PT  - JOURNAL ARTICLE
AU  - Shalchian, Bahareh
AU  - Rajabi, Hossein
AU  - Soltanian-Zadeh, Hamid
TI  - Assessment of the Wavelet Transform in Reduction of Noise from Simulated PET Images
AID  - 10.2967/jnmt.109.067454
DP  - 2009 Dec 01
TA  - Journal of Nuclear Medicine Technology
PG  - 223--228
VI  - 37
IP  - 4
4099  - http://tech.snmjournals.org/content/37/4/223.short
4100  - http://tech.snmjournals.org/content/37/4/223.full
SO  - J. Nucl. Med. Technol.2009 Dec 01; 37
AB  - An efficient method for tomographic imaging in nuclear medicine is PET. Higher sensitivity, higher spatial resolution, and more accurate quantification are advantages of PET, in comparison to SPECT. However, a high noise level in the images limits the diagnostic utility of PET. Noise removal in nuclear medicine is traditionally based on the Fourier decomposition of the images. This method is based on frequency components, irrespective of the spatial location of the noise or signal. The wavelet transform presents a solution by providing information on frequency contents while retaining spatial information, alleviating the shortcoming of Fourier transformation. Thus, wavelet transformation has been extensively used for noise reduction, edge detection, and compression. Methods: In this research, SimSET software was used for simulation of PET images of the nonuniform rational B-spline–based cardiac-torso phantom. The images were acquired using 250 million counts in 128 × 128 matrices. For a reference image, we acquired an image with high counts (6 billion). Then, we reconstructed these images using our own software developed in a commercially available program. After image reconstruction, a 250-million-count image (noisy image or test image) and a reference image were normalized, and then root mean square error was used to compare the images. Next, we wrote and applied denoising programs. These programs were based on using 54 different wavelets and 4 methods. Denoised images were compared with the reference image using root mean square error. Results: Our results indicate stationary wavelet transformation and global thresholding are more efficient at noise reduction than are other methods that we investigated. Conclusion: Wavelet transformation is a useful method for denoising simulated PET images. Noise reduction using this transform and loss of high-frequency information are simultaneous with each other. It seems we should attend to mutual agreement between noise reduction and visual quality of the image.