Wide-field fluorescence molecular tomography with compressive sensing based preconditioning

Abstract

Wide-field optical tomography based on structured light illumination and detection strategies enables efficient tomographic imaging of large tissues at very fast acquisition speeds. However, the optical inverse problem based on such instrumental approach is still ill-conditioned. Herein, we investigate the benefit of employing compressive sensing-based preconditioning to wide-field structured illumination and detection approaches. We assess the performances of Fluorescence Molecular Tomography (FMT) when using such preconditioning methods both in silico and with experimental data. Additionally, we demonstrate that such methodology could be used to select the subset of patterns that provides optimal reconstruction performances. Lastly, we compare preconditioning data collected using a normal base that offers good experimental SNR against that directly acquired with optimal designed base. An experimental phantom study is provided to validate the proposed technique.

Comparison of illumination geometry for lifetime‐based measurements in whole‐body preclinical imaging

“…We have developed and optimized an MFLI platform designed around a gated intensified CCD (ICCD) and structured light illumination. This platform, coupled with the computational tools required to perform optical tomography [13][14][15][16], enables performing 3D fluorescence imaging of live intact animals [17]. For tomography, the structured light illumination is implemented in transmittance for optimal performances [18].…”

Image reconstruction in fluorescence molecular tomography with sparsity-initialized maximum-likelihood expectation maximization

“…The theory of compressed sensing (CS) provides the conditions under which such approximate solvers are valid. Further, approaches based on singular value decomposition (SVD) can be applied to the sensitivity matrix to improve sparse reconstruction in FMT [33][34][35][36]. This technique is known as preconditioning of sensitivity matrix.…”

Improving mesoscopic fluorescence molecular tomography through data reduction

“…As a result, regularization methods have to be applied to obtain robust and accurate reconstructions. Herein, instead of the commonly used 2 L -norm regularization, also known as Tikhonov regularization, we take advantage of the sparsity of fluorophore distribution and adopt an 1 Lnorm regularization scheme [17,18], expressed as the following optimization problem:…”