[Fundamental evaluation of wavelet transform based noise reduction using soft threshold method in single photon emission computed tomography image].

PURPOSE The wavelet transform is a newly developed signal-processing tool that decomposes a signal into various levels of resolution. The wavelet transform based noise reduction has the characteristics of optimally separating signal from noise, preserving the rapid rises and falls of a signal, and reconstructing a smooth signal from noise-imposed observations. The aim of this study was to evaluate the effects of applying a new noise reduction technique, the wavelet transform based noise reduction, to single photon emission computed tomography (SPECT) images. METHODS Three experiments were performed using cylindrical phantom, line source, and hot-rod phantom, respectively. We acquired SPECT image datasets of each phantom, and reconstructed SPECT images using the wavelet transform based noise reduction with filter back projection (FBP). Images were de-noised by 3 parameters of wavelet transform based noise reduction: 1st wavelet weight (WW), 2nd WW, and 3rd WW, respectively. We evaluated the variances of full width at half maximum (FWHM), coefficients of variation (%CV), and frequency domains (radius direction distribution function in the power spectrum), respectively. RESULTS In the cylindrical phantom test, %CV was reduced from 27.92% to 15.38% using the wavelet approach. On the other hand, FWHM values showed no significant change. However, the increases of wavelet weights caused artifacts on the reconstructed images in some cases. CONCLUSIONS The wavelet based noise reduction had the significant potential to improve SPECT image. Therefore, the wavelet method should prove to be a robust approach to improve image quantification and fidelity.