In this work, we study total-variation (TV) regularization for model-based reconstruction from undersampled DTI data. Various TV regularization methods are examined. Using ex-vivo brain DTI data, we show that imposing TV constraints on DWI provide more reliable quantitative estimates of diffusion than those imposing TV constraints directly on the tensor. A gradient descent algorithm with line backtracking is used for better convergence to optimal solution. For highly undersampled data of 12 diffusion encoding directions and a reduction factor of R=4, we show that good estimates of primary eigen-vector, fractional anisotropy, and mean diffusivity can still be obtained using TV-based regularization.