Back

B1+-correction of MT saturation maps optimized for 7T postmortem MRI of the brain

Lipp, I.; Kirilina, E.; Edwards, L. J.; Pine, K. J.; Jaeger, C.; Graessle, T.; EBC consortium, ; Weiskopf, N.; Helms, G.

2022-07-14 neuroscience
10.1101/2022.07.12.498197 bioRxiv
Show abstract

PurposeMagnetization transfer saturation (MTsat) is a useful marker to probe tissue macromolecular content and myelination in the brain. The increased [Formula] -inhomogeneity at [≥] 7T and significantly larger saturation pulse flip angles which are often used for postmortem studies exceed the limits where previous MTsat [Formula] correction methods are applicable. Here, we develop a calibration-based correction model and procedure, and validate and evaluate it in postmortem 7T data of whole chimpanzee brains. TheoryThe [Formula] dependence of MTsat was investigated by varying the off-resonance saturation pulse flip angle. For the range of saturation pulse flip angles applied in typical experiments on postmortem tissue, the dependence was close to linear. A linear model with a single calibration constant C is proposed to correct bias in MTsat by mapping it to the reference value of the saturation pulse flip angle. MethodsC was estimated voxel-wise in five postmortem chimpanzee brains. "Individual-based global parameters" were obtained by calculating the mean C within individual specimen brains and "group-based global parameters" by calculating the means of the individual-based global parameters across the five brains. ResultsThe linear calibration model described the data well, though C was not entirely independent of the underlying tissue and [Formula]. Individual-based and group-based global correction parameters (C = 1.2) led to visible, quantifiable reductions of [Formula]-biases in high resolution MTsat maps. ConclusionThe presented model and calibration approach effectively corrects for [Formula] in-homogeneities in postmortem 7T data.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.