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Studies of non-invasive glucose measurement with optical coherence tomography (OCT) in tissue-simulating phantoms and biological tissues show that glucose has an effect on the OCT signal slope. Choosing an efficient fitting range to calculate the OCT signal slope is important because it helps to improve the precision of glucose measurement. In this paper, we study the problem in two ways: (1) scattering-induced change of OCT signal slope versus depth in intralipid suspensions with different concentrations based on Monte Carlo (MC) simulations and experiments and (2) efficient fitting range for glucose measurement in 3% and 10% intralipid. The results show that the OCT signal slope expresses a contrary change with scattering coefficient below a certain depth in high intralipid concentrations, so that there is an effective fitting depth. With an efficient fitting range from 100 μm to the effective fitting depth, the precision of glucose measurement can be 4.4 mM for 10% intralipid and 2.2 mM for 3% intralipid.

To provide a computational efficient forward model with moderate accuracy for rapid 3D optical tomography in small volumes, radiative transport in the delta-P1 approximation combined with the approximation of the reciprocity was examined. Perturbations of optical signals caused by absorption and fluorescence heterogeneities submerged in a resin-based liquid phantom with background parameters close to rat brain tissues were measured using a recently constructed laminar optical tomography system. These measured perturbations were used to examine the theoretically calculated fluence perturbations based on the delta-P1 approximation and the reciprocity approximation. Results show that the errors between the predicted and measured data are acceptable, especially for fluorescence perturbations.

In this paper, we consider the use of blind deconvolution for optoacoustic (photoacoustic) imaging and investigate the performance of the method as means for increasing the resolution of the reconstructed image beyond the physical restrictions of the system. The method is demonstrated with optoacoustic measurement obtained from six-day-old mice, imaged in the near-infrared using a broadband hydrophone in a circular scanning configuration. We find that estimates of the unknown point spread function, achieved by blind deconvolution, improve the resolution and contrast in the images and show promise for enhancing optoacoustic images.

Fluorescence molecular tomography (FMT) allows the detection and quantification of various biological processes in small animals in vivo, which expands the horizons of pre-clinical research and drug development. Efficient three-dimensional (3D) reconstruction algorithm is the key to accurate localization and quantification of fluorescent target in FMT. In this paper, 3D reconstruction of FMT is regarded as a sparse signal recovery problem and the compressive sampling matching pursuit (CoSaMP) algorithm is adopted to obtain greedy recovery of fluorescent signals. Moreover, to reduce the modeling error, the simplified spherical harmonics approximation to the radiative transfer equation (RTE), more specifically $S{P}_3$, is utilized to describe light propagation in biological tissues. The performance of the proposed reconstruction method is thoroughly evaluated by simulations on a 3D digital mouse model by comparing it with three representative greedy methods including orthogonal matching pursuit (OMP), stagewise OMP(StOMP), and regularized OMP (ROMP). The CoSaMP combined with $S{P}_3$ shows an improvement in reconstruction accuracy and exhibits distinct advantages over the comparative algorithms in multiple targets resolving. Stability analysis suggests that CoSaMP is robust to noise and performs stably with reduction of measurements. The feasibility and reconstruction accuracy of the proposed method are further validated by phantom experimental data.

We present for the first time in vivo imaging of rat brain using microwave-induced thermoacoustic tomography (TAT). The in vivo imaging of rat brain was realized through an unconventional delivery of microwave energy from the front of rat brain (while the transducer was scanned along coronal plane of the animal brain), which maximized the microwave penetration into the brain. In addition, we found that the imaging contrast was highly dependent on the direction of the electric field polarization (EFP) and that more tissue structures/compositions could be revealed when both $X$- and $Y$-EFPs were used for TAT. The in vivo TAT images of rat brain obtained were compared with the 3.0 T MRI images and histological photographs, and numerous important brain anatomical structures were identified. An example of our TAT approach for imaging a foreign object embedded in a rat brain was also demonstrated. This study suggests that TAT has a great potential to be used in neuroscience studies and in noninvasive imaging of brain disorders.

Learning-based methods have been proved to perform well in a variety of areas in the biomedical field, such as biomedical image segmentation, and histopathological image analysis. Deep learning, as the most recently presented approach of learning-based methods, has attracted more and more attention. For instance, massive researches of deep learning methods for image reconstructions of computed tomography (CT) and magnetic resonance imaging (MRI) have been reported, indicating the great potential of deep learning for inverse problems. Optical technology-related medical imaging modalities including diffuse optical tomography (DOT), fluorescence molecular tomography (FMT), bioluminescence tomography (BLT), and photoacoustic tomography (PAT) are also dramatically innovated by introducing learning-based methods, in particular deep learning methods, to obtain better reconstruction results. This review depicts the latest researches on learning-based optical tomography of DOT, FMT, BLT, and PAT. According to the most recent studies, learning-based methods applied in the field of optical tomography are categorized as kernel-based methods and deep learning methods. In this review, the former are regarded as a sort of conventional learning-based methods and the latter are subdivided into model-based methods, post-processing methods, and end-to-end methods. Algorithm as well as data acquisition strategy are discussed in this review. The evaluations of these methods are summarized to illustrate the performance of deep learning-based reconstruction.

Image reconstruction in fluorescence molecular tomography involves seeking stable and meaningful solutions via the inversion of a highly under-determined and severely ill-posed linear mapping. An attractive scheme consists of minimizing a convex objective function that includes a quadratic error term added to a convex and nonsmooth sparsity-promoting regularizer. Choosing ${\ell }_1$-norm as a particular case of a vast class of nonsmooth convex regularizers, our paper proposes a low per-iteration complexity gradient-based first-order optimization algorithm for the ${\ell }_1$-regularized least squares inverse problem of image reconstruction. Our algorithm relies on a combination of two ideas applied to the nonsmooth convex objective function: Moreau–Yosida regularization and inertial dynamics-based acceleration. We also incorporate into our algorithm a gradient-based adaptive restart strategy to further enhance the practical performance. Extensive numerical experiments illustrate that in several representative test cases (covering different depths of small fluorescent inclusions, different noise levels and different separation distances between small fluorescent inclusions), our algorithm can significantly outperform three state-of-the-art algorithms in terms of CPU time taken by reconstruction, despite almost the same reconstructed images produced by each of the four algorithms.