Improved image quality for Cherenkov-Excited Luminescence Scanned Tomography based on learned KSVD

Cherenkov-excited luminescence scanned imaging (CELSI) is a new emerging imaging modality, which uses linear accelerator (LINAC) to induce Cherenkov radiation, and then secondary excite molecular probes to produce luminescence. The tomographic distribution of the molecular probes can be recovered by a reconstruction algorithm. However, the reconstruction images usually suffer from many artifacts. To improve the image quality for tomographic reconstruction, we propose a reconstruction method based on learned KSVD. Numerical simulation experiments reveal that the proposed algorithm can reduce the artifacts in the reconstructed image. The quantitative results show that the structured similarity (SSIM) is improved more than 8.8% compared to the existing algorithms. In addition, our results also demonstrate that the proposed algorithm has the best performance under different noise levels (0.5%, 1%, 2%, and 4%).

Automated summary

Cherenkov-excited luminescence scanned imaging (CELSI) is a new emerging imaging modality that utilizes LINAC to induce Cherenkov radiation and generate luminescence through secondary excitation of molecular probes. The distribution of the molecular probes can be reconstructed using a reconstruction algorithm. However, reconstruction images often suffer from artifacts. To improve image quality, we propose a reconstruction method based on learned KSVD. Numerical simulation experiments demonstrate that the proposed algorithm can reduce artifacts in reconstructed images and improve structured similarity (SSIM) by more than 8.8% compared to existing algorithms. Additionally, our results show that the proposed algorithm performs best under different noise levels (0.5%, 1%, 2%, and 4%).