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Zero-Shot Low-Field MRI Enhancement via Denoising Diffusion Driven Neural ... Reconstructing Knee CT Volumes from Biplanar X-Rays Via Self-Supervised Neural Field.
Reconstructing Knee CT Volumes from Biplanar X-Rays Via Self-Supervised Neural Field · Shuyang LaiXuanyu Tian +5 authors. Yuyao Zhang. Engineering, Medicine.
Jul 18, 2024 · In this paper, we propose DiffuX2CT, which models CT reconstruction from orthogonal biplanar X-rays as a conditional diffusion process.
Missing: Supervised | Show results with:Supervised
We present an end-to-end Convolutional Neural Network (CNN) approach for 3D reconstruction of knee bones directly from two bi-planar X-ray images.
Apr 2, 2020 · An end-to-end Convolutional Neural Network approach for 3D reconstruction of knee bones directly from two bi-planar X-ray images, ...
Most use convolutional neural network (CNN) designs to fuse input x-rays together to predict a CT volume, supervised using pixel-wise reconstruction losses [3]- ...
Reconstructing Knee CT Volumes from Biplanar X-Rays Via Self-Supervised Neural Field. S Lai, X Tian, Q Wu, C Du, X Xu, H Wei, X Guan, Y Zhang. 2024 IEEE ...
Sep 10, 2023 · This paper presents a new self-driven generative adversarial network model (SdCT-GAN), which is motivated to pay more attention to image details.
Missing: Knee Volumes Supervised Neural Field.
A deep learning network, XctNet, is proposed to gain this prior knowledge from 2D pixels and produce volumetric data.