1. A radiation imaging method, comprising the steps of: collecting radiation emission data from a target including at least one radiation source, wherein the data includes measurements from at least two projections; classifying the data into at least one energy range; separating the data in each energy range into N independent radiation distributions, wherein N is a number of the radiation sources, and each of the N independent radiation distributions corresponds to each of the radiation sources; processing the data in each of the N independent radiation distributions to estimate its true distribution; and reconstructing a radiation distribution image of the target using the processed data wherein the step of separating the data into N independent radiation distributions comprises approximating the data in each of the energy ranges as a Gaussian mixture of N independent Gaussian distributions according to the equation: P ⁡ ( x ) = ∑ n = 1 N ⁢ ⁢ π n ⁢ N ⁡ ( x | μ n , Σ n ) wherein x=(x 1 , x 2 ,. .. , x d ) for d dimension and mixing coefficient π n is denoted weight of each component Gaussian distribution, which satisfies an equation: ∑ n = 1 N ⁢ ⁢ π n = 1 wherein each component N(x|μ n ,Σ n ) is multivariate Gaussian distribution: N ⁡ ( x | μ n , Σ n ) = 1 ( 2 ⁢ π ⁢  Σ n  ) 1 / 2 ⁢ ⅇ - 1 2 ⁢ ( x - μ n ) T ⁢ ∑ n - 1 ⁢ ⁢ ( x - μ n ) wherein μ n ,Σ n are marked mean and covariance of Gaussian distribution; and finding a solution that maximize the equation: ln ⁢ { p ⁡ ( X ) } = ∑ m = 1 M ⁢ ⁢ ln ⁢ ⁢ p ⁡ ( x m ) = ∑ m = 1 M ⁢ ⁢ ln ⁢ { ∑ n = 1 N ⁢ ⁢ π n ⁢ N ⁡ ( x m | μ n , Σ n ) } .