Defect Detection in Furniture Elements with the Hough Transform Applied to 3D Data

The quantitative use of electron diffraction intensity data for the determination of crystal structures represents the pioneering achievement in the electron crystallography of organic molecules, an effort largely begun by B. K. Vainshtein and his co-workers. However, despite numerous representative structure analyses yielding results consistent with X-ray determination, this entire effort was viewed with considerable mistrust by many crystallographers. This was no doubt due to the rather high crystallographic R-factors reported for some structures and, more importantly, the failure to convince many skeptics that the measured intensity data were adequate for ab initio structure determinations.We have recently demonstrated the utility of these data sets for structure analyses by direct phase determination based on the probabilistic estimate of three- and four-phase structure invariant sums. Examples include the structure of diketopiperazine using Vainshtein's 3D data, a similar 3D analysis of the room temperature structure of thiourea, and a zonal determination of the urea structure, the latter also based on data collected by the Moscow group.

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Finite element modeling of laser-generated ultrasound with application to defect detection

Research in Nondestructive Evaluation ◽

10.1080/09349847.2014.968748 ◽

2014 ◽

pp. 141008191338004

Author(s):

Peyman Soltani ◽

Negar Akbareian

Keyword(s):

Finite Element ◽

Finite Element Modeling ◽

Defect Detection ◽

Element Modeling

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Time consumption and quality of an automated fusion tool for SPECT and MRI images of the brain

Nuklearmedizin ◽

10.1055/s-0038-1625192 ◽

2003 ◽

Vol 42 (05) ◽

pp. 215-219

Author(s):

G. Platsch ◽

A. Schwarz ◽

K. Schmiedehausen ◽

B. Tomandl ◽

W. Huk ◽

...

Keyword(s):

Data Sets ◽

3D Mri ◽

Clinical Routine ◽

Fusion Procedure ◽

3D Data ◽

Mri Scans ◽

2D Data ◽

Time Required ◽

The Brain ◽

Manual Correction

Summary: Aim: Although the fusion of images from different modalities may improve diagnostic accuracy, it is rarely used in clinical routine work due to logistic problems. Therefore we evaluated performance and time needed for fusing MRI and SPECT images using a semiautomated dedicated software. Patients, material and Method: In 32 patients regional cerebral blood flow was measured using 99mTc ethylcystein dimer (ECD) and the three-headed SPECT camera MultiSPECT 3. MRI scans of the brain were performed using either a 0,2 T Open or a 1,5 T Sonata. Twelve of the MRI data sets were acquired using a 3D-T1w MPRAGE sequence, 20 with a 2D acquisition technique and different echo sequences. Image fusion was performed on a Syngo workstation using an entropy minimizing algorithm by an experienced user of the software. The fusion results were classified. We measured the time needed for the automated fusion procedure and in case of need that for manual realignment after automated, but insufficient fusion. Results: The mean time of the automated fusion procedure was 123 s. It was for the 2D significantly shorter than for the 3D MRI datasets. For four of the 2D data sets and two of the 3D data sets an optimal fit was reached using the automated approach. The remaining 26 data sets required manual correction. The sum of the time required for automated fusion and that needed for manual correction averaged 320 s (50-886 s). Conclusion: The fusion of 3D MRI data sets lasted significantly longer than that of the 2D MRI data. The automated fusion tool delivered in 20% an optimal fit, in 80% manual correction was necessary. Nevertheless, each of the 32 SPECT data sets could be merged in less than 15 min with the corresponding MRI data, which seems acceptable for clinical routine use.

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