MRI-Based Quantification of Magnetic Susceptibility in Gel Phantoms: Assessment of Measurement and Calculation Accuracy

Abstract

The local magnetic field inside and around an object in a magnetic resonance imaging unit depends on the magnetic susceptibility of the object being magnetized, in combination with its geometry/orientation. Magnetic susceptibility can thus be exploited as a source of tissue contrast, and susceptibility imaging may also become a useful tool in contrast agent quantification and for assessment of venous oxygen saturation levels. In this study, the accuracy of an established procedure for quantitative susceptibility mapping (QSM) was investigated. Three gel phantoms were constructed with cylinders of varying susceptibility and geometry. Experimental results were compared with simulated and analytically calculated data. An expected linear relationship between estimated susceptibility and concentration of contrast agent was observed. Less accurate QSM-based susceptibility values were observed for cylindrical objects at angles, relative to the main magnetic field, that were close to or larger than the magic angle. Results generally improved for large objects/high spatial resolution and large volume coverage. For simulated phase maps, accurate susceptibility quantification by QSM was achieved also for more challenging geometries. The investigated QSM algorithm was generally robust to changes in measurement and calculation parameters, but experimental phase data of sufficient quality may be difficult to obtain in certain geometries.

QSM reconstruction challenge 2.0: A realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures

“…Without a gold‐standard technique, it was not possible to obtain a meaningful in vivo reference susceptibility map through measurements. The committee had also discussed the design of real‐world test objects (phantoms) that are consistent with the QSM phase model 33,34 . Based on the committee members’ experience with phantom design and a literature research of previously used phantoms, it was concluded that the inclusion of sufficiently complex morphology and fine‐scale susceptibility features would be prohibitively challenging.…”

“…In the literature, most QSM algorithms were evaluated based on their visual appearance, 5,24,25 based on the RMS error (RMSE) of reconstructions of simple digital piece-wise constant phantoms consisting of geometrical shapes [25][26][27][28][29][30] or simplistic head phantoms. 22,31 Evaluation of the susceptibility quantification accuracy and precision typically relied on phantoms made of agar or aqueous solutions with varying concentrations of contrast agents such as gadolinium 20,26,[32][33][34][35] or iron oxide particles. 5,28,[36][37][38] Such measurements have been of great importance in establishing that QSM linearly maps the magnetic susceptibility property and that measurements across different platforms can be compared.…”

QSM Reconstruction Challenge 2.0: a realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures

“…In the literature, most QSM algorithms were evaluated based on their visual appearance (5,24,25), based on the root-mean-squared-error (RMSE) of reconstructions of simple digital piece-wise constant phantoms consisting of geometrical shapes (25)(26)(27)(28)(29)(30), or simplistic head phantoms (22,31). Evaluation of the susceptibility quantification accuracy and precision typically relied on phantoms made of agar or aqueous solutions with varying concentrations of contrast agents such as Gadolinium (20,26,(32)(33)(34)(35) or iron oxide particles (5,28,(36)(37)(38). Such measurements have been of great importance to establish that QSM linearly maps the magnetic susceptibility property and that measurements across different platforms can be compared.…”

“…Without a gold standard technique, it was not possible to obtain a meaningful in vivo reference susceptibility map through measurements. The committee had also discussed the design of real-world test objects (phantoms) that are consistent with the QSM phase model (33,34). Based on the committee members' experience with phantom design and a literature research of previously used phantoms, it was concluded that the inclusion of sufficiently complex morphology and fine-scale susceptibility features would be prohibitively challenging.…”

Dynamic contrast-enhanced QSM for perfusion imaging: a systematic comparison of ΔR2*- and QSM-based contrast agent concentration time curves in blood and tissue

“…However, obtaining pixel-wise information about CA concentration in structures of arbitrary shape and orientation relative to the main magnetic field is not straightforward, and QSM is an evolving approach where different algorithms have been proposed to solve the ill-posed problem of using MRI phase information to obtain information about magnetic susceptibility distributions [16]. While promising QSM reconstruction algorithms are continuously developed, there is still need for further evaluation of the performance in challenging geometries [19]. Also, to be able to compare susceptibility values from different measurements or different time points (and thus to quantify CA concentration), a reliable reference region is needed [12].…”