Model-based iterative reconstruction for synchrotron X-ray tomography

Synchrotron based X-ray tomography is widely used for three dimensional imaging of materials at the micron scale. Tomographic data collected from a synchrotron is often affected by non-idealities in the measurement system and sudden 'blinding' of detector pixels during the acquisition. Typically, reconstructions are done using analytical reconstruction techniques combined with pre/post-processing steps to correct for the non-idealities, resulting in loss of detail while still producing noisy reconstructions with some artifacts. In this paper, we present a model-based iterative reconstruction (MBIR) algorithm for synchrotron X-ray tomography that can automatically handle the non-idealities as a part of the reconstruction. First, we develop a forward model that accounts for the non-idealities in the measurement system and for the occurrence of outliers in the measurement. Next, we combine the forward model with a prior model of the object to formulate the MBIR cost function and propose an algorithm to minimize the cost. Results on a real data set show that the MBIR reconstructions are superior to the analytical reconstructions effectively suppressing noise as well as other artifacts.