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.