OATH
发表于 2025-3-28 14:37:08
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Outmoded
发表于 2025-3-28 21:35:12
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遗传
发表于 2025-3-29 00:38:13
Deep Learning Based Framework for Direct Reconstruction of PET Imagesase. In this paper, we propose a deep learning based framework for PET image reconstruction from sinogram domain directly. In the framework, conditional Generative Adversarial Networks (cGANs) is constructed to learn a mapping from sinogram data to reconstructed image and generate a well-trained mod
Armada
发表于 2025-3-29 06:05:08
Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction most works are limited in the sense that they assume equidistant rectilinear (Cartesian) data acquisition in 2D or 3D. In practice, a reconstruction from nonuniform samplings such as radial and spiral is an attractive choice for more efficient acquisitions. Nevertheless, it has less been explored a
Hallowed
发表于 2025-3-29 11:03:13
Reconstruction of Isotropic High-Resolution MR Image from Multiple Anisotropic Scans Using Sparse FiThe acquired images are thus anisotropic, with much lower inter-slice resolution than the intra-slice resolution. For better coverage of the organs of interest, multiple anisotropic scans, each of which focus to a certain scan direction, are usually acquired per patient. In this work, we propose a 3
俗艳
发表于 2025-3-29 12:23:56
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Highbrow
发表于 2025-3-29 16:16:28
Deep Gated Convolutional Neural Network for QSM Background Field Removalication of tissue magnetism within a volume of interest (VOI). Conventional state-of-the-art QSM background removal methods suffer from several limitations in accuracy and performance. To overcome these limitations, a 3D gated convolutional neural network was trained to infer whole brain local tissu
温顺
发表于 2025-3-29 19:58:13
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Fsh238
发表于 2025-3-30 03:49:59
RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Finoperties, such as T1 and T2 relaxation times, in one acquisition. To accelerate the data sampling in MRF, a variety of methods have been proposed to extract tissue properties from highly accelerated MRF signals. While these methods have demonstrated promising results, further improvement in the accu
STEER
发表于 2025-3-30 04:38:50
GANReDL: Medical Image Enhancement Using a Generative Adversarial Network with Real-Order Derivativeing and super-resolution, for instance). However, the central issue of recovering finer texture details in images still remains unsolved. State-of-the-art objective functions used in DCNN mostly focus on minimizing the mean squared reconstruction error. The resulting image estimates have high peak s