Dimov, Alexey

Magnetic materials in tissue, such as iron, calcium, or collagen, can be studied using quantitative susceptibility mapping (QSM). To date, QSM has been overwhelmingly applied in the brain, but is increasingly utilized outside the brain. QSM relies on the effect of tissue magnetic susceptibility sources on the MR signal phase obtained with gradient echo sequence. However, in the body, the chemical shift of fat present within the region of interest contributes to the MR signal phase as well. Therefore, correcting for the chemical shift effect by means of water‐fat separation is essential for body QSM. By employing techniques to compensate for cardiac and respiratory motion artifacts, body QSM has been applied to study liver iron and fibrosis, heart chamber blood and placenta oxygenation, myocardial hemorrhage, atherosclerotic plaque, cartilage, bone, prostate, breast calcification, and kidney stone.

BackgroundQuantitative susceptibility mapping (QSM) and dynamic contrast‐enhanced quantitative permeability (DCEQP) on magnetic resonance (MR) have been shown to correlate with neurovascular disease progression as markers of vascular leakage and hemosiderin deposition. Applying these techniques as monitoring biomarkers in clinical trials will be necessary; however, their validation across multiple MR platforms and institutions has not been rigorously verified.PurposeTo validate quantitative measurement of MR biomarkers on multiple instruments at different institutions.Study TypePhantom validation between platforms and institutions.Phantom ModelT1/susceptibility phantom, two‐compartment dynamic flow phantom.Field Strength/Sequence3T/QSM, T1 mapping, dynamic 2D SPGR.AssessmentPhilips Ingenia, Siemens Prisma, and Siemens Skyra at three different institutions were assessed. A QSM phantom with concentrations of gadolinium, corresponding to magnetic susceptibilities of 0, 0.1, 0.2, 0.4, and 0.8 ppm was assayed. DCEQP was assessed by measuring a MultiHance bolus as the consistency of the width ratio of the curves at the input and outputs over a range of flow ratios between outputs.Statistical TestsEach biomarker was assessed by measures of accuracy (Pearson correlation), precision (paired t‐test between repeated measurements), and reproducibility (analysis of covariance [ANCOVA] between instruments).ResultsQSM accuracy of r2 > 0.997 on all three platforms was measured. Precision (P = 0.66 Achieva, P = 0.76 Prisma, P = 0.69 Skyra) and reproducibility (P = 0.89) were good. T1 mapping of accuracy was r2 > 0.98. No significant difference between width ratio regression slopes at site 2 (P = 0.669) or site 3 (P = 0.305), and no significant difference between width ratio regression slopes between sites was detected by ANCOVA (P = 0.48).Data ConclusionThe phantom performed as expected and determined that MR measures of QSM and DCEQP are accurate and consistent across repeated measurements and between platforms.Level of Evidence: 1Technical Efficacy Stage: 2J. Magn. Reson. Imaging 2020;51:1192–1199.