Simultaneous Measurement of Regional Cerebral Blood Flow by Perfusion CT and Stable Xenon CT: A Validation Study
Max Wintermarka,
Jean-Philippe Thirana,
Philippe Maedera,
Pierre Schnydera and
Reto Meuli
,a
a From the Department of Diagnostic and Interventional Radiology (M.W., R.M., P.S., P.M.), University Hospital, and the Signal Processing Laboratory (J.-P.T.), Swiss Federal Institute of Technology, Lausanne, Switzerland.
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FIG 1. Images from the case of a 53-year-old woman with Moya Moya syndrome.
A, Ten-millimeter cerebral CT section shows encephalomalacia at the site of an excised right temporoparietal arteriovenous malformation.
B, Anteroposterior angiographic view displays Moya networks (arrows) due to the occlusion of the right MCA.
C, Perfusion CT studies led to a regional cerebral blood volume map (cc/100 g), resulting from a quantitative estimation of the partial volume averaging effect at each pixel. The cerebral blood volume map is normal, except in the resected area.
D, Mean transit time map inferred from a deconvolution operation. The mean transit time is abnormally prolonged in the right cerebral hemisphere.
E, Regional CBF map (cc/[100 g x min]), with the CBF at each pixel resulting from division of cerebral blood volume by relating mean transit time. CBF is abnormally lowered ipsilaterally, especially in the resected area.
F, Corresponding stable xenon CT scan is closely related to the CBF map shown in panel E.
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FIG 2. CBF maps of the same patient shown in figure 1 show perfusion CT studies of CBF (cc/[100 g x min]). The three CBF maps agree and show a 20% decrease in the left CBF. Cerebral regions with normal (blue ROI) and lowered (green ROI) CBF values are adequately displayed on the three CBF maps. With the LMS perfusion CT software, pixels with ACA and MCA branches (red ROI) show discretely increased CBF values, whereas they are filtered away with the SVD perfusion CT software. SVD and LMS perfusion CT software differ regarding the detection thresholds of vessels and spatial filtering for the final display.
A, Stable xenon CT scan.
B, SVD method.
C, LMS method
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FIG 3. Scatter plots between stable xenon CT and SVD or LMS deconvolution perfusion CT values of CBF (cc/[100 g x min]).
A, Image obtained using the SVD method. Healthy cerebral regions without ACA and MCA branches show linear regression with strong correlation and slopes close to unity.
B, Image obtained using the SVD method. In healthy cerebral regions with ACA and MCA branches, perfusion CT values of CBF tend to exceed stable xenon CT values.
C, Image obtained using the SVD method. Pathologically abnormal cerebral regions show linear regression with strong correlation and slopes close to unity.
D, Image obtained using the LMS method. Healthy cerebral regions without ACA and MCA branches show linear regression with strong correlation and slopes close to unity.
E, Image obtained using the LMS method. In healthy cerebral regions with ACA and MCA branches, perfusion CT values of CBF tend to exceed stable xenon CT values.
F, Image obtained using the LMS method. Pathologically abnormal cerebral regions show linear regression with strong correlation and slopes close to unity
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FIG 4. Images from the case of a 57-year-old woman with a diplopia while looking leftward.
A, Lateral angiographic view shows a left giant internal carotid artery aneurysm (arrow) at the C5 segment between the carotid siphon and the intrapetrous carotid artery. Diplopia related to compression of the left VIth cranial nerve by this aneurysm.
B, Analysis of the time-concentration curves in the ACA (circles) and in both MCA (crosses and squares) shows a cerebral flow alteration at the beginning of the left MCA (squares), downward of the aneurysm, featuring a delayed time-to-peak within this artery.
C, Stable xenon CT values of CBF.
D, Choice of the ACA as the reference artery results in a CBF map (cc/[100 g x min]) with underestimated CBF on the left side, compared with the stable xenon CT values of CBF.
E, Deconvolution of parenchymal time-concentration curves of each cerebral hemisphere by related profiles in ipsilateral MCA avoids such a pitfall
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