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Evaluation of the effect of filter apodization for volume PET imaging using the 3-D RP algorithm
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Citations
19
References
2003
Year
Computed TomographyEngineeringPet-mriAdvanced ImagingVolume PetBiomedical Engineering3-D Rp AlgorithmPositron Emission TomographySmall Lesion PhantomsCt ScanPhoton-counting Computed TomographyMolecular ImagingNuclear MedicineRadiologyHealth SciencesReconstruction TechniqueMedical ImagingMedical Image ComputingBiomedical ImagingCutoff FrequencyFilter Apodization3D Imaging
We investigated the influence of filter apodization and cutoff frequency on the image quality of volume positron emission tomography (PET) imaging using the three-dimensional reprojection (3-D RP) algorithm. An important parameter in 3-D RP and other filtered backprojection algorithms is the choice of the filter window function. In this study, the Hann, Hamming, and Butterworth low-pass window functions were investigated. For each window, a range of cutoff frequencies was considered. Projection data were acquired by scanning a uniform cylindrical phantom, a cylindrical phantom containing four small lesion phantoms having diameters of 3, 4, 5, and 6 mm and the 3-D Hoffman brain phantom. All measurements were performed using the high-resolution PET camera developed at the M.D. Anderson Cancer Center (MDAPET), University of Texas, Houston, TX. This prototype camera, which is a multiring scanner with no septa, has an intrinsic transaxial resolution of 2.8 mm. The evaluation was performed by computing the noise level in the reconstructed images of the uniform phantom and the contrast recovery of the 6-mm hot lesion in a warm background and also by visually inspecting images, especially those of the Hoffman brain phantom. For this work, we mainly studied the central slices which are less affected by the incompleteness of the 3-D data. Overall, the Butterworth window offered a better contrast-noise performance over the Hann and Hamming windows. For our high statistics data, for the Hann and Hamming apodization functions a cutoff frequency of 0.6-0.8 of the Nyquist frequency resulted in a reasonable compromise between the contrast recovery and noise level and for the Butterworth window a cutoff frequency of 0.4-0.6 of the Nyquist frequency was a reasonable choice. For the low statistics data, use of lower cutoff frequencies was more appropriate.
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