Fat quantification in dual-layer detector spectral CT: How to handle iron overload, varying tube voltage and radiation dose Indices.

OBJECTIVES: Opposed to other spectral CT techniques, fat quantification in dual-layer detector CT (dlCT) has only recently been developed. The impact of concomitant iron overload and dlCT-specific protocol settings such as the dose right index (DRI), a measure of image noise and tube current, on dlCT fat quantification was unclear. Further, spectral information became newly available <120 kV. Therefore, this study's objective was to evaluate the impact of iron, changing tube voltage, and DRI on dlCT fat quantification. MATERIAL AND METHODS: Phantoms with 0 and 8mg/cm3 iron; 0 and 5mg/cm3 iodine; 0, 10, 20, 35, 50, and 100% fat and liver equivalent, respectively, were scanned with a dlCT (CT7500, Philips, the Netherlands) at 100kV/20DRI, 120kV/20DRI, 140kV/20DRI, and at 120kV/16DRI, 120kV/24DRI. Material decomposition was done for fat, liver, and iodine (A1); for fat, liver, and iron (A2); and for fat, liver, and combined reference values of iodine and iron (A3). All scans were analyzed with reference values from 120kV/20DRI. For statistics, the intraclass correlation coefficient (ICC) and Bland-Altman analyses were used. RESULTS: In phantoms with iron and iodine, results were best for A3 with a mean deviation to phantom fat of 1.3±2.6% (ICC 0.999 [95%-confidence interval 0.996-1]). The standard approach A1 yielded a deviation of -2.5±3.0% (0.998[0.994-0.999]), A2 of 6.1±4.8% (0.991[0.974-0.997]). With A3 and changing tube voltage, the maximal difference between quantified fat and the phantom ground truth occurred at 100kV with 4.6±2.1%. Differences between scans were largest between 100kV and 140kV (2.0%[-7.1-11.2]). The maximal difference of changing DRI occurred between 16 and 24 DRI with 0.4%[-2.2-3.0]. CONCLUSION: For dlCT fat quantification in the presence of iron, material decomposition with combined reference values for iodine and iron delivers the most accurate results. Tube voltage-specific calibration of reference values is advisable while the impact of the DRI on dlCT fat quantification is neglectable.

Can Automated 3-Dimensional Dixon-Based Methods Be Used in Patients With Liver Iron Overload?

PURPOSE: Accurate quantification of liver iron concentration (LIC) can be achieved via magnetic resonance imaging (MRI). Maps of liver T2*/R2* are provided by commercially available, vendor-provided, 3-dimensional (3D) multiecho Dixon sequences and allow automated, inline postprocessing, which removes the need for manual curve fitting associated with conventional 2-dimensional (2D) gradient echo (GRE)-based postprocessing. The main goal of our study was to investigate the relationship among LIC estimates generated by 3D multiecho Dixon sequence to values generated by 2D GRE-based R2* relaxometry as the reference standard. METHODS: A retrospective review of patients who had undergone MRI scans for estimation of LIC with conventional T2* relaxometry and 3D multiecho Dixon sequences was performed. A 1.5 T scanner was used to acquire the magnetic resonance studies. Acquisition of standard multislice multiecho T2*-based sequences was performed, and R2* values with corresponding LIC were estimated. The comparison between R2* and corresponding LIC estimates obtained by the 2 methods was analyzed via the correlation coefficients and Bland-Altman difference plots. RESULTS: This study included 104 patients (51 male and 53 female patients) with 158 MRI scans. The mean age of the patients at the time of scan was 15.2 (SD, 8.8) years. There was a very strong correlation between the 2 LIC estimation methods for LIC values up to 3.2 mg/g (LIC quantitative multiecho Dixon [qDixon; from region of interest R2*] vs LIC GRE [in-house]: r = 0.83, P < 0.01; LIC qDixon [from segmentation volume R2*] vs LIC GRE [in-house]: r = 0.92, P < 0.01); and very weak correlation between the 2 methods at liver iron levels >7 mg/g. CONCLUSION: Three-dimensional-based multiecho Dixon technique can accurately measure LIC up to 7 mg/g and has the potential to replace 2D GRE-based relaxometry methods.

A fully automatic parenchyma extraction method for MRI T2* relaxometry of iron loaded liver in transfusion-dependent patients.

PURPOSE: To develop a fully automatic parenchyma extraction method for the T2* relaxometry of iron overload liver. METHODS: A retrospective multicenter collection of liver MR examinations from 177 transfusion-dependent patients was conducted. The proposed method extended a semiautomatic parenchyma extraction algorithm to a fully automatic approach by introducing a modified TransUNet on the R2* (1/T2*) map for liver segmentation. Axial liver slices from 129 patients at 1.5 T were allocated to training (85%) and internal test (15%) sets. Two external test sets separately included 1.5 T data from 20 patients and 3.0 T data from 28 patients. The final T2* measurement was obtained by fitting the average signal of the extracted liver parenchyma. The agreement between T2* measurements using fully and semiautomatic parenchyma extraction methods was assessed using coefficient of variation (CoV) and Bland-Altman plots. RESULTS: Dice of the deep network-based liver segmentation was 0.970 ± 0.019 on the internal dataset, 0.960 ± 0.035 on the external 1.5 T dataset, and 0.958 ± 0.014 on the external 3.0 T dataset. The mean difference bias between T2* measurements of the fully and semiautomatic methods were separately 0.12 (95% CI: -0.37, 0.61) ms, 0.04 (95% CI: -1.0, 1.1) ms, and 0.01 (95% CI: -0.25, 0.23) ms on the three test datasets. The CoVs between the two methods were 4.2%, 4.8% and 2.0% on the internal test set and two external test sets. CONCLUSIONS: The developed fully automatic parenchyma extraction approach provides an efficient and operator-independent T2* measurement for assessing hepatic iron content in clinical practice.

Pancreatic iron in pediatric transfusion-dependent beta-thalassemia patients: A longitudinal MRI study.

BACKGROUND: In pediatric transfusion-dependent thalassemia (TDT) patients, we evaluated the prevalence, pattern, and clinical associations of pancreatic siderosis and the changes in pancreatic iron levels and their association with baseline and changes in total body iron balance. PROCEDURE: We considered 86 pediatric TDT patients consecutively enrolled in the Extension-Myocardial Iron Overload in Thalassemia Network. Iron overload (IO) was quantified by R2* magnetic resonance imaging (MRI). RESULTS: Sixty-three (73%) patients had pancreatic IO (R2* > 38 Hz). Global pancreas R2* values were significantly correlated with mean serum ferritin levels, MRI liver iron concentration (LIC) values, and global heart R2* values. Global pancreas R2* values were significantly higher in patients with altered versus normal glucose metabolism. Thirty-one patients also performed the follow-up MRI at 18 ± 3 months. Higher pancreatic R2* values were detected at the follow-up, but the difference versus the baseline MRI was not significant. The 20% of patients with baseline pancreatic IO showed no pancreatic IO at the follow-up. The 46% of patients without baseline pancreatic IO developed pancreatic siderosis. The changes in global pancreas R2* between the two MRIs were not correlated with baseline serum ferritin levels, baseline, final, and changes in MRI LIC values, or baseline pancreatic iron levels. CONCLUSIONS: In children with TDT, pancreatic siderosis is a frequent finding associated with hepatic siderosis and represents a risk factor for myocardial siderosis and alterations of glucose metabolism. Iron removal from the pancreas is exceptionally challenging and independent from hepatic iron status.

Longitudinal prospective comparison of pancreatic iron by magnetic resonance in thalassemia patients transfusion-dependent since early childhood treated with combination deferiprone-desferrioxamine vs deferiprone or deferasirox monotherapy.

BACKGROUND: In transfusion-dependent thalassemia patients who started regular transfusions in early childhood, we prospectively and longitudinally evaluated the efficacy on pancreatic iron of a combined deferiprone (DFP) + desferrioxamine (DFO) regimen versus either oral iron chelator as monotherapy over a follow-up of 18 months. MATERIALS AND METHODS: We selected patients consecutively enrolled in the Extension-Myocardial Iron Overload in Thalassemia network who received a combined regimen of DFO+DFP (No.=28) or DFP (No.=61) or deferasirox (DFX) (No.=159) monotherapy between the two magnetic resonance imaging scans. Pancreatic iron overload was quantified by the T2* technique. RESULTS: At baseline no patient in the combined treatment group had a normal global pancreas T2* (≥26 ms). At follow-up the percentage of patients who maintained a normal pancreas T2* was comparable between the DFP and DFX groups (57.1 vs 70%; p=0.517).Among the patients with pancreatic iron overload at baseline, global pancreatic T2* values were significantly lower in the combined DFO+DFP group than in the DFP or DFX groups. Since changes in global pancreas T2* values were negatively correlated with baseline pancreas T2* values, the percent changes in global pancreas T2* values, normalized for the baseline values, were considered. The percent changes in global pancreas T2* values were significantly higher in the combined DFO+DFP group than in either the DFP (p=0.036) or DFX (p=0.030) groups. DISCUSSION: In transfusion-dependent patients who started regular transfusions in early childhood, combined DFP+DFO was significantly more effective in reducing pancreatic iron than was either DFP or DFX.

A short-TR single-echo spin-echo breath-hold method for assessing liver T2.

OBJECTIVE: Conventional single-echo spin-echo T2 mapping used for liver iron quantification is too long for breath-holding. This study investigated a short TR (~100 ms) single-echo spin-echo T2 mapping technique wherein each image (corresponding to a single TE) could be acquired in ~17 s-short enough for a breath-hold. TE images were combined for T2 fitting. To avoid T1 bias, each TE acquisition incremented TR to maintain a constant TR-TE. MATERIALS AND METHODS: Experiments at 1.5T validated the technique's accuracy in phantoms, 9 healthy volunteers, and 5 iron overload patients. In phantoms and healthy volunteers, the technique was compared to the conventional approach of constant TR for all TEs. Iron overload results were compared to FerriScan. RESULTS: In phantoms, the constant TR-TE technique provided unbiased estimates of T2, while the conventional constant TR approach underestimated it. In healthy volunteers, there was no significant discrepancy at the 95% confidence level between constant TR-TE and reference T2 values, whereas there was for constant TR scans. In iron overload patients, there was a high correlation between constant TR-TE and FerriScan T2 values (r2 = 0.95), with a discrepancy of 0.6+/- 1.4 ms. DISCUSSION: The short-TR single-echo breath-hold spin-echo technique provided unbiased estimates of T2 in phantoms and livers.

Technical Success and Reliability of Magnetic Resonance Elastography in Patients with Hepatic Iron Overload.

RATIONALE AND OBJECTIVES: This study aimed to evaluate the technical success rate and stiffness measurement reliability of two specific hepatic magnetic resonance elastography (MRE) sequences dedicated to solving susceptibility artifacts in patients with various degrees of hepatic iron overload. MATERIALS AND METHODS: Thirty-seven patients with iron-overloaded liver confirmed by R2* value measurement who underwent two-dimensional (2D) spin-echo (SE) MRE and 2D SE-echo-planar-imaging (EPI) MRE were reviewed retrospectively. According to four categories based on R2* value (mild, moderate, severe elevation, and extremely severe iron overload), we compared the success rate, quality score, and liver stiffness of the two sequences. In addition, Spearman's correlation was performed to evaluate the relationship between the R2* value and liver stiffness. RESULTS: The overall success rates of SE MRE and SE-EPI MRE in patients with hepatic iron overload were 91.89% and 78.38%, respectively, and 100% and 78.57%, respectively, for severe elevation iron overload. In all patients, the MRE quality scores were 54 and 48 for SE MRE and SE-EPI MRE, respectively (P = 0.107). There were no significant differences in liver stiffness measurements between the two MRE methods in patients with mild, moderate, and severe elevation iron-overloaded livers (P > 0.6 for all), respectively. For both MRE methods, R2* value had no significant effect on the liver stiffness measurements (correlation coefficient <0.1, P >0.6 for both). CONCLUSION: In the mild and moderate elevation iron-overloaded liver, both SE MRE and fast SE-EPI MRE can provide successful and reliable liver stiffness measurement. In severe elevation iron-overloaded livers, SE MRE may be a better choice than SE-EPI MRE.

Iron overload in hemodialysis patients: Comparison of serum iron parameters with T2* MRI sequence.

PURPOSE: To evaluate correlation between serum iron parameters and liver T2* value in hemodialysis patients with iron overload due to parenteral iron therapy. MATERIALS AND METHODS: We evaluated 30 hemodialysis patients using a multiecho T2*-weighted MRI sequence. Age, sex, duration of dialysis, iron and erythropoietin doses taken in the past year, and serum iron parameters were recorded. Liver T2* values were averaged from three distinct liver regions. A T2* value of 33 ± 7 ms is considered normal. Declines below 24, 21, and 14 ms signify iron overload grades 1, 2, and 3, respectively. RESULTS: There was no statistically significant difference comparing the measurements of 3 different ROIs (p > 0.05). A total of 23 patients (76.6%) had iron overload. Serum ferritin levels of patients with iron overload were significantly higher than those without iron overload (687.25 [186.5-1489] ng/mL vs. 371.25 [127.5-542.5] ng/mL, p = 0.008). No linear correlation was observed between age, dialysis duration, serum iron metrics, medication doses, and T2* values. Likewise, no significant differences were found among patients based on iron overload status or its grades concerning these parameters. CONCLUSION: While standard serum markers might overlook iron overload, elevated ferritin levels are promising. MRI reliably detects iron overload in patients receiving parenteral iron.

Four­Dimensional Echocardiographic Evaluation of Cardiac Iron Overload in Patients with Beta-Thalassemia Major.

Cardiac magnetic resonance imaging is the gold standard to detect cardiac iron overload in patients with beta-thalassemia. The aim of this study was to evaluate cardiac iron overload using four-dimensional transthoracic echocardiography in thalassemia patients with and without cardiac involvement detected by T2* value and to compare the two techniques. This cross-sectional and observational study was conducted in 44 patients diagnosed with thalassemia major. Left ventricular systolic function was assessed using four-dimensional speckle tracking echocardiography-derived global longitudinal (GLS), circumferential, radial, and area strain indices. Left ventricular ejection fraction, volumes, and mass index were similar between the patients with T2* values less than 20 ms as compared to those with T2* values greater than 20 ms. However, patients with lower T2* values had significantly higher GLS values (- 17.0% vs. - 19.8%, p < 0.001) compared with those with higher T2* values. GLS demonstrated a sensitivity of 91.7% and a specificity of 71.9% at a cut-off value of - 18.5%; however, sensitivity was 75%, and the specificity was 84.4% at a cut-off value of - 17.5%. For - 18.5%, the positive predictive value was 55%, and the negative predictive value was 95.8%; for - 17.5%, these values were 64.2 and 90%, respectively. This novel echocardiographic method, tested for the first time in our study in comparison with cardiac MRI in an adult patient group, has been shown to predict cardiac iron overload in thalassemia patients in the subclinical period without LVEF decline. Four-dimensional GLS is a marker with high sensitivity and negative predictive value.

CHMMOTv1 - cardiac and hepatic multi-echo (T2*) MRI images and clinical dataset for Iron overload on thalassemia patients.

INTRODUCTION: Regarding deep learning networks in medical sciences for improving diagnosis and treatment purposes and the existence of minimal resources for them, we decided to provide a set of magnetic resonance images of the cardiac and hepatic organs. DATABASE DESCRIPTION: The dataset included 124 patients (67 women and 57 men) with thalassemia (THM), the age range of (5-52) years. Patients were divided into two groups: with follow-up (1-5 times) at time intervals of about (5-6) months and without follow-up. T2* and, R2* values, the results of the Cardiac and Hepatic overload report (normal, mild, moderate, severe), and laboratory tests including Ferritin, Bilirubin (D, and T), AST, ALT, and ALP levels were provided as an Excel file. Also, the details of the patients' Echocardiogram data have been made available. This dataset CHMMOTv1) has been published in Mendeley Dataverse and also is accessible through the web at: http://databiox.com .

Hyperferritinemia and liver iron content determined with MRI: Reintroduction of the liver iron index.

BACKGROUND: Hyperferritinemia is found in around 12 % of the general population. Analyzing the cause can be difficult. In case of doubt about the presence of major iron overload most guidelines advice to perform a MRI as a reliable non-invasive marker to measure liver iron concentration (LIC). In general, a LIC of ≥ 36 µmol/g dw is considered the be elevated however in hyperferritinemia associated with, for example, obesity or alcohol (over)consumption the LIC can be ≥ 36 µmol/g dw in abscence of major iron overload. So, unfortunately a clear cut-off value to differentiate iron overload from normal iron content is lacking. Previously the liver iron index (LII) (LIC measured in liver biopsy (LIC-b)/age (years)), was introduced to differentiate between patients with major (LII ≥ 2) and minor or no iron overload (LII < 2). Based on the good correlation between the LIC-b and LIC determined with MRI (LIC-MRI), our goal was to investigate whether a LII_MRI ≥ 2 is a good indicator of major iron overload, reflected by a significantly higher amount of iron needed to be mobilized to reach iron depletion. METHODS: We compared the amount of mobilized iron to reach depletion and inflammation-related characteristics in two groups: LII-MRI ≥ 2 versus LII-MRI <2 in 92 hyperferritinemia patients who underwent HFE genotyping and MRI-LIC determination. RESULTS: Significantly more iron needed to be mobilized to reach iron depletion in the LII ≥ 2 group (mean 4741, SD ± 4135 mg) versus the LII-MRI <2 group (mean 1340, SD ± 533 mg), P < 0.001. Furthermore, hyperferritinemia in LII-MRI < 2 patients was more often related to components of the metabolic syndrome while hyperferritinemia in LII-MRI ≥ 2 patients was more often related to HFE mutations. ROC curve analysis showed good performance of LII =2 as cut-off value. However the calculations showed that the optimal cut-off for the LII = 3.4. CONCLUSION: The LII-MRI with a cut-off value of 2 is an effective method to differentiate major from minor iron overload in patients with hyperferritinemia. But the LII-MRI = 3.4 seems a more promising diagnostic test for major iron overload.

Impact of the COVID-19 Pandemic on Iron Overload Assessment by MRI in Patients with Hemoglobinopathies: The E-MIOT Network Experience.

BACKGROUND: The E-MIOT (Extension-Myocardial Iron Overload in Thalassemia) project is an Italian Network assuring high-quality quantification of tissue iron overload by magnetic resonance imaging (MRI). We evaluated the impact of the COVID-19 pandemic on E-MIOT services. METHODS: The activity of the E-MIOT Network MRI centers in the year 2020 was compared with that of 2019. A survey evaluated whether the availability of MRI slots for patients with hemoglobinopathies was reduced and why. RESULTS: The total number of MRI scans was 656 in 2019 and 350 in 2020, with an overall decline of 46.4% (first MRI: 71.7%, follow-up MRI: 36.9%), a marked decline (86.9%) in the period March-June 2020, and a reduction in the gap between the two years in the period July-September. A new drop (41.4%) was recorded in the period October-December for two centers, due to the general reduction in the total amount of MRIs/day for sanitization procedures. In some centers, patients refused MRI scans for fear of getting COVID. Drops in the MRI services >80% were found for patients coming from a region without an active MRI site. CONCLUSIONS: The COVID-19 pandemic had a strong negative impact on MRI multi-organ iron quantification, with a worsening in the management of patients with hemoglobinopathies.

Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms.

Heart failure caused by iron deposits in the myocardium is the primary cause of mortality in beta-thalassemia major patients. Cardiac magnetic resonance imaging (CMRI) T2* is the primary screening technique used to detect myocardial iron overload, but inherently bears some limitations. In this study, we aimed to differentiate beta-thalassemia major patients with myocardial iron overload from those without myocardial iron overload (detected by T2*CMRI) based on radiomic features extracted from echocardiography images and machine learning (ML) in patients with normal left ventricular ejection fraction (LVEF > 55%) in echocardiography. Out of 91 cases, 44 patients with thalassemia major with normal LVEF (> 55%) and T2* ≤ 20 ms and 47 people with LVEF > 55% and T2* > 20 ms as the control group were included in the study. Radiomic features were extracted for each end-systolic (ES) and end-diastolic (ED) image. Then, three feature selection (FS) methods and six different classifiers were used. The models were evaluated using various metrics, including the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Maximum relevance-minimum redundancy-eXtreme gradient boosting (MRMR-XGB) (AUC = 0.73, ACC = 0.73, SPE = 0.73, SEN = 0.73), ANOVA-MLP (AUC = 0.69, ACC = 0.69, SPE = 0.56, SEN = 0.83), and recursive feature elimination-K-nearest neighbors (RFE-KNN) (AUC = 0.65, ACC = 0.65, SPE = 0.64, SEN = 0.65) were the best models in ED, ES, and ED&ES datasets. Using radiomic features extracted from echocardiographic images and ML, it is feasible to predict cardiac problems caused by iron overload.

Simulation of a virtual liver iron overload model and R2 * estimation using multispectral fat-water models for GRE and UTE acquisitions at 1.5 T and 3 T.

R2 *-MRI has emerged as a noninvasive alternative to liver biopsy for assessment of hepatic iron content (HIC). Multispectral fat-water R2 * modeling techniques such as the nonlinear least squares (NLSQ) fitting and autoregressive moving average (ARMA) models have been proposed for the accurate assessment of iron overload by also considering fat, which can otherwise confound R2 *-based HIC measurements in conditions of coexisting iron overload and steatosis. However, the R2 * estimation by these multispectral models has not been systematically investigated for various acquisition methods in iron overload only conditions and across the full clinically relevant range of HICs (0-40 mg Fe/g dry liver weight). The purpose of this study is to evaluate the R2 * accuracy and precision of multispectral models for various multiecho gradient echo (GRE) and ultrashort echo time (UTE) imaging acquisitions by constructing virtual iron overload models based on true histology and synthesizing MRI signals via Monte Carlo simulations at 1.5 T and 3 T, and comparing their results with monoexponential model and published in vivo R2 *-HIC calibrations. The signals were synthesized with TE1 = 1.0 ms for GRE and TE1 = 0.1 ms for UTE acquisition for varying echo spacing, ΔTE (0.1, 0.5, 1, 2 ms), and maximum echo time, TEmax (2, 4, 6, 10 ms). An iron-doped phantom study is also conducted to validate the simulation results in experimental GRE (TE1 = 1.2 ms, ΔTE = 0.72 ms, TEmax = 6.24 ms) and UTE (TE1 = 0.1 ms, ΔTE = 0.5 ms, TEmax = 6.1 ms) acquisitions. For GRE acquisitions, the multispectral ARMA and NLSQ models produced higher slopes (0.032-0.035) compared with the monoexponential model and published in vivo R2 *-HIC calibrations (0.025-0.028). However, for UTE acquisition for shorter echo spacing (≤0.5 ms) and longer maximum echo time, TEmax (≥6 ms), the multispectral and monoexponential signal models produced similar R2 *-HIC slopes (1.5 T, 0.028-0.032; 3 T, 0.014-0.016) and precision values (coefficient of variation < 25%) across the full clinical spectrum of HICs at both 1.5 T and 3 T. The phantom analysis also showed that all signal models demonstrated a significant improvement in R2 * estimation for UTE acquisition compared with GRE, confirming our simulation findings. Future work should investigate the performance of multispectral fat-water models by simulating liver models in coexisting conditions of iron overload and steatosis for accurate R2 * and fat quantification.

Evaluation of proton density fat fraction (PDFF) obtained from a vendor-neutral MRI sequence and MRQuantif software.

OBJECTIVE: To validate the proton density fat fraction (PDFF) obtained by the MRQuantif software from 2D chemical shift encoded MR (CSE-MR) data in comparison with the histological steatosis data. METHODS: This study, pooling data from 3 prospective studies spread over time between January 2007 and July 2020, analyzed 445 patients who underwent 2D CSE-MR and liver biopsy. MR derived liver iron concentration (MR-LIC) and PDFF was calculated using the MRQuantif software. The histological standard steatosis score (SS) served as reference. In order to get a value more comparable to PDFF, histomorphometry fat fraction (HFF) were centrally determined for 281 patients. Spearman correlation and the Bland and Altman method were used for comparison. RESULTS: Strong correlations were found between PDFF and SS (rs = 0.84, p < 0.001) or HFF (rs = 0.87, p < 0.001). Spearman's coefficients increased to 0.88 (n = 324) and 0.94 (n = 202) when selecting only the patients without liver iron overload. The Bland and Altman analysis between PDFF and HFF found a mean bias of 5.4% ± 5.7 [95% CI 4.7, 6.1]. The mean bias was 4.7% ± 3.7 [95% CI 4.2, 5.3] and 7.1% ± 8.8 [95% CI 5.2, 9.0] for the patients without and with liver iron overload, respectively. CONCLUSION: The PDFF obtained by MRQuantif from a 2D CSE-MR sequence is highly correlated with the steatosis score and very close to the fat fraction estimated by histomorphometry. Liver iron overload reduced the performance of steatosis quantification and joint quantification is recommended. This device-independent method can be particularly useful for multicenter studies. CLINICAL RELEVANCE STATEMENT: The quantification of liver steatosis using a vendor-neutral 2D chemical-shift MR sequence, processed by MRQuantif, is well correlated to steatosis score and histomorphometric fat fraction obtained from biopsy, whatever the magnetic field and the MR device used. KEY POINTS: • The PDFF measured by MRQuantif from 2D CSE-MR sequence data is highly correlated to hepatic steatosis. • Steatosis quantification performance is reduced in case of significant hepatic iron overload. • This vendor-neutral method may allow consistent estimation of PDFF in multicenter studies.

Comparison between Gradient-Echo and Spin-Echo EPI MR Elastography at 3 T in quantifying liver stiffness of patients with and without iron overload; a prospective study.

OBJECTIVES: To compare the maximum axial area of the confidence mask and the calculated liver stiffness (LS) on gradient-echo (GRE) and spin-echo echo planar imaging (SE-EPI) MR elastography (MRE) in patients with and without iron deposition. METHODS: 104 patients underwent MRE by GRE and SE-EPI sequences at 3 T. R2* values >88 Hz in the liver were categorized in the iron overload group. The maximum axial area and the corresponding LS values were measured by manually contouring the whole area on one slice with the largest confidence mask at both GRE and SE-EPI sequences. RESULTS: In patients with iron overload, SE-EPI provided larger maximum axial confidence area in unfailed images (57.6 ± 41.7 cm2) compared to GRE (45.7 ± 29.1 cm2) (p-value = 0.007). In five patients with iron overload, imaging failed at GRE sequence, whereas at the SE-EPI sequence the maximum area of the confidence mask had a mean value of 33.5 ± 54.9 cm2. In livers without iron overload (R2*: 50.7 ± 13.1 Hz), the maximum area on the confidence mask was larger at SE-EPI (118.3 ± 41.2 cm2) than on GRE (105.1 ± 31.7 cm2) (P-value = 0.003). There was no significant difference in mean LS between SE-EPI (2.0 ± 0.3 kPa) and GRE (2.1 ± 0.5 kPa) in livers with iron overload (P value = 0.24). Similarly, in the group without iron overload, mean LS was 2.3 ± 0.7 kPa at SE-EPI and 2.4 ± 0.8 kPa at GRE sequences (P-value = 0.11). CONCLUSIONS: SE-EPI MRE can successfully provide similar LS measurements as GRE MRE. Furthermore, it provides a larger measurable area on the confidence mask in both groups with and without iron overload.

Liver Fibrosis, Fat, and Iron Evaluation with MRI and Fibrosis and Fat Evaluation with US: A Practical Guide for Radiologists.

Quantitative imaging biomarkers of liver disease measured by using MRI and US are emerging as important clinical tools in the management of patients with chronic liver disease (CLD). Because of their high accuracy and noninvasive nature, in many cases, these techniques have replaced liver biopsy for the diagnosis, quantitative staging, and treatment monitoring of patients with CLD. The most commonly evaluated imaging biomarkers are surrogates for liver fibrosis, fat, and iron. MR elastography is now routinely performed to evaluate for liver fibrosis and typically combined with MRI-based liver fat and iron quantification to exclude or grade hepatic steatosis and iron overload, respectively. US elastography is also widely performed to evaluate for liver fibrosis and has the advantage of lower equipment cost and greater availability compared with those of MRI. Emerging US fat quantification methods can be performed along with US elastography. The author group, consisting of members of the Society of Abdominal Radiology (SAR) Liver Fibrosis Disease-Focused Panel (DFP), the SAR Hepatic Iron Overload DFP, and the European Society of Radiology, review the basics of liver fibrosis, fat, and iron quantification with MRI and liver fibrosis and fat quantification with US. The authors cover technical requirements, typical case display, quality control and proper measurement technique and case interpretation guidelines, pitfalls, and confounding factors. The authors aim to provide a practical guide for radiologists interpreting these examinations. © RSNA, 2023 See the invited commentary by Ronot in this issue. Quiz questions for this article are available in the supplemental material.

Myocardial tissue characterization by segmental T2 mapping in thalassaemia major: detecting inflammation beyond iron.

AIMS: We measured myocardial T2 values by a segmental approach in thalassaemia major (TM) patients, comparing such values against T2* values for the detection of myocardial iron overload (MIO), evaluating their potential in detecting subclinical inflammation, and correlating with clinical status. METHODS AND RESULTS: One-hundred and sixty-six patients (102 females, 38.29 ± 11.49years) enrolled in the Extension-Myocardial Iron Overload in Thalassemia Network underwent magnetic resonance imaging for the assessment of hepatic, pancreatic, and cardiac iron overload (T2* technique), of biventricular function (cine images), and of replacement myocardial fibrosis [late gadolinium enhancement (LGE)]. T2 and T2* values were quantified in all 16 myocardial segments, and the global value was the mean of all segments. Global heart T2 values were significantly higher in TM than in a cohort of 80 healthy subjects. T2 and T2* values were significantly correlated. Out of the 25 patients with a decreased global heart T2* value, 11 (44.0%) had reduced T2 values. No patient with a normal T2* value had a decreased T2 value.Eleven (6.6%) patients had a decreased global heart T2 value, 74 (44.6%) a normal global heart T2 value, and 81 (48.8%) an increased global heart T2 value. Biventricular function was comparable amongst the three groups, whilst LGE was significantly more frequent in patients with reduced vs. increased global heart T2 value. Compared with the other two groups, patients with reduced T2 values had significantly higher hepatic and pancreatic iron deposition. CONCLUSION: In TM, T2 mapping does not offer any advantage in terms of sensitivity for MIO assessment but detects subclinical myocardial inflammation.

Quantification of cardiac iron in patients with thalassemia with 3-T MRI calibrated by 1.5-T MRI.

BACKGROUND: Due to the small sample size of many studies, it remained unclear what standardized reference range the T2* cutoff at 3 T would be used to assess the severity of cardiac iron load. In addition, the number of patients with moderate to severe cardiac iron load was small in some studies, especially the sample of patients with severe cardiac iron load. PURPOSE: To explore the feasibility, reproducibility, and reliability of using T2* values in quantifying cardiac iron load in patients with thalassemia at 3 T. MATERIAL AND METHODS: A total of 122 patients with thalassemia underwent cardiac T2* imaging at both 1.5 T and 3 T. Cardiac R2* (1000/T2*) values of the 100 patients at 3 T were fitted against the values at 1.5 T using linear regression and the prediction equation was derived. The remaining 22 cases were used to test the prediction accuracy of the equation. RESULTS: The combined R2* values exhibited a strong linear relationship between 1.5 T and 3 T (r = 0.830，P<0.001). At the center, it had a slope of 1.348 and an intercept of 37.279. According to the equation, the truncated T2* values of cardiac iron overload and cardiac heavy iron overload at 3 T were <10 ms and <6 ms, respectively. The two truncated T2* values were used to diagnose different levels of cardiac iron overloaded of 22 patients at 3 T; the accuracy rates were 95.5% and 100.0%, respectively. CONCLUSION: T2* quantification of cardiac iron load at 3 T MRI resulted to be feasible, reproducible, and reliable.

Pancreatic fatty replacement as risk marker for altered glucose metabolism and cardiac iron and complications in thalassemia major.

OBJECTIVES: This multicenter study assessed the extent of pancreatic fatty replacement and its correlation with demographics, iron overload, glucose metabolism, and cardiac complications in a cohort of well-treated patients with thalassemia major (TM). METHODS: We considered 308 TM patients (median age: 39.79 years; 182 females) consecutively enrolled in the Extension-Myocardial Iron Overload in Thalassemia Network. Magnetic resonance imaging was used to quantify iron overload (IO) and pancreatic fat fraction (FF) by T2* technique, cardiac function by cine images, and to detect replacement myocardial fibrosis by late gadolinium enhancement technique. The glucose metabolism was assessed by the oral glucose tolerance test. RESULTS: Pancreatic FF was associated with age, body mass index, and history of hepatitis C virus infection. Patients with normal glucose metabolism showed a significantly lower pancreatic FF than patients with impaired fasting glucose (p = 0.030), impaired glucose tolerance (p < 0.0001), and diabetes (p < 0.0001). A normal pancreatic FF (< 6.6%) showed a negative predictive value of 100% for abnormal glucose metabolism. A pancreatic FF > 15.33% predicted the presence of abnormal glucose metabolism. Pancreas FF was inversely correlated with global pancreas and heart T2* values. A normal pancreatic FF showed a negative predictive value of 100% for cardiac iron. Pancreatic FF was significantly higher in patients with myocardial fibrosis (p = 0.002). All patients with cardiac complications had fatty replacement, and they showed a significantly higher pancreatic FF than complications-free patients (p = 0.002). CONCLUSION: Pancreatic FF is a risk marker not only for alterations of glucose metabolism, but also for cardiac iron and complications, further supporting the close link between pancreatic and cardiac disease. KEY POINTS: • In thalassemia major, pancreatic fatty replacement by MRI is a frequent clinical entity, predicted by a pancreas T2* < 20.81 ms and associated with a higher risk of alterations in glucose metabolism. • In thalassemia major, pancreatic fatty replacement is a strong risk marker for cardiac iron, replacement fibrosis, and complications, highlighting a deep connection between pancreatic and cardiac impairment.