arXiv:2205.07866
공식 코드 · Papers with Code
Primal-Dual UNet for Sparse View Cone Beam Computed Tomography Volume Reconstruction
원문 초록
In this paper, the Primal-Dual UNet for sparse view CT reconstruction is modified to be applicable to cone beam projections and perform reconstructions of entire volumes instead of slices. Experiments show that the PSNR of the proposed method is increased by 10dB compared to the direct FDK reconstruction and almost 3dB compared to the modified original Primal-Dual Network when using only 23 projections. The presented network is not optimized wrt. memory consumption or hyperparameters but merely serves as a proof of concept and is limited to low resolution projections and volumes.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
X-GRM: Large Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomography
X-Field: A Physically Grounded Representation for 3D X-ray Reconstruction
TomoSelfDEQ: Self-Supervised Deep Equilibrium Learning for Sparse-Angle CT Reconstruction
Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction
Learning Difference-of-Convex Regularizers for Inverse Problems: A Flexible Framework with Theoretical Guarantees
Bigger Isn't Always Better: Towards a General Prior for Medical Image Reconstruction
토론
이 논문과 연결된 포럼 글이 아직 없습니다.