https://github.com/maybe198376/LEARN-Plus-Plus
공식 코드LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT
Papers with Code 아카이브
CT Reconstruction
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/meijitian/spener
공식 코드Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction
https://github.com/mozanunal/SparseCT
공식 코드https://github.com/odlgroup/odl
공식 코드https://github.com/phernst/pd-unet
공식 코드Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstruction
https://github.com/phernst/pd-unet-conebeam
공식 코드Primal-Dual UNet for Sparse View Cone Beam Computed Tomography Volume Reconstruction
https://github.com/qingze-bai/xctdiff
공식 코드https://github.com/qjy415417122/sa-roundtrip
공식 코드Bayesian imaging inverse problem with SA-Roundtrip prior via HMC-pCN sampler
https://github.com/rb876/deep_image_prior_extension
공식 코드An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction
https://github.com/sedaboni/TomoSelfDEQ
공식 코드TomoSelfDEQ: Self-Supervised Deep Equilibrium Learning for Sparse-Angle CT Reconstruction
https://github.com/sundeco/padis
공식 코드Learning Image Priors through Patch-based Diffusion Models for Solving Inverse Problems
https://github.com/sypsyp97/Trainable-Fourier-Series
공식 코드Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier Series
https://github.com/sypsyp97/eagle_loss
공식 코드EAGLE: An Edge-Aware Gradient Localization Enhanced Loss for CT Image Reconstruction
https://github.com/tonyckc/uda-bua
공식 코드Unsupervised Domain Adaptation for Low-dose CT Reconstruction via Bayesian Uncertainty Alignment
https://github.com/vlogroup/bigger-isnt-always-better
공식 코드Bigger Isn't Always Better: Towards a General Prior for Medical Image Reconstruction
https://github.com/wanderinrain/xray2ct
공식 코드CT Reconstruction from Few Planar X-rays with Application towards Low-resource Radiotherapy
https://github.com/wangyunpengbio/x-recon
공식 코드https://github.com/xmed-lab/dif-gaussian
공식 코드Learning 3D Gaussians for Extremely Sparse-View Cone-Beam CT Reconstruction
https://github.com/xmindflow/INCODE
공식 코드INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings
https://github.com/xylimeng/SDR-CT
공식 코드Scalable Double Regularization for 3D Nano-CT Reconstruction
https://github.com/yasminzhang/adcr
공식 코드https://github.com/yqx7150/gmsd
공식 코드Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction
https://github.com/yqx7150/osdm
공식 코드One Sample Diffusion Model in Projection Domain for Low-Dose CT Imaging
https://github.com/yqx7150/sword
공식 코드Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction
https://github.com/yransun/difr3ct
공식 코드DIFR3CT: Latent Diffusion for Probabilistic 3D CT Reconstruction from Few Planar X-Rays
https://github.com/zi-yuanyang/hyperfed
공식 코드Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging
https://github.com/Zakobian/CT_framework_
https://github.com/adler-j/learned_primal_dual
https://github.com/alexdenker/htc2022_lpd
https://github.com/brack-wang/zeco
X-Field: A Physically Grounded Representation for 3D X-ray Reconstruction
https://github.com/cmhungsteve/Awesome-Transformer-Attention
https://github.com/educating-dip/bayes_dip
An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction
https://github.com/educating-dip/educated_deep_image_prior
An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction
https://github.com/eunh/low_dose_CT
Wavelet Domain Residual Network (WavResNet) for Low-Dose X-ray CT Reconstruction
https://github.com/hanyoseob/roi-ct-cnn
One Network to Solve All ROIs: Deep Learning CT for Any ROI using Differentiated Backprojection
https://github.com/hj-harry/ddip3d
Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.