Learning observer design for object detection under complex background and variant locations: an application in IQ assessment with spectrum shaping technique for ultra-low dose lung imaging

We would propose a Deep Learning based Model Observer (DLMO) to assess performance of computed tomography (CT) images generated by applying tin-filter based spectral shaping technique. The DLMO was constructed based on a simplified VGG neural network trained from scratch. The training and test image datasets were obtained by scanning an anthropomorphic phantom with high-fidelity pulmonary structure at four dose levels with and without tin-filter, respectively. Spherical urethane foams were attached at variant positions of pulmonary tree to mimic ground glass nodule (GGN). These low dose CT scan images were assessed by the trained DLMO for lung nodule detection. The result demonstrated that spectral shaping by tin-filter can provide additional benefits on detection accuracy for certain ultra-low dose level scan (~0.2mGy), but faces challenges for extremely low dose level (~0.05mGy) due to significant noise. For normal dose range (~0.5 to 1mGy), both images from scan with and scan without tin-filter can achieve comparable detection accuracy on mimic GGN objects. A human observer (HO) study performed by 8 experienced CT image quality engineers on the same dataset as a signal-known-exactly (SKE) nodule detection task also indicated similar results.