Algorithm cross-correlation technique

Another main parameter, which needs to be determined in equation (1), is the local axial velocity. Applying cross-correlation techniques to determine the speed of moving profiles had been widely demonstrated (Beck Plaskowski [5]) and the mathematical principle behind this technique is also documented in statistical literature such as Bendat Piersol [10]. The basic function of the cross-correlation technique is to find the time offset between two signals where the similarities are most obvious. These signals can be of any value and is not limited to quantitative conductivity data. A back-projection-type qualitafive reconstruction algorithm requires less computational resources and will suffice Kotre [11]. 

The direct demodulation algorithm provides a general approach to handle a large variety of image restoration or reconstruction problems. Computer simulations and analysis results for COS-B and CGRO 7-ray data show that in comparison with traditional techniques, e.g. maximum entropy method, cross-correlation deconvolution or likelihood approach, the direct demodulation method has high sensitivity, high resolution ability and capability to effectively reduce the effect of statistical fluctuations and noise in data and to simultaneously restore both the extended and discrete features in the object. 

It is also possible to use p-PIV for temperature measurements. The technique is based on the precondition that Brownian motion will cause width-wise broadening of the cross-correlation peak. A correlation-based PIV algorithm detects the magnitude of Brownian particle motion and can be used to determine the temperature of the fluid [41]. 

Visualization of CT, MRI, and nuclear medicine images can greatly benefit fi om specialized visualization techniques, since they are series of two-dimensional cross sections. Instead of having the surgeon mentally correlate consecutive slices and create a mental three-dimensional view, it is desirable to directly reconstruct the three-dimensional information and show it as a new computed image. There are two families of visualization algorithms volume visualization and surface visualization. We describe them briefly next. 

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