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J Comput Assist Tomogr. 2013 Mar-Apr;37(2):222-32. doi: 10.1097/RCT.0b013e318280a465.

Assessment of tracer delay effect in whole-brain computed tomography perfusion: results for selected regions of interest in middle cerebral artery acute ischemic strokes.

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  • 1Department of Research and Education, Tier 7 Research and Development, Portland, OR 97229, USA.



The objective of this study was to compare the variability of computed tomography perfusion (CTP) results in identical data sets of middle cerebral artery (MCA) acute ischemic stroke (AIS) generated by standard singular value decomposition (sSVD) deconvolution and tracer delay-insensitive singular value decomposition (SVD+) algorithm analyses.


Whole-brain 320-detector-row CTP data sets from 9 unilateral MCA AIS cases and 9 controls were retrospectively analyzed. Computed tomography perfusion values for the combined core/penumbra, contralateral hemispheres and arterial territories were measured and compared with literature values. Simple linear regression models are provided to predict corresponding SVD+ value and sSVD CTP values.


In the core/penumbra, sSVD generated lower cerebral blood flow (CBF) values, higher mean transit time (MTT) values, and a broader range of CBF and MTT values as compared with SVD+. Mean transit time value differences between the core/penumbra and contralateral hemispheres were statistically significant using sSVD, whereas those of SVD+ were not. Goodness of fit between algorithms for the core/penumbra was lower for CBF (0.483) and MTT (0.494), as compared with time to peak (0.891) and cerebral blood volume (0.997).


In this study using identical source data for patients with MCA AIS, use of either sSVD or SVD+ analyses created statistically significant differences in the CTP value results. Tracer delay-sensitive and -insensitive algorithms impact CTP results in AIS and controls, highlighting the need to pursue additional studies that assess the variability, accuracy, and clinical implications of CTP results generated when using heterogeneous deconvolution algorithms.

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