Rapid automated liver quantitative susceptibility mapping

Abstract

Background: 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. Purpose: To develop a rapid, robust and automated liver QSM for clinical practice. Study Type: Prospective Population: 13 healthy subjects and 22 patients. Field strength/Sequences 1.5T and 3T / 3D multi-echo gradient-recalled echo (GRE) sequence. Assessment: Data were acquired using a 3D GRE sequence with an out-of-phase echo spacing with respect to each other. All odd echoes that were in-phase (IP) were used to initialize the fat-water separation and field estimation (T2*-IDEAL) before performing QSM. Liver QSM was generated through an automated pipeline without manual intervention. This IP echo-based initialization method was compared with an existing graph cuts initialization method (SPURS) in healthy subjects (n=5). Reproducibility was assessed over 4 scanners at 2 field strengths from 2 manufacturers using healthy subjects (n=8). Clinical feasibility was evaluated in patients (n=22). Statistical Tests: IP and SPURS initialization methods in both healthy subjects and patients were compared using paired t-test and linear regression analysis to assess processing time and ROI measurements. Reproducibility of QSM, R2*, and proton density fat fraction (PDFF) among the four different scanners was assessed using linear regression, Bland-Altman analysis, and the intraclass correlation coefficient (ICC). Results: Liver QSM using the IP method was found to be approximately 5.5 times faster than SPURS (P< 0.05) in initializing T2*-IDEAL with similar outputs. Liver QSM using the IP method were reproducibly generated in all four scanners (average coefficient of determination 0.95, average slope 0.90, average bias 0.002 ppm, 95% limits of agreement between −0.06 to 0.07 ppm, ICC 0.97). Conclusion: Use of IP echo-based initialization, enables robust water/fat separation and field estimation for automated, rapid and reproducible liver QSM for clinical applications.

Integrated quantitative susceptibility and R2* mapping for evaluation of liver fibrosis: An ex vivo feasibility study

“…Quantitative susceptibility mapping (QSM), 12 which measures susceptibility in a single orientation, can be performed in a single breath-hold and may thus be a more clinically useful method for liver fibrosis imaging. [13][14][15] Recently, a linear negative correlation of susceptibility measurements from QSM with collagen concentration was reported in a phantom study, while R 2 * values were positively associated with collagen. 15 R 2 * and QSM were both also positively associated with iron concentration, which may affect the utility of these MRI parameters for assessment of liver fibrosis, given that iron deposition is often present in the liver.…”

Ultrashort echo time quantitative susceptibility mapping (UTE‐QSM) for detection of hemosiderin deposition in hemophilic arthropathy: A feasibility study

“…By estimating the pixel‐wise susceptibility in an MR system, the distribution of targeted particles can be estimated. In the literature, QSM has been investigated extensively in neuro, 20‐22 body, 23,24 cardiovascular, 25 and MSK imaging 15‐18 …”

Abdominal single-step quantitative susceptibility mapping with spherical mean value filter and structure prior-based regularization

“…Phase unwrapping was unreliable with late echo times, and we used only early echo time images where the average value of the 3D phase derivative variance, used in the unwrapping algorithm, was ≤ 1.5 in the liver ROI (this threshold was chosen empirically based on the appearance of unwrapping artefacts). The relative difference field f was calculated taking into account the presence of fat using the T2*-IDEAL approach [33], in which the previously published 9-peak mouse fat spectral model was used [14]. Moreover, the quasi-Newton algorithm in the last step was replaced by the Levenberg-Marquardt algorithm.…”