Three-Dimensional Immersion Scanning Technique: A Scalable Low-Cost Solution for 3D Scanning Using Water-Based Fluid

Gonçalves, Ricardo Spyrides Boabaid Pimentel; Haueisen, Jens

doi:10.3390/s23063214

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Rapid and precise brain imaging allows for early diagnosis and evaluation of brain-related illnesses, as well as the establishment of therapeutic regimens. Despite the widespread use of MRI and conventional CT due to their high resolution (MRI: ~0.1 mm [1]; CT: ~0.23 mm [2]) [3], MRI is not suitable for people who are obese or have magnetic foreign bodies [4], and CT involves exposure to harmful ionizing radiation [5,6]. Additionally, these methods require bulky and expensive equipment and must be operated by healthcare professionals [7,8].…”

Section: Introductionmentioning

confidence: 99%

3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks

Ren,

Wang,

Liu

et al. 2023

Sensors

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Compared to magnetic resonance imaging (MRI) and X-ray computed tomography (CT), ultrasound imaging is safer, faster, and more widely applicable. However, the use of conventional ultrasound in transcranial brain imaging for adults is predominantly hindered by the high acoustic impedance contrast between the skull and soft tissue. This study introduces a 3D AI algorithm, Brain Imaging Full Convolution Network (BIFCN), combining waveform modeling and deep learning for precise brain ultrasound reconstruction. We constructed a network comprising one input layer, four convolution layers, and one pooling layer to train our algorithm. In the simulation experiment, the Pearson correlation coefficient between the reconstructed and true images was exceptionally high. In the laboratory, the results showed a slightly lower but still impressive coincidence degree for 3D reconstruction, with pure water serving as the initial model and no prior information required. The 3D network can be trained in 8 h, and 10 samples can be reconstructed in just 12.67 s. The proposed 3D BIFCN algorithm provides a highly accurate and efficient solution for mapping wavefield frequency domain data to 3D brain models, enabling fast and precise brain tissue imaging. Moreover, the frequency shift phenomenon of blood may become a hallmark of BIFCN learning, offering valuable quantitative information for whole-brain blood imaging.

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Section: Introductionmentioning

confidence: 99%

3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks

Ren,

Wang,

Liu

et al. 2023

Sensors

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Physics-Embedded Neural Networks for Enhanced Ultrasonic Brain Tomography

Ren,

Li,

Liu

2024

J. Phys.: Conf. Ser.

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Ultrasound imaging, valued for its non-invasiveness and cost-effectiveness, faces challenges in brain imaging due to acoustic impedance differences. This study introduces PEN-UBT, a deep learning-based method, combining Convolutional Neural Network for Forward Propagation (CN-FP) and Subnetwork for Inversion (SNI). CN-FP simplifies wavefield calculations, while SNI facilitates mapping from wavefield to model. PEN-UBT achieves high-fidelity imaging of the skull and soft tissues, excelling in scenarios with varying thrombus velocities. It demonstrates exceptional tissue resolution in brain slices, reducing imaging time to 1.13 seconds. PEN-UBT’s success extends its applicability beyond brain imaging, contributing to the broader field of medical imaging technologies.

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Fractal Characterization of Microstructure of Materials and Correlation with Their Properties on the Basis of Digital Materials Science Concept

Sychov,

Chekuryaev,

Mjakin

2023

Fractal Analysis - Applications and Updates

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The concept of Digital Materials Science supposes that materials are designed, fabricated, tested, studied, characterized, and optimized on the basis of digital technologies, including the analysis of fractal parameters (fractal dimension, lacunarity, scale invariance, Voronoi entropy, etc.) of materials’ microstructure. Many classes of materials may be considered as composites: polymer composites with inorganic fillers, alloys containing nonmetallic inclusions (oxides, carbides, nitrides, intermetallic ones, etc.), ceramic materials with pores and sintering additives, etc. The analysis of composition-technology-structure-properties relationships for such non-ordered composite materials requires the development of numerical tools for the characterization of their structure, including the interposition of phases. This chapter presents several examples of the implementation of this concept, including the study of filler distributions in dielectric composites, interposition of phases in special ceramic materials, distribution of nonmetallic inclusions in additively manufactured stainless steel, and structural features of tungsten oxide-based electrochromic materials. Based on the analysis of such characteristics as lacunarity and surface functionality, interrelations are established between technical properties of the studied materials and their structure providing approaches to the prediction and optimization of their target performances.

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Three-Dimensional Immersion Scanning Technique: A Scalable Low-Cost Solution for 3D Scanning Using Water-Based Fluid

Cited by 3 publications

References 17 publications

3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks

3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks

Physics-Embedded Neural Networks for Enhanced Ultrasonic Brain Tomography

Fractal Characterization of Microstructure of Materials and Correlation with Their Properties on the Basis of Digital Materials Science Concept

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