Efficient optimization for adaptive SPECT systems based on local shift-invariance

Adaptive SPECT systems automatically change some of their settings to maximize the image quality for a given subject and purpose. This approach has a lot of potential, and could lead to drastic improvements in performance. In particular, it would be very useful in high resolution pinhole SPECT, where the low sensitivity requires higher radiation doses or longer imaging times compared to other systems. In order to have adaptation in real-time, we need a fast method for optimizing the adaptive settings according to a given figure of merit. This is still a big challenge. Based on previous work, we address in this paper the issue of fast evaluation of image quality and optimization, for a class of adaptive SPECT systems. We evaluate the image quality in a voxel of interest, reconstructed using postfiltered MLEM, with the contrast-to-noise ratio (CNR). The CNR is computed analytically, using an approximation based on the Fisher information matrix and assuming local shift-invariance on the Fisher information matrices per adaptation parameter. We maximize the CNR with a gradient based optimization approach. We then test this method in the optimization of the angular sampling of a single-head SPECT system which rotates around a phantom. In this case, the method proved to be very efficient, and at the same time showed good agreement with previous results in literature and with the outcome from reconstructions.