Deep Network Interpolation for Accelerated Parallel MR Image Reconstruction
We present a deep network interpolation strategy for accelerated parallel MR image reconstruction. In particular, we examine the network interpolation in parameter space between a source model that is formulated in an unrolled scheme with L1 and SSIM losses and its counterpart that is trained with an adversarial loss. We show that by interpolating between the two different models of the same network structure, the new interpolated network can model a trade-off between perceptual quality and fidelity.
Code (0)
등록된 구현이 없습니다.
Tasks
Image ReconstructionSSIMSimilar Papers 제목 키워드 기반
Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks
Accelerated magnetic resonance (MR) scan acquisition with compressed sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time. However, many reconstruction algorithms have high computational …
compressed sensingDeep Learning Methods for Parallel Magnetic Resonance Image Reconstruction
Following the success of deep learning in a wide range of applications, neural network-based machine learning techniques have received interest as a means of accelerating magnetic resonance imaging (MRI). A number of ide…
BIG-bench Machine Learningcompressed sensingDeep LearningImage Reconstruction+1Iterative training of robust k-space interpolation networks for improved image reconstruction with limited scan specific training samples
Purpose: To evaluate an iterative learning approach for enhanced performance of Robust Artificial-neural-networks for K-space Interpolation (RAKI), when only a limited amount of training data (auto-calibration signals, A…
Data AugmentationImage Reconstructionk-Space Deep Learning for Parallel MRI: Application to Time-Resolved MR Angiography
Time-resolved angiography with interleaved stochastic trajectories (TWIST) has been widely used for dynamic contrast enhanced MRI (DCE-MRI). To achieve highly accelerated acquisitions, TWIST combines the periphery of the…
A review and experimental evaluation of deep learning methods for MRI reconstruction
Following the success of deep learning in a wide range of applications, neural network-based machine-learning techniques have received significant interest for accelerating magnetic resonance imaging (MRI) acquisition an…
compressed sensingDeep LearningImage ReconstructionMRI Reconstruction