A single photon emission computed tomography (SPECT) imaging system can be modeled by a linear operator
H that maps from object space to detector pixels in image space. The singular vectors and singular-value spectra
of H provide useful tools for assessing system performance. The number of voxels used to discretize object space
and the number of collection angles and pixels used to measure image space make the matrix dimensions H
large. As a result, H must be stored sparsely which renders several conventional singular value decomposition
(SVD) methods impractical. We used an iterative power methods SVD algorithm (Lanczos) designed to operate
on very large sparsely stored matrices to calculate the singular vectors and singular-value spectra for two small
animal pinhole SPECT imaging systems: FastSPECT II and M3R. The FastSPECT II system consisted of two
rings of eight scintillation cameras each. The resulting dimensions of H were 68921 voxels by 97344 detector
pixels. The M3R system is a four camera system that was reconfigured to measure image space using a single
scintillation camera. The resulting dimensions of H were 50864 voxels by 6241 detector pixels. In this paper we
present results of the SVD of each system and discuss calculation of the measurement and null space for each
system.
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