Quantification of superparamagnetic iron oxide using inversion recovery balanced steady-state free precession.

Cellular and molecular MRI trafficking studies using superparamagnetic iron oxide (SPIO) have greatly improved non-invasive investigations of disease progression and drug efficacy, but thus far, these studies have largely been restricted to qualitative assessment of hypo- or hyperintense areas near SPIO. In this work, SPIO quantification using inversion recovery balanced steady-state free precession (IR-bSSFP) was demonstrated at 3T by extracting R2 values from a monoexponential model (P. Schmitt et al., 2004). A low flip angle was shown to reduce the apparent recovery rate of the IR-bSSFP time course, thus extending the dynamic range of quantification. However, low flip angle acquisitions preclude the use of traditional methods for combining RF phase-cycled images to reduce banding artifacts arising from off-resonance due to B0 inhomogeneity. To achieve R2 quantification of SPIO, we present a new algorithm applicable to low flip angle IR-bSSFP acquisitions that is specifically designed to identify on-resonance acquisitions. We demonstrate in this work, using both theoretical and empirical methods, that the smallest estimated R2 from multiple RF phase-cycled acquisitions correspond well to the on-resonance time course. Using this novel minimum R2 algorithm, homogeneous R2 maps and linear R2 calibration curves were created up to 100μg(Fe)/mL with 20° flip angles, despite substantial B0 inhomogeneity. In addition, we have shown this technique to be feasible for pre-clinical research: the minimum R2 algorithm was resistant to off-resonance in a single slice mouse R2 map, whereas maximum intensity projection resulted in banding artifacts and overestimated R2 values. With the application of recent advances in accelerated acquisitions, IR-bSSFP has the potential to quantify SPIO in vivo, thus providing important information for oncology, immunology, and regenerative medicine MRI studies.

Duke Scholars

Author Nikki Pelot