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Robust dual‐module velocity‐selective arterial spin labeling (dm‐VSASL) with velocity‐selective saturation and inversion
PURPOSE: Compared to conventional arterial spin labeling (ASL) methods, velocity‐selective ASL (VSASL) is more sensitive to artifacts from eddy currents, diffusion attenuation, and motion. Background suppression is typically suboptimal in VSASL, especially of CSF. As a result, the temporal SNR and q...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9792445/ https://www.ncbi.nlm.nih.gov/pubmed/36336852 http://dx.doi.org/10.1002/mrm.29513 |
Sumario: | PURPOSE: Compared to conventional arterial spin labeling (ASL) methods, velocity‐selective ASL (VSASL) is more sensitive to artifacts from eddy currents, diffusion attenuation, and motion. Background suppression is typically suboptimal in VSASL, especially of CSF. As a result, the temporal SNR and quantification accuracy of VSASL are compromised, hindering its application despite its advantage of being delay‐insensitive. METHODS: A novel dual‐module VSASL (dm‐VSASL) strategy is developed to improve the SNR efficiency and the temporal SNR with a more balanced gradient configuration in the label/control image acquisition. This strategy applies for both VS saturation (VSS) and VS inversion (VSI) labeling. The dm‐VSASL schemes were compared with single‐module labeling and a previously developed multi‐module schemes for the SNR performance, background suppression efficacy, and sensitivity to artifacts in simulation and in vivo experiments, using pulsed ASL as the reference. RESULTS: Dm‐VSASL enabled more robust labeling and efficient backgroud suppre across brain tissues, especially of CSF, resulting in significantly reduced artifacts and improved temporal SNR. Compared to single‐module labeling, dm‐VSASL significantly improved the temporal SNR in gray (by 90.8% and 94.9% for dm‐VSS and dm‐VSI, respectively; P < 0.001) and white (by 41.5% and 55.1% for dm‐VSS and dm‐VSI, respectively; P < 0.002) matter. Dm‐VSI also improved the SNR of VSI by 5.4% (P = 0.018). CONCLUSION: Dm‐VSASL can significantly improve the robustness of VS labeling, reduce artifacts, and allow efficient background suppression. When implemented with VSI, it provides the highest SNR efficiency among VSASL methods. Dm‐VSASL is a powerful ASL method for robust, accurate, and delay‐insensitive perfusion mapping. |
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