Defect Detection Imaging
Defect Detection Imaging - Related Videos
02:40
Second Harmonic Generation Imaging in a Rat Model to Study Tubulin Defects
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2025
Source: Piazza, V., et al., Label-Free Non-Linear Optics for the Study of Tubulin-Dependent Defects in Central Myelin. J. Vis. Exp. (2023)This video demonstrates the procedure for imaging microtubule abnormalities in a rat brain tissue slice using a two-photon excitation microscope. It employs second harmonic generation (SHG) signals to detect tubulin defects and reduced myelin production.
09:15
Light Enhanced Hydrofluoric Acid Passivation: A Sensitive Technique for Detecting Bulk Silicon Defects
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Cited by 6 •
2016
A RT liquid surface passivation technique to investigate the recombination activity of bulk silicon defects is described. For the technique to be successful, three critical steps are required: (i) chemical cleaning and etching of silicon, (ii) immersion of silicon in 15% hydrofluoric acid and (iii) illumination for 1 min.
04:39
Implantation of Ferumoxides Labeled Human Mesenchymal Stem Cells in Cartilage Defects
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Cited by 8 •
2010
Goal of the presentation is to demonstrate a highly reproducible method to generate matrix associated stem cell implants in cartilage defects, which can be visualized with MR imaging. Stem cells are labeled with FDA-approved Ferumoxides, mixed with agarose, implanted into cartilage defects and imaged with a 7T MR scanner.
10:48
How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging
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Cited by 27 •
2013
This article describes how to record amygdala activity with magnetoencephalography (MEG). In addition this article will describe how to conduct trace fear conditioning without awareness, a task that activates the amygdala. It will cover 3 topics: 1) Designing a trace conditioning paradigm using backward masking to manipulate awareness. 2) Recording brain activity during the task using magnetoencephalography. 3) Using source imaging to recover signal from subcortical structures.
07:56
A Full Skin Defect Model to Evaluate Vascularization of Biomaterials In Vivo
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Cited by 13 •
2014
Vascularization is key to approaches in successful tissue engineering. Therefore, reliable technologies are required to evaluate the development of vascular networks in tissue-constructs. Here we present a simple and cost-effective method to visualize and quantify vascularization in vivo.
FAQs
The system relies on differences in spatially resolved signals between defective and intact regions. Contrast enhancement makes these differences more visible, while pattern recognition and image analysis help separate features such as cracks, voids, inclusions, corrosion, or surface irregularities from normal material structure. This combination supports more consistent identification than relying on visual appearance alone.
Optical, thermal, radiographic, and other modalities respond differently to material features and can reveal defects at different depths and scales. A surface irregularity may be visible with one approach, whereas another may provide information from deeper within a component. Matching the modality to the suspected flaw and inspection target therefore affects what damage can be detected and assessed.
Defect characterization depends on the contrast between the flaw and surrounding material, the spatial scale of the feature, and how deeply it is located. Image enhancement can clarify weak differences, while pattern recognition and analysis help assess the observed feature. These factors influence whether an inspection merely locates a possible defect or also supports evaluation of its nature and extent.
A typical workflow captures images or other spatially resolved signals from the engineered material, enhances relevant contrast, and analyzes the resulting patterns. The analysis distinguishes suspected defects from sound regions and supports their location and characterization. The selected sequence can be adapted to optical, thermal, radiographic, or other imaging systems according to the inspection objective.
Selection should reflect the type of feature being investigated and the depth and scale that matter for the inspection. Optical imaging can address visible surface conditions, while thermal or radiographic approaches may provide different spatially resolved information, including indications associated with subsurface regions. Using the appropriate modality improves the likelihood that relevant damage will be revealed.
The approach supports quality control by improving inspection consistency and helps identify damage before it contributes to failure. In predictive maintenance, repeated or targeted imaging can inform assessment of component condition and potential deterioration. Because inspection can often proceed without causing damage, engineers can evaluate materials, components, or structures while preserving their continued use.