In very-high-spatial-resolution gamma-ray imaging applications, such as preclinical PET and SPECT, estimation of 3D interaction location inside the detector crystal can be used to minimize parallax error in the imaging system. In this work, we investigate the effect of bias voltage setting on depth-of-interaction (DOI) estimates for a semiconductor detector with a double-sided strip geometry. We first examine the statistical properties of the signals and develop expressions for likelihoods for given gamma-ray interaction positions. We use Fisher Information to quantify how well (in terms of variance) the measured signals can be used for DOI estimation with different bias-voltage settings. We performed measurements of detector response versus 3D position as a function of applied bias voltage by scanning with highly collimated synchrotron radiation at the Advanced Photon Source at Argonne National Laboratory. Experimental and theoretical results show that the optimum bias setting depends on whether or not the estimated event position will include the depth of interaction. We also found that for this detector geometry, the z-resolution changes with depth.

Multi-anode photomultiplier tubes (MAPMTs) offer high spatial resolution with their small size anodes that may range from 64 to 1024 in number per tube. In order to increase detector size, MAPMT modules can be arranged in arrays and combined in a single modular scintillation camera. However, then the large number of channels that require amplification and digitization become practically not feasible unless signals are combined or reduced in some manner. Conventional approaches use resistive charge division readouts with a centroid algorithm (or a variant of it) for simplicity in the electronic circuitry implementation and fast execution. However, coupling signals from many anodes may cause significant information loss and limit achievable resolution. In this study, a new approach for optimizing readout-electronics design for MAPMTs based on an analysis of information content in the signals is presented. An adaptive read-out scheme to be used with maximum-likelihood estimation methods is proposed. This scheme achieves precision in estimating event parameters that is close to what is achieved by retaining all signals.

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