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  Fast interleaved multislice T1 mapping: Model-based reconstruction of single-shot inversion-recovery radial FLASH.

Wang, X., Voit, D., Roeloffs, V. B., Uecker, M., & Frahm, J. (2018). Fast interleaved multislice T1 mapping: Model-based reconstruction of single-shot inversion-recovery radial FLASH. Computational and Mathematical Methods in Medicine, 2018: 2560964. doi:10.1155/2018/2560964.

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 Urheber:
Wang, X.1, Autor           
Voit, D.1, Autor           
Roeloffs, V. B.1, Autor           
Uecker, M., Autor
Frahm, J.1, Autor           
Affiliations:
1Biomedical NMR Research GmbH, MPI for Biophysical Chemistry, Max Planck Society, ou_578634              

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 Zusammenfassung: Purpose. To develop a high-speed multislice T1 mapping method based on a single-shot inversion-recovery (IR) radial FLASH acquisition and a regularized model-based reconstruction. Methods. Multislice radial k-space data are continuously acquired after a single nonselective inversion pulse using a golden-angle sampling scheme in a spoke-interleaved manner with optimized flip angles. Parameter maps and coil sensitivities of each slice are estimated directly from highly undersampled radial k-space data using a model-based nonlinear inverse reconstruction in conjunction with joint sparsity constraints. The performance of the method has been validated using a numerical and experimental T1 phantom as well as demonstrated for studies of the human brain and liver at 3T. Results. The proposed method allows for 7 simultaneous T1 maps of the brain at 0.5 x 0.5 x 4 mm(3) resolution within a single IR experiment of 4 s duration. Phantom studies confirm similar accuracy and precision as obtained for a single-slice acquisition. For abdominal applications, the proposed method yields three simultaneous T1 maps at 1.25 x 1.25 x 6 mm(3) resolution within a 4 s breath hold. Conclusion. Rapid, robust, accurate, and precise multislice T1 mapping may be achieved by combining the advantages of a model-based nonlinear inverse reconstruction, radial sampling, parallel imaging, and compressed sensing.

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Sprache(n): eng - English
 Datum: 2018-08-13
 Publikationsstatus: Online veröffentlicht
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 Ort, Verlag, Ausgabe: -
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1155/2018/2560964
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Titel: Computational and Mathematical Methods in Medicine
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: -
Seiten: 8 Band / Heft: 2018 Artikelnummer: 2560964 Start- / Endseite: - Identifikator: -