MAGIC: Manifold and Graph Integrative Convolutional Network for Low-Dose CT Reconstruction
نویسندگان
چکیده
منابع مشابه
A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction.
PURPOSE Due to the potential risk of inducing cancer, radiation exposure by X-ray CT devices should be reduced for routine patient scanning. However, in low-dose X-ray CT, severe artifacts typically occur due to photon starvation, beam hardening, and other causes, all of which decrease the reliability of the diagnosis. Thus, a high-quality reconstruction method from low-dose X-ray CT data has b...
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Regularization methods are commonly used in X-ray CT image reconstruction. Different regularization methods reflect the characterization of different prior knowledge of images. In a recent work, a new regularization method called a low-dimensional manifold model (LDMM) is investigated to characterize the low-dimensional patch manifold structure of natural images, where the manifold dimensionali...
متن کاملGamma regularization based reconstruction for low dose CT.
Reducing the radiation in computerized tomography is today a major concern in radiology. Low dose computerized tomography (LDCT) offers a sound way to deal with this problem. However, more severe noise in the reconstructed CT images is observed under low dose scan protocols (e.g. lowered tube current or voltage values). In this paper we propose a Gamma regularization based algorithm for LDCT im...
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ژورنال
عنوان ژورنال: IEEE Transactions on Medical Imaging
سال: 2021
ISSN: 0278-0062,1558-254X
DOI: 10.1109/tmi.2021.3088344