Technical Note: Intrinsic raw data‐based CT misalignment correction without redundant data

Abstract

Purpose: CT image reconstruction requires accurate knowledge of the used geometry or image quality might be degraded by misalignment artifacts. To overcome this issue, an intrinsic method, that is, a method not requiring a dedicated calibration phantom, to perform a raw data-based misalignment correction for CT is proposed herein that does not require redundant data and hence is applicable to measurements with less than 180 plus fan-angle of data. Methods: The forward projection of a volume reconstructed from a misaligned geometry resembles the acquired raw data if no redundant data are used, that is, if less than 180 plus fan-angle are used for image reconstruction. Hence, geometric parameters cannot be deduced from such data by an optimization of the geometry-dependent raw data fidelity. We propose to use a nonlinear transform applied to the reconstructed volume to introduce inconsistencies in the raw data that can be employed to estimate geometric parameters using less than 180 plus fan-angle of data. The proposed method is evaluated using simulations of the FORBILD head phantom and using actual measurements of a contrast-enhanced scan of a mouse acquired using a micro-CT. Results: Noisy simulations and actual measurements demonstrate that the proposed method is capable of correcting for artifacts arising from a misaligned geometry without redundant data while ensuring raw data fidelity. Conclusions: The proposed method extends intrinsic raw data-based misalignment correction methods to an angular range of 180 or less and is thus applicable to systems with a limited scan range.