Subjective reading is still the majority way in current medical image diagnostics, and the visualization effect of images is one important factor which may affect the reading performance or diagnostic quality. In computed tomography (CT), CT numbers are converted into greyscale images by the display window settings. Therefore, settings of display window width and window level significantly influence the image visibility, and the object detectability could be enhanced with appropriate display window settings. In this study, we propose a new idea that the window settings can be automatically adjusted based on greyscale-based contrast-to-noise ratio, which takes into account the effect of the window settings on image quality. With optimized window settings, the greyscale-based image contrast is enhanced and reading performance is improved.
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.
Attenuation information from Localizer Radiograph (LR) is the basis for Automatic Exposure Control. However, the total achievable dose optimization could be significantly affected by either LR based attenuation calculation or patient positioning. To avoid those aspects, we proposed an integrated procedure for more robust and accurate attenuation calculation as well as possible automatic patient centering combined with the Rotating Projection base Localizer Radiograph (RPLR). A 3D attenuation map with more accurate attenuation information and automatic patient centering can be realized with one pre-view scan, so that a complete automatic workflow with always optimized dose modulation can be enabled.
A 3D printed heart chamber phantom was developed to work combined with other commercially available phantom kit. Based on CT image combined with traditional phantom and anatomic structures, the 3D model was generated and input for printing with selected materials. The 3D printed phantom could realize multi-dimensional motion and deformation similarity, improved HU behavior as contrast enhanced tissue mimic, biological closed anthropomorphic structure including cardiac chambers and coronary arteries with contrast agents, as well as inserts for anatomic or functional abnormalities simulation. With those properties the proposed 3D printed phantom could potentially be used for either CCTA imaging performance or CCTA scan strategy verifications combined with scanner and patient properties.
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