2019/04/11 by Juan Liu, Liu, Juan, Kevin M. Koch +1 · 1 voice · 5 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Medicine · Physics and Astronomy · #Advanced MRI Techniques and Applications #Advanced Neuroimaging Techniques and Applications #Algorithm #Artificial Intelligence (cs.AI) #Artificial intelligence #Computation #Computer science #Computer vision #Convolutional neural network #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Magnetic resonance imaging #Medical Physics (physics.med-ph) #Pattern recognition (psychology) #Photoacoustic and Ultrasonic Imaging #Quantitative Methods (q-bio.QM) #Quantitative susceptibility mapping #Streaking #Translation (biology) #cs.AI #physics.med-ph #q-bio.QM
paper · pdf · doi:10.48550/arxiv.1904.05493
published in arXiv (Cornell University) (Cornell University)
arxiv created 2019/04/11 · arxiv published 2019/04/11 · arxiv updated 2019/04/12
Quantitative Susceptibility Mapping (QSM) reconstruction is a challenging inverse problem driven by ill conditioning of its field-to -susceptibility transformation. State-of-art QSM reconstruction methods either suffer from image artifacts or long computation times, which limits QSM clinical translation efforts. To overcome these limitations, a non-locally encoder-decoder gated convolutional neural network is trained to infer whole brain susceptibility map, using the local field and brain mask as the inputs. The performance of the proposed method is evaluated relative to synthetic data, a publicly available challenge dataset, and clinical datasets. The proposed approach can outperform existing methods on quantitative metrics and visual assessment of image sharpness and streaking artifacts. The estimated susceptibility maps can preserve conspicuity of fine features and suppress streaking artifacts. The demonstrated methods have potential value in advancing QSM clinical research and aiding in the translation of QSM to clinical operations.