Improving mesoscopic fluorescence molecular tomography via preconditioning and regularization

Reconstruction of fluorophore absorption and fluorescence lifetime using early photon mesoscopic fluorescence molecular tomography: a phantom study

SIGNIFICANCE

Fluorescence molecular lifetime tomography (FMLT) plays an increasingly important role in experimental oncology. The article presents and experimentally verifies an original method of mesoscopic time domain FMLT, based on an asymptotic approximation to the fluorescence source function, which is valid for early arriving photons.

AIM

The aim was to justify the efficiency of the method by experimental scanning and reconstruction of a phantom with a fluorophore. The experimental facility included the TCSPC system, the pulsed supercontinuum Fianium laser, and a three-channel fiber probe. Phantom scanning was done in mesoscopic regime for three-dimensional (3D) reflectance geometry.

APPROACH

The sensitivity functions were simulated with a Monte Carlo method. A compressed-sensing-like reconstruction algorithm was used to solve the inverse problem for the fluorescence parameter distribution function, which included the fluorophore absorption coefficient and fluorescence lifetime distributions. The distributions were separated directly in the time domain with the QR-factorization least square method.

RESULTS

3D tomograms of fluorescence parameters were obtained and analyzed using two strategies for the formation of measurement data arrays and sensitivity matrices. An algorithm is developed for the flexible choice of optimal strategy in view of attaining better reconstruction quality. Variants on how to improve the method are proposed, specifically, through stepped extraction and further use of a posteriori information about the object.

CONCLUSIONS

Even if measurement data are limited, the proposed method is capable of giving adequate reconstructions but their quality depends on available a priori (or a posteriori) information. Further research aims to improve the method by implementing the variants proposed.

Accurate and fast reconstruction for bioluminescence tomography based on adaptive Newton hard thresholding pursuit algorithm

As a promising noninvasive medical imaging technique, bioluminescence tomography (BLT) dynamically offers three-dimensional visualization of tumor distribution in living animals. However, due to the high ill-posedness caused by the strong scattering property of biological tissues and the limited boundary measurements with noise, BLT reconstruction still cannot meet actual preliminary clinical application requirements. In our research, to recover 3D tumor distribution quickly and precisely, an adaptive Newton hard thresholding pursuit (ANHTP) algorithm is proposed to improve the performance of BLT. The ANHTP algorithm fully combines the advantages of sparsity constrained optimization and convex optimization to guarantee global convergence. More precisely, an adaptive sparsity adjustment strategy was developed to obtain the support set of the inverse system matrix. Based on the strong Wolfe line search criterion, a modified damped Newton algorithm was constructed to obtain optimal source distribution information. A series of numerical simulations and phantom and in vivo experiments show that ANHTP has high reconstruction accuracy, fast reconstruction speed, and good robustness. Our proposed algorithm can further increase the practicality of BLT in biomedical applications.

Deep tissue localization and sensing using optical microcavity probes

Optical microcavities and microlasers were recently introduced as probes inside living cells and tissues. Their main advantages are spectrally narrow emission lines and high sensitivity to the environment. Despite numerous novel methods for optical imaging in strongly scattering biological tissues, imaging at single-cell resolution beyond the ballistic light transport regime remains very challenging. Here, we show that optical microcavity probes embedded inside cells enable three-dimensional localization and tracking of individual cells over extended time periods, as well as sensing of their environment, at depths well beyond the light transport length. This is achieved by utilizing unique spectral features of the whispering-gallery modes, which are unaffected by tissue scattering, absorption, and autofluorescence. In addition, microcavities can be functionalized for simultaneous sensing of various parameters, such as temperature or pH value, which extends their versatility beyond the capabilities of standard fluorescent labels.

Compressed sensing in fluorescence microscopy

Compressed sensing (CS) is a signal processing approach that solves ill-posed inverse problems, from under-sampled data with respect to the Nyquist criterium. CS exploits sparsity constraints based on the knowledge of prior information, relative to the structure of the object in the spatial or other domains. It is commonly used in image and video compression as well as in scientific and medical applications, including computed tomography and magnetic resonance imaging. In the field of fluorescence microscopy, it has been demonstrated to be valuable for fast and high-resolution imaging, from single-molecule localization, super-resolution to light-sheet microscopy. Furthermore, CS has found remarkable applications in the field of mesoscopic imaging, facilitating the study of small animals' organs and entire organisms. This review article illustrates the working principles of CS, its implementations in optical imaging and discusses several relevant uses of CS in the field of fluorescence imaging from super-resolution microscopy to mesoscopy.

Accelerating vasculature imaging in tumor using mesoscopic fluorescence molecular tomography via a hybrid reconstruction strategy

Mesoscopic fluorescent molecular tomography (MFMT) enables to image fluorescent molecular probes beyond the typical depth limits of microscopic imaging and with enhanced resolution compared to macroscopic imaging. However, MFMT is a scattering-based inverse problem that is an ill-posed inverse problem and hence, requires relative complex iterative solvers coupled with regularization strategies. Inspired by the potential of deep learning in performing image formation tasks from raw measurements, this work proposes a hybrid approach to solve the MFMT inverse problem. This methodology combines a convolutional symmetric network and a conventional iterative algorithm to accelerate the reconstruction procedure. By the proposed deep neural network, the principal components of the sensitivity matrix are extracted and the accompanying noise in measurements is suppressed, which helps to accelerate the reconstruction and improve the accuracy of results. We apply the proposed method to reconstruct in silico and vascular tree models. The results demonstrate that reconstruction accuracy and speed are highly improved due to the reduction of redundant entries of the sensitivity matrix and noise suppression.

Intravital mesoscopic fluorescence molecular tomography allows non-invasive in vivo monitoring and quantification of breast cancer growth dynamics

Preclinical breast tumor models are an invaluable tool to systematically study tumor progression and treatment response, yet methods to non-invasively monitor the involved molecular and mechanistic properties under physiologically relevant conditions are limited. Here we present an intravital mesoscopic fluorescence molecular tomography (henceforth IFT) approach that is capable of tracking fluorescently labeled tumor cells in a quantitative manner inside the mammary gland of living mice. Our mesoscopic approach is entirely non-invasive and thus permits prolonged observational periods of several months. The relatively high sensitivity and spatial resolution further enable inferring the overall number of oncogene-expressing tumor cells as well as their tumor volume over the entire cycle from early tumor growth to residual disease following the treatment phase. Our IFT approach is a promising method for studying tumor growth dynamics in a quantitative and longitudinal fashion in-vivo.

Model-based system matrix for iterative reconstruction in sub-diffuse angular-domain fluorescence optical projection tomography

This work concerns a fluorescence optical projection tomography system for low scattering tissue, like lymph nodes, with angular-domain rejection of highly scattered photons. In this regime, filtered backprojection (FBP) image reconstruction has been shown to provide reasonable quality images, yet here a comparison of image quality between images obtained by FBP and iterative image reconstruction with a Monte Carlo generated system matrix, demonstrate measurable improvements with the iterative method. Through simulated and experimental phantoms, iterative algorithms consistently outperformed FBP in terms of contrast and spatial resolution. Moreover, when projection number was reduced, in order to reduce total imaging time, iterative reconstruction suppressed artifacts that hampered the performance of FBP reconstruction (structural similarity of the reconstructed images with "truth" was improved from 0.15 ± 1.2 × 10^-3 to 0.66 ± 0.02); and although the system matrix was generated for homogenous optical properties, when heterogeneity (62.98 cm^-1 variance in µ_s ) was introduced to simulated phantoms, the results were still comparable (structural similarity homo: 0.67 ± 0.02 vs hetero: 0.66 ± 0.02).

Early-photon reflectance fluorescence molecular tomography for small animal imaging: Mathematical model and numerical experiment

The paper presents an original approach to time-domain reflectance fluorescence molecular tomography (FMT) of small animals. It is based on the use of early arriving photons and state-of-the-art compressed-sensing-like reconstruction algorithms and aims to improve the spatial resolution of fluorescent images. We deduce the fundamental equation that models the imaging operator and derive analytical representations for the sensitivity functions which are responsible for the reconstruction of the fluorophore absorption coefficient. The idea of fluorescence lifetime tomography with our approach is also discussed. We conduct a numerical experiment on 3D reconstruction of box phantoms with spherical fluorescent inclusions of small diameters. For modeling measurement data and constructing the sensitivity matrix we assume a virtual fluorescence tomograph with a scanning fiber probe that illuminates and collects light in reflectance geometry. It provides for large source-receiver separations which correspond to the macroscopic regime. Two compressed-sensing-like reconstruction algorithms are used to solve the inverse problem. These are the algebraic reconstruction technique with total variation regularization and our modification of the fast iterative shrinkage-thresholding algorithm. Results of our numerical experiment show that our approach is capable of achieving as good spatial resolution as 0.2 mm and even better at depths to 9 mm inclusive.

System configuration optimization for mesoscopic fluorescence molecular tomography

Tissue engineering applications demand 3D, non-invasive, and longitudinal assessment of bioprinted constructs. Current emphasis is on developing tissue constructs mimicking in vivo conditions; however, these are increasingly challenging to image as they are typically a few millimeters thick and turbid, limiting the usefulness of classical fluorescence microscopic techniques. For such applications, we developed a Mesoscopic Fluorescence Molecular Tomography methodology that collects high information content data to enable high-resolution tomographic reconstruction of fluorescence biomarkers at millimeters depths. This imaging approach is based on an inverse problem; hence, its imaging performances are dependent on critical technical considerations including optode sampling, forward model design and inverse solver parameters. Herein, we investigate the impact of the optical system configuration parameters, including detector layout, number of detectors, combination of detector and source numbers, and scanning mode with uncoupled or coupled source and detector array, on the 3D imaging performances. Our results establish that an MFMT system with a 2D detection chain implemented in a de-scanned mode provides the optimal imaging reconstruction performances.