Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks

• Published in: IEEE transactions on biomedical engineering Vol. 65; no. 9; pp. 1985 - 1995
• Main Authors: Lee, Dongwook, Yoo, Jaejun, Tak, Sungho, Ye, Jong Chul
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Algorithms; Aliasing; Brain - diagnostic imaging; Compressed sensing MRI; Computer applications; Computing time; Data processing; deep convolutional framelets; Deep Learning; Humans; Image acquisition; Image Processing, Computer-Assisted - methods; Image reconstruction; Interpolation; Iterative algorithms; Iterative methods; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Networks; parallel imaging

Objective: Accelerated magnetic resonance (MR) image 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 costs. To address this, we investigate deep residual learning networ...