SIRT methods for the iterative solution of sparse OPT data reconstruction

Abstract

Optical projection tomography (OPT) is an advanced three-dimensional (3D) imaging technology, which uses the filtered backprojection (FBP) algorithm to recover the 3D volume with sufficient number of projections. As to in vivo imaging, it is urgent to reduce the number of projections because the acquisition time could be minimized. However, reconstructing from undersampled OPT data can lead to artifacts and the decline in image quality. The simultaneous iterative reconstruction technique (SIRT) is introduced to remove artifacts and improve the image quality. The image qualities reconstructed from FBP and SIRT separately are compared, and the structural similarity and peak signal-to-noise ratio are calculated. Through simulated phantoms and OPT data of in vivo zebrafish embryo, SIRT consistently outperforms FBP in terms of reduced artifacts and enhanced image contrast especially when the projection numbers are reduced. SIRT method can provide high-quality reconstruction with 50 or fewer projections, thereby significantly reducing the minimum acquisition time and light dose while maintaining reconstruction quality. Through optimization and GPU acceleration, the SIRT algorithm can converge faster so as to reduce the image processing time. To our knowledge, this is the first time one SIRT algorithm is used for reconstruction of sparse OPT data. The experimental results show that SIRT algorithm outperform FBP especially when the number of projections is reduced. In addition, SIRT performs better in the preservation of vascular signal, which is significant for the monitoring of angiogenesis.