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.

In this paper, we propose a dual-excitation-mode methodology for three-dimensional (3D) fluorescence molecular tomography (FMT). For this modality, an effective reconstruction algorithm is developed to reconstruct fluorescent yield and lifetime using finite element techniques. In the steady state mode, a direct linear relationship is established between measured optical data on the body surface of a small animal and the unknown fluorescent yield inside the animal, and the reconstruction of fluorescent yield is formulated as a linear least square minimization problem. In the frequency domain mode, based on localization results of the fluorescent probe obtained using the first mode, the reconstruction of fluorescent lifetime is transformed into a relatively simple optimization problem. This algorithm helps overcome the ill-posedness with FMT. The effectiveness of the proposed method is numerically demonstrated using a heterogeneous mouse chest phantom, showing good accuracy, stability, noise characteristics and computational efficiency.

Quantitative computed tomography based finite element analysis of the femur is currently being investigated as a method for non-invasive stiffness and strength predictions of the proximal femur. The specific objective of this study was to determine better conversion relationships from QCT-derived bone density to elastic modulus, in order to achieve accurate predictions of the overall femoral stiffness in a fall-on-the-hip loading configuration. Twenty-two femurs were scanned, segmented and meshed for finite element analysis. The elastic moduli of the elements were assigned according to the average density in the element. The femurs were then tested to fracture and force-displacement data was collected to calculate femoral stiffness. Using a training set of nine femurs, finite element analyses were performed and the parameters of the density-elastic modulus relationship were iteratively adjusted to obtain optimal stiffness predictions in a least-squares sense. The results were then validated on the remaining 13 femurs. Our novel procedure resulted in parameter identification of both power and sigmoid functions for density-elastic modulus conversion for this specific loading scenario. Our in situ estimated power law achieved improved predictions compared to published power laws, and the sigmoid function yielded even smaller prediction errors. In the future, these results will be used to further improve the femoral strength predictions of our finite element models.

Bioluminescent imaging has proven to be a valuable tool for monitoring physiological and pathological activities at cellular and molecular levels in living small animals. Using biological techniques, target cells can be tagged with reporters encoding several kinds of luciferase enzymes, which generate characteristic photons in a wide spectrum covering the infrared range. Part of the diffused light can reach the body surface of the small animal, be separated into several spectral bands using appropriate filters, and collected by a sensitive CCD camera. Here we present a bioluminescence tomography (BLT) method for a bioluminescent source reconstruction from multispectral data measured on the external surface, and demonstrate the advantages of multispectral BLT in a numerical study using a heterogeneous mouse chest phantom. The results show that the multispectral approach significantly improves the accuracy and stability of the BLT reconstruction even if the data are highly noisy.

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