LAUSR

Photon counting detection is a promising approach toward dose reduction in X-ray computed tomography (CT). Full CT reconstruction from a fraction of the detected photons required by energy-integrating detectors has been demonstrated. In medical and industrial CT applications, projection truncation to the region-of-interest (ROI) is another effective way of dose reduction, as information from the ROI is usually sufficient for diagnostic purposes. Truncated projections pose an ill-conditioned inverse problem, which can be improved by including measurements from the exterior region. However, this tradeoff between the interior reconstruction quality and the additional exterior measurement (extra dose) has not been studied. In this paper, we explore the number of detected X-ray photons as a new dimension for measurement engineering. Specifically, we design a flexible, photon-efficient measurement strategy for ROI reconstruction by incorporating the photon statistics at extremely low flux level (16 photons per pixel). The optimized photon-allocation strategy shows a 10- to 15-fold lower normalized mean square error (NMSE) in ROI than truncated projections, and a 2-fold lower NMSE in ROI than whole-volume CT scan. Our analysis in few-photon interior tomography could serve as a new framework for dose-efficient, task-specific X-ray image acquisition design.