Wenxiang Cong

Practical reconstruction method for bioluminescence tomography

Bioluminescence tomography (BLT) is used to localize and quantify bioluminescent sources in a small living animal. By advancing bioluminescent imaging to a tomographic framework, it helps to diagnose diseases, monitor therapies and facilitate drug development. In this paper, we establish a direct linear relationship between measured surface photon density and an unknown bioluminescence source distribution by using a finite-element method based on the diffusion approximation to the photon propagation in biological tissue. We develop a novel reconstruction algorithm to recover the source distribution. This algorithm incorporates a priori knowledge to define the permissible source region in order to enhance numerical stability and efficiency. Simulations with a numerical mouse chest phantom demonstrate the feasibility of the proposed BLT algorithm and reveal its performance in terms of source location, density, and robustness against noise. Lastly, BLT experiments are performed to identify the location and power of two light sources in a physical mouse chest phantom.

A multilevel adaptive finite element algorithm for bioluminescence tomography.

As a new mode of molecular imaging, bioluminescence tomography (BLT) has become a hot topic over the past two years. In this paper, a multilevel adaptive finite element algorithm is developed for BLT reconstruction. In this algorithm, the mesh is adaptively refined according to a posteriori error estimation, which helps not only to improve localization and quantification of sources but also to enhance the robustness and efficiency of reconstruction. In the numerical simulation, bioluminescent signals on the body surface of a heterogeneous phantom are synthesized in a molecular optical simulation environment (MOSE) that we developed to model the photon transportation via Monte Carlo simulation. The performance of the algorithm is evaluated in numerical tests involving single and multiple sources in various arrangements. The results demonstrate the merits and potential of the multilevel adaptive approach for BLT.

In vivo mouse studies with bioluminescence tomography.

Bioluminescence tomography (BLT) is a new molecular imaging mode, which is being actively developed to reveal molecular and cellular signatures as labeled by bioluminescent probes in a living small animal. This technology can help diagnose diseases, evaluate therapies, and facilitate drug development with mouse models. In this paper, we describe in vivo mouse experiments with BLT, and propose the reconstruction procedure of bioluminescent sources from optical data measured on the body surface of the mouse using a modality fusion approach. The results show the feasibility of our methodology for localization and quantification of the bioluminescent activities in vivo.

Spectrally resolved bioluminescence tomography with adaptive finite element analysis: methodology and simulation

As a molecular imaging technique, bioluminescence tomography (BLT) with its highly sensitive detection and facile operation can significantly reveal molecular and cellular information in vivo at the whole-body small animal level. However, because of complex photon transportation in biological tissue and boundary detection data with high noise, bioluminescent sources in deeper positions generally cannot be localized. In our previous work, we used achromatic or monochromatic measurements and an a priori permissible source region strategy to develop a multilevel adaptive finite-element algorithm. In this paper, we propose a spectrally solved tomographic algorithm with a posteriori permissible source region selection. Multispectral measurements, and anatomical and optical information first deal with the nonuniqueness of BLT and constrain the possible solution of source reconstruction. The use of adaptive mesh refinement and permissible source region based on a posteriori measures not only avoids the dimension disaster arising from the multispectral measured data but also reduces the ill-posedness of BLT and therefore improves the reconstruction quality. Reconsideration of the optimization method and related modifications further enhance reconstruction robustness and efficiency. We also incorporate into the method some improvements for reducing computational burdens. Finally, using a whole-body virtual mouse phantom, we demonstrate the capability of the proposed BLT algorithm to reconstruct accurately bioluminescent sources in deeper positions. In terms of optical property errors and two sources of discernment in deeper positions, this BLT algorithm represents the unique predominance for BLT reconstruction.

Mathematical theory and numerical analysis of bioluminescence tomography

Molecular imaging is widely recognized as the main stream in the next generation of biomedical imaging. Bioluminescence tomography (BLT) is a rapidly developing new area of molecular imaging. The goal of BLT is to provide quantitative three-dimensional reconstruction of a bioluminescent source distribution within a small animal from optical signals on the surface of the animal body. In this paper, a mathematical framework is established for BLT. Solution existence and uniqueness are established. Continuous dependence of the solution is demonstrated with respect to data. Stable BLT schemes are studied, leading to error estimates and convergence of the methods. A numerical example is presented to illustrate the algorithmic performance.

A born-type approximation method for bioluminescence tomography.

In this paper, we present a Born-type approximation method for bioluminescence tomography (BLT), which is to reconstruct an internal bioluminescent source from the measured bioluminescent signal on the external surface of a small animal. Based on the diffusion approximation for the photon propagation in biological tissue, this BLT method utilizes the Green function to establish a linear relationship between the measured bioluminescent signal and the internal bioluminescent source distribution. The Green function can be modified to describe a heterogeneous medium with an arbitrary boundary using the Born approximation. The BLT reconstruction is formulated in a linear least-squares optimization framework with simple bounds constraint. The performance of this method is evaluated in numerical simulation and phantom experiments.

Overview of bioluminescence tomography--a new molecular imaging modality.

According to the NIH roadmap, optical molecular imaging has an instrumental role in the development of molecular medicine. Great efforts, including those with bioluminescent imaging techniques, have been made to understand the linkage between genes and phenotypic expressions in normal and disease biology. Currently, bioluminescent techniques are widely used in small animal studies. However, most of the current bioluminescent imaging techniques are done in the 2D mode. In this overview, we review bioluminescence tomography (3D mode), elaborate on its principle and multi-spectral extension, describe associated image unmixing and normalization techniques, and discuss a number of directions for technical improvements and biomedical applications.

Image reconstruction for bioluminescence tomography from partial measurement

The bioluminescence tomography is a novel molecular imaging technology for small animal studies. Known reconstruction methods require the completely measured data on the external surface, although only partially measured data is available in practice. In this work, we formulate a mathematical model for BLT from partial data and generalize our previous results on the solution uniqueness to the partial data case. Then we extend two of our reconstruction methods for BLT to this case. The first method is a variant of the well-known EM algorithm. The second one is based on the Landweber scheme. Both methods allow the incorporation of knowledge-based constraints. Two practical constraints, the source non-negativity and support constraints, are introduced to regularize the BLT problem and produce stability. The initial choice of both methods and its influence on the regularization and stability are also discussed. The proposed algorithms are evaluated and validated with intensive numerical simulation and a physical phantom experiment. Quantitative results including the location and source power accuracy are reported. Various algorithmic issues are investigated, especially how to avoid the inverse crime in numerical simulations.

Towards Omni-Tomography-Grand Fusion of Multiple Modalities for Simultaneous Interior Tomography

We recently elevated interior tomography from its origin in computed tomography (CT) to a general tomographic principle, and proved its validity for other tomographic modalities including SPECT, MRI, and others. Here we propose “omni-tomography”, a novel concept for the grand fusion of multiple tomographic modalities for simultaneous data acquisition in a region of interest (ROI). Omni-tomography can be instrumental when physiological processes under investigation are multi-dimensional, multi-scale, multi-temporal and multi-parametric. Both preclinical and clinical studies now depend on in vivo tomography, often requiring separate evaluations by different imaging modalities. Over the past decade, two approaches have been used for multimodality fusion: Software based image registration and hybrid scanners such as PET-CT, PET-MRI, and SPECT-CT among others. While there are intrinsic limitations with both approaches, the main obstacle to the seamless fusion of multiple imaging modalities has been the bulkiness of each individual imager and the conflict of their physical (especially spatial) requirements. To address this challenge, omni-tomography is now unveiled as an emerging direction for biomedical imaging and systems biomedicine.

A practical method to determine the light source distribution in bioluminescent imaging

Optical signatures of tumor cells may be generated by expression of reporter genes encoding bioluminescent/fluorescent proteins. Bioluminescent imaging is a novel technique that identifies such light sources from the light flux detected on the surface of a small animal. This technique can effectively evaluate tumor cell growth and regression in response to various therapies in medical research, drug development and gene therapy. In this paper, the diffusion approximation is employed to describe the propagation of photons through biological tissues. Then, a practical method is proposed for localizing and quantifying bioluminescent sources from external bioluminescent signals. This method incorporates prior knowledge on permissible source regions, and transforms the inverse bioluminescent problem into a finite element-based constrained optimization procedure. This approach is validated and evaluated with ideal and noise data in numerical simulation.