSE, local lesion conspicuity using mean absolute error (MAE) and local contrast, and the clinical relevance of uncertainty maps employing correlation between the uncertainty measures and the dose reduction factor (DRF). Our NPB-LDPET reconstruction method exhibits a significantly superior global reconstruction accuracy for various DRFs (paired t-test, $p<0.0001$ , N=10, 631). Moreover, we demonstrate a 21% reduction in MAE (573.54 vs. 723.70, paired t-test, $p<0.0001$ , N=28) and an 8.3% improvement in local lesion contrast (2.077 vs. 1.916, paired t-test, $p<0.0001$ , N=28). Furthermore, our framework exhibits a stronger correlation between the predicted uncertainty 95th percentile score and the DRF ( $r^2=0.9174$ vs. $r^2=0.6144$ , N=10, 631). The proposed framework has the potential to improve clinical decision-making for LD PET imaging by providing a more accurate and informative reconstruction while reducing radiation exposure.

Adolescent idiopathic scoliosis (AIS) is a three-dimensional spine deformity governed of the spine. A child's Risser stage of skeletal maturity must be carefully considered for AIS evaluation and treatment. However, there are intra-observer and inter-observer inaccuracies in the Risser stage manual assessment. A multi-task learning approach is proposed to address the low precision issue of manual assessment. With our developed multi-task learning approach, the iliac area is extracted and forwarded to the improved Swin Transformer for Risser stage assessment. The spatial and channel reconstruction convolutional Swin block is adapted to each stage of the Swin Transformer to achieve better performance. The Risser stage assessment based on iliac region extraction had an overall accuracy of 81.53%. The accuracy increased in comparison to ResNet50, ResNet101, Uni-former, Next-ViT, ConvNeXt, and the original Swin Transformer by 5.85%, 4.6%, 3.6%, 2.7%, 2.25%, and 1.8%, respectively. The Grad-CAM visualization is used to understand the interpretability of our proposed model. The results show that the proposed multi-task learning strategy performs well on the Risser stage assessment. Our proposed automatic Risser stage assessment method benefits the clinical evaluation of AIS. Project address: https://github.com/xyz911015/Risser-stage-assessment.

The identification and categorization of circulating tumor cells (CTCs) in peripheral blood are imperative for advancing cancer diagnostics and prognostics. The intricacy of various CTCs subtypes, coupled with the difficulty in developing exhaustive datasets, has impeded progress in this specialized domain. To date, no methods have been dedicated exclusively to overcoming the classification challenges of CTCs. To address this deficit, we have developed CTCDet, a large-scale dataset meticulously annotated based on the distinctive pathological characteristics of CTCs, aimed at advancing the application of deep learning techniques in oncological research. Furthermore, we introduce CTCNet, an innovative hybrid architecture that merges the capabilities of CNNs and Transformers to achieve precise classification of CTCs. This architecture features the Parallel Token mixer, which integrates local window self-attention with large-kernel depthwise convolution, enhancing the network's ability to model intricate channel and spatial relationships. Additionally, the Deformable Large Kernel Attention (DLKAttention) module leverages deformable convolution and large-kernel operations to adeptly delineate the nuanced features of CTCs, substantially boosting classification efficacy. Comprehensive evaluations on the CTCDet dataset validate the superior performance of CTCNet, confirming its ability to outperform other general methods in accurate cell classification. Moreover, the generalizability of CTCNet has been established across various datasets, establishing its robustness and applicability. What is more, our proposed method can lead to clinical applications and provide some help in assisting cancer diagnosis and treatment. Code and Data are available at https://github.com/JasonWu404/CTCs_Classification .