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Magn Reson Med ; 78(2): 429-440, 2017 08.
Artículo en Inglés | MEDLINE | ID: mdl-27548836

RESUMEN

PURPOSE: To compare a new parallel imaging (PI) method for multislice proton magnetic resonance spectroscopic imaging (1 H-MRSI), termed (2 + 1)D-CAIPIRINHA, with two standard PI methods: 2D-GRAPPA and 2D-CAIPIRINHA at 7 Tesla (T). METHODS: (2 + 1)D-CAIPIRINHA is a combination of 2D-CAIPIRINHA and slice-CAIPIRINHA. Eight healthy volunteers were measured on a 7T MR scanner using a 32-channel head coil. The best undersampling patterns were estimated for all three PI methods. The artifact powers, g-factors, Cramér-Rao lower bounds (CRLB), and root mean square errors (RMSE) were compared quantitatively among the three PI methods. Metabolic maps and spectra were compared qualitatively. RESULTS: (2 + 1)D-CAIPIRINHA allows acceleration in three spatial dimensions in contrast to 2D-GRAPPA and 2D-CAIPIRINHA. Thus, this sequence significantly decreased the RMSE of the metabolic maps by 12.1 and 6.9%, on average, for 4 < R < 11, compared with 2D-GRAPPA and 2D-CAIPIRINHA, respectively. The artifact power was 22.6 and 8.4% lower, and the CRLB were 3.4 and 0.6% lower, respectively. CONCLUSION: (2 + 1)-CAIPIRINHA can be implemented for multislice MRSI in the brain, enabling higher accelerations than possible with two-dimensional (2D) parallel imaging methods. An eight-fold acceleration was still feasible in vivo with negligible PI artifacts with lipid decontamination, thus decreasing the measurement time from 120 to 15 min for a 64 × 64 × 4 matrix. Magn Reson Med 78:429-440, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.


Asunto(s)
Encéfalo/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador/métodos , Imagen por Resonancia Magnética/métodos , Algoritmos , Humanos
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