Joshua D. Evans

TL;DR: The results of this simulation study imply that penalized AM has the potential to reconstruct images with similar noise and resolution using a fraction of FBP dose, and imply that the advantage of AM can be maximized by optimizing the nonquadratic penalty function to the specific imaging task of interest.

TL;DR: A commercially available real-time dosimeter probe was found to be a convenient and accurate instrument for measuring fan beam profiles and could handle a wide range of collimation widths by explicitly considering the finite width of the ion chamber.

TL;DR: A framework for characterizing the data needed to implement a polyenergetic model-based statistical reconstruction algorithm, Alternating Minimization (AM), on a commercial fan-beam CT scanner and a novel method for assessing the accuracy of the commissioned data model are presented.

TL;DR: Using a small fraction of the computation time of dual-energy alternating minimization, LIAM achieves better accuracy of the component images in the presence of Poisson noise for simulated data reconstruction and achieves the same level of accuracy for real data reconstruction.

TL;DR: The investigated statistical reconstruction algorithm, AM, reduced random measurement uncertainty relative to FBP owing to improved noise performance and these early results also support efforts to increase DE spectral separation, which can further reduce the pDECT sensitivity to measurement uncertainty.