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Found 14 papers, 1 papers with code
One Sample Diffusion Model in Projection Domain for Low-Dose CT Imaging
Therefore, we propose a fully unsupervised one sample diffusion model (OSDM)in projection domain for low-dose CT reconstruction.
Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction
When the number of projection view changes, the DL network should be retrained with updated sparse-view/full-view CT image pairs.
Spectral2Spectral: Image-spectral Similarity Assisted Spectral CT Deep Reconstruction without Reference
The photon-counting detector (PCD) based spectral computed tomography attracts much more attentions since it has the capability to provide more accurate identification and quantitative analysis for biomedical materials.
Multi-domain Integrative Swin Transformer network for Sparse-View Tomographic Reconstruction
To improve image quality from sparse-view data, a Multi-domain Integrative Swin Transformer network (MIST-net) was developed in this article.
Stationary Multi-source AI-powered Real-time Tomography (SMART)
The reconstructed volumetric images convincingly demonstrate the merits of the SMART system using the AI-empowered interior tomography approach, enabling cardiac micro-CT with the unprecedented temporal resolution of 30ms, which is an order of magnitude higher than the state of the art.
AI-Enabled Ultra-Low-Dose CT Reconstruction
By the ALARA (As Low As Reasonably Achievable) principle, ultra-low-dose CT reconstruction is a holy grail to minimize cancer risks and genetic damages, especially for children.
Deep Iteration Assisted by Multi-level Obey-pixel Network Discriminator (DIAMOND) for Medical Image Recovery
Image restoration is a typical ill-posed problem, and it contains various tasks.
Suppression of Correlated Noise with Similarity-based Unsupervised Deep Learning
Limited by the independent noise assumption, current unsupervised denoising methods cannot process correlated noises as in CT images.
Deep Learning based Spectral CT Imaging
To address the image deblurring problem associated with the $L_2^2$-loss, we propose a general $L_p^p$-loss, $p>0$ Furthermore, the images from different energy bins share similar structures of the same object, the regularization characterizing correlations of different energy bins is incorporated into the $L_p^p$-loss function, which helps unify the deep learning based methods with traditional compressed sensing based methods.
Stabilizing Deep Tomographic Reconstruction
ACID synergizes a deep reconstruction network trained on big data, kernel awareness from CS-inspired processing, and iterative refinement to minimize the data residual relative to real measurement.
DLIMD: Dictionary Learning based Image-domain Material Decomposition for spectral CT
Second, we employ the direct inversion (DI) method to obtain initial material decomposition results, and a set of image patches are extracted from the mode-1 unfolding of normalized material image tensor to train a united dictionary by the K-SVD technique.
Block Matching Frame based Material Reconstruction for Spectral CT
Spectral computed tomography (CT) has a great potential in material identification and decomposition.
Non-local Low-rank Cube-based Tensor Factorization for Spectral CT Reconstruction
Then, as a new regularizer, Kronecker-Basis-Representation (KBR) tensor factorization is employed into a basic spectral CT reconstruction model to enhance the ability of extracting image features and protecting spatial edges, generating the non-local low-rank cube-based tensor factorization (NLCTF) method.
Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary
The L0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT images.
