Abstract
The arterial concentration of the radiopharmaceutical \({}^{18}\text {F}\)-choline is needed to estimate its absorption by tumors and other tissues. The blood concentration of \({}^{18}\text {F}\)-choline changes as it interacts with tissues, and so it is represented as a function with respect to time, the so-called Input Function (IF). In this paper, we present the estimation of an arterial whole-blood Image-Derived Input Function (IDIF) from the PET image, needed to model its absorption. The sagittal and transverse brain venous sinuses are automatically segmented based on the top-hat morphological transform. Such segmentation provides an estimation of the venous whole-blood IDIF. It is then corrected to obtain the arterial whole-blood IDIF by relating the amount of radioactivity material entering the brain region with the amount leaving it and the amount remaining. We compare the automatic venous whole-blood IDIF with a whole-blood venous IDIF from a region manually segmented. Also, we compare the automatic arterial whole-blood IDIF with the arterial IF obtained with serial blood samples on the radial artery. Quantitative measures indicate the overall accuracy of the estimation.
This work was supported by the project 2015 PI15-01653 of the Carlos III Health Institute and by the project TIN 2016-75404-P AEI/FEDER.
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González, C., Bibiloni, P., González-Hidalgo, M., Mir, A., Rubí, S. (2019). Automatic Image-Derived Estimation of the Arterial Whole-Blood Input Function from Dynamic Cerebral PET with \(^{18}\)F-Choline. In: Riaño, D., Wilk, S., ten Teije, A. (eds) Artificial Intelligence in Medicine. AIME 2019. Lecture Notes in Computer Science(), vol 11526. Springer, Cham. https://doi.org/10.1007/978-3-030-21642-9_43
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DOI: https://doi.org/10.1007/978-3-030-21642-9_43
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