Quantification of the liver iron concentration (LIC) is critical for the diagnosis for patients with β-thalassaemia, hereditary hemochromatosis, and sickle-cell disease (1,2). It is also indispensable in monitoring chelation therapies for patients undergoing blood transfusion (2). LIC is conventionally measured using R₂/R₂* relaxation rate mapping methods (1,3). However, these methods have limited accuracy and precision, partly due to the non-linear relation between relaxation rate and iron concentration (4). Particularly, R₂* may fail completely for measuring high iron overload (2). Quantitative susceptibility mapping (QSM) is a method which extracts susceptibility from magnetic field variations (5). In the past, QSM has been shown to be effective in quantifying cerebral iron deposition. However, there are a few challenges using QSM for LIC quantification, including dealing with air-tissue interface in the abdomen, background field removal and solving the ill-posed inverse problem (4,6). In this abstract, we show a method which uses the apparent susceptibility of the liver vessels to quantify iron concentration of the liver tissue itself. The field variation is essentially a function of the local susceptibility differences. For an infinitely long cylinder, the field variation is given as: ΔB(r)=½B₀(χ_in-χ_out)∙(a²/r²)∙sin²θ∙cos2φ, where χ_in and χ_out are the susceptibilities inside and outside the cylinder, respectively (7). For a vessel in the liver, the field variation is dependent on its apparent susceptibility (Δχ= χ_in-χ_out). For patients with iron overload, the liver vessels can be considered as in vivo probes which reflect the changes of susceptibility or iron concentration in the liver tissue, assuming that the changes of susceptibility inside the vessels is negligible compared to those outside. To prove the feasibility of this method, in vivo data for 7 healthy controls and 4 patients were collected on a 3T Siemens scanner, using a 3D double-echo sequence. Imaging parameters were: TE=5/10ms, TR=15ms, FA=15^o, BW/pixel =427Hz/pixel, voxel size = 1.67x1.67x2 mm³, matrix size = 192x144x16. The phase images from the first echo were used to generate susceptibility maps. Binary masks for the liver region were generated manually, and SHARP (8) was used to remove the background phase in the liver. Susceptibility maps were created using the iterative SWIM algorithm (9), with the geometries of the vessels extracted from the magnitude images from the first echo. R₂* maps were generated by fitting the magnitude images in both echoes to an exponential decay curve. The apparent susceptibilities of the vessels and the R₂* values of the liver tissue were measured.The healthy controls show the usual contrast for veins being paramagnetic relative to the background tissue (liver in this case, Figure 1.c). On the other hand, the severe iron overload in the patient is reflected by the reduced intensity in magnitude images (Figure 1.e), the increased R₂* values (Figure 1.h) and the reversed sign of the phase of vessels in the liver (Figure 1.f), compared those in the healthy control (Figure 1.a, 1.d and 1.b). This leads to negative apparent susceptibility of the vessels (Figure 1.g). The apparent susceptibilities measured in healthy controls and patients were correlated with the measured R₂* in the liver tissue (Figure 2). Specifically, higher LIC was associated with higher R₂* values of the liver tissues and lower apparent susceptibilities (i.e., more diamagnetic in nature) of the liver vessels.The main advantages of the proposed method include the simplicity in data processing and the accuracy in quantifying susceptibility of the vessels. It is assumed that the changes in susceptibility of the vessels is negligible from healthy controls to patients. In fact, the actual susceptibility of the vessels can be more accurately estimated by utilizing the R₂* of the vessels. On the other hand, there are a few QSM studies which try to estimate the susceptibility of the liver tissue directly (4,6). However, this requires accurate phase unwrapping in not only the regions inside the liver but also the regions outside. In that case, both phase unwrapping and background field removal may fail due to low SNR and the multiple air-tissue interfaces in the abdomen, preventing a reliable measurement of LIC. The iterative SWIM algorithm is the ideal QSM algorithm for quantifying apparent susceptibility of vessels, since the geometry of the vessels can be faithfully obtained from magnitude images. In conclusion, this study demonstrated the feasibility of using apparent susceptibility of liver vessels to estimate liver iron concentration. The proposed method may provide reliable and automatic measurement of liver iron concentration in the future and may be able to handle a more accurate quantification of liver iron for higher iron concentrations.