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Advances in 3D fusion of multimodal medical images: 3D reconstruction of bone, muscle, and ligament structures under load from radiographs and magnetic resonance imaging

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Research on Biomedical Engineering 2025年第1期41卷 1-35页

作者： da Silva, Daniel S. Astolfi, Rodrigo S. Jagatheesaperumal, Senthil K. Selvaraj, Priyadharsini Russel, Newlin Shebiah de Albuquerque, Victor Hugo C. Post-Graduation Program of Medical Surgical Sciences Federal University of Ceara CE Fortaleza60455-970 Brazil Department of Teleinformatics Engineering Federal University of Ceara CE Fortaleza60455-970 Brazil Department of Electronics and Communication Engineering Mepco Schlenk Engineering College TN Sivakasi626005 India Department of Computer Science and Engineering Mepco Schlenk Engineering College TN Sivakasi626005 India

Purpose: Foot and ankle pathologies are among the most prevalent conditions within the human locomotor system. Imaging examinations are crucial for diagnosing, treating, and achieving satisfactory functional outcomes for these conditions. However, conventional imaging exams often fail to capture all the necessary details for precise analysis, necessitating the use of multiple imaging modalities to evaluate bones, ligaments, cartilage, and other structures. Objective: This research aims to explore computational technologies and algorithms for 3D reconstruction and fusion of foot and ankle images, alongside the analysis, segmentation, classification, and evaluation techniques applied to these images. Methods: The study investigates different computational approaches for 3D reconstruction and multimodal image fusion, integrating X-ray and MRI data. Advanced segmentation and classification techniques are applied to enhance the precision of structural assessment. Additionally, we assess the availability and quality of databases for training and validating these models. Results: Our findings highlight a significant gap in research concerning these specific applications, as well as a shortage of suitable databases for 3D reconstruction and fusion of images from X-rays and MRI. Conclusion: The study emphasizes the need for dedicated datasets and further research to develop effective computational solutions for multimodal foot and ankle imaging. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering 2025.

关键词： Magnetic resonance imaging

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Low Dose CT Filtering in the Image Domain Using MAP Algorithms

Low Dose CT Filtering in the Image Domain Using MAP Algorith...

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作者： Geraldo, Rafael J. Cura, Luis M.V. Cruvinel, Paulo E. Mascarenhas, Nelson D.A. Institute of Mathematics and Computer Science-USP Saõ Carlos13566-590 Brazil Computing Department Federal University of Saõ Carlos Saõ Carlos Brazil Faculdade Campo Limpo Paulista Campo Limpo Paulista Brazil Embrapa Instrumentation Center Saõ Carlos Brazil Post-Graduation Program in Computer Sciences Federal University of Saõ Carlos Saõ Carlos Brazil

The purpose of this paper is to present two new noise reduction filters techniques in the CT image space, in order to provide a better quality to the images acquired with low radiation exposure. For the noise reduction, a new denoising technique is presented based on a pointwise Maximum a posteriori (MAP). The noise is considered Gaussian with zero mean, as observed experimentally, and the variance is estimated considering a signal-independent noise. For the a priori density of the signal, we used different non-negative probability densities (reflecting the fact that the pixels of an image are non-negative). In another approach, the histogram of the images were segmented into unimodal parts and each segment was filtered using the filter based on the MAP criterion with the a priori density that best fits it. After filtering, the evaluation of the method is performed using the following criteria: Peak Signal-to-Noise Ratio, Universal Image Quality Index and Structural Similarity Index. The 2D filtering results are compared with the results obtained by pointwise Wiener filter. Simulation and real CT images results show that the proposed techniques increase the image quality and improve the use of a low-dose CT protocol. © 2017 IEEE.

关键词： Image denoising

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