*In vivo* small animal optical imaging has become an important tool of biological discovery and preclinical applications [

1][

2][

3]. When mouse models are labeled using optical molecular probes, the probes acting as light sources, reflect corresponding biological information through the emission of visible or near infrared (NIR) light photons. Optical molecular imaging equipment is used to detect the photon distribution over the surface of the small animal to non-invasively investigate these models [

4]. In recent years, planar optical molecular imaging, and more specifically bioluminescence imaging, has been extensively applied in tumorigenesis studies, cancer diagnosis, metastasis detection, drug discovery and development, and gene therapies given its convenience and ease operation [

5][

6]. The technology that is capable to acquire three dimensional information of the light sources will become a next generation instrument for optical molecular imaging. Bioluminescence tomography (BLT) is one such instrument being developed for this purpose [

7].

An indispensable parameter for bioluminescence tomography is

*a priori* information, which can be used to localize the optical sources. Theoretically, the source uniqueness proof gives us an important reference [

8]. Practically, the richer the

*a priori* information we apply, the further improvements BLT reconstruction can yield. Currently, three types of

*a priori* information are verified and extensively applied in reconstruction algorithms. These include anatomical information [

9][

10], spectrally-resolved measurements [

11][

12][

9][

13], and the distribution of surface photons [

14]. Anatomical information is used to assign relevant optical properties to organs. Spectrally resolved data considers the source spectrum and the tissue absorption and scattering characteristics. The use of these

*a priori* information significantly improves source reconstruction. The temperature dependent source spectral shift has recently led to a temperature-modulated bioluminescence tomography method which uses a focused ultrasound array [

15]. In principle, this should belong to spectrally-resolved

*a priori* information. The

*a priori* permissible source region is defined by the surface photon distribution and improves the reconstruction by constraining the permissible source position [

14]. Overall, it is necessary to define additional

*a priori* parameters for BLT reconstruction.

The diffusion approximation is extensively used in BLT reconstruction despite the fact that higher approximations of the radiative transfer equation lead to improved reconstructed results in some situations [

16]. The finite element method (FEM), analytical formulations and Born approximation theory have been applied in combination with the diffusion equation [

17]. The FEM has become popular due to its ability to process complex heterogeneous geometries. The adaptive strategy has also been developed to further improve the reconstruction based on the FEM [

18][

19]. In BLT, although nonlinear optimization strategies [

20] used in diffuse optical tomography (DOT) and expectation maximization (EM) algorithms [

9] similar with that in positron emission tomography (PET) are used, a linear least square (LS) problem is easily obtained because of the linear nature of the BLT problem [

14]. Meanwhile, the

*inverse crime* needs to be carefully considered especially when new algorithms are evaluated using synthetic data [

21].

BLT reconstruction is an ill-posed problem. Inhibiting noise in measured data and reducing the ill-posedness is necessary to obtain BLT reconstructions. Regularization is a useful method for such problems. Currently, the weighted least square method is used to reduce the measured noise effects [

22][

14]. The Tikhonov regularization is a popular method and is extensively applied in BLT reconstruction [

23][

19]. Mathematically, the Tikhonov method is aiming to stabilize the inverse of an ill-conditioned operator by minimizing a trade-off between a loss function and the

*l*_{2}-norm of the signals. The advantage of the

*l*_{2} norm is that the associated optimization problem can be efficiently solved using a classic quadratic minimization algorithm. The disadvantage is that the solution obtained is often smoothed everywhere, resulting the loss of high frequency structures of the original signal, especially in the case of noise. Over the past several years, the

*l*_{1} norm regularization has been investigated in the signal and image processing fields, such as wavelet thresholding denoising [

24], basis pursuit [

25], and total variation for edge preserving reconstruction [

26]. Moreover, a new sampling theory related to

*l*_{1} minimization, known as compressed sensing (or compressed sampling) provides a strong theoretical foundation for sparse approximations [

27][

28]. More accurately, this theory allows an exact reconstruction from a greatly reduced number of measurements through the use of convex programming. In other words, if the real signals or images are sparse on some basis and the measurement operator and sparsity basis satisfy certain coherent conditions, then the original sparse signal can be reconstructed with a greatly reduced sampling rate. In BLT, the unknown sources are contained in the reconstructed domain (such as a mouse). Non-invasive measurements only acquire the surface distribution of photons emitted by bioluminescence sources. When using small elements (such as tetrahedron or hexahedron) to discretize the whole domain, the number of the surface discretized points is significantly fewer than that of the volumetric discretized points. The undersampling is inevitable for BLT reconstruction. Compared with single view measurements, multi-view data acquisition improves the BLT reconstruction to a certain degree [

29][

30], but it limits the high throughput ability of optical imaging. Single view measurements need to be further investigated for improved BLT reconstruction.

Fortunately, when we use optical probes to observe the specified biological process of interest, the domain of the light source is relatively small and sparse compared with the entire reconstruction domain, in this case the mouse body. Here, by a combination of this *a priori* information and compressed sensing theory, a novel spectrally-resolved bioluminescence reconstruction algorithm is proposed. Specifically, based on the diffusion approximation model, the linear relationship between the spectrally-resolved measured data and the unknown source distribution is established by using the FEM. The *l*_{1} norm as a regularization term is combined into the BLT least squares problem, realizing the compressed sensing method. In order to reduce memory and time cost, a limited memory variable metric optimization method is used to solve the bound-constrained BLT problem. In numerical verifications, the *inverse crime* is demonstrated for different synthetic data sets from different finite element meshes and different simulation methods, showing that the Monte Carlo method is necessary for accurate simulation tests. Furthermore, BLT reconstructions with different noise levels and different source depths demonstrate the usefulness of the compressing sensing method-based *l*_{1} norm regularization, especially for sources located deep within tissues and having high noise. Finally, the proposed algorithm is further tested by experimental reconstruction. In the next section, we present the spectrally-resolved BLT framework based on *l*_{1} regularization. In the third section, we evaluate the performance of the proposed algorithm with various source settings. In the final section, we discuss the relevant issues and conclusions. To the best of the authors knowledge, this contribution represents the first time that bioluminescence source sparse characteristic is used to improve BLT reconstruction with the compressed sensing method.