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Benign and malignant lung nodules classification is an important task in the diagnosis of lung cancer. In this study, lung nodules are classified based on growth changes feature and registration technique. Firstly, this paper combine the global rigid registration with local elastic registration method, which can extract the growth changes of a region of interest. Secondly, the benign and malignant...
Extracting prior knowledge from previous high quality normal-dose computed tomography (NdCT) data for Bayesian reconstruction of current low-dose CT (LdCT) images has attracted great research interests recently. While most efforts focused on registering the previous NdCT data to the current LdCT reconstruction, this work investigated an alternative strategy by extracting the local structure-specific...
Low-dose X-ray computed tomography (CT) imaging is desirable for various clinical applications due to the growing concerns about excessive radiation exposure to the patients. One strategy to achieve low-dose CT imaging is to lower the number of projection views per rotation during data acquisition. However, the resulting image by the conventional filtered back-projection method may suffer from view-aliasing...
Statistical iterative reconstruction (SIR) algorithms have shown advantages over the conventional filtered back-projection method for low-dose computed tomography (CT) reconstruction. For the SIR algorithms, the regularization term plays a critical role on determining the performance. One commonly used regularization is the quadratic-form Gaussian Markov random field (MRF), which penalizes differences...
In single photon emission computed tomography (SPECT), the Poisson noise in sinogram data is one of the major degrading factors that jeopardize the quality of reconstructed images. The common strategy to reduce noise in SPECT images is to apply low-pass pre- or post-processing filters, which suppress the noise by attenuating the high frequency components that can contain valuable edge/detail information...
Previous works have demonstrated that the low-dose CT images could be reconstructed by minimizing the total variation (TV) or adaptive weighted total variation (AwTV) of the desired images incorporate data fidelity term. However, due to the piecewise constant assumption of TV model, the reconstructed images are frequently reported to suffer from the undesired patchy artifacts or stair-case effects...
Computer-aided detection (CADe) of pulmonary nodules from computer tomography (CT) scans is critical for assisting radiologists to identify lung lesions at an early stage. In this paper, we propose a novel CADe system for lung nodule detection based on a vector quantization (VQ) approach. Compared to existing CADe systems, the extraction of lungs from the chest CT image is fully automatic, and the...
We address the problem of optimizing data acquisition for photon-counting CT. We formulate a task-driven approach using a clinically relevant task of detection and localization of a lesion in a search region. The appropriate scalar measure of task performance is ALROC, the area under the LROC curve. For hardware optimization, the observer performing the task should operate on the raw (sinogram) data...
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