WE‐EF‐207‐03: Design and Optimization of a CBCT Head Scanner for Detection of Acute Intracranial Hemorrhage

Abstract

Purpose:To design a dedicated x‐ray cone‐beam CT (CBCT) system suitable to deployment at the point‐of‐care and offering reliable detection of acute intracranial hemorrhage (ICH), traumatic brain injury (TBI), stroke, and other head and neck injuries.Methods:A comprehensive task‐based image quality model was developed to guide system design and optimization of a prototype head scanner suitable to imaging of acute TBI and ICH. Previously reported models were expanded to include the effects of x‐ray scatter correction necessary for detection of low contrast ICH and the contribution of bit depth (digitization noise) to imaging performance. Task‐based detectablity index provided the objective function for optimization of system geometry, x‐ray source, detector type, anti‐scatter grid, and technique at 10–25 mGy dose. Optimal characteristics were experimentally validated using a custom head phantom with 50 HU contrast ICH inserts imaged on a CBCT imaging bench allowing variation of system geometry, focal spot size, detector, grid selection, and x‐ray technique.Results:The model guided selection of system geometry with a nominal source‐detector distance 1100 mm and optimal magnification of 1.50. Focal spot size ∼0.6 mm was sufficient for spatial resolution requirements in ICH detection. Imaging at 90 kVp yielded the best tradeoff between noise and contrast. The model provided quantitation of tradeoffs between flat‐panel and CMOS detectors with respect to electronic noise, field of view, and readout speed required for imaging of ICH. An anti‐scatter grid was shown to provide modest benefit in conjunction with post‐acquisition scatter correction. Images of the head phantom demonstrate visualization of millimeter‐scale simulated ICH.Conclusions:Performance consistent with acute TBI and ICH detection is feasible with model‐based system design and robust artifact correction in a dedicated head CBCT system. Further improvements can be achieved with incorporation of model‐based iterative reconstruction techniques also within the scope of the task‐based optimization framework.David Foos and Xiaohui Wang are employees of Carestream Health