Compressed sensing (CS) computed tomography has been proven to be important for several clinical applications, such as sparse-view computed tomography (CT), digital tomosynthesis and interior tomography. Traditional compressed sensing focuses on the design of handcrafted prior regularizers, which are usually image-dependent and time-consuming. Inspired by recently proposed deep learning-based CT reconstruction models, we extend the state-of-the-art LEARN model to a dual-domain version, dubbed LEARN++. Different from existing iteration unrolling methods, which only involve projection data in the data consistency layer, the proposed LEARN++ model integrates two parallel and interactive subnetworks to perform image restoration and sinogram inpainting operations on both the image and projection domains simultaneously, which can fully explore the latent relations between projection data and reconstructed images. The experimental results demonstrate that the proposed LEARN++ model achieves competitive qualitative and quantitative results compared to several state-of-the-art methods in terms of both artifact reduction and detail preservation.
The patient motion can damage the quality of computed tomography images, which are typically acquired in cone-beam geometry. The rigid patient motion is characterized by six geometric parameters and are more challenging to correct than in fan-beam geometry. We extend our previous rigid patient motion correction method based on the principle of locally linear embedding (LLE) from fan-beam to cone-beam geometry and accelerate the computational procedure with the graphics processing unit (GPU)-based all scale tomographic reconstruction Antwerp toolbox. The major merit of our method is that we need neither fiducial markers nor motion-tracking devices. The numerical and experimental studies show that the LLE-based patient motion correction is capable of calibrating the six parameters of the patient motion simultaneously, reducing patient motion artifacts significantly.
X-ray MicroandNano imaging is developed based on the conventional x–ray tomography, it can not only provide nondestructive testing with higher resolution measurement, but also be used to examine the material or the structure with low atomic number and low density. The source with micro-focal spot size is one of the key components of x-ray MicroandNano imaging. The focused electron beam from SEM bombarding the metal target can generate x-ray with ultra-small size. It is convenient to set up x-ray microscopy based on SEM for laboratory use. This paper describes a new x-ray microscopy using reflection targets based on FEI Quanta600 SEM with tungsten filament. The flat panel detector is placed outside of the vacuum chamber with 300μm thickness Be-window to isolate vacuum from the air. A stage with 3 DOFs is added to adjust the positions of the target, the SEM’s sample stage is used to move sample. And the shape of target is designed as cone with 60° half cone angle to get the maximum x-ray dosage. The attenuation coefficient of Bewindow for x-ray is about 25%. Finally, the line pair card is used to evaluate the resolution and the result shows that the resolution of the system can receive less than 750nm, when the acceleration voltage is 30keV, the beam current is 160nA, the SEM working distance is 5mm and the acquisition time of the detector is 60s.
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