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

ABSTRACT: 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.

SUBMITTER: Tipirneni-Sajja A

PROVIDER: S-EPMC5927847 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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

Tipirneni-Sajja Aaryani A Song Ruitian R McCarville M Beth MB Loeffler Ralf B RB Hankins Jane S JS Hillenbrand Claudia M CM

Journal of magnetic resonance imaging : JMRI 20171030 6

<h4>Background</h4>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.<h4>Purpose</h4>To implement and evaluate an automatic hepatic vessel exclusion and parenchyma extraction technique for accurate ass ...[more]

PMID: 29083524

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Project description:BackgroundMRI T2* and R2* mapping have gained clinical acceptance for noninvasive assessment of iron overload. Lower field MRI may offer increased measurement dynamic range in patients with high iron concentration and may potentially increase MRI accessibility, but it is compromised by lower signal-to-noise ratio that reduces measurement precision.PurposeTo characterize a high-performance 0.55 T MRI system for evaluating patients with liver iron overload.Study typeProspective.PopulationForty patients with known or suspected iron overload (sickle cell anemia [n = 5], ß-thalassemia [n = 3], and hereditary spherocytosis [n = 2]) and a liver iron phantom.Field strength/sequenceA breath-held multiecho gradient echo sequence at 0.55 T and 1.5 T.AssessmentPatients were imaged with T2*/R2* mapping 0.55 T and 1.5 T within 24 hours, and 16 patients returned for follow-up exams within 6-16 months, resulting in 56 paired studies. Liver T2* and R2* measurements and standard deviations were compared between 0.55 T and 1.5 T and used to validate a predictive model between field strengths. The model was then used to classify iron overload at 0.55 T.Statistical testsLinear regression and Bland-Altman analysis were used for comparisons, and measurement precision was assessed using the coefficient of variation. A P-value < 0.05 was considered statistically significant.ResultsR2* was significantly lower at 0.55 T in our cohort (488 ± 449 s-1 at 1.5 T vs. 178 ± 155 s-1 at 0.55 T, n = 56 studies) and in the patients with severe iron overload (937 ± 369 s-1 at 1.5 T vs. 339 ± 127 s-1 at 0.55 T, n = 23 studies). The coefficient of variation indicated reduced precision at 0.55 T (3.5 ± 2.2% at 1.5 T vs 6.9 ± 3.9% at 0.55 T). The predictive model accurately predicted 1.5 T R2* from 0.55 T R2* (Bland Altman bias = -6.6 ± 20.5%). Using this model, iron overload at 0.55 T was classified as: severe R2* > 185 s-1 , moderate 81 s-1 < R2* < 185 s-1 , and mild 45 s-1 < R2* < 91 s-1 .Data conclusionWe demonstrated that 0.55 T provides T2* and R2* maps that can be used for the assessment of liver iron overload in patients.Evidence level2 TECHNICAL EFFICACY: Stage 2.

Project description:BackgroundR2*-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.PurposeTo 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 typePhantom study and in vivo cohort.SubjectsNine iron phantoms covering clinically relevant R2* range (20-1200/second) and 48 patients (22 male, 26 female, median age 18 years).Field strength/sequenceThree-dimensional (3D) and two-dimensional (2D) multi-echo gradient echo (GRE) at 1.5 T.AssessmentARMA-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 testsLinear 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.ResultsIn 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 conclusionThis 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 evidence3 TECHNICAL EFFICACY: Stage 2.

Project description:The treatment of sickle cell disease (SCD) is mainly supportive, except for a minority, who receive bone marrow transplantation (BMT). Serum ferritin (SF) is routinely available but is notoriously unreliable as a tool for iron-overload assessment since it is an acute-phase reactant. Although blood transfusion is one of the most effective ways to deal with specific acute and chronic complications of SCD, this strategy is often associated with alloimmunization, iron overload, and hemolytic reactions. This study, thus, aims to evaluate iron overload in patients with SCD on chronic blood transfusions and specifically, correlate SF with the current standard of care of iron-overload assessment using MRI-based imaging techniques. Amongst a historic cohort of 58 chronically transfused patients with SCD, we were able to evaluate 44 patients who are currently alive and had multiple follow-up testing. Their mean age (±SD) was 35 (9) years and comprised of 68.2% of women. The studied iron-overload parameters included cardiac T2* MRI, liver iron concentration (LIC) by Liver T2* MRI, and serial SF levels. Additionally, in a smaller cohort, we also studied LIC by FerriScan© R2-MRI. Chronic blood transfusions were necessary for severe vaso-occlusive crisis (VOC) (38.6%), severe symptomatic anemia (38.6%), past history of stroke (15.9%), and recurrent acute chest syndrome (6.9%). About 14 (24%) patients among the original cohort died following SCD-related complications. Among the patients currently receiving chelation, 26 (96%) are on Deferasirox (DFX) [Jadenu® (24) or Exjade® (2)], with good compliance and tolerance. However, one patient is still receiving IV deferoxamine (DFO), in view of the significantly high systemic iron burden. In this evaluable cohort of 44 patients, the mean SF (±SD) reduced marginally from 4,311 to 4,230 ng/ml, mean Liver T2* MRI dropped from 12 to 10.3 mg/gm dry weight, while the mean cardiac T2*MRI improved from 36.8 to 39.5 ms. There was a mild to moderate correlation between the baseline and final values of SF ng/ml, r = 0.33, p = 0.01; Cardiac T2* MRI ms, r = 0.3, p = 0.02 and Liver T2* MRI mg/kg dry weight, r = 0.6, p < 0.001. Overall, there was a positive correlation between SF and Liver T2* MRI (Pearson's r = 0.78, p < 0.001). Cardiac T2*MRI increased with the decreasing SF concentration, showing a negative correlation which was statistically significant (Pearson's r = -0.6, p < 0.001). Furthermore, there was an excellent correlation between SF ng/ml and LIC by FerriScan© R2-MRI mg/g or mmol/kg (Spearmen's rho = -0.723, p < 0.008) in a small subset of patients (n = 14) who underwent the procedure. In conclusion, our study demonstrated a good correlation between serial SF and LIC by either Liver MRI T2* or by FerriScan© R2-MRI, even though SF is an acute-phase reactant. It also confirms the cardiac sparing effect in patients with SCD, even with the significant transfusion-related iron burden. About 14 (24%) patients of the original cohort died over the past 15 years, indicative of a negative impact of iron overload on disease morbidity and mortality.

Project description:ObjectivesMRI-based R2* mapping may enable reliable and rapid quantification of liver iron concentration (LIC). However, the performance and reproducibility of R2* across acquisition protocols remain unknown. Therefore, the objective of this work was to evaluate the performance and reproducibility of complex confounder-corrected R2* across acquisition protocols, at both 1.5 T and 3.0 T.MethodsIn this prospective study, 40 patients with suspected iron overload and 10 healthy controls were recruited with IRB approval and informed written consent and imaged at both 1.5 T and 3.0 T. For each subject, acquisitions included four different R2* mapping protocols at each field strength, and an FDA-approved R2-based method performed at 1.5 T as a reference for LIC. R2* maps were reconstructed from the complex data acquisitions including correction for noise effects and fat signal. For each subject, field strength, and R2* acquisition, R2* measurements were performed in each of the nine liver Couinaud segments and the spleen. R2* measurements were compared across protocols and field strength (1.5 T and 3.0 T), and R2* was calibrated to LIC for each acquisition and field strength.ResultsR2* demonstrated high reproducibility across acquisition protocols (p > 0.05 for 96/108 pairwise comparisons across 2 field strengths and 9 liver segments, ICC > 0.91 for each field strength/segment combination) and high predictive ability (AUC > 0.95 for four clinically relevant LIC thresholds). Calibration of R2* to LIC was LIC = - 0.04 + 2.62 × 10-2 R2* at 1.5 T and LIC = 0.00 + 1.41 × 10-2 R2* at 3.0 T.ConclusionsComplex confounder-corrected R2* mapping enables LIC quantification with high reproducibility across acquisition protocols, at both 1.5 T and 3.0 T.Key points• Confounder-corrected R2* of the liver provides reproducible R2* across acquisition protocols, including different spatial resolutions, echo times, and slice orientations, at both 1.5 T and 3.0 T. • For all acquisition protocols, high correlation with R2-based liver iron concentration (LIC) quantification was observed. • The calibration between confounder-corrected R2* and LIC, at both 1.5 T and 3.0 T, is determined in this study.

Project description:Background and objectivesIron overload among hemodialysis patients was previously considered rare but is now an increasingly recognized clinical situation. We analyzed correlations between iron biomarkers and the liver iron concentration (LIC) measured by magnetic resonance imaging (MRI), and examined their diagnostic accuracy for iron overload.Design, setting, participants and measurementsWe performed a prospective cross-sectional study from 31 January 2005 to 31 August 2013 in the dialysis centre of a French community-based private hospital. A cohort of 212 hemodialysis patients free of overt inflammation or malnutrition, were treated for anemia with parenteral iron-sucrose and an erythropoesis-stimulating agent, in keeping with current clinical guidelines. Blinded measurements of hepatic iron stores were performed by T1 and T2* contrast MRI, and relationships were analysed using Spearman's coefficient, logistic regression and receiver-operator characteristic (ROC) curves.ResultsAmong the biological markers, only serum ferritin showed a strong correlation with LIC (rho= 0.52, 95% CI: 0.41-0.61, p< 0.0001, Spearman test). In logistic analysis, only serum ferritin correctly classified the overall cohort into patients with normal liver iron stores (LIC ? 50 ?mol/g) and those with elevated liver iron stores (LIC > 50 ?mol/g) (odds ratio 1.007; 95% CI: 1.004-1.010). Serum ferritin was the iron biomarker with the best discriminatory capacity in ROC curves analysis (area under the curve (AUC) = 0.767; 95% CI: 0.698-0.835). The optimal serum ferritin cutoffs were 160 ?g/L for LIC > 50 ?mol/g (mild iron overload) and 290 ?g/L for LIC > 200 ?mol/g (severe iron overload).ConclusionsFor clinical purposes, serum ferritin correctly reflects liver iron stores, as assessed by MRI, in hemodialysis patients without overt inflammation or malnutrition. These results strongly suggest that current ferritin target values should be lowered to avoid iron overload.Trial registrationISRCTN Registry 80100088.

Project description:Purpose To quantify liver fat and liver iron content by measurement of confounder-corrected proton density fat fraction (PDFF) and R2* and to identify clinical associations for fatty liver disease and liver iron overload and their prevalence in a large-scale population-based study. Materials and Methods From 2008 to 2013, 2561 white participants (1336 women; median age, 52 years; 25th and 75th quartiles, 42 and 62 years) were prospectively recruited to the Study of Health in Pomerania (SHIP). Complex chemical shift-encoded magnetic resonance (MR) examination of the liver was performed, from which PDFF and R2* were assessed. On the basis of previous histopathologic calibration, participants were stratified according to their liver fat and iron content as follows: none (PDFF, ≤5.1%; R2*, ≤41.0 sec-1), mild (PDFF, >5.1%; R2*, >41 sec-1), moderate (PDFF, >14.1%; R2*, >62.5 sec-1), high (PDFF: >28.0%; R2*: >70.1 sec-1). Prevalence of fatty liver diseases and iron overload was calculated (weighted by probability of participation). Clinical associations were identified by using boosting for generalized linear models. Results Median PDFF was 3.9% (range, 0.6%-41.5%). Prevalence of fatty liver diseases was 42.2% (1082 of 2561 participants); mild, 28.5% (730 participants); moderate, 12.0% (307 participants); high content, 1.8% (45 participants). Median R2* was 34.4 sec-1 (range, 14.0-311.8 sec-1). Iron overload was observed in 17.4% (447 of 2561 participants; mild, 14.7% [376 participants]; moderate, 0.8% [20 participants]; high content, 2.0% [50 participants]). Liver fat content correlated with waist-to-height ratio, alanine transaminase, uric acid, serum triglycerides, and blood pressure. Liver iron content correlated with mean serum corpuscular hemoglobin, male sex, and age. Conclusion In a white German population, the prevalence of fatty liver diseases and liver iron overload is 42.2% (1082 of 2561) and 17.4% (447 of 2561). Whereas liver fat is associated with predictors related to the metabolic syndrome, liver iron content is mainly associated with mean serum corpuscular hemoglobin. © RSNA, 2017 Online supplemental material is available for this article.

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

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

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