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Updated: Jan 20, 2026
Measuring Grey Matter Differences with Voxel-based Morphometry: The Musical Brain
Published on: April 30, 2023
Optimizing ultra-rapid compressed-sensing MPRAGE acquisitions for brain morphometry
Lindsay C Hanford1,2,3, Tom Hilbert4,5,6, Tobias Kober4,5,6
1Center for Brain Science, Harvard University, Cambridge, MA, United States.
Purpose:
Compressed-sensing (CS) methods can decrease the acquisition time for T1-weighted (T1w) structural MRI images to 1-2 min. Rapid acquisitions reduce participant burden, reduce the risk of motion artifacts, and allow for repeat scans to be acquired within a session. This study investigated the tradeoffs of sparse sampling and CS image reconstruction for brain morphometric applications.
Methods:
Magnetization-Prepared Rapid Gradient Echo (MPRAGE) images were acquired at 1.0 mm spatial resolution. The effects of the acceleration factor (x2 to x8) and regularization factor were examined. Subcortical volumes and regional cortical thickness estimates of brain structure were obtained for all T1w images. Within-sequence agreement was evaluated by comparing estimates obtained using the same protocol in the same imaging session. Between-sequence agreement was evaluated by comparing estimates from a fully sampled MPRAGE protocol to the novel CS-accelerated MPRAGE protocols within the same session.
Results:
Higher acceleration lowered the SNR in white matter but not in gray matter. SNR could be further manipulated by the regularization parameter. Within-sequence agreement was comparable across all protocols. In fact, the spread in estimates from the 58-s CSx8 protocol was similar to those from the fully sampled protocol. Similarly, high agreement was found between estimates from the fully sampled and under-sampled protocols for all acceleration levels up to eight. Modifying the regularization factor had a quantifiable effect on image smoothness, however it had minimal impact on the agreement of morphometric estimates.
Conclusion:
Accelerated CS imaging protocols show comparable performance to traditional longer protocols for morphometric brain estimates.
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