A rapid, robust multi-echo phase unwrapping method for quantitative susceptibility mapping (QSM) using strategically acquired gradient echo (STAGE) data acquisition

Author(s):  
Yongsheng Chen ◽  
Saifeng Liu ◽  
Yan Kang ◽  
E. Mark Haacke
2020 ◽  
Vol 14 ◽  
Author(s):  
Sara Gharabaghi ◽  
Saifeng Liu ◽  
Ying Wang ◽  
Yongsheng Chen ◽  
Sagar Buch ◽  
...  

2021 ◽  
Author(s):  
Oliver C. Kiersnowski ◽  
Anita Karsa ◽  
Stephen J. Wastling ◽  
John S. Thornton ◽  
Karin Shmueli

Purpose: Quantitative susceptibility mapping (QSM) is increasingly used for clinical research where oblique image acquisition is commonplace but its effects on QSM accuracy are not well understood. Theory and Methods: The QSM processing pipeline involves defining the unit magnetic dipole kernel, which requires knowledge of the direction of the main magnetic field B0 with respect to the acquired image volume axes. The direction of B0 is dependent upon the axis and angle of rotation in oblique acquisition. Using both a numerical brain phantom and in-vivo acquisitions, we analysed the effects of oblique acquisition on magnetic susceptibility maps. We compared three tilt correction schemes at each step in the QSM pipeline: phase unwrapping, background field removal and susceptibility calculation, using the root-mean-squared error and QSM-tuned structural similarity index (XSIM). Results: Rotation of wrapped phase images gave severe artefacts. Background field removal with projection onto dipole fields gave the most accurate susceptibilities when the field map was first rotated into alignment with B0. LBV and VSHARP background field removal methods gave accurate results without tilt correction. For susceptibility calculation, thresholded k-space division, iterative Tikhonov regularisation and weighted linear total variation regularisation all performed most accurately when local field maps were rotated into alignment with B0 before susceptibility calculation. Conclusion: For accurate QSM, oblique acquisition must be taken into account. Rotation of images into alignment with B0 should be carried out after phase unwrapping and before background field removal. We provide open-source tilt-correction code to incorporate easily into existing pipelines: https://github.com/o-snow/QSM_TiltCorrection.git.


2013 ◽  
Vol 27 (2) ◽  
pp. 219-227 ◽  
Author(s):  
Wei Li ◽  
Alexandru V. Avram ◽  
Bing Wu ◽  
Xue Xiao ◽  
Chunlei Liu

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Kathryn E. Keenan ◽  
Ben P. Berman ◽  
Slávka Rýger ◽  
Stephen E. Russek ◽  
Wen-Tung Wang ◽  
...  

Quantitative Susceptibility Mapping (QSM) is an MRI tool with the potential to reveal pathological changes from magnetic susceptibility measurements. Before phase data can be used to recover susceptibility ( Δ χ ), the QSM process begins with two steps: data acquisition and phase estimation. We assess the performance of these steps, when applied without user intervention, on several variations of a phantom imaging task. We used a rotating-tube phantom with five tubes ranging from Δ χ = 0.05 ppm to Δ χ = 0.336  ppm. MRI data was acquired at nine angles of rotation for four different pulse sequences. The images were processed by 10 phase estimation algorithms including Laplacian, region-growing, branch-cut, temporal unwrapping, and maximum-likelihood methods, resulting in approximately 90 different combinations of data acquisition and phase estimation methods. We analyzed errors between measured and expected phases using the probability mass function and Cumulative Distribution Function. Repeatable acquisition and estimation methods were identified based on the probability of relative phase errors. For single-echo GRE and segmented EPI sequences, a region-growing method was most reliable with Pr (relative error <0.1) = 0.95 and 0.90, respectively. For multiecho sequences, a maximum-likelihood method was most reliable with Pr (relative error <0.1) = 0.97. The most repeatable multiecho methods outperformed the most repeatable single-echo methods. We found a wide range of repeatability and reproducibility for off-the-shelf MRI acquisition and phase estimation approaches, and this variability may prevent the techniques from being widely integrated in clinical workflows. The error was dominated in many cases by spatially discontinuous phase unwrapping errors. Any postprocessing applied on erroneous phase estimates, such as QSM’s background field removal and dipole inversion, would suffer from error propagation. Our paradigm identifies methods that yield consistent and accurate phase estimates that would ultimately yield consistent and accurate Δ χ estimates.


Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Shuo Wang ◽  
Min Lou ◽  
Tian Liu ◽  
xiaomei chen ◽  
Yi Wang

Introduction T2* weighted gradient echo MRI has been increasingly recognized as a sensitive tool in detecting intracerebral hemorrhage. However, its blooming artifacts is highly dependent on imaging parameters including TE, field strength and voxel size, making it difficult to reliably estimate the hematoma volume, a key predictor of morbidity and mortality of hemorrhage. Recently, a novel quantitative susceptibility mapping (QSM) technology has been developed for processing gradient echo MRI data to map tissue susceptibility property without blooming artifacts and dependence on imaging parameters. Hypothesis We assessed the hypothesis that hematoma volume measurement on QSM is independent of imaging parameters, eliminating its TE dependence on gradient echo MRI. Method A retrospective image analysis of MRI was approved by our IRB with HIPPA compliance. We randomly selected 16 patients who underwent intracerebral hemorrhage MRI including a 3D multiecho T2*w sequence: 8-11 echoes with first echo TE/ echo spacing/ TR= 5/5/50 msec. Postprocessed images of gradient echo MRI included susceptibility weighted imaging (SWI), R2* (quantitative 1/T2* mapping), and QSM at various TEs. Hematoma volumes were measured from all these images. Results Linear regression of hematoma volume vs TE over all subjects showed substantial slopes for gradient echo magnitude (0.45±0.31 L/s), SWI (0.52±0.46) and R2* (0.39±0.30) but nearly zero slope for QSM (0.01±0.05). At TE=20 msec, hematoma volume on QSM was 0.80x that on gradient echo magnitude image (R2=0.99), and hematoma volume on CT is also 0.8x that on gradient echo magnitude image according to literature (Stroke 2008;39:2017-2020). Conclusion In conclusion, quantitative susceptibility mapping can provide reliable measurement of hematoma volume, independent echo time and similar to CT.


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