TIMBIR: A Method for Time-Space Reconstruction From Interlaced Views

Abstract

Synchrotron X-ray computed tomography (SXCT) is increasingly being used for 3-D imaging of material samples at micron and finer scales. The success of these techniques has increased interest in 4-D reconstruction methods that can image a sample in both space and time. However, the temporal resolution of widely used 4-D reconstruction methods is severely limited by the need to acquire a very large number of views for each reconstructed 3-D volume. Consequently, the temporal resolution of current methods is insufficient to observe important physical phenomena. Furthermore, measurement nonidealities also tend to introduce ring and streak artifacts into the 4-D reconstructions. In this paper, we present a time-interlaced model-based iterative reconstruction (TIMBIR) method, which is a synergistic combination of two innovations. The first innovation, interlaced view sampling, is a novel method of data acquisition, which distributes the view angles more evenly in time. The second innovation is a 4-D model-based iterative reconstruction algorithm (MBIR), which can produce time-resolved volumetric reconstruction of the sample from the interlaced views. In addition to modeling both the sensor noise statistics and the 4-D object, the MBIR algorithm also reduces ring and streak artifacts by more accurately modeling the measurement nonidealities. We present reconstructions of both simulated and real X-ray synchrotron data, which indicate that TIMBIR can improve temporal resolution by an order of magnitude relative to existing approaches.