16 April 2012 Comparison of TTP and Tmax estimation techniques in perfusion-weighted MR datasets for tissue-at-risk definition
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Acute stroke is a major cause for death and disability among adults in the western hemisphere. Time-resolved perfusion-weighted (PWI) and diffusion-weighted (DWI) MR datasets are typically used for the estimation of tissue-at-risk, which is an important variable for acute stroke therapy decision-making. Although several parameters, which can be estimated based on PWI concentration curves, have been proposed for tissue-at-risk definition in the past, the time-to-peak (TTP) or time-to-max (Tmax) parameter is used most frequently in recent trials. Unfortunately, there is no clear consensus which method should be used for estimation of Tmax or TTP maps. Consequently, tissue-at-risk estimations and following treatment decision might vary considerably with the method used. In this work, 5 PWI datasets of acute stroke patients were used to calculate TTP or Tmax maps using 10 different estimation techniques. The resulting maps were segmented using a typical threshold of +4s and the corresponding PWI-lesions were calculated. The first results suggest that the TTP or Tmax method used has a major impact on the resulting tissue-at-risk volume. Numerically, the calculated volumes differed up to a factor of 3. In general, the deconvolution-based Tmax techniques estimate the ischemic penumbra rather smaller compared to direct TTP based techniques. In conclusion, the comparison of different methods for TTP or Tmax estimation revealed high variations regarding the resulting tissue-at-risk volume, which might lead to different therapy decisions. Therefore, a consensus how TTP or Tmax maps should be calculated seems necessary.
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Nils Daniel Forkert, Philipp Kaesemann, Jens Fiehler, Götz Thomalla, "Comparison of TTP and Tmax estimation techniques in perfusion-weighted MR datasets for tissue-at-risk definition", Proc. SPIE 8317, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging, 83171R (16 April 2012); doi: 10.1117/12.911017; https://doi.org/10.1117/12.911017

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Diffusion weighted imaging



Brain mapping

Data modeling

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