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Found 21 papers, 6 papers with code
Deep Few-view High-resolution Photon-counting Extremity CT at Halved Dose for a Clinical Trial
Despite the success of deep learning methods for 2D few-view reconstruction, applying them to HR volumetric reconstruction of extremity scans for clinical diagnosis has been limited due to GPU memory constraints, training data scarcity, and domain gap issues.
LoMAE: Low-level Vision Masked Autoencoders for Low-dose CT Denoising
Recently, transformer models emerged as a promising avenue to enhance LDCT image quality.
Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse Problems
Score-based models have proven to be effective in addressing different inverse problems encountered in CT and MRI, such as sparse-view CT and fast MRI reconstruction.
Masked Autoencoders for Low dose CT denoising
A plethora of transformer models have been developed recently to improve LDCT image quality.
Spectral2Spectral: Image-spectral Similarity Assisted Spectral CT Deep Reconstruction without Reference
Spectral computed tomography based on a photon-counting detector (PCD) attracts more and more attentions since it has the capability to provide more accurate identification and quantitative analysis for biomedical materials.
SQ-Swin: a Pretrained Siamese Quadratic Swin Transformer for Lettuce Browning Prediction
To the best of our knowledge, it is the first-of-its-kind on deep learning for lettuce browning prediction using a pretrained Siamese Quadratic Swin (SQ-Swin) transformer with several highlights.
CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoising
Furthermore, an overlapped inference mechanism is introduced to effectively eliminate the boundary artifacts that are common for encoder-decoder-based denoising models.
Manifoldron: Direct Space Partition via Manifold Discovery
A neural network with the widely-used ReLU activation has been shown to partition the sample space into many convex polytopes for prediction.
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.
TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoising
The model is free of convolution blocks and consists of a symmetric encoder-decoder block with sole transformer.
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.
EZLDA: Efficient and Scalable LDA on GPUs
LDA is a statistical approach for topic modeling with a wide range of applications.
MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstruction
Other components, such as image priors and hyperparameters, are kept as the original design.
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.
On a Sparse Shortcut Topology of Artificial Neural Networks
In this paper, we investigate the expressivity and generalizability of a novel sparse shortcut topology.
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.
Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity.
Deep Learning for the Classification of Lung Nodules
Deep learning, as a promising new area of machine learning, has attracted a rapidly increasing attention in the field of medical imaging.
Dictionary-Learning-Based Reconstruction Method for Electron Tomography
In this paper, we evaluate the EST, ADSIR and an ordered-subset simultaneous algebraic reconstruction technique (OS-SART), and compare the ES and equally angled (EA) data acquisition modes.
