Experimental study of the fast non-local means noise reduction algorithm using the Hoffman 3D brain phantom in nuclear medicine SPECT image

Abstract Gamma imaging of the brain in nuclear medicine usually contains a noise distribution owing to attenuation and scattering effects. This study aimed at improving the quality of the image of the brain acquired by single photon emission computed tomography (SPECT) by applying the fast non-local means (FNLM) algorithm, which an excellent noise reduction technique, using a Hoffman 3D brain phantom filled with 99mTc. Gaussian noise was added to the acquired image using MATLAB and the image quality evaluated using conventional noise reduction filters and the proposed FNLM algorithm. The contrast to noise ratio (CNR), peak signal to noise ratio, and root mean square error were used for quantitative evaluation. Comparing the image qualities obtained using various filters and algorithms, the CNR results of the images with Gaussian and median filters, and FNLM algorithm were found to surpass those of the noise image by 1.06, 1.22, and 1.41 times, respectively. In addition, similarity analysis indicated that the image evaluated using the FNLM algorithm showed excellent quality. Thus, our results demonstrated that the FNLM algorithm provided the greatest improvement in image quality based on quantitative evaluation results in the SPECT system.