9 March 2012 Experimental validation of an OSEM-type iterative reconstruction algorithm for inverse geometry computed tomography
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Iterative reconstruction methods have emerged as a promising avenue to reduce dose in CT imaging. Another, perhaps less well-known, advance has been the development of inverse geometry CT (IGCT) imaging systems, which can significantly reduce the radiation dose delivered to a patient during a CT scan compared to conventional CT systems. Here we show that IGCT data can be reconstructed using iterative methods, thereby combining two novel methods for CT dose reduction. A prototype IGCT scanner was developed using a scanning beam digital X-ray system - an inverse geometry fluoroscopy system with a 9,000 focal spot x-ray source and small photon counting detector. 90 fluoroscopic projections or "superviews" spanning an angle of 360 degrees were acquired of an anthropomorphic phantom mimicking a 1 year-old boy. The superviews were reconstructed with a custom iterative reconstruction algorithm, based on the maximum-likelihood algorithm for transmission tomography (ML-TR). The normalization term was calculated based on flat-field data acquired without a phantom. 15 subsets were used, and a total of 10 complete iterations were performed. Initial reconstructed images showed faithful reconstruction of anatomical details. Good edge resolution and good contrast-to-noise properties were observed. Overall, ML-TR reconstruction of IGCT data collected by a bench-top prototype was shown to be viable, which may be an important milestone in the further development of inverse geometry CT.
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Sabrina David, Steve Burion, Alan Tepe, Brian Wilfley, Daniel Menig, and Tobias Funk "Experimental validation of an OSEM-type iterative reconstruction algorithm for inverse geometry computed tomography", Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 83133M (9 March 2012); doi: 10.1117/12.913335; https://doi.org/10.1117/12.913335

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