Effect of Inversion Recovery Fat Suppression on Hepatic R2* Quantitation in Transfusional Siderosis

Magnetic Resonance in Med

Song

et al. 2017

Purpose Hepatic iron content (HIC) quantification via R2*-MRI using multi gradient echo (mGRE) imaging is compromised towards high HIC or at higher fields due to the rapid signal decay. Our study aims at presenting an optimized 2D UTE sequence for R2* quantification to overcome these limitations. Methods 2D UTE imaging was realized via half pulse excitation and radial center-out sampling. The sequence includes CHESS pulses to reduce streaking artifacts from subcutaneous fat and spatial saturation (sSAT) bands to suppress out-of-slice signals. The sequence employs interleaved multi-echo readout trains to achieve dense temporal sampling of rapid signal decays. Evaluation at 1.5T and 3T was done in phantoms and clinical applicability demonstrated in five patients with biopsy-confirmed massively high HIC levels (>25 mg Fe/g dry weight liver tissue). Results In phantoms, the sSAT pulses were found to remove out-of-slice contamination, and R2* results were in excellent agreement to reference mGRE R2* results (slope of linear regression: 1.02/1.00 for 1.5/3T). UTE-based R2* quantification in patients with massive iron overload proved successful at both field strengths and was consistent with biopsy HIC values. Conclusion The UTE sequence provides a means to measure R2* in patients with massive iron overload both, at 1.5T and 3T.

Section: Discussionmentioning

confidence: 99%

Quantitative ultrashort echo time imaging for assessment of massive iron overload at 1.5 and 3 Tesla

Magnetic Resonance in Med

Song

et al. 2017

“…Third, the UTE‐A sequence uses fat suppression pulses to reduce streaking artifacts arising from the bright subcutaneous fat; otherwise, the streaking artifacts can distort the signal in the liver and cause R2* bias . Recent GRE studies have shown that application of fat suppression pulses in iron overloaded cases, even without fat, can lead to R2* underestimation . However, Krafft et al reported that there were only minor differences in R2* values due to application of fat suppression pulses in UTE imaging …”

Section: Discussionmentioning

confidence: 99%

“…11 Recent GRE studies have shown that application of fat suppression pulses in iron overloaded cases, even without fat, can lead to R2* underestimation. 17,24 However, Krafft et al reported that there were only minor differences in R2* values due to application of fat suppression pulses in UTE imaging. 11 Fourth, the optimal sequence among the five acquisition methods investigated was identified based on simulations only.…”

Section: Discussionmentioning

confidence: 99%

Ultrashort echo time imaging for quantification of hepatic iron overload: Comparison of acquisition and fitting methods via simulations, phantoms, and in vivo data

Tipirneni-Sajja

Magnetic Resonance Imaging

et al. 2018

Background Current R2*‐MRI techniques for measuring hepatic iron content (HIC) use various acquisition types and fitting models. Purpose To evaluate the accuracy and precision of R2*‐HIC acquisition and fitting methods. Study Type Signal simulations, phantom study, and prospective in vivo cohort. Population In all, 132 patients (58/74 male/female, mean age 17.7 years). Field Strength/Sequence 2D‐multiecho gradient‐echo (GRE) and ultrashort echo time (UTE) acquisitions at 1.5T. Assessment Synthetic MR signals were created to mimic published GRE and UTE methods, using different R2* values (25–2000 s−1) and signal‐to‐noise ratios (SNR). Phantoms with varying iron concentrations were scanned at 1.5T. In vivo data were analyzed from 132 patients acquired at 1.5T. R2* was estimated by fitting using three signal models. Accuracy and precision of R2* measurements for UTE acquisition parameters (SNR, echo spacing [ΔTE], maximum echo time [TEmax]) and fitting methods were compared for simulated, phantom, and in vivo datasets. Statistical Tests R2* accuracy was determined from the relative error and by linear regression analysis. Precision was evaluated using coefficient of variation (CoV) analysis. Results In simulations, all models had high R2* accuracy (error <5%) and precision (CoV <10%) for all SNRs, shorter ΔTE (≤0.5 msec), and longer TEmax (≥10.1 msec); except the constant offset model overestimated R2* at the lowest SNR. In phantoms and in vivo, all models produced similar R2* values for different SNRs and shorter ΔTEs (slopes: 0.99–1.06, R2 > 0.99, P < 0.001). In all experiments, R2* results degraded for high R2* values with longer ΔTE (≥1 msec). In vivo, shorter and longer TEmax gave similar R2* results (slopes: 1.02–1.06, R2 > 0.99, P < 0.001) for the noise subtraction model for 25≤R2*≤2000 s−1. However, both quadratic and constant offset models, using shorter TEmax (≤4.7 msec) overestimated R2* and yielded high CoVs up to ∼170% for low R2* (<250 s−1). Data Conclusion UTE with TEmax ≥ 10.1 msec and ΔTE ≤ 0.5 msec yields accurate R2* estimates over the entire clinical HIC range. Monoexponential fitting with noise subtraction is the most robust signal model to changes in UTE parameters and achieves the highest R2* accuracy and precision. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1475–1488.

“…Another solution is to suppress the fat signal via inversion‐recovery or chemically selective fat saturation. However, recent studies show that these strategies can lead to R2* underestimation in cases of iron overload, even without fat . Another limitation of both concepts is that they do not consider the spectral complexity of fat, which comprises multiple lipid peaks …”

mentioning

confidence: 99%

Autoregressive moving average modeling for hepatic iron quantification in the presence of fat

Tipirneni-Sajja

Magnetic Resonance Imaging

et al. 2019

Background Measuring hepatic R2* by fitting a monoexponential model to the signal decay of a multigradient‐echo (mGRE) sequence noninvasively determines hepatic iron content (HIC). Concurrent hepatic steatosis introduces signal oscillations and confounds R2* quantification with standard monoexponential models. Purpose To evaluate an autoregressive moving average (ARMA) model for accurate quantification of HIC in the presence of fat using biopsy as the reference. Study Type Phantom study and in vivo cohort. Population Twenty iron–fat phantoms covering clinically relevant R2* (30–800 s‐1) and fat fraction (FF) ranges (0–40%), and 10 patients (four male, six female, mean age 18.8 years). Field Strength/Sequence 2D mGRE acquisitions at 1.5 T and 3 T. Assessment Phantoms were scanned at both field strengths. In vivo data were analyzed using the ARMA model to determine R2* and FF values, and compared with biopsy results. Statistical Tests Linear regression analysis was used to compare ARMA R2* and FF results with those obtained using a conventional monoexponential model, complex‐domain nonlinear least squares (NLSQ) fat–water model, and biopsy. Results In phantoms and in vivo, all models produced R2* and FF values consistent with expected values in low iron and low/high fat conditions. For high iron and no fat phantoms, monoexponential and ARMA models performed excellently (slopes: 0.89–1.07), but NLSQ overestimated R2* (slopes: 1.14–1.36) and produced false FFs (12–17%) at 1.5 T; in high iron and fat phantoms, NLSQ (slopes: 1.02–1.16) outperformed monoexponential and ARMA models (slopes: 1.23–1.88). The results with NLSQ and ARMA improved in phantoms at 3 T (slopes: 0.96–1.04). In patients, mean R2*‐HIC estimates for monoexponential and ARMA models were close to biopsy‐HIC values (slopes: 0.90–0.95), whereas NLSQ substantially overestimated HIC (slope 1.4) and produced false FF values (4–28%) with very high SDs (15–222%) in patients with high iron overload and no steatosis. Data Conclusion ARMA is superior in quantifying R2* and FF under high iron and no fat conditions, whereas NLSQ is superior for high iron and concurrent fat at 1.5 T. Both models give improved R2* and FF results at 3 T. Level of Evidence: 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1620–1632.