Multiscale local sparsity and prior learning algorithm for Cherenkov-excited luminescence scanned tomography reconstruction.

Cherenkov-excited luminescence scanned tomography (CELST) is an emerging imaging technique and its potential applications during radiation therapy have just recently been explored. The aim of CELST is to recover the distribution of luminescent probes from emission photons. However, CELST images tend to suffer from low resolution and degraded image quality due to light multiple scattering and limited boundary measurements. Therefore, inaccurate information about the status of the luminescent probe is provided. To accurately capture the sparsity characterization of a luminescent probe and achieve the high-quality image, a novel reconstruction method, to our knowledge, is proposed for CELST by combining a sparse prior with an attention network, termed LKSVD-Net. A multiscale learned KSVD is first incorporated to obtain the local sparsity information of a luminescent probe. Subsequently, a prior attention network is designed to leverage the prior features related to the measurements. The multiscale sparsity and prior features are finally combined to complete the image reconstruction. Experimental results demonstrate that the LKSVD-Net can notably enhance image quality even in a 20 dB signal-to-noise ratio (SNR). Furthermore, the proposed LKSVD-Net yields improved quantitative accuracy for 4 mm diameter probes with an edge-to-edge distance of 2 mm. The results demonstrate that LKSVD-Net improves the peak signal-to-noise ratio (PSNR) by approximately 15.1%, structural similarity index measure (SSIM) by about 95.8%, and Pearson correlation (PC) by around 3% compared to Tikhonov regularization.