50 citations · 125 across the 14 of their papers we have counts for
6 papers · 1 filter
Instant tissue field and magnetic susceptibility mapping from MR raw phase using Laplacian enabled deep neural networks
Yang Gao, Zhuang Xiong, Amir Fazlollahi +8
Quantitative susceptibility mapping (QSM) is a valuable MRI post-processing technique that quantifies the magnetic susceptibility of body tissue from phase data. However, the tradi…
CAN3D: Fast 3D Medical Image Segmentation via Compact Context Aggregation
Wei Dai, Boyeong Woo, Siyu Liu +6
Direct automatic segmentation of objects from 3D medical imaging, such as magnetic resonance (MR) imaging, is challenging as it often involves accurately identifying a number of in…
Bespoke Fractal Sampling Patterns for Discrete Fourier Space via the Kaleidoscope Transform
Jacob M. White, Stuart Crozier, Shekhar S. Chandra
Sampling strategies are important for sparse imaging methodologies, especially those employing the discrete Fourier transform (DFT). Chaotic sensing is one such methodology that em…
Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning
Xuanyu Zhu, Yang Gao, Feng Liu +2
Introduction: Quantitative Susceptibility Mapping (QSM) is generally acquired with full brain coverage, even though many QSM brain-iron studies focus on the deep grey matter (DGM)…
Deep Simultaneous Optimisation of Sampling and Reconstruction for Multi-contrast MRI
Xinwen Liu, Jing Wang, Fangfang Tang +3
MRI images of the same subject in different contrasts contain shared information, such as the anatomical structure. Utilizing the redundant information amongst the contrasts to sub…
Accelerating Quantitative Susceptibility Mapping using Compressed Sensing and Deep Neural Network
Yang Gao, Martijn Cloos, Feng Liu +3
Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing method that quantifies tissue magnetic susceptibility distributions. However, QSM acquisitions are r…