Optimized super-selective Arterial Spin Labeling for quantitative flow territory mapping
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Optimized super-selective Arterial Spin Labeling for quantitative flow territory mapping. / Lindner, Thomas; Jansen, Olav; Helle, Michael.
In: MAGN RESON IMAGING, Vol. 53, 11.2018, p. 14-19.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Optimized super-selective Arterial Spin Labeling for quantitative flow territory mapping
AU - Lindner, Thomas
AU - Jansen, Olav
AU - Helle, Michael
N1 - Copyright © 2018 Elsevier Inc. All rights reserved.
PY - 2018/11
Y1 - 2018/11
N2 - Flow territory mapping using Arterial Spin Labeling (ASL) methods allows for deeper insights into the physiology of the brain. However, in most approaches assumptions of the labeling efficiency are used for calculation of brain perfusion which might result in inaccuracies. This becomes more important in super-selective ASL as the labeling focus could be positioned not exactly at the artery of interest. Therefore, measuring the labeling efficiency of the current scan seems important to obtain reliable results and to correct for potential errors. In this study, an optimized super-selective ASL tagging scheme is presented and the labeling efficiency is measured using quantitative phase-contrast angiography of the tagged artery and considering the volume of the supplied flow territory. The aim then is to evaluate the labeling efficiency of super-selective ASL and considering these values when shifting the labeling spot away from the artery of interest. The measured efficiency is compared to simulations performed for the optimized super-selective ASL approach. Considering the values of labeling efficiency after shifting the labeling focus, the values of cerebral blood flow still show accurate results despite the expected lower SNR up to an offset of 3 mm. Following this, to obtain accurate results for quantifying super-selective ASL perfusion images, measuring the labeling efficiency seems necessary to prevent false results.
AB - Flow territory mapping using Arterial Spin Labeling (ASL) methods allows for deeper insights into the physiology of the brain. However, in most approaches assumptions of the labeling efficiency are used for calculation of brain perfusion which might result in inaccuracies. This becomes more important in super-selective ASL as the labeling focus could be positioned not exactly at the artery of interest. Therefore, measuring the labeling efficiency of the current scan seems important to obtain reliable results and to correct for potential errors. In this study, an optimized super-selective ASL tagging scheme is presented and the labeling efficiency is measured using quantitative phase-contrast angiography of the tagged artery and considering the volume of the supplied flow territory. The aim then is to evaluate the labeling efficiency of super-selective ASL and considering these values when shifting the labeling spot away from the artery of interest. The measured efficiency is compared to simulations performed for the optimized super-selective ASL approach. Considering the values of labeling efficiency after shifting the labeling focus, the values of cerebral blood flow still show accurate results despite the expected lower SNR up to an offset of 3 mm. Following this, to obtain accurate results for quantifying super-selective ASL perfusion images, measuring the labeling efficiency seems necessary to prevent false results.
KW - Adult
KW - Arteries/diagnostic imaging
KW - Blood Flow Velocity
KW - Brain/diagnostic imaging
KW - Cerebrovascular Circulation
KW - Computer Simulation
KW - Female
KW - Humans
KW - Magnetic Resonance Angiography/methods
KW - Male
KW - Perfusion
KW - Spin Labels
KW - Young Adult
U2 - 10.1016/j.mri.2018.06.020
DO - 10.1016/j.mri.2018.06.020
M3 - SCORING: Journal article
C2 - 29966693
VL - 53
SP - 14
EP - 19
JO - MAGN RESON IMAGING
JF - MAGN RESON IMAGING
SN - 0730-725X
ER -
