We investigate performance of a convex nonquadratic (CNQ) spline regularization method applied to limited-angle tomography reconstruction. Since limited-angle data lack projections over a certain range of view angles, they produce poor reconstructions with streak artifacts and geometric distortions. To obtain a good solution, a feasible prior that can eliminate or reduce artifacts and distortions is necessary. The CNQ prior used in this paper is expressed as a linear combination of the first- and the second-order spatial derivatives and applied to a CNQ penalty function. To determine a solution efficiently, we use the fast globally convergent block sequential regularized expectation maximization algorithm. Our experimental results demonstrate that the hybrid CNQ spline prior outperforms conventional nonquadratic priors in eliminating limited-angle artifacts.