Whole head quantitative susceptibility mapping using a least-norm direct dipole inversion method

Sun, Hongfu; Ma, Yuhan; MacDonald, M. Ethan; Pike, G. Bruce

doi:10.1016/j.neuroimage.2018.06.036

Cited by 36 publications

(42 citation statements)

References 57 publications

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“…This would provide advantages in clinical applications as has already been shown in QSM algorithms that can invert the total field. 44,45 A study by Liu et al 46 also proposed background field removal with similar results using simulated background field distributions.…”

Section: Background Field Removalmentioning

confidence: 84%

Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities

2020

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Quantitative susceptibility mapping (QSM) has gained broad interests in the field by extracting biological tissue properties, predominantly myelin, iron and calcium from magnetic resonance imaging (MRI) phase measurements in vivo. Thereby, QSM can reveal pathological changes of these key components in a variety of diseases. QSM requires multiple processing steps such as phase unwrapping, background field removal and field-to-source-inversion. Current state of the art techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and require a careful choice of regularization parameters. With the recent success of deep learning using convolutional neural networks for solving ill-posed reconstruction problems, the QSM community also adapted these techniques and demonstrated that the QSM processing steps can be solved by efficient feed forward multiplications not requiring iterative optimization nor the choice of regularization parameters.Here, we review the current status of deep learning based approaches for processing QSM, highlighting limitations and potential pitfalls, and discuss the future directions the field may take to exploit the latest advances in deep learning for QSM.

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Section: Background Field Removalmentioning

confidence: 84%

Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities

2020

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“…Another advantage of our method is the newly proposed least‐norm QSM method that we incorporated for QSM‐OEF analysis. Least‐norm QSM is a 1‐step method that bypasses the procedure of background field removal and has been shown to reduce reconstruction artifacts compared with using a total‐variation regularization alone and performs equally well in deep GM susceptibility measurements, compared with other commonly used QSM methods . Moreover, it preserves the outer brain surface to enable susceptibility measurements in the cortex and the superior sagittal sinus.…”

Section: Discussionmentioning

confidence: 99%

Cerebral OEF quantification: A comparison study between quantitative susceptibility mapping and dual‐gas calibrated BOLD imaging

Sun

Cho

et al. 2019

Magnetic Resonance in Med

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Purpose To compare regional oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen consumption (CMRO2) quantified from the microvascular quantitative susceptibility mapping (QSM) using a hypercapnic gas challenge with those measured by the dual‐gas calibrated BOLD imaging (DGC‐BOLD) in healthy subjects. Methods Ten healthy subjects were scanned using a 3T MR system. The QSM data were acquired with a multi‐echo gradient‐echo sequence at baseline and hypercapnia. Cerebral blood flow data were acquired using the pseudo‐continuous arterial spin labeling technique. Baseline OEF and CMRO2 were calculated using QSM and cerebral blood flow measurements. The DGC‐BOLD data were also collected under a hypercapnic and a hyperoxic condition to yield baseline OEF and CMRO2. The QSM‐OEF and CMRO2 maps were compared with DGC‐BOLD OEF and CMRO2 maps using region of interest (vascular territories) analysis and Bland‐Altman plots. Results Hypercapnia is a robust stimulus for mapping OEF in combination with QSM. Average OEF in 16 vascular territory regions of interest across 10 subjects was 0.40 ± 0.04 by QSM‐OEF and 0.38 ± 0.09 by DGC‐BOLD. The average CMRO2 was 176 ± 35 and 167 ± 53 μmol O2/min/100g by QSM‐OEF and DGC‐BOLD, respectively. A Bland‐Altman plot of regional OEF and CMRO2 in regions of interest revealed a statistically significant but small difference (OEF difference = 0.02, CMRO2 difference = 9 μmol O2/min/100g, p < .05) between the 2 methods for the 10 healthy subjects. Conclusion Hypercapnic challenge–assisted QSM‐OEF is a feasible approach to quantify regional brain OEF and CMRO2. Compared with DGC‐BOLD, hypercapnia QSM‐OEF results in smaller intersubject variability and requires only 1 gas challenge.

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“…The LN-QSM and TFI methods perform dipole inversion directly on the total field instead of on the filtered phase and thus avoid the Laplacian operator, but still require brain masks to aid QSM reconstruction. Additionally, it was shown that the reconstruction speed and the quantification accuracy are both influenced by the choice of the preconditioner in TFI and regularization parameters in LN-QSM Sun et al, 2018). In this study, the trained neural network enables end-to-end single-step QSM processing and it does not require explicit regularization parameters.…”

Section: Discussionmentioning

confidence: 94%

Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction

et al. 2019

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Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing steps. These steps involve phase unwrapping, brain volume extraction, background phase removal and solving an illposed inverse problem relating the tissue phase to the underlying susceptibility distribution. The resulting susceptibility map is known to suffer from inaccuracy near the edges of the brain tissues, in part due to imperfect brain extraction, edge erosion of the brain tissue and the lack of phase measurement outside the brain. This inaccuracy has thus hindered the application of QSM for measuring susceptibility of tissues near the brain edges, e.g., quantifying cortical layers and generating superficial venography. To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and background phase removal, referred to as autoQSM. The neural network has a modified U-net structure and is trained using QSM maps computed by a twostep QSM method. 209 healthy subjects with ages ranging from 11 to 82 years were employed for patch-wise network training. The network was validated on data dissimilar to the training data, e.g. in vivo mouse brain data and brains with lesions, which suggests that the network generalized and learned the underlying mathematical relationship between magnetic field perturbation and magnetic susceptibility. Quantitative and qualitative comparisons were performed between autoQSM and other two-step QSM methods. AutoQSM was able to recover magnetic susceptibility of anatomical structures near the edges of the brain including the veins covering the cortical surface, spinal cord and nerve tracts near the mouse brain boundaries. The advantages of high-quality maps, no need for brain volume extraction, and high reconstruction speed demonstrate autoQSM's potential for future applications.

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Whole head quantitative susceptibility mapping using a least-norm direct dipole inversion method

Cited by 36 publications

References 57 publications

Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities

Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities

Cerebral OEF quantification: A comparison study between quantitative susceptibility mapping and dual‐gas calibrated BOLD imaging

Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction

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