Linear scan Computed Tomography (LCT) has emerged as a promising technique in fields like industrial scanning and security inspection due to its straight-line source trajectory and high scanning speed. However, in practical applications of LCT, the ordinary algorithms suffer from serious artifacts owing to the limited-angle and insufficient data. In this paper, a new method which reconstructs image from partial Fourier data sampled in pseudo polar grid based on alternating direction anisometric total variation minimization has been proposed. The main idea is to reform the image reconstruction problem into solving an under-determined linear equation, and then reconstruct image by applying the popular total variation (TV) minimization to reform an unconstraint optimization by means of augmented Lagrange method and using the alternating minimization method of multiplier (ADMM) which contributes to the fast convergence. The proposed method is practical in the large-scale task of reconstruction due to its algorithmic simplicity and computational efficiency and reconstructs better images. The results of the numerical simulations and pseudo real data reconstructions from the linear scan validate that the proposed method is both efficient and accurate.