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We present a new decomposition approach for dual-energy computed tomography (DECT) called SIRZ that provides precise and accurate material description, independent of the scanner, over diagnostic energy ranges (30 to 200 keV). System independence is achieved by explicitly including a scanner-specific spectral description in the decomposition method, and a new X-ray-relevant feature space. The feature space consists of electron density, ${\rho _{\rm e}}$ , and a new effective atomic number, ${{\rm Z}_{\rm e}}$ , which is based on published X-ray cross sections. Reference materials are used in conjunction with the system spectral response so that additional beam-hardening correction is not necessary. The technique is tested against other methods on DECT data of known specimens scanned by diverse spectra and systems. Uncertainties in accuracy and precision are less than 3% and 2% respectively for the ( ${\rho _{\rm e}}$ , ${{\rm Z}_{\rm e}}$ ) results compared to prior methods that are inaccurate and imprecise (over 9%).

In this paper, we describe the work done in order to run the CT 3-D reconstruction algorithm on the 120 GB raw data from the more than 25\thinspace000 radiographs acquired from the Kongo Rikishi (XIII century) Japanese wooden statue. The work was done using the Microsoft (Redmond) HPC cluster and then on a local cluster at the INFN of Bologna. A speed-up factor of 75 was reached.

An efficient method for accurately calculating the center-of-rotation, or projection center, for parallel computed tomography projection data, or sinograms, is described. This method uses all the data in the sinogram to estimate the center by a least-squares technique and requires no previous calibration scan. The method also finds the object's center-of-mass without reconstructing its image. Since the method uses the measured data, it is sensitive to noise in the measurements, but that sensitivity is relatively small compared to other techniques. Examples of its use on simulated and actual data are included. For fan-beam data over 360 degrees , two related methods are described to find the center in the presence or absence of a midline offset. >