Cherenkov-excited luminescence scanned tomography reconstruction based on Unet

Cherenkov-excited luminescence scanned tomography (CELST) is an emerging tomographic optical imaging modality. However, recovering spatial distribution of luminescent source from boundary measurements is a typically ill-posed problem. To improve the performance of CELST reconstruction, an end-to-end reconstruction algorithm is developed by combining dilated convolution and attention mechanism based on Unet (DA-Unet). Its performance is validated with numerical simulations. The results reveal that DA-Unet has superior reconstruction performance with high spatial resolution. It achieves image quality with PSNR of more than 35 dB and SSIM of larger than 0.95. Furthermore, the DA-Unet can reconstruct luminescent source even with less boundary measurements.

Automated summary

This paper introduces Cherenkov-excited luminescence scanned tomography (CELST) as a novel tomographic optical imaging modality, and proposes an improved reconstruction algorithm DA-Unet, which combines dilated convolution and attention mechanism based on Unet for end-to-end reconstruction. Numerical simulations validate the superior reconstruction performance and high spatial resolution of DA-Unet, achieving image quality with PSNR of more than 35 dB and SSIM of larger than 0.95. Furthermore, DA-Unet can reconstruct luminescent source even with less boundary measurements.