ArticleMagnetic resonance in medicine2026
Optimized T
Article in Magnetic resonance in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Optimized TMagnetic resonance in medicine · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo propose and evaluate an optimized MP-RAGE protocol for rapid T
methodsIncoherent and coherent steady state free precession (SSFP) RAGE kernels with constant and variable excitation angles were investigated in terms of the white matter SNR and the white matter-gray matter signal difference. Potential edge smearing from the transient signal readout was assessed based on a differential point spread function analysis. Finally, the prospects of a deep-learning reconstruction (DLR) method for accelerated MP-RAGE MRI of undersampled data were evaluated for the best performing variant.
resultsMP-RAGE imaging with a variable flip angle (vFA) SSFP-FID kernel outperformed all other investigated variants. As compared to the standard MPRAGE sequence using a spoiled gradient echo kernel with constant flip angle, vFA SSFP-FID offered an average gain in the white matter SNR of 21% ± 2% and an average improvement for the white matter-gray matter signal difference for cortical gray matter of 47% ± 7%. The differential point spread function was narrowest for the spoiled gradient echo but slightly increased by 8% for vFA SSFP-FID. For vFA SSFP-FID, DLR offered a considerable decrease in the overall scan time from 5:17 min down to 2:46 min without noticeable image artifacts and degradations.
conclusionsAt 0.55 T, a vFA MP-RAGE variant using an SSFP-FID kernel combined with a DLR method offers excellent prospects for rapid T
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