Micro-Computed Tomography: A Method for the Non-Destructive Evaluation of the Three-Dimensional Structure of Biological Specimens

2008 ◽  
pp. 273-292 ◽  
Author(s):  
Martin Stauber ◽  
Ralph Müller
Keyword(s):  
Computed Tomography ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Micro Computed Tomography ◽  
Three Dimensional Structure ◽  
Non Destructive Evaluation ◽  
Non Destructive

Distinguishing foliage from branches in the non-destructive measurement of the three-dimensional structure of mountain forest canopies

2003 ◽  
Vol 79 (2) ◽  
pp. 313-317 ◽  
Author(s):  
Tanaka Takafumi ◽  
Park Hotaek ◽  
Hattori Shigeaki
Keyword(s):  
Forest Canopy ◽  
Three Dimensional ◽  
Ccd Camera ◽  
Dimensional Structure ◽  
Mountain Forest ◽  
Forest Canopies ◽  
Three Dimensional Structure ◽  
Range Finding ◽  
Non Destructive ◽  
Finding Method

Mountainous forest canopies usually present a slanted, rough and porous surface. To clarify the effect of forest on radiative and convective exchanges, the three-dimensional structure of the canopy should be measured. An earlier study examined the laser plane range-finding method as a new non-destructive way to measure it. In this study, to distinguish foliage from branches using the results of measurements, detected values of reflection were adjusted to compensate for varying distances from the detector to canopy elements. When the laser reflection values were adjusted by using the 1.5-th power of the distance, the calculations could distinguish foliage from stems. Key words: Mountainous forest, canopy, non-destructive, three-dimensional structure, laser, range-finding method, NDVI, CCD camera

Modeling Dental Implants With Finite Element Analysis and Micro-Computed Tomography

2009 ◽  
Author(s):  
Naomi Tsafnat
Keyword(s):  
Computed Tomography ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Three Dimensional ◽  
Structural Response ◽  
Micro Computed Tomography ◽  
Element Analysis ◽  
Numerical Modeling And Simulation ◽  
Non Destructive ◽  
Digital Nature

X-ray micro-computed tomography (microCT) allows us to construct three-dimensional images of specimens at the micron scale in a non-destructive manner. The digital nature of the microCT images, which are in voxel form, make them ideal candidates for use in numerical modeling and simulation [1]. Finite element analysis (FEA) is a well-known technique for modeling the structural response of a system to mechanical loading, and is most useful in modeling complex systems which cannot be analyzed analytically. MicroCT datasets can be converted into finite element models, directly incorporating both the geometry of the specimen and information about the different materials in it. This method is known as micro-finite element analysis (microFEA). It is especially useful in the study of materials with complex microstructures.

scholarly journals Three-dimensional Non-destructive Visualization of Teeth Enamel Microcracks Using X-ray Micro-computed Tomography

2021 ◽  
Author(s):  
Irma Dumbryte ◽  
Arturas Vailionis ◽  
Edvinas Skliutas ◽  
Saulius Juodkazis ◽  
Mangirdas Malinauskas
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
3D Visualization ◽  
Three Dimensional ◽  
Potential Effect ◽  
Micro Computed Tomography ◽  
X Ray ◽  
Tooth Structure ◽  
Structure Damage ◽  
Non Destructive

Abstract Although the topic of tooth fractures has been extensively analyzed in the dental literature, there is still insufficient information on the potential effect of enamel microcracks (EMCs) to the underlying tooth structures. For precise examination of tooth structure damage in the area of EMCs (i.e. whether it crosses the dentin-enamel junction (DEJ) and reaches dentin or pulp), volumetric (three-dimensional (3D)) evaluation of EMCs is necessary. The aim of this study was to present an X-ray micro-computed tomography (μCT) as a technique suitable for 3D non-destructive visualization and qualitative analysis of different severity teeth EMCs. Extracted human maxillary premolars were examined using a μCT instrument ZEISS Xradia 520 Versa. In order to separate (segment) cracks from the rest of the tooth a Deep Learning Tool was utilized within the ORS Dragonfly software. The scanning technique used allowed for the recognition and detection of EMCs that are not only visible on the outer surface but also those that are deeply buried inside the tooth. The 3D visualization combined with Deep Learning segmentation enabled evaluation of EMC dynamics as it extends from the cervical to the occlusal part of the tooth, and precise examination of EMC position with respect to the DEJ.

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