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.
X-ray scatter is a major limit for good CT image quality. Apart from using hardware approach (e.g. anti-scatter grid), computational algorithms based on Monte-Carlo simulation or convolution kernels have been proven to be valid for compensating scatter effect. However, computational algorithms always have to take care about the balance between complexity and efficiency, so the performance has some limitation when scatter contribution is large. In this paper we proposed a deep learning based approach by adopting a convolutional neuro-network (CNN) to predict the scatter distribution on projection domain. The performance of the CNN-based model is validated in both projection domain as well as reconstructed image domain. The result shows that the scatter correction algorithm with learning approach is able to compensate the artifact from scatter radiations under various complicated scenarios, resulting in equivalent or even better image quality than commercially used kernel-based scatter correction algorithm.
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