A dense search challenge phantom fabricated with pixel-based 3D printing for precise detectability assessment.

Abstract

The performance of a CT scanner for detectability tasks is difficult to precisely measure. Metrics such as contrast-to-noise ratio, modulation transfer function, and noise power spectrum do not predict detectability in the context of nonlinear reconstruction. We propose to measure detectability using a dense search challenge: a phantom is embedded with hundreds of target objects at random locations, and a human or numerical observer analyzes the reconstruction and reports on suspected locations of all target objects. The reported locations are compared to ground truth to produce a figure of merit, such as area under the curve (AUC), that is sensitive to the acquisition dose and the dose efficiency of the CT scanner. We used simulations to design such a dense search challenge phantom and found that detectability could be measured with precision better than 5%. Test 3D prints using the PixelPrint technique showed the feasibility of this technique.