Hadamard multiplexed fluorescence tomography

Abstract

Depth-resolved three-dimensional (3D) reconstruction of fluorophore-tagged inclusions in fluorescence tomography (FT) poses a highly ill-conditioned problem as depth information must be extracted from boundary data. Due to the ill-posed nature of the FT inverse problem, noise and errors in the data can severely impair the accuracy of the 3D reconstructions. The signal-to-noise ratio (SNR) of the FT data strongly affects the quality of the reconstructions. Additionally, in FT scenarios where the fluorescent signal is weak, data acquisition requires lengthy integration times that result in excessive FT scan periods. Enhancing the SNR of FT data contributes to the robustness of the 3D reconstructions as well as the speed of FT scans. A major deciding factor in the SNR of the FT data is the power of the radiation illuminating the subject to excite the administered fluorescent reagents. In existing single-point illumination FT systems, the source power level is limited by the skin maximum radiation exposure levels. In this paper, we introduce and study the performance of a multiplexed fluorescence tomography system with orders-of-magnitude enhanced data SNR over existing systems. The proposed system allows for multi-point illumination of the subject without jeopardizing the information content of the FT measurements and results in highly robust reconstructions of fluorescent inclusions from noisy FT data. Improvements offered by the proposed system are validated by numerical and experimental studies. tomography of sub-surface heterogeneities using spatially modulated structured light," Opt.

Generalized mesh-based Monte Carlo for wide-field illumination and detection via mesh retessellation

“…Such modeling should encompass structured illumination strategies for spectroscopy as performed in spatial-frequency domain imaging (SFDI) [39-41], or compressive sensing-based optical tomography using quantized low frequency [42][43][44], k-space [45][46][47], wavelet [48,49], measurement-driven [50,51] or theoretically prior-driven [52] patterns to form structured illumination bases. Such modeling has also proven to be useful for applications using raster scanning of fixed extended sources [53][54][55] or spatial integration of punctual source illumination schemes [56]. Additionally, it should also enable wide-field detection strategies that includes spatial post processing from camera-based detection schemes [43,46,57] and single-pixel camera implementations [42,58,59].…”