Quantitative comparison of planar coded aperture imaging reconstruction methods

Abstract

Imaging distributions of radioactive sources plays a substantial role in nuclear medicine as well as in monitoring nuclear waste and its deposit. Coded Aperture Imaging (CAI) has been proposed as an alternative to parallel or pinhole collimators, but requires image reconstruction as an extra step. Multiple reconstruction methods with varying run time and computational complexity have been proposed. Yet, no quantitative comparison between the different reconstruction methods has been carried out so far. This paper focuses on a comparison based on three sets of hot-rod phantom images captured with an experimental γ-camera consisting of a Tungsten-based MURA mask with a 2 mm thick 256 × 256 pixelated CdTe semiconductor detector coupled to a Timepix© readout circuit. Analytical reconstruction methods, MURA Decoding, Wiener Filter and a convolutional Maximum Likelihood Expectation Maximization (MLEM) algorithm were compared to data-driven Convolutional Encoder-Decoder (CED) approaches. The comparison is based on the contrast-to-noise ratio as it has been previously used to assess reconstruction quality. For the given set-up, MURA Decoding, the most commonly used CAI reconstruction method, provides robust reconstructions despite the assumption of a linear model. For single image reconstruction, however, MLEM performed best of all analytical reconstruction methods, but took on average 45 times longer than MURA Decoding. The fastest reconstruction method is the Wiener Filter with a run time 4.3 times faster compared to MURA Decoding and a mediocre quality. The CED with a specifically tailored training set was able to succeed the most commonly used MURA decoding on average by a factor between 1.37 and 2.60 and an equal run time.

Coded Aperture Imaging for Electron Pitch Angle Observations

“…However, optimizing random masks for various applications is not trivial and often requires artificial intelligence search methods (e.g., McMillan et al (2015)). A recent study by Meiaaner et al (2023) compared additional analytical reconstruction techniques such as the Maximum Likelihood Expectation Maximization (MLEM) algorithm and Wiener filtering to standard decoding, as well as Convolutional Encoder-Decoder methods. The authors found that MLEM provides the highest reconstruction quality but at the expense of nearly a 45× increase in computation time.…”

“…The authors found that MLEM provides the highest reconstruction quality but at the expense of nearly a 45× increase in computation time. Meiaaner et al (2023) did suggest, however, that a Convolutional Encoder-Decoder neural network could outperform all analytical decoding techniques, producing images with better contrast. Neural networks have been found optimal over analytical methods in other coded aperture studies (e.g., Zhang et al (2019)), and even for identifying sources within the PCFOV (e.g., Liu et al (2021)).…”

“…Meiaaner et al. (2023) did suggest, however, that a Convolutional Encoder‐Decoder neural network could outperform all analytical decoding techniques, producing images with better contrast. Neural networks have been found optimal over analytical methods in other coded aperture studies (e.g., Zhang et al.…”

Intraoperative Gamma Cameras: A Review of Development in the Last Decade and Future Outlook

“…Collimator performance dominates current IGC capabilities; this, therefore, represents a research area with a high potential to improve overall performance. Whilst pinhole and parallel-hole collimator geometries are well-established and understood, diverging and coded-aperture designs are still in flux, with active research into both collimator design and image reconstruction [122][123][124]. This development is supported by the rapid progression of additive manufacturing for high-Z materials, although currently cost remains a barrier to this technology's uptake.…”

“…Charge-sharing correction algorithms, which have already been implemented for multiple detector systems, offer the ability to recover the spectral performance lost by small-anode-pixel semiconductor detectors and allow for exceptional energy resolution and spatial resolution simultaneously [118,147,148]. The application of deep learning and neural networks to the optimisation of current dataprocessing tasks looks to advance IGCs in a range of ways, including: improved energy resolution reconstruction, sub-pixel event positioning, improved event localisation, and improved near-field coded-aperture image reconstruction [119,124,149,150].…”