Purpose: Currently, the intra-operative visualization of vessels during endovascular aneurysm repair (EVAR) relies on contrast-based imaging modalities. Moreover, traditional image fusion techniques lack a continuous and automatic update of the vessel configuration, which changes due to the insertion of stiff guidewires. The purpose of this work is to develop and evaluate a novel approach to improve image fusion, that takes into account the deformations, combining electromagnetic (EM) tracking technology and finite element modeling (FEM).
Methods: To assess whether EM tracking can improve the prediction of the numerical simulations, a patient-specific model of abdominal aorta was segmented and manufactured. A database of simulations with different insertion angles was created. Then, an ad hoc sensorized tool with three embedded EM sensors was designed, enabling tracking of the sensors' positions during the insertion phase. Finally, the corresponding cone beam computed tomography (CBCT) images were acquired and processed to obtain the ground truth aortic deformations of the manufactured model.
Results: Among the simulations in the database, the one minimizing the in silico versus in vitro discrepancy in terms of sensors' positions gave the most accurate aortic displacement results.
Conclusions: The proposed approach suggests that the EM tracking technology could be used not only to follow the tool, but also to minimize the error in the predicted aortic roadmap, thus paving the way for a safer EVAR navigation.
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http://dx.doi.org/10.1007/s11548-024-03187-y | DOI Listing |
Sci Rep
December 2024
Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, China.
The intelligent identification of wear particles in ferrography is a critical bottleneck that hampers the development and widespread adoption of ferrography technology. To address challenges such as false detection, missed detection of small wear particles, difficulty in distinguishing overlapping and similar abrasions, and handling complex image backgrounds, this paper proposes an algorithm called TCBGY-Net for detecting wear particles in ferrography images. The proposed TCBGY-Net uses YOLOv5s as the backbone network, which is enhanced with several advanced modules to improve detection performance.
View Article and Find Full Text PDFSci Rep
December 2024
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325035, China.
Addressing the issues of a single-feature input channel structure, scarcity of training fault data, and insufficient feature learning capabilities in noisy environments for intelligent diagnostic models of mechanical equipment, we propose a method based on a one-dimensional and two-dimensional dual-channel feature information fusion convolutional neural network (1D_2DIFCNN). By constructing a one-dimensional and two-dimensiona dual-channel feature information fusion convolutional network and introducing a Convolutional Block Attention Mechanism, we utilize Random Overlapping Sampling Technique to process raw vibration signals. The model takes as inputs both one-dimensional data and two-dimensional Continuous Wavelet Transform images.
View Article and Find Full Text PDFHealth Inf Sci Syst
December 2025
Department of Electrical Engineering, Iqra National University, Peshawar, 25000 Pakistan.
Leukemia, a life-threatening form of cancer, poses a significant global health challenge affecting individuals of all age groups, including both children and adults. Currently, the diagnostic process relies on manual analysis of microscopic images of blood samples. In recent years, machine learning employing deep learning approaches has emerged as cutting-edge solutions for image classification problems.
View Article and Find Full Text PDFBMC Oral Health
December 2024
Department of Orthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration & Shandong Provincial Clinical Research Center for Oral Diseases, Jinan, China.
Background: This study aims to evaluate the impact of different thresholds and voxel sizes on the accuracy of Cone-beam computed tomography (CBCT) tooth reconstruction and to assess the accuracy of fused CBCT and intraoral scanning (IOS) tooth models using curvature continuity algorithms under varying thresholds and voxel conditions.
Methods: Thirty-two isolated teeth were digitized using IOS and CBCT at two voxel sizes and five threshold settings. Crown-root fusion was performed using a curvature continuity algorithm.
Prog Neuropsychopharmacol Biol Psychiatry
December 2024
Tianjin Fourth Central Hospital, The Affiliated Hospital of Tianjin Medical University, Tianjin 300140, China. Electronic address:
Background: The mechanisms underlying the complex relationship between autoimmune hypothyroidism and neurological disorders remain unclear. We conducted a comprehensive analysis of associations between alternative splicing, transcriptomics, and proteomics data and autoimmune hypothyroidism.
Methods: Splicing-Wide association studies (SWAS), proteome-wide association studies (PWAS), and transcriptome-wide association studies (TWAS) were used to identify genes and proteins that regulate autoimmune hypothyroidism within the brain axis.
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