Evaluation of median filtering after reconstruction with maximum likelihood expectation maximization (ML-EM) by real space and frequency space

• Published in: Nippon Hōshasen Gijutsu Gakkai zasshi Vol. 58; no. 5; p. 670
• Main Authors: Matsumoto, Keiichi, Fujita, Toru, Oogari, Koji
• Format: Journal Article
• Language: Japanese
• Published: Japan 01-05-2002
• Subjects: Filtration; Image Enhancement - methods; Image Processing, Computer-Assisted - methods; Phantoms, Imaging; Tomography, Emission-Computed, Single-Photon - instrumentation; Tomography, Emission-Computed, Single-Photon - methods
• ISSN: 0369-4305
• DOI: 10.6009/jjrt.KJ00001364426

Maximum likelihood expectation maximization (ML-EM) image quality is sensitive to the number of iterations, because a large number of iterations leads to images with checkerboard noise. The use of median filtering in the reconstruction process allows both noise reduction and edge preservation. We examined the value of median filtering after reconstruction with ML-EM by comparing filtered back projection (FBP) with a ramp filter or ML-EM without filtering. SPECT images were obtained with a dual-head gamma camera. The acquisition time was changed from 10 to 200 (seconds/frame) to examine the effect of the count statistics on the quality of the reconstructed images. First, images were reconstructed with ML-EM by changing the number of iterations from 1 to 150 in each study. Additionally, median filtering was applied following reconstruction with ML-EM. The quality of the reconstructed images was evaluated in terms of normalized mean square error (NMSE) values and two-dimensional power spectrum analysis. Median filtering after reconstruction by the ML-EM method provided stable NMSE values even when the number of iterations was increased. The signal element of the image was close to the reference image for any repetition number of iterations. Median filtering after reconstruction with ML-EM was useful in reducing noise, with a similar resolution achieved by reconstruction with FBP and a ramp filter. Especially in images with poor count statistics, median filtering after reconstruction with ML-EM is effective as a simple, widely available method.