The restriction of CBCT to micro-coil imaging allows for integrating key a priori knowledge that a coil is a high-density spatially sparse curvilinear structure. In this paper, we investigate acquisition patterns specifically designed for biplane systems allowing a faster workflow and reduced dose. Each pattern is a subsampling of a standard tomographic acquisition reconstructed with an 1-constrained algorithm to promote sparsity together with diffusion filters that promote the curvilinear nature of the coil. Three tensor-based 3D diffusion filters are investigated. Quantitative and qualitative results are provided for one coil and four patients datasets. They show how the reconstruction performs according to the selected acquisition pattern (uniform versus non-uniform subsampling), the quantity of missing information and the selected diffusion filter. We observed a systematically better recovery of the coil in reconstructions obtained using a uniform subsampling pattern but at the cost of being systematically noisier than those obtained with a non-uniform subsampling pattern. Diffusion filtering significantly reduced this structural noise.