Modern X-ray techniques opened the possibility to reconstruct phase contrast (PC) information. This provides
significantly improved soft-tissue contrast when compared to conventional computed tomography (CT). While PCCT
significantly ameliorates contrast information, radiation dose continues to be an issue when translated to the clinic.
Possible dose reduction can be achieved by using more efficient reconstruction algorithms. In this work, dose reduction
is achieved by applying a compressed sensing (CS) reconstruction to a highly sparse set of PCCT projections. The
applied reconstruction algorithm is based on a non-uniform fast Fourier transform (NUFFT), where sparse sets of
projections are reconstructed with a CS algorithm, employing wavelet domain sparsity and finite differences
minimization. We evaluated this approach with both phantom and real data. Measured data from a conventional X-ray
source were acquired using grating-based interferometry. The resulting reconstructions are compared visually, and
quantitatively on the basis of standard deviation within different regions-of-interest. The assessment of phantom and
measured data demonstrated the possibility to reconstruct from drastically fewer projections than the Nyquist-theorem
demands. The measured standard deviations were comparable or even lower compared to full dose reconstructions. In
this initial evaluation of CS-based methods in PCCT, we presented a considerable reduction of necessary projections.
Thus, radiation dose can be reduced while maintaining the superior soft-tissue contrast and image quality of PCCT. In
the future, approaches such as the presented, will enable 4D PCCT, for instance in cardiac applications.