Iterative tomographic X-Ray phase reconstruction

Phase contrast imaging has been of growing interest in the biomedical field, since it provides an enhanced contrast compared to attenuation-based imaging. Actually, the phase shift of the incoming X-ray beam induced by an object can be up to three orders of magnitude higher than its attenuation, particularly for soft tissues in the imaging energy range. Phase contrast can be, among others existing techniques, achieved by letting a coherent X-ray beam freely propagate after the sample. In this case, the obtained and recorded signals can be modeled as Fresnel diffraction patterns. The challenge of quantitative phase imaging is to retrieve, from these diffraction patterns, both the attenuation and the phase information of the imaged object, quantities that are non-linearly entangled in the recorded signal. In this work we consider developments and applications of X-ray phase micro and nano-CT. First, we investigated the reconstruction of seeded bone scaffolds using sed multiple distance phase acquisitions. Phase retrieval is here performed using the mixed approach, based on a linearization of the contrast model, and followed by filtered-back projection. We implemented an automatic version of the phase reconstruction process, to allow for the reconstruction of large sets of samples. The method was applied to bone scaffold data in order to study the influence of different bone cells cultures on bone formation. Then, human bone samples were imaged using phase nano-CT, and the potential of phase nano-imaging to analyze the morphology of the lacuno-canalicular network is shown. We applied existing tools to further characterize the mineralization and the collagen orientation of these samples. Phase retrieval, however, is an ill-posed inverse problem. A general reconstruction method does not exist. Existing methods are either sensitive to low frequency noise, or put stringent requirements on the imaged object. Therefore, we considered the joint inverse problem of combining both phase retrieval and tomographic reconstruction. We proposed an innovative algorithm for this problem, which combines phase retrieval and tomographic reconstruction into a single iterative regularized loop, where a linear phase contrast model is coupled with an algebraic tomographic reconstruction algorithm. This algorithm is applied to numerical simulated data.