A method for estimating and removing streaking artifacts in quantitative susceptibility mapping
By Wei Li, Nian Wang, Fang Yu, Hui Han, Wei Cao, R. Romero, B. Tantiwongkosi, T. Duong, Chunlei Liu. NeuroImage, 2014
325 citations recorded by the source corpus.
Quantitative susceptibility mapping (QSM) is a novel MRI method for quantifying tissue magnetic property. In the brain, it reflects the molecular composition and microstructure of the local tissue. However, susceptibility maps reconstructed from single-orientation data still suffer from streaking artifacts which obscure structural details and small lesions. We propose and have developed a general method for estimating streaking artifacts and subtracting them from susceptibility maps. Specifically, this method uses a sparse linear equation and least-squares (LSQR)-algorithm-based method to derive an initial estimation of magnetic susceptibility, a fast quantitative susceptibility mapping method to estimate the susceptibility boundaries, and an iterative approach to estimate the susceptibility artifact from ill-conditioned k-space regions only. With a fixed set of parameters for the initial susceptibility estimation and subsequent streaking artifact estimation and removal, the method provides an unbiased estimate of tissue susceptibility with negligible streaking artifacts, as compared to multi-orientation QSM reconstruction. This method allows for improved delineation of white matter lesions in patients with multiple sclerosis and small structures of the human brain with excellent anatomical details. The proposed methodology can be extended to other existing QSM algorithms.
Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/2585ab1e36ba93d4b2b07d93728cb4ced99f04eb
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In Molecular Biology
In Radiology, Nuclear Medicine and Imaging
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Citations in the corpus
- Quantitative susceptibility mapping using deep neural network: QSMnet (2018, methodology): “For Model loss, the L1 difference between the dipole convolution of the label and the output was measured as follows: lossModel ¼ jd χ d yjj1 (1) where d is the dipole kernel (Li et al., 2011; Liu et al., 2015; Wang and Liu, 2015), * is the symbol for convolution, χ is the output, and y is the label”
- DeepQSM - Using Deep Learning to Solve the Dipole Inversion for MRI Susceptibility Mapping (2018, unknown): “One example to solve this equation system is the LSQR algo-rithm (34, 35).”