Table 1.
Definitions of parameters and assumed values for ASL signal simulation.
Table 2.
Patient characteristics, clinical data, and MRI findings.
Fig 1.
ROI selection for ROI-based comparison between PET-CBF and ASL-CBF maps.
a) PET-CBF map, b) ASL-CBF map. ROI selections for 12 gray matter and 4 white matter areas were exactly the same for PET-CBF and ASL-CBF maps.
Fig 2.
ASL signal simulation and ATT dependency.
a) Changes of the ASL signal showing passage of the labeled spins through brain parenchyma with various model assumptions. Signals are calculated based on equations (S1)–(S3) and (T1)-(T5) in S1 File. Signal dynamics are different depending on the model assumptions, as shown in the graph explanatory examples. b) Changes of the ASL signal depending on a fixed value of ATT showing different dependent patterns according to the model assumptions shown in graph explanatory examples. Signals are calculated based on equations (S1)–(S5) and (T1)-(T8) in S1 File.
Fig 3.
The ASL signal changes for model parameters.
a) ASL signal dependency to T1e. The single-compartment model is constant along with T1e value, since labeled spins are retained in the vascular space and correspond to the relaxation of T1b, while the other model assumption with venous outflow has an increasing trend along with the increase of T1e. b) ASL signal dependency to CBV. ASL signal has an increasing trend along with CBV increase. Changes are quite stable in the normal brain CBV range (2.0 to 5.0 ml/100 g) (see text). c) ASL signal dependency to PS. PS is also constant despite the wide range of 20 to 200 ml/min/g, which adequately covers the physiological cerebral blood flow range.
Fig 4.
Comparison between MR and PET in a typical case of left ICA obstruction.
a) Comparison between PET-CBF and ASL-CBF. From top to bottom rows, PET-CBF, ASL-CBF, ASL-ATT (transit time map) are calculated from the multiple PLD data and ASL-CBF calculated from single PLD (1.5 s) data, based on the simple single-compartment model (see text). The decreased signal in the right frontal cortex corresponds to cystic change after infarction. b) MRA revealing the complete obstruction of left ICA suggests the left MCA territory is fed through collaterals of A-Com and/or left IC-PC. c) 2D-plots of PET and ASL-CBF on pixel-by-pixel basis. The plotted CBF images through ventricle body level correspond to the third column images from the right side in the 1st and 2nd rows. Abscissa and ordinate axes represent PET and ASL CBF, respectively. Scale bars and units as indicated.
Fig 5.
Comparison between MR and PET in a typical case of left MCA severe stenosis.
a) Comparison between PET-CBF and ASL-CBF. From top to bottom rows, PET-CBF, ASL-CBF, ASL-ATT (transit time map) are calculated from the multiple PLD data and ASL-CBF calculated from single PLD (1.5s) data based on the simple single-compartment model (see text). b) MRA reveals that left MCA branches are less bright than those of the contralateral side. c) 2D-plots of PET and ASL-CBF on a pixel-by-pixel basis. The plotted CBF images through the ventricle body level correspond to third column images from the right side in 1st and 2nd rows. Abscissa and ordinate axes represent PET and ASL CBF, respectively.
Table 3.
The comparison between PET and ASL CBF on Bland-Altman plots.
Fig 6.
Linear regression analysis of PET-CBF and ASL-CBF.
The ROI values from affected and contralateral normal sides are plotted using a diamond shapes (◊) and crosses (x), respectively. a) 2D-plots of PET and ASL CBF using gray and white matter ROIs in both affected and contralateral cerebral hemispheres. The regression lines and the coefficient of determination (R2) are shown as insets on the graph. b) Bland-Altman plots of ROI-based CBF comparison between ASL-CBF and PET-CBF. Bias and mean ± 2 SD precision lines are drawn on the graph as thick lines and dashed lines, respectively. c) Extracted Bland-Altman plots of affected ROI of ASL-CBF data calculated from multi-PLD data. There is no significant regressed line. The regression lines and the coefficient of determination (R2) are shown as insets of the graph, but were not significant statistically.
Fig 7.
Linear regression analyses of PET-CBF and ASL-CBF.
ROI values from affected and contralateral normal sides are plotted as diamond shapes (◊) and crosses (x), respectively. a) 2D-plots between PET and ASL CBF using gray and white matter ROIs in both affected and contralateral cerebral hemispheres. The regression lines and the coefficient of determination (R2) are shown as insets of the graph. b) Bland-Altman plots of ROI-based CBF comparison between ASL-CBF and PET-CBF. Bias and mean ± 2 SD precision lines are drawn on the graph as thick lines and dashed lines, respectively. c) Extracted Bland-Altman plots of affected ROI of ASL-CBF data calculated from single PLD = 1.5 s data. A significant regressed line is obtained (p <0.05). The regression line and the coefficient of determination (R2) are shown as insets of the graph.
Fig 8.
Linear regression analyses of PET-CBF and ASL-CBF.
ROI values from affected and contralateral normal sides are plotted as diamond shapes (◊) and crosses (x), respectively. a) 2D-plots between PET and ASL CBF using gray and white matter ROIs in both affected and contralateral cerebral hemispheres. The regression lines and the coefficient of determination (R2) are shown as insets of the graph. b) Bland-Altman plots of ROI-based CBF comparison between ASL-CBF and PET-CBF. Bias and mean ± 2 SD precision lines are drawn on the graph as thick lines and dashed lines, respectively. c) Extracted Bland-Altman plots of affected ROI of ASL-CBF data calculated from single PLD = 2.0 s data. The regression lines and the coefficient of determination (R2) are shown as insets of the graph, but were not significant statistically.