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Is quantification of bolus tracking MRI reliable without deconvolution?

✍ Scribed by Joanna E. Perthen; Fernando Calamante; David G. Gadian; Alan Connelly


Publisher
John Wiley and Sons
Year
2001
Tongue
English
Weight
156 KB
Volume
47
Category
Article
ISSN
0740-3194

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✦ Synopsis


Abstract

Bolus tracking data obtained with paramagnetic intravascular tracers are commonly analyzed and quantified by the direct measurement of properties of the tissue concentration‐time curve (e.g., time to peak (TTP)). The measurement of these “summary parameters” is used as an accessible alternative approach to the complex deconvolution procedure, and provides indirect measures of perfusion. However, summary parameters do not take into account differences in arterial input functions (AIFs) or residue functions (R(t)) between patients or studies. Simulations were performed to assess the variability of summary parameters over a realistic range of AIFs and for differing R(t), to establish whether they can be used as reliable measures of tissue perfusion status. Results showed that the value of each summary parameter investigated is highly dependent upon both the AIF and R(t). The referencing of summary parameters to their corresponding value in the AIF or in normal tissue is a method commonly used to normalize results, but this approach did not lead to any measures that were independent of both the AIF and R(t) in this study. The results presented here show that the use of summary parameters requires considerable caution, since tissue or patient types can easily be incorrectly classified due to the effect of variations in patient AIF and R(t). Magn Reson Med 47:61–67, 2002. © 2002 Wiley‐Liss, Inc.


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Quantification of bolus-tracking MRI: Im
✍ Fernando Calamante; David G. Gadian; Alan Connelly 📂 Article 📅 2003 🏛 John Wiley and Sons 🌐 English ⚖ 306 KB

## Abstract Quantification of cerebral blood flow (CBF) and the tissue residue function (__R__) using bolus‐tracking MRI requires deconvolution of the arterial input function (AIF). Currently, the most commonly used deconvolution method is singular value decomposition (SVD), which has been shown to