Sub-resolution modeling of the apparent mass loss in quantitative broadband X-ray radiography

The quantitative impact of image blur on calculated object mass in X-ray radiography is explored. Radiographed object masses are initially estimated through experimental calibration and compared against their known true mass, with the deviation, or "apparent mass loss". Apparent mass loss occurs due to the effect of spatial blur on X-ray transmission images that reduces the measured mass because while the image intensity is conserved with spatial blur, the nonlinear conversion to path length is not. A synthetic model is proposed where an object's apparent mass loss is estimated through the amount of blur present within the radiograph image. The image blur is inferred through a combination of readily available image parameters related to the signal levels, object shape, and experimental extrinsics. A regularized regression model is built that correlates these blur parameters to the expected mass loss. The model is first trained on 2100 and tested on 140 synthetically created objects, and then later verified on two separate experimental setups of type 304 stainless steel objects imaged with a portable tube source at 150 kVp and type 6061 aluminum objects at 80 kVp. The proposed model allows for a simple post hoc reduction in errors of approximately 20% in object mass through X-ray radiography.