Estimation of the depth-dependent component of the point spread function of SPECT

The point spread function (PSF) of a gamma camera describes the photon count density distribution at the detector surface when a point source is imaged. Knowledge of the PSF is important for computer simulation and accurate image reconstruction of single photon emission computed tomography (SPECT) images. To reduce the number of measurements required for PSF characterization and the amount of computer memory to store PSF tables, and to enable generalization of the PSF to different collimator-to-source distances, the PSF may be modeled as the two-dimensional (2D) convolution of the depth-dependent component which is free of detector blurring (PSF(ideal)) and the distance-dependent detector response. Owing to limitations imposed by the radioactive strength of point sources, extended sources have to be used for measurements. Therefore, if PSF(ideal) is estimated from measured responses, corrections have to be made for both the detector blurring and for the extent of the source. In this paper, an approach based on maximum likelihood expectation-maximization (ML-EM) is used to estimate PSF(ideal). In addition, a practical measurement procedure which avoids problems associated with commonly used line-source measurements is proposed. To decrease noise and to prevent nonphysical solutions, shape constraints are applied during the estimation of PSF(ideal). The estimates are generalized to depths other than those which have been measured and are incorporated in a SPECT simulator. The method is validated for Tc-99m and T1-201 by means of measurements on physical phantoms. The corrected responses have the desired shapes and simulated responses closely resemble measured responses. The proposed methodology may, consequently, serve as a basis for accurate three-dimensional (3D) SPECT reconstruction.