Papers CT Reconstruction
“CT Reconstruction” 태그가 달린 논문 235편 · 필터 해제
Latent Space Consistency for Sparse-View CT Reconstruction
Computed Tomography (CT) is a widely utilized imaging modality in clinical settings. Using densely acquired rotational X-ray arrays, CT can capture 3D spatial features. However, it is confronted with challenged such as s…
Computed Tomography (CT)Contrastive LearningCT ReconstructionImage Generation+1ResPF: Residual Poisson Flow for Efficient and Physically Consistent Sparse-View CT Reconstruction
Sparse-view computed tomography (CT) is a practical solution to reduce radiation dose, but the resulting ill-posed inverse problem poses significant challenges for accurate image reconstruction. Although deep learning an…
Computed Tomography (CT)CT ReconstructionImage GenerationImage ReconstructionAre Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction?
Widely adopted evaluation metrics for sparse-view CT reconstruction--such as Structural Similarity Index Measure and Peak Signal-to-Noise Ratio--prioritize pixel-wise fidelity but often fail to capture the completeness o…
AnatomyCT ReconstructionAdvancing Limited-Angle CT Reconstruction Through Diffusion-Based Sinogram Completion
Limited Angle Computed Tomography (LACT) often faces significant challenges due to missing angular information. Unlike previous methods that operate in the image domain, we propose a new method that focuses on sinogram i…
CT ReconstructionTowards Prospective Medical Image Reconstruction via Knowledge-Informed Dynamic Optimal Transport
Medical image reconstruction from measurement data is a vital but challenging inverse problem. Deep learning approaches have achieved promising results, but often requires paired measurement and high-quality images, whic…
CT ReconstructionImage ReconstructionX-GRM: Large Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomography
Computed Tomography serves as an indispensable tool in clinical workflows, providing non-invasive visualization of internal anatomical structures. Existing CT reconstruction works are limited to small-capacity model arch…
CT ReconstructionLearning Wavelet-Sparse FDK for 3D Cone-Beam CT Reconstruction
Cone-Beam Computed Tomography (CBCT) is essential in medical imaging, and the Feldkamp-Davis-Kress (FDK) algorithm is a popular choice for reconstruction due to its efficiency. However, FDK is susceptible to noise and ar…
CT ReconstructionOrdered-subsets Multi-diffusion Model for Sparse-view CT Reconstruction
Score-based diffusion models have shown significant promise in the field of sparse-view CT reconstruction. However, the projection dataset is large and riddled with redundancy. Consequently, applying the diffusion model …
CT ReconstructionNonperiodic dynamic CT reconstruction using backward-warping INR with regularization of diffeomorphism (BIRD)
Dynamic computed tomography (CT) reconstruction faces significant challenges in addressing motion artifacts, particularly for nonperiodic rapid movements such as cardiac imaging with fast heart rates. Traditional methods…
Computed Tomography (CT)CT ReconstructionAccelerated Optimization of Implicit Neural Representations for CT Reconstruction
Inspired by their success in solving challenging inverse problems in computer vision, implicit neural representations (INRs) have been recently proposed for reconstruction in low-dose/sparse-view X-ray computed tomograph…
Computed Tomography (CT)CT ReconstructionVirtual-mask Informed Prior for Sparse-view Dual-Energy CT Reconstruction
Sparse-view sampling in dual-energy computed tomography (DECT) significantly reduces radiation dose and increases imaging speed, yet is highly prone to artifacts. Although diffusion models have demonstrated potential in …
CT ReconstructionSynthetic CT Generation from Time-of-Flight Non-Attenutaion-Corrected PET for Whole-Body PET Attenuation Correction
Positron Emission Tomography (PET) imaging requires accurate attenuation correction (AC) to account for photon loss due to tissue density variations. In PET/MR systems, computed tomography (CT), which offers a straightfo…
Computed Tomography (CT)CT ReconstructionX$^{2}$-Gaussian: 4D Radiative Gaussian Splatting for Continuous-time Tomographic Reconstruction
Four-dimensional computed tomography (4D CT) reconstruction is crucial for capturing dynamic anatomical changes but faces inherent limitations from conventional phase-binning workflows. Current methods discretize tempora…
CT ReconstructionDecoderDVG-Diffusion: Dual-View Guided Diffusion Model for CT Reconstruction from X-Rays
Directly reconstructing 3D CT volume from few-view 2D X-rays using an end-to-end deep learning network is a challenging task, as X-ray images are merely projection views of the 3D CT volume. In this work, we facilitate c…
CT ReconstructionImage to 3DX-Field: A Physically Grounded Representation for 3D X-ray Reconstruction
X-ray imaging is indispensable in medical diagnostics, yet its use is tightly regulated due to potential health risks. To mitigate radiation exposure, recent research focuses on generating novel views from sparse inputs …
3D ReconstructionComputed Tomography (CT)CT ReconstructionNovel View SynthesisX-LRM: X-ray Large Reconstruction Model for Extremely Sparse-View Computed Tomography Recovery in One Second
Sparse-view 3D CT reconstruction aims to recover volumetric structures from a limited number of 2D X-ray projections. Existing feedforward methods are constrained by the limited capacity of CNN-based architectures and th…
16kCT ReconstructionTop-K Maximum Intensity Projection Priors for 3D Liver Vessel Segmentation
Liver-vessel segmentation is an essential task in the pre-operative planning of liver resection. State-of-the-art 2D or 3D convolution-based methods focusing on liver vessel segmentation on 2D CT cross-sectional views, w…
CT ReconstructionSegmentationTomoSelfDEQ: Self-Supervised Deep Equilibrium Learning for Sparse-Angle CT Reconstruction
Deep learning has emerged as a powerful tool for solving inverse problems in imaging, including computed tomography (CT). However, most approaches require paired training data with ground truth images, which can be diffi…
Computed Tomography (CT)CT ReconstructionUnsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction
Emerging unsupervised implicit neural representation (INR) methods, such as NeRP, NeAT, and SCOPE, have shown great potential to address sparse-view computed tomography (SVCT) inverse problems. Although these INR-based m…
CT ReconstructionSparse Measurement Medical CT Reconstruction using Multi-Fused Block Matching Denoising Priors
A major challenge for medical X-ray CT imaging is reducing the number of X-ray projections to lower radiation dosage and reduce scan times without compromising image quality. However these under-determined inverse imagin…
CT ReconstructionDenoisingImage Reconstruction