Accelerated Magnetic Resonance Spectroscopy with Model-inspired Deep Learning

Adaptive convolutional neural networks for accelerating magnetic resonance imaging via k-space data interpolation

Cardiac Functional Analysis with Cine MRI via Deep Learning Reconstruction

Compressed sensing MRI via a fusion model based on image and gradient priors

Author:Yuxiang Dai  Cheng yan Wang  He Wang  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Image Reconstruction  

Session Name:Machine Learning to Reconstruct Accelerated Scans  

Program Number:1980  

Room Session:Concurrent 1  

Institution:Fudan University  

DL2 - Deep Learning + Dictionary Learning-based Regularization for Accelerated 2D Dynamic Cardiac MR Image Reconstruction

Improved CNN-based Image reconstruction using regularly under-sampled signal obtained in phase scrambling Fourier transform imaging

Author:Satoshi ITO  Shun UEMATSU  

Session Type:Digital Poster  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Image Reconstruction  

Session Name:Machine Learning to Reconstruct Accelerated Scans  

Program Number:1974  

Room Session:Concurrent 1  

Institution:Utsunomiya University  

Joint-ISTA-Net: A model-based deep learning network for multi-contrast CS-MRI reconstruction

Kernel-based Fast EPTI Reconstruction with Neural Network

A Modified Generative Adversarial Network using Spatial and Channel-wise Attention for Compressed Sensing MRI Reconstruction

Multi-Mask Self-Supervised Deep Learning for Highly Accelerated Physics-Guided MRI Reconstruction

PIC-GAN: A Parallel Imaging Coupled Generative Adversarial Network for Accelerated Multi-Channel MRI Reconstruction

Progressive Volumetrization for Data-Efficient Image Recovery in Accelerated Multi-Contrast MRI

Training- and Database-free Deep Non-Linear Inversion (DNLINV) for Highly Accelerated Parallel Imaging and Calibrationless PI&CS MR Imaging

Using Untrained Convolutional Neural Networks to Accelerate MRI in 2D and 3D

Zero-shot Learning for Unsupervised Reconstruction of Accelerated MRI Acquisitions