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Feasibility of Rapid Automated Liver Quantitative Susceptibility Mapping in a Patient Population1Cornell University, New York, NY, United States, 2Weill Cornell Medicine, New York, NY, United States, 3Southern Medical University, Guangzhou, China, 4Columbia University Medical Center, New York, NY, United States
Synopsis
Accurate measurement of the liver iron concentration (LIC) is needed to guide iron-chelating therapy for patients with transfusional iron overload. In this work, we investigate the feasibility of automated quantitative susceptibility mapping (QSM) to measure the LIC in clinical practice. We propose to incorporate In-phase echo acquisition for rapid, robust initialization of the field in water-fat separation problem (T2*-IDEAL).
Introduction
Quantitative Susceptibility Mapping (QSM) is an emerging noninvasive MRI method to quantify iron, calcium and other susceptibility sources (1,2). In patients with transfusional iron overload, QSM enables accurate noninvasive monitoring of liver iron to guide iron-chelating therapy (3). A major challenge in liver QSM is to solve the nonlinear water-fat separation, R2* and field estimation problem (4,5). This nonlinear optimization is highly dependent on the initial guess and can converge to local minima. We propose to use in-phase (IP) echoes for rapid initialization of the field and R2*(6). This method was compared with a 3D graph cuts field initialization method (SPURS) (7) to (i) assess reproducibility of liver QSM in healthy subjects for two field strengths, two manufacturers, and (ii) evaluate its feasibility within clinical practice.Methods
The T2*-IDEAL problem estimates fat content $$$(F)$$$, water content $$$(W)$$$, susceptibility induced field $$$(f)$$$ and $$$R_2^*$$$ decay by modeling the complex gradient-recalled echo (GRE) signal $$$S$$$ shown in Eq. 1. We propose 1) using out-of-phase echo spacing (∆TE= 2.3 msec at 1.5T and 1.15 msec at 3T) , and 2) obtain initial guesses $$$f_{IP}$$$ and $$$R_{2,IP}^*$$$ for Eq.1 using the echoes that are in-phase with respect to the first echo, i.e., the odd echoes, by solving Eqs. 2 & 3.
$$(W,F,f,R_2^* )=argmin\sum_{j=1}^N||S(t_j )-e^{-R_2^* t_j } e^{-i2πft_j} (W+Fe^{-i2πν_F t_j })||_2^2, [1]$$
$$f_{IP} =argmin\sum_{j_{odd}}||S(t_j )-|S(t_j )|e^{-i2πf_{IP} t_j })||_2^2, [2]$$
$$(a,{R_2^*}_{IP})=argmin\sum_{j_{odd}}|||S(t_j )|-a.e^{{R_2^*}_{IP} t_j })||_2^2, [3]$$
QSM is reconstructed in a Bayesian setting (2),
$$χ=argmin\frac{1}{2}||w(e^{-if}-e^{-i(d*χ)})||_2^2+λ_1||M_G\triangledown χ||_1+λ_2||M_{aorta}(χ-\overline{χ}_{aorta})||^2_2, [4]$$
assuming Gaussian noise (8), $$$χ$$$ is susceptibility, $$$w$$$ noise weighting, $$$f$$$ the local field, $$$d$$$ the dipole kernel, $$$M_G$$$ the binary edge mask, , $$$λ_1, λ_2$$$ regularization parameters, and $$$M_{aorta}$$$ the binary mask of abdominal aorta used for zero-referencing (9).
Reproducibility of liver QSM was studied in n=8 healthy subjects using a breath-hold multi-echo 3D gradient echo (GRE) sequence across 4 scanners including two 1.5T GE scanners (S1, S2), one 1.5T (S3) and one 3T (S4) Siemens scanner. Clinical feasibility was assessed in n=19 patients at scanners S1 and S2.
For reproducibility tests, an axial slice depicting approximately the same part of the liver was used for ROI analysis, using a large hepatic vein on R2* as a landmark. ROIs were drawn on the liver avoiding vessels and inhomogeneous regions. R2*, and susceptibility values in the liver were calculated comparing both IP and SPURS methods. Regression analysis (coefficient of determination and slope) and Bland-Altman analysis (bias and 95% limits of agreement LoA) was performed for each scanner pair.
An experienced radiologist (25 years of experience) read all images. Measurements of initialization execution time, PDFF, R2*, and susceptibility on ROI of a homogenous region of liver avoiding vessels are reported for both IP and SPURS methods. For statistical analysis paired t-test and linear regression were performed.
Results
In Figure 1, PDFF, R2*, and susceptibility maps of the same liver structure in a healthy subject scanned at four scanners shows similar intensity and variation across 4 scanners except for R2* maps at S4 which is a 3T scanner. Table 1 shows good agreement between scanner pairs and both initialization methods.
Figure 2 shows the magnitude, PDFF, R2*, and QSM maps in 4 thalassemia major patients. Subjects had iron levels from low (Fig.2a) to high (Fig.2d). Water-fat separation was successful with low fat for these livers (PDFF in the second row). The R2* increased from 34 Hz to 240 Hz (third row), suggesting normal or low to moderate iron overload in these patients. QSM values ranged from 0.13 ppm to 0.4 ppm (last row). ROI analysis in all 19 patients in Table 2 and IP method showed PDFF range from 1% to 8.6%, R2* range from 25 Hz to 388 Hz, and susceptibility from 0.04 ppm to 0.57 ppm.
Conclusion
Our data demonstrate that automated liver QSM is feasible and reproducible across different manufacturers and models of scanners at both 1.5T and 3T. The in-phase echoes (IP) initialization of the T2*-IDEAL problem provides approximately a 5.7-fold improvement in speed compared to the current SPURS initialization with no loss of quality. Data acquisition can be performed within a breath-hold using a 3D multi-echo gradient echo sequence. We demonstrate feasibility of liver QSM in patients with iron overload in clinical practice.Acknowledgements
No acknowledgement found.References
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