The influence of wavelet function shape on the results of NMR data processing
Ladislav Valkovic, Slovak Academy of Sciences, Bratislava, Slovakia

Seminar • 06 December 2010

Abstract
The wavelet transform (WT) has been proved in both its forms (discrete and continuous) to be a useful tool in processing NMR datasets. Discrete WT can be usedmainly for image (and spectral) filtering and contrast enhancement. The continuous WT is more suited for fMRI data analysis because of its ability of tracking signal changes in time. Although WT has been found useful and applicable, still other methods are more common. This is probably a result of not optimal setting of the WT used. Wavelet function as a basic parameter of the WT has a lot of possible shapes defined and basically a lot more are possible. The influence of the chosen function shape on the result of the WT is obvious and therefore an optimal function shape has to be defined for each application. Our first experiments were with fast low field high-resolution MRI, therefore discrete WT was used (for image filtering). Fiftydifferent function shapes along with different thresholds and WT order (1500 different options altogether) were compared. Different performance of the filter was observed as expected and according to two parameters (SNR and residual difference) an optimal setting was found. As every sequence has its own features it is expected to find different optimums for each imaging sequence. In the field of fMRI is WT used to find the signal changes and also to measure the hemo-dynamic response curve (HDR) of the activated region. We suspect that the function of the shape closest to the HDR will have the best results in activation definition. Again a variety of optimal wavelet functions is expected for different fMRI paradigms. Not even different regions, but also the same regions activated by different stimulusmight resolve in slight differences in HDR. Knowing the exact HDR is very important for future experimentsetting. We believe that with proper setting is WT going to be very precise and thus commonly used tool in fMRI data analysis.