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CT Reconstruction
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Masaaki-75/proct
공식 코드Prompted Contextual Transformer for Incomplete-View CT Reconstruction
https://github.com/achaman2/truly_shift_invariant_cnns
공식 코드Truly shift-equivariant convolutional neural networks with adaptive polyphase upsampling
https://github.com/alexanderauras/convergent-data-driven-regularizations-for-ct-reconstruction
공식 코드Convergent Data-driven Regularizations for CT Reconstruction
https://github.com/alperelli/MCAOL
공식 코드Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction
https://github.com/anonyr7/Sinogram-Inpainting
공식 코드2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual Network
https://github.com/brack-wang/x-field
공식 코드X-Field: A Physically Grounded Representation for 3D X-ray Reconstruction
https://github.com/caiyuanhao1998/sax-nerf
공식 코드https://github.com/caiyuanhao1998/x-gaussian
공식 코드Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis
https://github.com/caiziruo/nf-ula
공식 코드NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems
https://github.com/chrisdcs/LAMA-Learned-Alternating-Minimization-Algorithm
공식 코드Learned Alternating Minimization Algorithm for Dual-domain Sparse-View CT Reconstruction
https://github.com/csyben/PYRO-NN
공식 코드https://github.com/csyben/PYRO-NN-Layers
공식 코드https://github.com/cuhk-aim-group/x-grm
공식 코드X-GRM: Large Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomography
https://github.com/dek924/perx2ct
공식 코드Perspective Projection-Based 3D CT Reconstruction from Biplanar X-rays
https://github.com/educating-dip/svd_dip
공식 코드SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction
https://github.com/esizikova/medsynth_public
공식 코드Improving Computed Tomography (CT) Reconstruction via 3D Shape Induction
https://github.com/faebstn96/helix2fan
공식 코드On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT Setting
https://github.com/fanxiaohong/mvms-rcn
공식 코드https://github.com/hanyoseob/framing-u-net
공식 코드Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT
https://github.com/hanyoseob/hdd-dl-for-svct
공식 코드Hierarchical Decomposed Dual-domain Deep Learning for Sparse-View CT Reconstruction
https://github.com/hj-harry/dds
공식 코드Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems
https://github.com/hmshan/MAP-NN
공식 코드Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?
https://github.com/howardheaton/feasibility_fixed_point_networks
공식 코드https://github.com/iwuqing/scope
공식 코드Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomography
https://github.com/jcy132/DML_CT
공식 코드Patch-wise Deep Metric Learning for Unsupervised Low-Dose CT Denoising
https://github.com/kylekma/X2CT
공식 코드X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks
https://github.com/linfengyu77/irldct
공식 코드https://github.com/llnl/leap
공식 코드Differentiable Forward Projector for X-ray Computed Tomography
https://github.com/lzhengchun/tomotx
공식 코드https://github.com/m-lantz/ct_recon_sigpro
공식 코드https://github.com/masaaki-75/freeseed
공식 코드FreeSeed: Frequency-band-aware and Self-guided Network for Sparse-view CT Reconstruction
https://github.com/masilab/s-efov
공식 코드https://github.com/matteo-ronchetti/torch-radon
공식 코드TorchRadon: Fast Differentiable Routines for Computed Tomography
https://github.com/matthewachan/hyperdm
공식 코드Estimating Epistemic and Aleatoric Uncertainty with a Single Model
https://github.com/matthewyccheung/conformal-metric
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.