The role of MRI to estimate liver iron concentration (LIC) for identifying patients with iron overload and guiding the titration of chelation therapy is increasingly established for routine clinical practice. However, the existence of multiple MRI-based LIC quantification techniques limits standardization and widespread clinical adoption. In this article, we review the existing and widely accepted MRI-based LIC estimation methods at 1.5 T and 3 T: signal intensity ratio (SIR) and relaxometry (R2 and R2*) and discuss the basic principles, acquisition and analysis protocols, and MRI-LIC calibrations for each technique. Further, we provide an up-to-date information on MRI vendor implementations and available offline commercial and free software for each MRI-based LIC quantification approach. We also briefly review the emerging and advanced MRI techniques for LIC estimation and their current limitations for clinical use. Lastly, we discuss the implications of MRI-based LIC measurements on clinical use and decision-making in the management of patients with iron overload. Some of the key highlights from this review are as follows: 1) Both R2 and R2* can estimate accurate and reproducible LIC, when validated acquisition parameters and analysis protocols are applied, 2) Although the Ferriscan R2 method has been widely used, recent consensus and guidelines endorse R2*-MRI as the most accurate and reproducible method for LIC estimation, 3) Ongoing efforts aim to establish R2*-MRI as the standard approach for quantifying LIC, and 4) Emerging R2*-MRI techniques employ radial sampling strategies and offer improved motion compensation and broader dynamic range for LIC estimation. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Quantitative abdominal magnetic resonance imaging (MRI) offers non-invasive, objective assessment of diseases in the liver, pancreas, and other organs and is increasingly being used in the pediatric population. Certain quantitative MRI techniques, such as liver proton density fat fraction (PDFF), R2* mapping, and MR elastography, are already in wide clinical use. Other techniques, such as liver T1 mapping and pancreas quantitative imaging methods, are emerging and show promise for enhancing diagnostic sensitivity and treatment monitoring. Quantitative imaging techniques have historically required a breath-hold, making them more difficult to implement in the pediatric population. However, technological advances, including free-breathing techniques and compressed sensing imaging, are making these techniques easier to implement. The purpose of this article is to review current liver and pancreas quantitative techniques and to provide a practical guide for implementing these techniques in pediatric practice. Future directions of liver and pancreas quantitative imaging will be briefly discussed.

This study assessed the clinical classification performance of an R2*-based MRI technique for LIC quantification relative to FerriScan, with intra-patient FerriScan LIC uncertainty taken into account. The variabilities of R2* and FerriScan LIC were also assessed.

This was an ethics approved retrospective study, performed on patients undergoing chelation treatment for iron overload. 126 patients (69 women, 57 men), with an age of 42 +/- 16 years (range 19-86 years) were included. FerriScan and R2* MRI at 1.5 T were performed as part of a routine liver iron assessment protocol. For R2* MRI, a commercially available pulse sequence and reconstruction implementation was used, together with a previously derived calibration curve to convert R2* to LIC. Clinical classifications arising from R2*-derived LIC estimates were compared to those based on FerriScan. The accuracy and precision of the R2* technique was calculated. The variabilities of FerriScan- and R2*-derived estimates of LIC were compared with a Wilcoxon Signed Rank test. Significance was set at the 95% confidence level.

The precision of R2* ranged from 0.59 to 0.92, with an overall accuracy of 72%. When intra-patient FerriScan LIC uncertainty was considered, precision and accuracy increased to >94% and 97% respectively. The R2*-LIC variability (=17%) was significantly lower than the FerriScan-LIC variability (34%) at the 95% confidence level (p < 10-3).

MRI R2*-based LIC estimates provided a similar clinical classification as FerriScan. The intra-patient uncertainty of R2*-based LIC estimates was significantly lower than FerriScan.

To evaluate the causal relationship between abdominal multi-organ iron content and PD risk using publicly available genome-wide association study (GWAS) data.

We conducted MR analysis to assess the effects of iron content in various abdominal organs on PD risk, followed by reverse analysis. Additionally, MVMR analysis evaluated the independent effects of organ-specific iron content on PD. We utilized genetic variation data from the UK Biobank, including liver iron content (n = 32,858), spleen iron content (n = 35,324), and pancreas iron content (n = 25,617), as well as summary-level data for Parkinson's disease from the FinnGen (n = 218,473) and two other large GWAS datasets of European populations (First dataset n = 480,018; Second dataset n = 2,829). The primary MR analysis used the inverse variance-weighted (IVW) method, confirmed by MR-Egger and weighted median methods. Sensitivity analysis was performed to address potential pleiotropy and heterogeneity. Observational cohort results were validated through replication cohort analysis, followed by meta-analysis.

IVW analysis revealed a causal relationship between increased liver iron content and elevated risk of PD (OR = 1.27; 95% CI: 1.05-1.53; p = 0.015). No significant causal relationship was observed between spleen (OR = 1.00; 95% CI: 0.76-1.32; p = 0.983) and pancreatic (OR = 0.93; 95% CI: 0.72-1.20; p = 0.573) iron content and increased risk of PD. Meta-analysis of GWAS data for PD from three different sources using the random-effects IVW method showed a statistically significant causal relationship between liver iron content and the occurrence of PD (OR = 1.17, 95% CI: 1.01-1.35; p = 0.012).

This study presents evidence from Mendelian randomization (MR) analysis indicating a significant causal link between increased liver iron content and a higher risk of Parkinson's disease (PD). These findings suggest that interventions targeting body iron metabolism, particularly liver iron levels, may be effective in preventing PD.

Hepatic dysfunction represents a wide spectrum of pathological changes, which can be frequently found in hepatitis, cholestasis, metabolic diseases, and focal liver lesions. As hepatic dysfunction is often clinically silent until advanced stages, there remains an unmet need to identify affected patients at early stages to enable individualized intervention which can improve prognosis. Passive liver function tests include biochemical parameters and clinical grading systems (e.g., the Child-Pugh score and Model for End-Stage Liver Disease score). Despite widely used and readily available, these approaches provide indirect and limited information regarding hepatic function. Dynamic quantitative tests of liver function are based on clearance capacity tests such as the indocyanine green (ICG) clearance test. However, controversial results have been reported for the ICG clearance test in relation with clinical outcome and the accuracy is easily affected by various factors. Imaging techniques, including ultrasound, computed tomography, and magnetic resonance imaging, allow morphological and functional assessment of the entire hepatobiliary system, hence demonstrating great potential in evaluating hepatic dysfunction noninvasively. In this article, we provide a state-of-the-art summary of noninvasive imaging modalities for hepatic dysfunction assessment along the pathophysiological track, with special emphasis on the imaging modality comparison and selection for each clinical scenario.

Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics offer great promise for clinical use. However, many of these methods have limited clinical adoption, in part due to issues of generalizability, that is, the ability to translate methods and models across institutions. Researchers can assess generalizability through measurement of repeatability and reproducibility, thus quantifying different aspects of measurement variance. In this article, we review the challenges to ensuring repeatability and reproducibility of image quantitation methods as well as present strategies to minimize their variance to enable wider clinical implementation. We present possible solutions for achieving clinically acceptable performance of image quantitation methods and briefly discuss the impact of minimizing variance and achieving generalizability towards clinical implementation and adoption.

Iron overload is a common clinical problem resulting from hereditary hemochromatosis or secondary hemosiderosis (mainly associated with transfusion therapy), being also associated with chronic liver diseases and metabolic disorders. Excess of iron accumulates in organs like the liver, pancreas and heart. Without treatment, patients with iron overload disorders will develop liver cirrhosis, diabetes and cardiomyopathy. Iron quantification is therefore crucial not only for diagnosis of iron overload but also to monitor iron-reducing therapies. Liver iron concentration is considered the surrogate marker of total body iron stores. Because liver biopsy is invasive and prone to high variability and sampling bias, MR imaging has emerged as a non-invasive method and gained wide acceptance, now being considered the standard of care for assessing iron overload. Nevertheless, there are different MR techniques for iron quantification and there is still no consensus about the best technique or postprocessing tool for hepatic iron quantification, with the choice of imaging technique depending mainly on the local expertise as well on the available equipment and software. Because different methods should not be used interchangeably, it is important to choose one method and use the same one when following up patients over time.