Ali Behrooz

Total variation regularization for 3D reconstruction in fluorescence tomography: experimental phantom studies

Fluorescence tomography (FT) is depth-resolved three-dimensional (3D) localization and quantification of fluorescence distribution in biological tissue and entails a highly ill-conditioned problem as depth information must be extracted from boundary measurements. Conventionally, L2 regularization schemes that penalize the euclidean norm of the solution and possess smoothing effects are used for FT reconstruction. Oversmooth, continuous reconstructions lack high-frequency edge-type features of the original distribution and yield poor resolution. We propose an alternative regularization method for FT that penalizes the total variation (TV) norm of the solution to preserve sharp transitions in the reconstructed fluorescence map while overcoming ill-posedness. We have developed two iterative methods for fast 3D reconstruction in FT based on TV regularization inspired by Rudin-Osher-Fatemi and split Bregman algorithms. The performance of the proposed method is studied in a phantom-based experiment using a noncontact constant-wave trans-illumination FT system. It is observed that the proposed method performs better in resolving fluorescence inclusions at different depths.

High-contrast subcutaneous vein detection and localization using multispectral imaging.

Abstract. Multispectral imaging has shown promise in subcutaneous vein detection and localization in human subjects. While many limitations of single-wavelength methods are addressed in multispectral vein detection methods, their performance is still limited by artifacts arising from background skin reflectance and optimality of postprocessing algorithms. We propose a background removal technique that enhances the contrast and performance of multispectral vein detection. We use images acquired at visible wavelengths as reference for removing skin reflectance background from subcutaneous structures in near-infrared images. Results are validated by experiments on human subjects.

Adaptive row-action inverse solver for fast noise-robust three-dimensional reconstructions in bioluminescence tomography: theory and dual-modality optical/computed tomography in vivo studies

Abstract. A novel approach is presented for obtaining fast robust three-dimensional (3-D) reconstructions of bioluminescent reporters buried deep inside animal subjects from multispectral images of surface bioluminescent photon densities. The proposed method iteratively acts upon the equations relating the multispectral data to the luminescent distribution with high computational efficiency to provide robust 3-D reconstructions. Unlike existing algebraic reconstruction techniques, the proposed method is designed to use adaptive projections that iteratively guide the updates to the solution with improved speed and robustness. Contrary to least-squares reconstruction methods, the proposed technique does not require parameter selection or optimization for optimal performance. Additionally, optimized schemes for thresholding, sampling, and ordering of the bioluminescence tomographic data used by the proposed method are presented. The performance of the proposed approach in reconstructing the shape, volume, flux, and depth of luminescent inclusions is evaluated in a multitude of phantom-based and dual-modality in vivo studies in which calibrated sources are implanted in animal subjects and imaged in a dual-modality optical/computed tomography platform. Statistical analysis of the errors in the depth and flux of the reconstructed inclusions and the convergence time of the proposed method is used to demonstrate its unbiased performance, low error variance, and computational efficiency.

Hadamard multiplexed fluorescence tomography

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.

Fast total variation regularization for higher resolution in fluorescence tomography: A split Bregman iteration approach

We present an edge-preserving regularization for fluorescence tomography that improves its resolution and noise robustness through penalizing the total variation (TV) norm of the reconstructed fluorescent distribution. Results are validated by numerical and phantom-based studies.

Toward robust high resolution fluorescence tomography: a hybrid row-action edge preserving regularization

Depth-resolved localization and quantification of fluorescence distribution in tissue, called Fluorescence Molecular Tomography (FMT), is highly ill-conditioned as depth information should be extracted from limited number of surface measurements. Inverse solvers resort to regularization algorithms that penalize Euclidean norm of the solution to overcome ill-posedness. While these regularization algorithms offer good accuracy, their smoothing effects result in continuous distributions which lack high-frequency edge-type features of the actual fluorescence distribution and hence limit the resolution offered by FMT. We propose an algorithm that penalizes the total variation (TV) norm of the solution to preserve sharp transitions and high-frequency components in the reconstructed fluorescence map while overcoming ill-posedness. The hybrid algorithm is composed of two levels: 1) An Algebraic Reconstruction Technique (ART), performed on FMT data for fast recovery of a smooth solution that serves as an initial guess for the iterative TV regularization, 2) A time marching TV regularization algorithm, inspired by the Rudin-Osher-Fatemi TV image restoration, performed on the initial guess to further enhance the resolution and accuracy of the reconstruction. The performance of the proposed method in resolving fluorescent tubes inserted in a liquid tissue phantom imaged by a non-contact CW trans-illumination FMT system is studied and compared to conventional regularization schemes. It is observed that the proposed method performs better in resolving fluorescence inclusions at higher depths.

Hadamard multiplexed fluorescence molecular tomography: Theory and numerical studies

Inspired by Hadamard multiplexing technique, a method is proposed to improve noise robustness and minimize estimation error in fluorescence molecular tomography (FMT). Theoretical results are validated by numerical studies of 2D simulated FMT data.

An information-theoretic treatment of fluorescent molecular tomography

Depth-resolved imaging of fluorescent molecules in tissue using a non-invasive optical modality called fluorescent molecular tomography (FMT) has found applications in pre-clinical and clinical studies. While FMT offers unique and affordable functional imaging capabilities, its resolution is limited due to the diffusive nature of light propagation in tissue. In this paper we offer a framework for investigating the resolution of FMT using information-theoretic concepts. Specifically, we analyze the amount of useful information that exists in a set of emission measurements. The information content of the measurements directly affects the actual resolution that can be achieved in the reconstructed threedimensional fluorescence images. The relationship between this information content and the measurement geometry is further discussed where it is shown that expanding the measurement size does not necessarily increase the information content. The concept of capacity as defined for multi-input multi-output channels is applied to the linear model of FMT. Assuming a uniform non-zero a priori probability distribution for the fluorophore concentrations in the volume voxels, we derive an expression for the information capacity of the FMT system matrix. This capacity essentially indicates an upper limit on the amount of data that can be extracted from emission measurements. The capabilities of various detector configurations in resolving fluorescent tubes inserted in a gel-based tissue phantom are analyzed in a continuous-wave FMT system using the proposed framework. It is observed that the information capacity of source-detector configurations of different scales directly affects the performance in terms of resolution in the reconstructed fluorescent images.

A multi-resolution approach toward robust fluorescent molecular tomography: Experimental phantom results

We propose a method to improve depth resolution and accuracy of fluorescent molecular tomography (FMT) by applying a spatial constraint to obtain a low-resolution fluorophore presence map. Results are verified using a CW FMT system.

A row-action based L 1 -minimization approach to robust fluorescent tomography

We present a row-action method based on minimization of the L1 norm for improving the accuracy of fluorescent tomography in reconstruction of fluorescent objects. The method is validated using a CW system and milk-based phantoms.