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Found 8 papers, 1 papers with code
MB-DECTNet: A Model-Based Unrolled Network for Accurate 3D DECT Reconstruction
Therefore, to improve its convergence, we have embedded DECT SIR into a deep learning model-based unrolled network for 3D DECT reconstruction (MB-DECTNet) that can be trained in an end-to-end fashion.
A Metal Artifact Reduction Scheme For Accurate Iterative Dual-Energy CT Algorithms
We compared DEAM with the proposed method to the original DEAM and vendor reconstructions with and without metal-artifact reduction for orthopedic implants (O-MAR).
A Machine-learning Based Initialization for Joint Statistical Iterative Dual-energy CT with Application to Proton Therapy
Dual-energy CT (DECT) has been widely investigated to generate more informative and more accurate images in the past decades.
Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations
The proposed registration approach is then built on the factorized latent shape code, with the assumption that the intrinsic shape deformation existing in original image domain is preserved in this latent space.
Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation
In this paper, we propose a novel model framework for learning automatic X-ray image parsing from labeled CT scans.
Dilated FCN for Multi-Agent 2D/3D Medical Image Registration
Second, to handle various artifacts in 2D X-ray images, multiple local agents are employed efficiently via FCN-based structures, and an auto attention mechanism is proposed to favor the proposals from regions with more reliable visual cues.
An Artificial Agent for Robust Image Registration
The resulting registration approach inherently encodes both a data-driven matching metric and an optimal registration strategy (policy).
Real-time 2D/3D Registration via CNN Regression
In this paper, we present a Convolutional Neural Network (CNN) regression approach for real-time 2-D/3-D registration.
