Phase processing for quantitative susceptibility mapping of regions with large susceptibility and lack of signal

Purpose Quantitative susceptibility mapping (QSM) is heavily impacted by phase processing of gradient echo imaging data. So far, phase unwrapping algorithms have mostly been developed and tested for neuroimaging applications. In this work, a numerical human abdomen phantom was created and used to assess the feasibility of different phase unwrapping algorithms in abdominal QSM. Furthermore, in vivo data were acquired to evaluate consistency with the simulations. Methods Laplacian‐based, quality‐guided region growing and graph‐cuts unwrapping techniques were evaluated using the numerical phantom as well as an in vivo measurement. As a quality metric, root mean square error (RMSE) was calculated in order to analyze the performance of the examined unwrapping algorithms. Subsequently, susceptibility maps were generated from the resulting phase maps and compared to the ground truth. The evaluation was carried out on the whole phantom as well as individual organs. Results Graph‐cuts led to the most accurate and robust results among the investigated unwrapping methods. The other algorithms showed severe errors in regions with large susceptibility changes (i.e., around the lungs). Deviations from the ground‐truth susceptibility were higher than in the previous brain simulations for all tested algorithms. Conclusion Graph‐cuts–based unwrapping algorithms should be preferred in QSM studies in the human abdomen, where large susceptibility changes occur. For further improvement of QSM studies, unwrapping algorithms should be optimized for abdominal applications.

Section: Discussionsupporting

confidence: 91%

Section: Discussionsupporting

confidence: 91%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of different phase unwrapping methods to optimize quantitative susceptibility mapping in the abdomen

Bechler

Stabinska

Wittsack

2019

“…For each scan, the total magnetic field map was generated from the DICOM images using a nonlinear voxel‐wise fit with a correction to remove residual echo‐to‐echo phase inconsistencies in the readout direction when present . Phase processing was performed using quality guided field unwrapping and Laplacian boundary value (LBV) background field removal, because these methods have been shown to produce the best performance in QSM processing . The QSM map was reconstructed from the local field map using the MEDI+0 method, which minimized variation in the agarose gel medium of the phantom according to the expression:

χ^{*} = \underset{χ}{argmin} \frac{1}{2} {false| false| w (e^{- i f} - e^{- i false(d * χ false)}) false| false|}_{2}^{2} + λ_{1} {false| false| M_{G} \nabla χ false| false|}_{1} + λ_{2} {false| false| M_{AGAROSE} false(χ - \bar{χ_{AGAROSE}} false) false| false|}_{2}^{2},

…”

Section: Methodsmentioning

confidence: 99%

Multicenter reproducibility of quantitative susceptibility mapping in a gadolinium phantom using MEDI+0 automatic zero referencing

Deh

Kawaji

Bulk

et al. 2018

Purpose To determine the reproducibility of quantitative susceptibility mapping at multiple sites on clinical and preclinical scanners (1.5 T, 3 T, 7 T, and 9.4 T) from different vendors (Siemens, GE, Philips, and Bruker) for standardization of multicenter studies. Methods Seven phantoms distributed from the core site, each containing 5 compartments with gadolinium solutions with fixed concentrations between 0.625 mM and 10 mM. Multi‐echo gradient echo scans were performed at 1.5 T, 3 T, 7 T, and 9.4 T on 12 clinical and 3 preclinical scanners. DICOM images from the scans were processed into quantitative susceptibility maps using the Laplacian boundary value (LBV) and MEDI+0 automatic uniform reference algorithm. Region of interest (ROI) analyses were performed by a physicist to determine agreement between results from all sites. Measurement reproducibility was assessed using regression, Bland‐Altman plots, and the intra‐class correlation coefficient (ICC). Results Quantitative susceptibility mapping (QSM) from all scanners had similar, artifact‐free visual appearance. Regression analysis showed a linear relationship between gadolinium concentrations and average QSM measurements for all phantoms (y = 350x – 0.0346, r2>0.99). The SD of measurements increased almost linearly from 32 ppb to 230 ppb as the measured susceptibility increased from 0.26 ppm to 3.56 ppm. A Bland‐Altman plot showed the bias, upper, and lower limits of agreement for all comparisons were −10, −210, and 200 ppb, respectively. The ICC was 0.991 with a 95% CI (0.973, 0.99). Conclusions QSM shows excellent multicenter reproducibility for a large range of susceptibility values encountered in cranial and extra‐cranial applications on a diverse set of scanner platforms.

The 2016 QSM Challenge: Lessons learned and considerations for a future challenge design

Milovic

Tejos

Acosta‐Cabronero³

et al. 2020

Purpose: The 4th International Workshop on MRI Phase Contrast and QSM (2016, Graz, Austria) hosted the first QSM Challenge. A single-orientation gradient recalled echo acquisition was provided, along with COSMOS and the χ 33 STI component as ground truths. The submitted solutions differed more than expected depending on the error metric used for optimization and were generally over-regularized. This raised (unanswered) questions about the ground truths and the metrics utilized. Methods: We investigated the influence of background field remnants by applying additional filters. We also estimated the anisotropic contributions from the STI tensor to the apparent susceptibility to amend the χ 33 ground truth and to investigate the impact on the reconstructions. Lastly, we used forward simulations from the COSMOS reconstruction to investigate the impact noise had on the metric scores. Results: Reconstructions compared against the amended STI ground truth returned lower errors. We show that the background field remnants had a minor impact in the errors. In the absence of inconsistencies, all metrics converged to the same regularization weights, whereas structural similarity index metric was more insensitive to such inconsistencies. Conclusion: There was a mismatch between the provided data and the ground truths due to the presence of unaccounted anisotropic susceptibility contributions and noise.