Bone Sparsity Model For Computed Tomography Image Reconstruction

Gradient sparsity regularization is an effective way to mitigate artifacts due to sparse-view sampling or data noise in computed tomography (CT) image reconstruction. The effectiveness of this type of regularization relies on the scanned object being approximately piecewise constant. Trabecular bone tissue is also technically piecewise constant, but the fine internal structure varies at a spatial scale that is smaller than the resolution of a typical CT scan; thus it is not clear what form of sparsity regularization is most effective for this type of tissue. In this conference submission, we develop a pixel-sparsity regularization model, which is observed to be effective at reducing streak artifacts due to sparse-view sampling and noise. Comparison with gradient sparsity regularization is also shown.