Introducing a novel fast algebraic reconstruction technique and advancing 3D image reconstruction in a specialized bioimaging system

In this study, our primary goal was to remarkably reduce computation time and enhance the efficiency of 3D image reconstruction in bioimaging applications by focusing on iterative image reconstruction methods, particularly algebraic reconstruction technique (ART). To achieve this, we introduced a novel fast algebraic reconstruction technique called the mining-ART, consisting of two versions. We validated our proposed method using the mini-Opto tomography device, a specialized bioimaging system, and a synthetic biological phantom. This phantom, developed in our laboratory for bioimaging experiments, was composed of polydimethylsiloxane (PDMS) and had dimensions of 8 mm x 8 mm x 500 mu m. We acquired two-dimensional (2D) projections of the phantom from 11 different angles using the bioimaging device, and then reconstructed these projections in 3D using both ART and the mining-ART. The dimensions of the 3D reconstructed images ranged from 100 x 100 x 50 to 800 x 800 x 50, and voxel resolutions varied correspondingly from 80 x 80 x 10 mu m to 10 x 10 x 10 mu m. Our experimental results demonstrated that the proposed mining-ART outperformed ART in terms of superior 3D image reconstruction speed across various sizes, while maintaining similar image quality. The mining-ART achieved a significant acceleration in computation time, ranging from 5.89 to 92.77 times faster than ART, depending on the dimensions. Furthermore, we extensively explored the feasibility of integrating compressed sensing-based three-dimensional total variation (3DTV) into the mining-ART. In conclusion, our proposed mining-ART demonstrated its potential in dramatically enhancing the computational performance of image reconstruction methods in bioimaging and made a significant contribution to advancing 3D image reconstruction in various research fields.