Generative adversarial networks (GANs) have proven useful for several medical imaging tasks, including image reconstruction and stochastic object model generation. Thus far, most of the work with GANs has been constrained to twodimensional images. Considering that medical imaging data are often inherently three-dimensional (3D), a 3D GAN would be a more principled way to synthesize realistic volumes. Training a 3D GAN is both computationally and memory intensive. However, prior work has not considered the anisotropic nature of many medical imaging systems. In this paper, the SlabGAN is proposed to reduce the inefficiencies associated with training a 3D GAN. The SlabGAN uses the progressive GAN architecture extended to 3D, but removes the requirement of the three dimensions being equal sizes. This permits the generation of anisotropic 3D volumes with large x and y dimensions. The SlabGAN is trained on MRI brain images from the fastMRI dataset to generate images of dimension 256×256×16. The x and y dimensions of these images are comparable to previously published results while requiring significantly fewer computational resources to generate. The trained SlabGAN is applicable to tasks such as 3D medical image reconstruction and thin-slice MR super resolution.