Abstract
Purpose A major obstacle to the clinical implementation of quantitative MR is the lengthy acquisition time required to derive multi-contrast parametric maps. We sought to reduce the acquisition time for quantitative susceptibility mapping (QSM) and macromolecular tissue volume (MTV) by acquiring both contrasts simultaneously by leveraging their redundancies. The Joint Virtual Coil concept with generalized autocalibrating partially parallel acquisitions (JVC-GRAPPA) was applied to reduce acquisition time further.
Methods Three adult volunteers were imaged on a 3T scanner using a multi-echo 3D GRE sequence acquired at three head orientations. MTV, QSM, R2*, T1, and proton density maps were reconstructed. The same sequence (GRAPPA R=4) was performed in subject #1 with a single head orientation for comparison. Fully sampled data was acquired in subject #2, from which retrospective undersampling was performed (R=6 GRAPPA and R=9 JVC-GRAPPA). Prospective undersampling was performed in subject #3 (R=6 GRAPPA and R=9 JVC-GRAPPA) using gradient blips to shift k-space sampling in later echoes.
Results Subject #1’s multi-orientation and single-orientation MTV maps were not significantly different based on RMSE. For subject #2, the retrospectively undersampled JVC-GRAPPA and GRAPPA generated similar results as fully sampled data. This approach was validated with the prospectively undersampled images in subject #3. Using QSM, R2*, and MTV, the contributions of myelin and iron content to susceptibility was estimated.
Conclusion We have developed a novel strategy to simultaneously acquire data for the reconstruction of five intrinsically co-registered 1-mm isotropic resolution multi-parametric maps, with a scan time of 6 minutes using JVC-GRAPPA.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Research reported in this publication was supported by the National Institutes of Health (NIBIB R01 EB028797 [B.B.], U01 EB025162 [B.B.], U01 EB026996 [B.B.], P41 EB030006 [B.B.], NINDS K23 NS096056 [S.Y.H.], NIA R21AG067562 [S.Y.H.]), NVIDIA GPU grant (B.B.), American Society of Neuroradiology Boerger Research Fund in Alzheimer’s Disease and Neurocognitive Disorders (S.Y.H.), Marilyn Hilton Award for Innovation in MS Research from the Conrad N. Hilton Foundation (S.Y.H.), MGH Claflin Distinguished Scholar Award (S.Y.H.).
The authors do not have any conflicts of interest to report.
