计算机科学
迭代重建
人工神经网络
变量(数学)
深度学习
算法
网(多面体)
编码(集合论)
缩小
人工智能
数学
几何学
数学分析
集合(抽象数据类型)
程序设计语言
作者
Jinming Duan,Jo Schlemper,Qin Chen,Cheng Ouyang,Wenjia Bai,Carlo Biffi,Ghalib Bello,Ben Statton,Declan P. O’Regan,Daniel Rueckert
标识
DOI:10.1007/978-3-030-32251-9_78
摘要
In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high-quality reconstruction of undersampled multi-coil MR data. We formulate the generalized parallel compressed sensing reconstruction as an energy minimization problem, for which a variable splitting optimization method is derived. Based on this formulation we propose a novel, end-to-end trainable deep neural network architecture by unrolling the resulting iterative process of such variable splitting scheme. VS-Net is evaluated on complex valued multi-coil knee images for 4-fold and 6-fold acceleration factors. We show that VS-Net outperforms state-of-the-art deep learning reconstruction algorithms, in terms of reconstruction accuracy and perceptual quality. Our code is publicly available at https://github.com/j-duan/VS-Net .
科研通智能强力驱动
Strongly Powered by AbleSci AI