Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details
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 a...
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| Format: | Article |
| Language: | English |
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Wiley
2011-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2011/467563 |
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| _version_ | 1832554676068810752 |
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| author | Andreas Fieselmann Markus Kowarschik Arundhuti Ganguly Joachim Hornegger Rebecca Fahrig |
| author_facet | Andreas Fieselmann Markus Kowarschik Arundhuti Ganguly Joachim Hornegger Rebecca Fahrig |
| author_sort | Andreas Fieselmann |
| collection | DOAJ |
| description | 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 physiological model that are necessary in order to apply it to measured data acquired with current CT and MR
scanners. |
| format | Article |
| id | doaj-art-fd608cb2493348dea23020a0e0fe2a99 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-fd608cb2493348dea23020a0e0fe2a992025-02-03T05:50:54ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962011-01-01201110.1155/2011/467563467563Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation DetailsAndreas Fieselmann0Markus Kowarschik1Arundhuti Ganguly2Joachim Hornegger3Rebecca Fahrig4Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nuremberg, Martensstraße 3, 91058 Erlangen, GermanySiemens AG, Healthcare Sector, Angiography & Interventional X-Ray Systems, Siemensstraße 1, 91301 Forchheim, GermanyDepartment of Radiology, Lucas MRS Center, Stanford University, 1201 Welch Road, Palo Alto, CA 94305, USAPattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nuremberg, Martensstraße 3, 91058 Erlangen, GermanyDepartment of Radiology, Lucas MRS Center, Stanford University, 1201 Welch Road, Palo Alto, CA 94305, USADeconvolution-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 physiological model that are necessary in order to apply it to measured data acquired with current CT and MR scanners.http://dx.doi.org/10.1155/2011/467563 |
| spellingShingle | Andreas Fieselmann Markus Kowarschik Arundhuti Ganguly Joachim Hornegger Rebecca Fahrig Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details International Journal of Biomedical Imaging |
| title | Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details |
| title_full | Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details |
| title_fullStr | Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details |
| title_full_unstemmed | Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details |
| title_short | Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details |
| title_sort | deconvolution based ct and mr brain perfusion measurement theoretical model revisited and practical implementation details |
| url | http://dx.doi.org/10.1155/2011/467563 |
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