Assessment of volumetric noise and resolution performance for linear and nonlinear CT reconstruction methods : Assessing CT noise and resolution for nonlinear reconstruction

To explain the technical principles of and differences between commercially available iterative reconstruction (IR) algorithms for computed tomography (CT) in non-mathematical terms for radiologists and clinicians. Technical details of the different proprietary IR techniques were distilled from available scientific articles and manufacturers' white papers and were verified by the manufacturers. Clinical results were obtained from a literature search spanning January 2006 to January 2012, including only original research papers concerning IR for CT. IR for CT iteratively reduces noise and artefacts in either image space or raw data, or both. Reported dose reductions ranged from 23 % to 76 % compared to locally used default filtered back-projection (FBP) settings, with similar noise, artefacts, subjective, and objective image quality. IR has the potential to allow reducing the radiation dose while preserving image quality. Disadvantages of IR include blotchy image appearance and longer computational time. Future studies need to address differences between IR algorithms for clinical low-dose CT. • Iterative reconstruction technology for CT is presented in non-mathematical terms. • IR reduces noise and artefacts compared to filtered back-projection. • IR can improve image quality in routine-dose CT and lower the radiation dose. • IR's disadvantages include longer computation and blotchy appearance of some images.

This paper examines the spatial resolution properties of penalized-likelihood image reconstruction methods by analyzing the local impulse response. The analysis shows that standard regularization penalties induce space-variant local impulse response functions, even for space-invariant tomographic systems. Paradoxically, for emission image reconstruction, the local resolution is generally poorest in high-count regions. We show that the linearized local impulse response induced by quadratic roughness penalties depends on the object only through its projections. This analysis leads naturally to a modified regularization penalty that yields reconstructed images with nearly uniform resolution. The modified penalty also provides a very practical method for choosing the regularization parameter to obtain a specified resolution in images reconstructed by penalized-likelihood methods.

To investigate a measurement method for evaluating the resolution properties of CT imaging systems across reconstruction algorithms, dose, and contrast.