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Neutron computed tomography (nCT) is a 3D characterization technique used toimage the internal morphology or chemical composition of samples in biology andmaterials sciences. A typical workflow involves placing the sample in the pathof a neutron beam, acquiring projection data at a predefined set oforientations, and processing the resulting data using an analyticreconstruction algorithm. Typical nCT scans require hours to days to completeand are then processed using conventional filtered back-projection (FBP), whichperforms poorly with sparse views or noisy data. Hence, the main methods inorder to reduce overall acquisition time are the use of an improved samplingstrategy combined with the use of advanced reconstruction methods such asmodel-based iterative reconstruction (MBIR). In this paper, we propose anadaptive orientation selection method in which an MBIR reconstruction onpreviously-acquired measurements is used to define an objective function onorientations that balances a data-fitting term promoting edge alignment and aregularization term promoting orientation diversity. Using simulated andexperimental data, we demonstrate that our method produces high-qualityreconstructions using significantly fewer total measurements than theconventional approach.

An Edge Alignment-based Orientation Selection Method for Neutron Tomography