Hepatic Iron Quantification Using a Free-Breathing 3D Radial Gradient Echo Technique and Validation With a 2D Biopsy-Calibrated R2* Relaxometry Method.

BACKGROUND: Hepatic iron content (HIC) is an important parameter for the management of iron overload. Non-invasive HIC assessment is often performed using biopsy-calibrated two-dimensional breath-hold Cartesian gradient echo (2D BH GRE) R2* -MRI. However, breath-holding is not possible in most pediatric patients or those with respiratory problems, and three-dimensional free-breathing radial GRE (3D FB rGRE) has emerged as a viable alternative. PURPOSE: To evaluate the performance of a 3D FB rGRE and validate its R2* and fat fraction (FF) quantification with 3D breath-hold Cartesian GRE (3D BH cGRE) and biopsy-calibrated 2D BH GRE across a wide range of HICs. STUDY TYPE: Retrospective. SUBJECTS: Twenty-nine patients with hepatic iron overload (22 females, median age: 15 [5-25] years). FIELD STRENGTH/SEQUENCE: Three-dimensional radial and 2D and 3D Cartesian multi-echo GRE at 1.5 T. ASSESSMENT: R2* and FF maps were computed for 3D GREs using a multi-spectral fat model and 2D GRE R2* maps were calculated using a mono-exponential model. Mean R2* and FF values were calculated via whole-liver contouring and T2* -thresholding by three operators. STATISTICAL TESTS: Inter- and intra-observer reproducibility was assessed using Bland-Altman and intraclass correlation coefficient (ICC). Linear regression and Bland-Altman analysis were performed to compare R2* and FF values among the three acquisitions. One-way repeated-measures ANOVA and Wilcoxon signed-rank tests, respectively, were used to test for significant differences between R2* and FF values obtained with different acquisitions. Statistical significance was assumed at P < 0.05. RESULTS: The mean biases and ICC for inter- and intra-observer reproducibility were close to 0% and >0.99, respectively for both R2* and FF. The 3D FB rGRE R2* and FF values were not significantly different (P > 0.44) and highly correlated (R2 ≥ 0.98) with breath-hold Cartesian GREs, with mean biases ≤ ±2.5% and slopes 0.90-1.12. In non-breath-holding patients, Cartesian GREs showed motion artifacts, whereas 3D FB rGRE exhibited only minimal streaking artifacts. DATA CONCLUSION: Free-breathing 3D radial GRE is a viable alternative in non-breath-hold patients for accurate HIC estimation. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Quantitative Susceptibility Mapping Using a Multispectral Autoregressive Moving Average Model to Assess Hepatic Iron Overload.

BACKGROUND: R2*-MRI is clinically used to noninvasively assess hepatic iron content (HIC) to guide potential iron chelation therapy. However, coexisting pathologies, such as fibrosis and steatosis, affect R2* measurements and may thus confound HIC estimations. PURPOSE: To evaluate whether a multispectral auto regressive moving average (ARMA) model can be used in conjunction with quantitative susceptibility mapping (QSM) to measure magnetic susceptibility as a confounder-free predictor of HIC. STUDY TYPE: Phantom study and in vivo cohort. SUBJECTS: Nine iron phantoms covering clinically relevant R2* range (20-1200/second) and 48 patients (22 male, 26 female, median age 18 years). FIELD STRENGTH/SEQUENCE: Three-dimensional (3D) and two-dimensional (2D) multi-echo gradient echo (GRE) at 1.5 T. ASSESSMENT: ARMA-QSM modeling was performed on the complex 3D GRE signal to estimate R2*, fat fraction (FF), and susceptibility measurements. R2*-based dry clinical HIC values were calculated from the 2D GRE acquisition using a published R2*-HIC calibration curve as reference standard. STATISTICAL TESTS: Linear regression analysis was performed to compare ARMA R2* and susceptibility-based estimates to iron concentrations and dry clinical HIC values in phantoms and patients, respectively. RESULTS: In phantoms, the ARMA R2* and susceptibility values strongly correlated with iron concentrations (R2 ≥ 0.9). In patients, the ARMA R2* values highly correlated (R2  = 0.97) with clinical HIC values with slope = 0.026, and the susceptibility values showed good correlation (R2  = 0.82) with clinical dry HIC values with slope = 3.3 and produced a dry-to-wet HIC ratio of 4.8. DATA CONCLUSION: This study shows the feasibility that ARMA-QSM can simultaneously estimate susceptibility-based wet HIC, R2*-based dry HIC and FFs from a single multi-echo GRE acquisition. Our results demonstrate that both, R2* and susceptibility-based wet HIC values estimated with ARMA-QSM showed good association with clinical dry HIC values with slopes similar to published R2*-biopsy HIC calibration and dry-to-wet tissue weight ratio, respectively. Hence, our study shows that ARMA-QSM can provide potentially confounder-free assessment of hepatic iron overload. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

GSTM1 and Liver Iron Content in Children with Sickle Cell Anemia and Iron Overload.

Chronic blood transfusions in patients with sickle cell anemia (SCA) cause iron overload, which occurs with a degree of interpatient variability in serum ferritin and liver iron content (LIC). Reasons for this variability are unclear and may be influenced by genes that regulate iron metabolism. We evaluated the association of the copy number of the glutathione S-transferase M1 (GSTM1) gene and degree of iron overload among patients with SCA. We compared LIC in 38 children with SCA and ≥12 lifetime erythrocyte transfusions stratified by GSTM1 genotype. Baseline LIC was measured using magnetic resonance imaging (MRI), R2*MRI within 3 months prior to, and again after, starting iron unloading therapy. After controlling for weight-corrected transfusion burden (mL/kg) and splenectomy, mean pre-chelation LIC (mg/g dry liver dry weight) was similar in all groups: GSTM1 wild-type (WT) (11.45, SD±6.8), heterozygous (8.2, SD±4.52), and homozygous GSTM1 deletion (GSTM1-null; 7.8, SD±6.9, p = 0.09). However, after >12 months of chelation, GSTM1-null genotype subjects had the least decrease in LIC compared to non-null genotype subjects (mean LIC change for GSTM1-null = 0.1 (SD±3.3); versus -0.3 (SD±3.0) and -1.9 (SD±4.9) mg/g liver dry weight for heterozygous and WT, respectively, p = 0.047). GSTM1 homozygous deletion may prevent effective chelation in children with SCA and iron overload.

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

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.

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

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.

Automated vessel exclusion technique for quantitative assessment of hepatic iron overload by R2*-MRI.

BACKGROUND: Extraction of liver parenchyma is an important step in the evaluation of R2*-based hepatic iron content (HIC). Traditionally, this is performed by radiologists via whole-liver contouring and T2*-thresholding to exclude hepatic vessels. However, the vessel exclusion process is iterative, time-consuming, and susceptible to interreviewer variability. PURPOSE: To implement and evaluate an automatic hepatic vessel exclusion and parenchyma extraction technique for accurate assessment of R2*-based HIC. STUDY TYPE: Retrospective analysis of clinical data. SUBJECTS: Data from 511 MRI exams performed on 257 patients were analyzed. FIELD STRENGTH/SEQUENCE: All patients were scanned on a 1.5T scanner using a multiecho gradient echo sequence for clinical monitoring of HIC. ASSESSMENT: An automated method based on a multiscale vessel enhancement filter was investigated for three input data types-contrast-optimized composite image, T2* map, and R2* map-to segment blood vessels and extract liver tissue for R2*-based HIC assessment. Segmentation and R2* results obtained using this automated technique were compared with those from a reference T2*-thresholding technique performed by a radiologist. STATISTICAL TESTS: The Dice similarity coefficient was used to compare the segmentation results between the extracted parenchymas, and linear regression and Bland-Altman analyses were performed to compare the R2* results, obtained with the automated and reference techniques. RESULTS: Mean liver R2* values estimated from all three filter-based methods showed excellent agreement with the reference method (slopes 1.04-1.05, R2 > 0.99, P < 0.001). Parenchyma areas extracted using the reference and automated methods had an average overlap area of 87-88%. The T2*-thresholding technique included small vessels and pixels at the vessel/tissue boundaries as parenchymal area, potentially causing a small bias (<5%) in R2* values compared to the automated method. DATA CONCLUSION: The excellent agreement between reference and automated hepatic vessel segmentation methods confirms the accuracy and robustness of the proposed method. This automated approach might improve the radiologist's workflow by reducing the interpretation time and operator dependence for assessing HIC, an important clinical parameter that guides iron overload management. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1542-1551.

Fast quantitative parameter maps without fitting: Integration yields accurate mono-exponential signal decay rates.

PURPOSE: To develop a computationally fast and accurate algorithm for mono-exponential signal modelling and validate the new technique in the context of R2* mapping for iron overload assessment. METHODS: An algorithm is introduced that directly calculates R2* values from a series of images based on integration of the mono-exponential signal decay curve. The algorithm is fast, because fitting is avoided and only arithmetic computations without iterations are applied. Precision and accuracy of the method is determined in comparison to the conventional log-linear (LL), nonlinear least-squares-based Levenberg-Marquardt (NLM), and squared nonlinear Levenberg-Marquardt (SQNLM) methods, which rely on iterative curve fitting. RESULTS: In simulations, the signal integration based method consistently had the same or better accuracy than the LL, NLM, and SQNLM algorithms for R2* values ranging from 50 s-1 to 1200 s-1 . In phantoms and in vivo (12 participants), this method was robust over a wide range of R2* values and signal-to-noise ratios. Computation times were approximately 100, 1460, and 930 times faster than those of the LL, NLM, and SQNLM methods, respectively. CONCLUSIONS: The fast signal integration method accurately calculates R2* maps. It has the potential to replace conventional, mono-exponential fitting methods for quantitative MRI such as R2* parameter mapping. Magn Reson Med 79:2978-2985, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Removal of Arterial Vessel Contributions in Susceptibility-Weighted Images for Quantification of Normalized Visible Venous Volume in Children with Sickle Cell Disease.

PURPOSE: To evaluate a new postprocessing framework that eliminates arterial vessel signal contributions in the quantification of normalized visible venous volume (NVVV, a ratio between venous and brain volume) in susceptibility-weighted imaging (SWI) exams in patients with sickle cell disease (SCD). MATERIALS AND METHODS: We conducted a retrospective study and qualitatively reviewed for hypointense arterial vessel contamination in SWI exams from 21 children with SCD. We developed a postprocessing framework using magnetic resonance angiography in combination with SWI to provide a more accurate quantification of NVVV. NVVV was calculated before and after removing arterial vessel contributions to determine the error from hypointense arterial vessels in quantifying NVVV. RESULTS: Hypointense arterial vessel contamination was observed in 86% SWI exams and was successfully corrected by the proposed method. The contributions of hypointense arterial vessels in the original SWI were significant and accounted for approximately 33% of the NVVV [uncorrected NVVV = 0.012 ± 0.005 versus corrected NVVV = 0.008 ± 0.003 (mean ± SD), P < 0.01]. CONCLUSION: Hypointense arterial vessel contamination occurred in the majority of SWI exams and led to a sizeable overestimation of the visible venous volume. A prospective longitudinal study is needed to evaluate if quantitation of NVVV was improved and to assess the role of NVVV as a biomarker of SCD severity or stroke risk.

Removal of Arterial Vessel Contributions in Susceptibility-Weighted Images for Quantification of Normalized Visible Venous Volume in Children with Sickle Cell Disease

Purpose: To evaluate a new postprocessing framework that eliminates arterial vessel signal contributions in the quantification of normalized visible venous volume (NVVV, a ratio between venous and brain volume) in susceptibility-weighted imaging (SWI) exams in patients with sickle cell disease (SCD). Materials and Methods: We conducted a retrospective study and qualitatively reviewed for hypointense arterial vessel contamination in SWI exams from 21 children with SCD. We developed a postprocessing framework using magnetic resonance angiography in combination with SWI to provide a more accurate quantification of NVVV. NVVV was calculated before and after removing arterial vessel contributions to determine the error from hypointense arterial vessels in quantifying NVVV. Results: Hypointense arterial vessel contamination was observed in 86% SWI exams and was successfully corrected by the proposed method. The contributions of hypointense arterial vessels in the original SWI were significant and accounted for approximately 33% of the NVVV [uncorrected NVVV = 0.012 ± 0.005 versus corrected NVVV = 0.008 ± 0.003 (mean ± SD), P < 0.01]. Conclusion: Hypointense arterial vessel contamination occurred in the majority of SWI exams and led to a sizeable overestimation of the visible venous volume. A prospective longitudinal study is needed to evaluate if quantitation of NVVV was improved and to assess the role of NVVV as a biomarker of SCD severity or stroke risk.

Radial Ultrashort TE Imaging Removes the Need for Breath-Holding in Hepatic Iron Overload Quantification by R2* MRI.

OBJECTIVE: The objective of this study is to evaluate radial free-breathing (FB) multiecho ultrashort TE (UTE) imaging as an alternative to Cartesian FB multiecho gradient-recalled echo (GRE) imaging for quantitative assessment of hepatic iron content (HIC) in sedated patients and subjects unable to perform breath-hold (BH) maneuvers. MATERIALS AND METHODS: FB multiecho GRE imaging and FB multiecho UTE imaging were conducted for 46 test group patients with iron overload who could not complete BH maneuvers (38 patients were sedated, and eight were not sedated) and 16 control patients who could complete BH maneuvers. Control patients also underwent standard BH multiecho GRE imaging. Quantitative R2* maps were calculated, and mean liver R2* values and coefficients of variation (CVs) for different acquisitions and patient groups were compared using statistical analysis. RESULTS: FB multiecho GRE images displayed motion artifacts and significantly lower R2* values, compared with standard BH multiecho GRE images and FB multiecho UTE images in the control cohort and FB multiecho UTE images in the test cohort. In contrast, FB multiecho UTE images produced artifact-free R2* maps, and mean R2* values were not significantly different from those measured by BH multiecho GRE imaging. Motion artifacts on FB multiecho GRE images resulted in an R2* CV that was approximately twofold higher than the R2* CV from BH multiecho GRE imaging and FB multiecho UTE imaging. The R2* CV was relatively constant over the range of R2* values for FB multiecho UTE, but it increased with increases in R2* for FB multiecho GRE imaging, reflecting that motion artifacts had a stronger impact on R2* estimation with increasing iron burden. CONCLUSION: FB multiecho UTE imaging was less motion sensitive because of radial sampling, produced excellent image quality, and yielded accurate R2* estimates within the same acquisition time used for multiaveraged FB multiecho GRE imaging. Thus, FB multiecho UTE imaging is a viable alternative for accurate HIC assessment in sedated children and patients who cannot complete BH maneuvers.

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

PURPOSE: Hepatic iron content (HIC) quantification via transverse relaxation rate (R2*)-MRI using multi-gradient echo (mGRE) imaging is compromised toward high HIC or at higher fields due to the rapid signal decay. Our study aims at presenting an optimized 2D ultrashort echo time (UTE) sequence for R2* quantification to overcome these limitations. METHODS: Two-dimensional UTE imaging was realized via half-pulse excitation and radial center-out sampling. The sequence includes chemically selective saturation 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 was done at 1.5 Tesla (T) and 3T in phantoms, and clinical applicability was 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. Magn Reson Med 78:1839-1851, 2017. © 2017 Wiley Periodicals, Inc.

Can multi-slice or navigator-gated R2* MRI replace single-slice breath-hold acquisition for hepatic iron quantification?

BACKGROUND: Liver R2* values calculated from multi-gradient echo (mGRE) magnetic resonance images (MRI) are strongly correlated with hepatic iron concentration (HIC) as shown in several independently derived biopsy calibration studies. These calibrations were established for axial single-slice breath-hold imaging at the location of the portal vein. Scanning in multi-slice mode makes the exam more efficient, since whole-liver coverage can be achieved with two breath-holds and the optimal slice can be selected afterward. Navigator echoes remove the need for breath-holds and allow use in sedated patients. OBJECTIVE: To evaluate if the existing biopsy calibrations can be applied to multi-slice and navigator-controlled mGRE imaging in children with hepatic iron overload, by testing if there is a bias-free correlation between single-slice R2* and multi-slice or multi-slice navigator controlled R2*. MATERIALS AND METHODS: This study included MRI data from 71 patients with transfusional iron overload, who received an MRI exam to estimate HIC using gradient echo sequences. Patient scans contained 2 or 3 of the following imaging methods used for analysis: single-slice images (n = 71), multi-slice images (n = 69) and navigator-controlled images (n = 17). Small and large blood corrected region of interests were selected on axial images of the liver to obtain R2* values for all data sets. Bland-Altman and linear regression analysis were used to compare R2* values from single-slice images to those of multi-slice images and navigator-controlled images. RESULTS: Bland-Altman analysis showed that all imaging method comparisons were strongly associated with each other and had high correlation coefficients (0.98 ≤ r ≤ 1.00) with P-values ≤0.0001. Linear regression yielded slopes that were close to 1. CONCLUSION: We found that navigator-gated or breath-held multi-slice R2* MRI for HIC determination measures R2* values comparable to the biopsy-validated single-slice, single breath-hold scan. We conclude that these three R2* methods can be interchangeably used in existing R2*-HIC calibrations.

Paired self-compensated spin-lock preparation for improved T1ρ quantification.

PURPOSE: Spin-lock (SL) imaging allows quantification of the spin-lattice relaxation time in the rotating frame (T1ρ). B0 and B1 inhomogeneities impact T1ρ quantification because the preparatory block in SL imaging is sensitive to the field heterogeneities. Here, a modified preparatory block (PSC-SL) is proposed that attempts to alleviate SL sensitivity to field inhomogeneities in scenarios where existing approaches fail, i.e. high SL frequencies. METHODS: Computer simulations, phantom and in vivo experiments were used to determine the effect of field inhomogeneities on T1ρ quantification. Existing SL preparations were compared with PSC-SL in different conditions to assess the advantages and disadvantages of each method. RESULTS: Phantom experiments and computer modeling demonstrate that PSC-SL provides superior T1ρ quantification at high SL frequencies in situations where the existing SL preparation methods fail. This result has been confirmed in pre-clinical neuro and body imaging at 7T. CONCLUSION: PSC-SL complements existing methods by increasing the accuracy of T1ρ quantification at high spin-lock frequencies when large field inhomogeneities are present. A-priory information about the experimental conditions such, as field distribution and spinlock frequency are useful for selecting an appropriate spin-lock preparation for specific applications.

Measurement of glomerular filtration rate by dynamic contrast-enhanced magnetic resonance imaging using a subject-specific two-compartment model.

Measuring glomerular filtration rate (GFR) by dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) as part of standard of care clinicalMRIexams (e.g., in pediatric solid tumor patients) has the potential to reduce diagnostic burden. However, enthusiasm for this relatively newGFRtest may be curbed by the limited amount of cross-calibration studies with referenceGFRtechniques and the vast variety ofMRtracer model algorithms causing confusion on the choice of model. To advanceMRI-basedGFRquantification via improvedGFRmodeling and comparison with associated(99m)Tc-DTPA-GFR, 29 long-term Wilms' tumor survivors (19.0-43.3 years, [median 32.0 ± 6.0 years]) treated with nephrectomy, nonnephrotoxic chemotherapy ± radiotherapy underwentMRIwith Gd-DTPAadministration and a(99m)Tc-DTPA GFRtest. ForDCE-MRI-basedGFRestimation, a subject-specific two-compartment (SS-2C) model was developed that uses individual hematocrit values, automatically defines subject-specific uptake intervals, and fits tracer-uptake curves by incorporating these measures. The association between reference(99m)Tc-DTPA GFRandMR-GFRs obtained bySS-2C, three published 2C uptake, and inflow-outflow models was investigated via linear regression analysis. Uptake intervals varied from 64 sec to 141 sec [96 sec ± 21 sec] and hematocrit values ranged from 30% to 49% [41% ± 4%]; these parameters can therefore not be assumed as constants in 2C modeling. OurMR-GFRestimates using theSS-2C model showed accordingly the highest correlation with(99m)Tc-DTPA-GFRs (R(2) = 0.76,P < 0.001) compared with other models (R(2)-range: 0.36-0.66). In conclusion,SS-2C modeling ofDCE-MRIdata improved the association betweenGFRobtained by(99m)Tc-DTPAand Gd-DTPA DCE-MRIto such a degree that this approach could turn into a viable, diagnosticGFRassay without radiation exposure to the patient.

Does fat suppression via chemically selective saturation affect R2*-MRI for transfusional iron overload assessment? A clinical evaluation at 1.5T and 3T.

PURPOSE: Fat suppression (FS) via chemically selective saturation (CHESS) eliminates fat-water oscillations in multiecho gradient echo (mGRE) R2*-MRI. However, for increasing R2* values as seen with increasing liver iron content (LIC), the water signal spectrally overlaps with the CHESS band, which may alter R2*. We investigated the effect of CHESS on R2* and developed a heuristic correction for the observed CHESS-induced R2* changes. METHODS: Eighty patients [female, n = 49; male, n = 31; mean age (± standard deviation), 18.3 ± 11.7 y] with iron overload were scanned with a non-FS and a CHESS-FS mGRE sequence at 1.5T and 3T. Mean liver R2* values were evaluated using three published fitting approaches. Measured and model-corrected R2* values were compared and statistically analyzed. RESULTS: At 1.5T, CHESS led to a systematic R2* reduction (P < 0.001 for all fitting algorithms) especially toward higher R2*. Our model described the observed changes well and reduced the CHESS-induced R2* bias after correction (linear regression slopes: 1.032/0.927/0.981). No CHESS-induced R2* reductions were found at 3T. CONCLUSION: The CHESS-induced R2* bias at 1.5T needs to be considered when applying R2*-LIC biopsy calibrations for clinical LIC assessment, which were established without FS at 1.5T. The proposed model corrects the R2* bias and could therefore improve clinical iron overload assessment based on linear R2*-LIC calibrations. Magn Reson Med 76:591-601, 2016. © 2015 Wiley Periodicals, Inc.

Automated T(2) * measurements using supplementary field mapping to assess cardiac iron content.

PURPOSE: To develop and evaluate an algorithm that automatically identifies high-susceptibility areas and excludes them from T(2) * measurements in the left ventricle (LV) for myocardial iron measurements. MATERIALS AND METHODS: An autoregressive moving average (ARMA) model was implemented on multigradient echo scans of 24 patients (age range 3-45 years, 10 male/14 female). Voxels with relatively high susceptibility (>3 Hz/mm) were flagged and deselected from the T(2) * calculations for iron quantification. The mean, standard deviation, and coefficient of variation (CoV) of the ARMA-defined region were compared to the CoV of four distinct regions of the LV and the entire LV using a Student's t-test (α = 0.05). RESULTS: The CoV of T(2) * values obtained by the ARMA method are comparable with that in the interventricular septum (IS), where susceptibility was the lowest (CoV = 0.31). The ARMA method provides a greater area (51.9 ± 13.7% of the LV) to measure T(2) * than that using the IS alone (21.1 ± 3.4%, P < 0.0001). Areas where low susceptibility are measured corroborate with areas reported in previous studies that investigated T(2) * variations throughout the LV. CONCLUSION: An automated method to measure T(2) * relaxation in the LV with minimal effects from susceptibility has been developed. Variability is reduced while covering more regions for cardiac T2 * calculation.

A dedicated automated injection system for dynamic contrast-enhanced MRI experiments in mice.

PURPOSE: To develop a reproducible small-animal dynamic contrast-enhanced (DCE) MRI set-up for mice through which volumes <100 µL can be accurately and safely injected and to test this set-up by means of DCE measurements in resting muscle and tumor tissue. MATERIALS AND METHODS: The contrast agent (CA) injection system comprised 2 MR-compatible syringe pumps placed 50 cm from the 7T magnet bore where the fringe field is approximately 40 mT. Microbore tubing and T-connector, close to the injection site, minimized dead volume (<10 µL). For DCE-MRI measurements in 8 CB-17 SCID mice with 1500-2500 mm(3) large orthotopic neuroblastoma, a bolus of 10-fold-diluted Gd-DTPA CA solution (0.1 mmol/kg) was delivered (5 µL/s), followed by a 50-µL saline flush. Retro-orbital injections were given instead of tail vein injections, because the peripheral vasculature was reduced because of large tumor burden. RESULTS: The CA injection was successful in 19 of 24 experiments. Optical assessment showed minimal dispersion of ink-colored CA bolus. Mean (± SD) pharmacokinetic parameters retrieved from DCE-MRI examinations in resting muscle (K(trans) = 0.038 ± 0.025 min(-1), k(ep) = 0.66 ± 0.48 min(-1), v(e) = 0.060 ± 0.014, v(p) = 0.033 ± 0.021) and tumor (K(trans) = 0.082 ± 0.071 min(-1), k(ep) = 0.82 ± 0.80 min(-1), v(e) = 0.121 ± 0.075, v(p) = 0.093 ± 0.051) agreed with those reported previously. CONCLUSION: We successfully designed and implemented a DCE-MRI set-up system with short injection lines and low dead volume. The system can be used at any field strength with the syringe pumps placed at a sufficiently low fringe field (<40 mT).

Simultaneous field and R2 mapping to quantify liver iron content using autoregressive moving average modeling.

PURPOSE: To investigate the use of a complex multigradient echo (mGRE) acquisition and an autoregressive moving average (ARMA) model for simultaneous susceptibility and R 2 measurements for the assessment of liver iron content (LIC) in patients with iron overload. MATERIALS AND METHODS: Fifty magnetic resonance imaging (MRI) exams with magnitude and phase mGRE images were processed using the ARMA model, which provides fat-separated field maps, R 2 maps, and T(1) -W imaging. The LIC was calculated by measuring the susceptibility between the liver and the right transverse abdominal muscle from the field maps. The relationship between LIC derived from susceptibility measurements and LIC from R 2 measurements was determined using linear least-squares regression analysis. RESULTS: LIC measured from R 2 is highly correlated to the LIC with the susceptibility method (mg/g dry = 8.99 ± 0.15 × [mg Fe/mL of wet liver] -2.38 ± 0.29, R(2) = 0.94). The field inhomogeneity in the liver is correlated with R 2 (R(2) = 0.85). CONCLUSION: By using the ARMA model on complex mGRE images, both susceptibility and R 2-based LIC measurements can be made simultaneously. The susceptibility measurement can be used to help verify R 2 measurements in the assessment of iron overload.