245. A protocol set-up for fast brain T1 mapping of subcortical tissues

Abstract

PurposeThis work is part of a larger cohort study aimed at investigating the long term effects of exposure to manganese [1]. In this study we set up a protocol for brain fast T1 mapping and segmentation of subcortical tissues, in particular globus pallidus (GP), that is reported to be a target tissue for manganese deposition in brain. MethodsThe variable flip angle model was used for T1 mapping. Acquisition protocol, set up on a 3T Siemens Skyra MR system, includes 10 RF spoiled 3D gradient echoes (VIBE) with flip angles from 2° to 30° and a reduced scanned volume to cover GP in the shortest possible time, an extra 30° VIBE covering the whole brain (WB- VIBE), two fast acquisitions with different TEs for B1 map calculation, used to correct nominal flip angles, and a 3D-MPRAGE acquisition of the whole brain with the same WB-WIBE geometry. T1 and error maps were calculated offline with a bespoke Matlab script, checking accuracy with calibrated gels. The most efficient pipeline for post-processing, shown in Fig. 1, is as follows: T1 map calculation of the reduced volume, and zero padding of the missing slices to get the same WB-VIBE volume (WB-T1MAP); 3D-MPRAGE and WB-T1MAP coregistration using the WB-VIBE to 3D transformation matrix (WB-T1MAP-COR); GP automatic segmentation of the 3D-MPRAGE and, with the output mask, GP statistics calculation of the WB-T1MAP-COR. In order to cancel possible partial volume effects, segmented regions were eroded by 1 pixels and T1 results were compared. The protocol was checked on a group of 10 adolescents that were exposed to Mn during childhood. ResultsT1 accuracy was 4.3% (median) and within 13.8% (interquartile distance). GP-T1 average values were 1314,65 ms and 1302,92 ms after erosion, close to the values reported in other papers, with a coefficient of variation respectively decreased from 6.8%, to 5.6%. ConclusionsWe set up a fast and automated T1 mapping protocol with good accuracy and optimised data variability in vivo.