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Radiology. 2013 Apr;267(1):201-11. doi: 10.1148/radiol.12112618. Epub 2012 Dec 6.

Accuracy and reliability assessment of CT and MR perfusion analysis software using a digital phantom.

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Division of Ultra-High Field MRI and Department of Radiology, Iwate Medical University, 19-1 Uchimaru, Morioka 020-8505, Japan.



To design a digital phantom data set for computed tomography (CT) perfusion and perfusion-weighted imaging on the basis of the widely accepted tracer kinetic theory in which the true values of cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), and tracer arrival delay are known and to evaluate the accuracy and reliability of postprocessing programs using this digital phantom.


A phantom data set was created by generating concentration-time curves reflecting true values for CBF (2.5-87.5 mL/100 g per minute), CBV (1.0-5.0 mL/100 g), MTT (3.4-24 seconds), and tracer delays (0-3.0 seconds). These curves were embedded in human brain images. The data were analyzed by using 13 algorithms each for CT and magnetic resonance (MR), including five commercial vendors and five academic programs. Accuracy was assessed by using the Pearson correlation coefficient (r) for true values. Delay-, MTT-, or CBV-dependent errors and correlations between time to maximum of residue function (Tmax) were also evaluated.


In CT, CBV was generally well reproduced (r > 0.9 in 12 algorithms), but not CBF and MTT (r > 0.9 in seven and four algorithms, respectively). In MR, good correlation (r > 0.9) was observed in one-half of commercial programs, while all academic algorithms showed good correlations for all parameters. Most algorithms had delay-dependent errors, especially for commercial software, as well as CBV dependency for CBF or MTT calculation and MTT dependency for CBV calculation. Correlation was good in Tmax except for one algorithm.


The digital phantom readily evaluated the accuracy and characteristics of the CT and MR perfusion analysis software. All commercial programs had delay-induced errors and/or insufficient correlations with true values, while academic programs for MR showed good correlations with true values.


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