Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019; 22nd International C Dinggang Shen,Tianming Liu,Ali Khan Conferen

OATH

Outmoded

遺傳

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

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

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

俗艷

Highbrow

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

溫順

Fsh238

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

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