Interleaved signal multiplexing readout in depth encoding Prism-PET detectors

Abstract

Background: Given the large number of readout pixels in clinical positron emission tomography (PET) scanners, signal multiplexing is an indispensable feature to reduce scanner complexity, power consumption, heat output, and cost.

Purpose: In this paper, we introduce interleaved multiplexing (iMux) scheme that utilizes the characteristic light-sharing pattern of depth-encoding Prism-PET detector modules with single-ended readout.

Methods: In the iMux readout, four anodes from every other silicon photomultiplier (SiPM) pixels across rows and columns, which overlap with four distinct light guides, are connected to the same application-specific integrated circuit (ASIC) channel. The 4-to-1 coupled Prism-PET detector module was used which consisted of a 16 × 16 array of 1.5 × 1.5 × 20 mm³ lutetium yttrium oxyorthosilicate (LYSO) scintillator crystals coupled to an 8 × 8 array with 3 × 3 mm² SiPM pixels. A deep learning-based demultiplexing model was investigated to recover the encoded energy signals. Two different experiments were performed with non-multiplexed and multiplexed readouts to evaluate the spatial, depth of interaction (DOI), and timing resolutions of our proposed iMux scheme.

Results: The measured flood histograms, using the decoded energy signals from our deep learning-based demultiplexing architecture, achieved perfect crystal identification of events with negligible decoding error. The average energy, DOI, and timing resolutions were 9.6 ± 1.5%, 2.9 ± 0.9 mm, and 266 ± 19 ps for non-multiplexed readout and 10.3 ± 1.6%, 2.8 ± 0.8 mm, and 311 ± 28 ps for multiplexed readout, respectively.

Conclusions: Our proposed iMux scheme improves on the already cost-effective and high-resolution Prism-PET detector module and provides 16-to-1 crystal-to-readout multiplexing without appreciable performance degradation. Also, only four SiPM pixels are shorted together in the 8 × 8 array to achieve 4-to-1 pixel-to-readout multiplexing, resulting in lower capacitance per multiplexed channel.

Keywords: Prism-PET; deep learning; demultiplexing; signal multiplexing.