Adaptive kernel-based scatter correction for multi-source stationary CT with non-circular geometry

Tomographic systems based on stationary arrangements of compact x-ray sources coupled to curved panel detectors have shown great potential for point-of-care brain imaging, but suffer from large, non-isotropic x-ray scatter. This work presents an adaptive kernel strategy to efficiently estimate scatter in stationary multi-source CT. The adaptive scatter estimation handles non-circular geometries, by the addition of pre- and post-processing steps to projection domain scatter estimators. The method was calibrated and evaluated on simulated data for a previously presented system with 31 x-ray sources on a circular arc coupled to a curved detector. Further assessment was obtained on experimental data obtained with an imaging testbench including a compact CNT-based x-ray source and simulating the scanner geometry. The method achieved accurate air-normalized scatter distributions across x-ray source positions and detector pixels, yielding a mean absolute error of 1.98𝑥10−3 with respect to the Monte-Carlo ground truth. Air-gap compensation had the largest impact on final accuracy. Image quality for simulated data showed consistent mitigation of scatter artifacts and reduction in non-uniformity from NU = 109 HU to 24 HU, with comparable performance for variations in cranium size, ranging in length from 161 mm (NU =14 HU) to 246 mm (NU = 15 HU). The experimental data showed comparable performance with error attributable to slight simulation infidelity. This work presents an adaptive approach to scatter compensation in multi-source, non-circular geometries using warping and weighting operations coupled to kernel-based scatter estimation on a virtual circular geometry, with immediate extension to other projection-based scatter compensation strategies.