In X-ray computed tomography(CT)for limited-view reconstruction,there are great challenges for accurate reconstruction as complete projections cannot be obtained.Iterative image reconstruction(IIR)with sparsity-exploiting methods,such as total variation(TV)minimization,inspired by compressive sensing(CS),potentially claims large reductions in sampling requirements.However,quantitative notion of this claim is non-trivial,for the reduction in sampling admitted by sparsity-exploiting method is ill-defined.In this paper,sufficient and necessary condition to the uniqueness of solution is used to study accurate reconstruction sampling condition for limited-view problem in TV minimization.A new computational method is proposed for solution uniqueness testing by converting the sufficient and necessary condition into convex optimization problem.Using this method,the sufficient sampling number of accurate reconstruction is able to be quantified for any fixed phantom and settled geometrical parameter in limited-view problem.The experiment results show the quantified sampling number and indicate that as the scanning range becomes narrower,object can be accurately reconstructed by increasing sampling number.Nevertheless,a lower bound of scanning range is presented that accurate reconstruction cannot be obtained once the projection angle is not more than that.This paper gives a reference for quantifying the sampling condition of accurate reconstruction in CT,and the lower bound is useful to evaluate the phantom recoverability from limited-view.