Fluorescence molecular tomography (FMT) is a promising tool for real time in vivo quantification of neurotransmission (NT) as we pursue in our BRAIN initiative effort. However, the acquired image data are noisy and the reconstruction problem is ill-posed. Further, while spatial sparsity of the NT effects could be exploited, traditional compressive-sensing methods cannot be directly applied as the system matrix in FMT is highly coherent. To overcome these issues, we propose and assess a three-step reconstruction method. First, truncated singular value decomposition is applied on the data to reduce matrix coherence. The resultant image data are input to a homotopy-based reconstruction strategy that exploits sparsity via ℓ<sub>1</sub> regularization. The reconstructed image is then input to a maximum-likelihood expectation maximization (MLEM) algorithm that retains the sparseness of the input estimate and improves upon the quantitation by accurate Poisson noise modeling. The proposed reconstruction method was evaluated in a three-dimensional simulated setup with fluorescent sources in a cuboidal scattering medium with optical properties simulating human brain cortex (reduced scattering coefficient: 9.2 cm<sup>−1</sup>, absorption coefficient: 0.1 cm<sup>−1</sup> and tomographic measurements made using pixelated detectors. In different experiments, fluorescent sources of varying size and intensity were simulated. The proposed reconstruction method provided accurate estimates of the fluorescent source intensity, with a 20% lower root mean square error on average compared to the pure-homotopy method for all considered source intensities and sizes. Further, compared with conventional ℓ<sub>2</sub> regularized algorithm, overall, the proposed method reconstructed substantially more accurate fluorescence distribution. The proposed method shows considerable promise and will be tested using more realistic simulations and experimental setups.
Using optical tweezers combined with video based methods we probe non-specific interactions between polystyrene beads and surfaces at different electrolyte concentrations. We present force-distance relationships and using statiscally based maximum likelihood methods, which significantly improves spatial resolutions, the interaction potentials are also found. At low electrolyte concentrations the interaction potentials are in agreement with DLVO theory, but at high electrolyte concentrations the interaction potentials deviate both from DVLE and Lifshitz theories. Also, we probe the specific interaction between biotin and streptavidin. However, these measurements are complicated because the interaction is of very short range and a streptavidin coated polystyrene bead adheres to a biotin coated surface as soon as it encounters the surface during its Brownian motion in the trap.