Optimizing background suppression for dual-module velocity-selective arterial spin labeling: Without using additional background-suppression pulses.
Optimizing background suppression for dual-module velocity-selective arterial spin labeling: Without using additional background-suppression pulses.
复制标题
优化双模块速度选择性动脉自旋标记的背景抑制:不使用额外的背景抑制脉冲。
DOI:
10.1002/mrm.29995
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发表时间:
2024
影响因子:
3.3
通讯作者:
Guo,Jia
中科院分区:
文献类型:
--
作者:
Guo,Jia
PurposeBackground suppression (BS) is recommended in arterial spin labeling (ASL) for improved SNR but is difficult to optimize in existing velocity‐selective ASL (VSASL) methods. Dual‐module VSASL (dm‐VSASL) enables delay‐insensitive, robust, and SNR‐efficient perfusion imaging, while allowing efficient BS, but its optimization has yet to be thoroughly investigated.MethodsThe inversion effects of the velocity‐selective labeling pulses, such as velocity‐selective inversion (VSI), can be used for BS, and were modeled for optimizing BS in dm‐VSASL. In vivo experiments using dual‐module VSI (dm‐VSI) were performed to compare two BS strategies: a conventional one with additional BS pulses and a new one without any BS pulse. Their BS performance, temporal noise, and temporal SNR were examined and compared, with pulsed and pseudo‐continuous ASL (PASL and PCASL) as the reference.ResultsThe in vivo experiments validated the BS modeling. Strong positive linear correlations (r> 0.82,p< 0.0001) between the temporal noise and the tissue signal were found in PASL/PCASL and dm‐VSI. Optimal BS can be achieved with and without additional BS pulses in dm‐VSI; the latter improved the ASL signals by 8.5% in gray matter (p= 0.006) and 12.2% in white matter (p= 0.014) and tended to provide better temporal SNR. The dm‐VSI measured significantly higher ASL signal (p< 0.016) and temporal SNR (p< 0.018) than PASL and PCASL. Complex reconstruction was found necessary with aggressive BS.ConclusionGuided by modeling, optimal BS can be achieved without any BS pulse in dm‐VSASL, further improving the ASL signal and the SNR performance.
影响因子:
3.3
作者:
Hernandez-Garcia, Luis;Aramendia-Vidaurreta, Veronica;Bolar, Divya S.;Dai, Weiying;Fernandez-Seara, Maria A.;Guo, Jia;Madhuranthakam, Ananth J.;Mutsaerts, Henk;Petr, Jan;Qin, Qin;Schollenberger, Jonas;Suzuki, Yuriko;Taso, Manuel;Thomas, David L.;van Osch, Matthias J. P.;Woods, Joseph;Yan, Lirong;Wang, Ze;Zhao, Li;Zhao, Moss Y.;Okell, Thomas W.
通讯作者:
Okell, Thomas W.
影响因子:
3.3
作者:
Alsop, David C.;Detre, John A.;Golay, Xavier;Guenther, Matthias;Hendrikse, Jeroen;Hernandez-Garcia, Luis;Lu, Hanzhang;MacIntosh, Bradley J.;Parkes, Laura M.;Smits, Marion;van Osch, Matthias J. P.;Wang, Danny J. J.;Wong, Eric C.;Zaharchuk, Greg
通讯作者:
Zaharchuk, Greg