We present here a new fluorescence molecular tomographic model that can provide ultrahigh spatial and temporal resolution reconstruction through sparsity constrained dimensional reduction. The new method implements a novel sparsity function to asymptotically enforce the sparsest representation of fluorescent targets while reducing the problem dimension based correlation between sensing matrix and measurement. Parameterized temporal data (TD) L(S), available from the Laplace transform, is used here as input to the inverse model for their computational efficiency and accuracy and robustness to noise. We use radiative transfer equation (RTE) as a light propagation model as it provides more accurate predictions of light propagation in small-volume tissue. The performance of this new method is evaluated through numerical phantoms, focusing on spatial resolution and computational speed. The results show that the sparsity constrained dimensional reduction inverse model can achieve near cellular resolution (∼1mm spatial resolution) at depth of 70 mean free paths (MFPs) within ∼25 milliseconds.