ArticleNMR in biomedicine2017
Single-step quantitative susceptibility mapping with variational penalties.
Article in NMR in biomedicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Does quantitative susceptibility mapping elucidate age-related alterations in deep gray matter iron deposition? A systematic review.Radiological physics and technology · 2026Review
- Accelerated susceptibility-driven positive contrast MRI reconstruction based on primal-dual optimization with minimal parameter tuning.Quantitative imaging in medicine and surgery · 2026Article
- Midbrain and pallidal iron changes identify patients with REM sleep behaviour disorder and Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Data adaptive regularization with reference tissue constraints for liver quantitative susceptibility mapping.Magnetic resonance in medicine · 2023Article
- Investigating the effect of oblique image acquisition on the accuracy of QSM and a robust tilt correction method.Magnetic resonance in medicine · 2023Article
- Article
- Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs.Frontiers in neuroscience · 2023Article
- Comparison of quantitative susceptibility mapping methods on evaluating radiation-induced cerebral microbleeds and basal ganglia at 3T and 7T.NMR in biomedicine · 2022Article
- Single-step calculation of susceptibility through multiple orientation sampling.NMR in biomedicine · 2021Article
- Non-Invasive Evaluation of Cerebral Microvasculature Using Pre-Clinical MRI: Principles, Advantages and Limitations.Diagnostics (Basel, Switzerland) · 2021Review
- Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM).NMR in biomedicine · 2020Article
- Spatially Adaptive Regularization in Total Field Inversion for Quantitative Susceptibility Mapping.iScience · 2020Article
- Methodological Considerations for Neuroimaging in Deep Brain Stimulation of the Subthalamic Nucleus in Parkinson's Disease Patients.Journal of clinical medicine · 2020Article
- The 2016 QSM Challenge: Lessons learned and considerations for a future challenge design.Magnetic resonance in medicine · 2020Article
- Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction.NeuroImage · 2019Article
- Improvement of Signal Inhomogeneity Induced by Radio-frequency Transmit-related Phase Error for Single-step Quantitative Susceptibility Mapping Reconstruction.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2019Article
- Susceptibility mapping of the dural sinuses and other superficial veins in the brain.Magnetic resonance imaging · 2019Article
- On the sensitivity of quantitative susceptibility mapping for measuring trabecular bone density.Magnetic resonance in medicine · 2019Article
- A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults.Scientific data · 2019Article
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11 authors.
Funding
Abstract
Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from the gradient echo (GRE) phase signal through background phase removal and dipole inversion steps. Each of these steps typically requires the solution of an ill-posed inverse problem and thus necessitates additional regularization. Recently developed single-step QSM algorithms directly relate the unprocessed GRE phase to the unknown susceptibility distribution, thereby requiring the solution of a single inverse problem. In this work, we show that such a holistic approach provides susceptibility estimation with artifact mitigation and develop efficient algorithms that involve simple analytical solutions for all of the optimization steps. Our methods employ total variation (TV) and total generalized variation (TGV) to jointly perform the background removal and dipole inversion in a single step. Using multiple spherical mean value (SMV) kernels of varying radii permits high-fidelity background removal whilst retaining the phase information in the cortex. Using numerical simulations, we demonstrate that the proposed single-step methods reduce the reconstruction error by up to 66% relative to the multi-step methods that involve SMV background filtering with the same number of SMV kernels, followed by TV- or TGV-regularized dipole inversion. In vivo single-step experiments demonstrate a dramatic reduction in dipole streaking artifacts and improved homogeneity of image contrast. These acquisitions employ the rapid three-dimensional echo planar imaging (3D EPI) and Wave-CAIPI (controlled aliasing in parallel imaging) trajectories for signal-to-noise ratio-efficient whole-brain imaging. Herein, we also demonstrate the multi-echo capability of the Wave-CAIPI sequence for the first time, and introduce an automated, phase-sensitive coil sensitivity estimation scheme based on a 4-s calibration acquisition. Copyright © 2016 John Wiley & Sons, Ltd.
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