The technique of Localizer Radiography (LR) can realize patient oriented automatic exposure control based on attenuation information. Rotating Projection based Localizer Radiography (RPLR), as a dynamic tube positioned scanning, aims to improve the whole clinical workflow. However, topogram (topo) reconstruction in RPLR is affected by sparse sampling. This paper proposed a deep learning model which contains transformers (power in modeling long-term relationship) and CNNs (high texture modeling capacity) to implement projection context restoration for topo reconstruction. With a coarse topo prior generated by the transformers based on sparse sampling data, high-fidelity topo texture can be rendered with CNNs, which reveals great potential for topo reconstruction in RPLR.
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.
Subjective reading is still the majority way in current medical image diagnostics, and the image visualization effect to the observer is very important for the reading performance. And the display window settings play the significant role on the display quality of CT images. To improve the greyscale-based image contrast detectability, we propose a new idea that the window settings can be automatically adjusted in accordance with human visual properties. With the optimized window settings, the greyscalebased image contrast is enhanced, reading performance is improved by maximizing the visibility of targeting objects which the observer focusing on, and image impression is maintained as some level of consistency.
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