Matrix computations (3rd ed.)
Deconvolution of images and spectra (2nd ed.)
Deconvolution of images and spectra (2nd ed.)
Discrete Inverse Problems: Insight and Algorithms
Discrete Inverse Problems: Insight and Algorithms
Low-noise dynamic reconstruction for X-ray tomographic perfusion studies using low sampling rates
Journal of Biomedical Imaging
Group-wise motion correction of brain perfusion images
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
| Hi-index | 0.00 |
Deconvolution-based analysis of CT and MR brain perfusion data is widely used in clinical practice and it is still a topic of ongoing research activities. In this paper, we present a comprehensive derivation and explanation of the underlying physiological model for intravascular tracer systems. We also discuss practical details that are needed to properly implement algorithms for perfusion analysis. Our description of the practical computer implementation is focused on the most frequently employed algebraic deconvolution methods based on the singular value decomposition. In particular, we further discuss the need for regularization in order to obtain physiologically reasonable results. We include an overview of relevant preprocessing steps and provide numerous references to the literature. We cover both CT and MR brain perfusion imaging in this paper because they share many common aspects. The combination of both the theoretical as well as the practical aspects of perfusion analysis explicitly emphasizes the simplifications to the underlying physiologicalmodel that are necessary in order to apply it to measured data acquired with current CT and MR scanners.