The low sensitivity of conventional single photon emission computed tomography (SPECT) presents challenges for ultra-low activity imaging, such as targeted alpha therapy (TAT) imaging. Our previous work showed that collimatorless imaging can achieve remarkable sensitivity but suffers from deteriorated resolution and inaccurate reconstructions, even with advanced reconstruction algorithms. To address these limitations, in this study, we design the dual-layer collimatorless SPECT systems specifically for fast or ultra-low activity imaging. These dual-layer systems combine collimatorless imaging with collimated acquisition using slit or slat collimators: the inner layer maximizes sensitivity with collimatorless imaging, while the outer layer provides additional spatial information through collimated imaging. Unlike general-purpose SPECT systems, these are tailored to significantly improve sensitivity while maintaining satisfactory spatial resolution and accurate reconstruction for fast or ultra-low activity imaging. In this study, our primary focus is on small animal imaging, particularly preclinical TAT imaging using 225 Ac as an exemplar for ultra-low activity imaging. We evaluate the performance of these dual-layer systems through extensive Monte Carlo simulations and reconstructions with various phantoms. The results show that relying solely on collimatorless imaging from the inner layer or collimated imaging from the outer layer leads to inaccuracies and suboptimal reconstructions in ultra-low activity imaging. The combined imaging from both layers enhances sensitivity, spatial resolution, and overall image quality. The proposed dual-layer collimatorless SPECT systems achieve a sensitivity higher than 29% for 218 keV gamma rays and an image resolution of about 5 mm, making them promising and suitable for ultra-low activity imaging.

In our previous work on image reconstruction for single-layer collimatorless scintigraphy, we introduced the min-min weighted robust least squares (WRLS) optimization algorithm to address the challenge of reconstructing images when both the system matrix and the projection data are uncertain. Whereas the WRLS algorithm has been successful in two-dimensional (2D) reconstruction, expanding it to three-dimensional (3D) reconstruction is difficult since the WRLS optimization problem is neither smooth nor strongly-convex. To overcome these difficulties and achieve robust image reconstruction in the presence of system uncertainties and projection noise, we propose a generalized iterative method based on the maximum likelihood expectation maximization (MLEM) algorithm, hereinafter referred to as the Masked-MLEM algorithm. In the Masked-MLEM algorithm, only selected subsets (“masks”) in the system matrix and the projection contribute to the image update to satisfy the constraints imposed by the system uncertainties. We validate the Masked-MLEM algorithm and compare it to the standard MLEM algorithm using data from both collimated and uncollimated imaging instruments, including parallel-hole collimated SPECT, 2D collimatorless scintigraphy, and 3D collimatorless tomography. The results show that the Masked-MLEM and standard MLEM reconstructions are similar in SPECT imaging, while the Masked-MLEM algorithm outperforms the standard MLEM algorithm in collimatorless imaging. A good choice of system uncertainty can make the Masked-MLEM reconstruction more robust than the standard MLEM reconstruction, effectively reducing the likelihood of reconstructing higher activities in regions without radioactive sources.