In this paper we present a compressed sensing (CS) method adapted to 3D ultrasound imaging (US). In contrast to previous work, we propose a new approach based on the use of learned overcomplete dictionaries. Such dictionaries allow for much sparser representations of the signals since they are optimized for a particular class of images such as US images. In this study, the dictionary was learned using the K-SVD algorithm on patches extracted from a training dataset and the reconstruction was performed from 3D volumes not included in the training dataset. In each case, CS reconstruction was performed on the non-log envelope data by removing 20% to 80% of the original samples and the accuracy of the reconstruction was evaluated in terms of the normalized root mean square error relative to the original volume. Using numerically simulated data, we evaluate the influence of the training parameters and the influence of the sampling strategy. The latter is done by comparing the two most common sampling patterns, i.e. point-wise and line-wise random patterns. The results show in particular that line-wise sampling yields an accuracy comparable to the conventional point-wise sampling. This indicates that CS acquisition of 3D data is feasible in a relatively simple setting, and thus offers the perspective of increasing the frame rate by simply skipping the acquisition of many lines among the several thousands required in 3D imaging. We then evaluate the approach on US volumes of several ex vivo and in vivo organs. We first show that the learned dictionary approach yields better performances than conventional sparsifying dictionaries based on fixed transforms such as Fourier or discrete cosine. Finally, we investigate the generality of the learned dictionary approach and show that it is possible to build a general dictionary allowing to reliably reconstruct different volumes of different ex vivo or in vivo organs. The difference between the reconstruction error obtained with a- specific dictionary and the one obtained with the general dictionary is minimal