Evaluation and Optimization of MR-Based Attenuation Correction Methods in Combined Brain PET/MR
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Combined PET/MR provides simultaneous molecular and functional information in an anatomical context with unique soft tissue contrast. However, PET/MR does not support direct derivation of attenuation maps of objects and tissues within the measured PET field-of-view. Valid attenuation maps are required for quantitative PET imaging, specifically for scientific brain studies. Therefore, several methods have been proposed for MR-based attenuation correction (MR-AC). Last year, we performed an evaluation of different MR-AC methods, including simple MR thresholding, atlas- and machine learning-based MR-AC. CT-based AC served as gold standard reference. RoIs from 2 anatomic brain atlases with different levels of detail were used for evaluation of correction accuracy. We now extend our evaluation of different MR-AC methods by using an enlarged dataset of 23 patients from the integrated BrainPET/MR (Siemens Healthcare). Further, we analyze options for improving the MR-AC performance in terms of speed and accuracy. Finally, we assess the impact of ignoring BrainPET positioning aids during the course of MR-AC. This extended study confirms the overall prediction accuracy evaluation results of the first evaluation in a larger patient population. Removing datasets affected by metal artifacts from the Atlas-Patch database helped to improve prediction accuracy, although the size of the database was reduced by one half. Significant improvement in prediction speed can be gained at a cost of only slightly reduced accuracy, while further optimizations are still possible.
| Author(s): | Mantlik, F. and Hofmann, M. and Bezrukov, I. and Schmidt, H. and Kolb, A. and Beyer, T. and Reimold, M. and Schölkopf, B. and Pichler, BJ. |
| Links: | |
| Volume: | 2011 |
| Number (issue): | MIC18.M-96 |
| Year: | 2011 |
| Month: | October |
| Day: | 0 |
| BibTeX Type: | Poster (poster) |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Event Name: | 2011 IEEE Nuclear Science Symposium, Medical Imaging Conference (NSS-MIC 2011) |
| Event Place: | Valencia, Spain |
BibTeX
@poster{MantlikHBSKBRSP2011,
title = {Evaluation and Optimization of MR-Based Attenuation Correction Methods in Combined Brain PET/MR},
abstract = {Combined PET/MR provides simultaneous molecular and functional information in an anatomical context with unique soft tissue contrast. However, PET/MR does not support direct derivation of attenuation maps of objects and tissues within the measured PET field-of-view. Valid attenuation maps are required for quantitative PET imaging, specifically for scientific brain studies. Therefore, several methods have been proposed for MR-based attenuation correction (MR-AC). Last year, we performed an evaluation of different MR-AC methods, including simple MR thresholding, atlas- and machine learning-based MR-AC. CT-based AC served as gold standard reference. RoIs from 2 anatomic brain atlases with different levels of detail were used for evaluation of correction accuracy. We now extend our evaluation of different MR-AC methods by using an enlarged dataset of 23 patients from the integrated BrainPET/MR (Siemens Healthcare). Further, we analyze options for improving the MR-AC performance in terms of speed and accuracy. Finally, we assess the impact of ignoring BrainPET positioning aids during the course of MR-AC. This extended study confirms the overall prediction accuracy evaluation results of the first evaluation in a larger patient population. Removing datasets affected by metal artifacts from the Atlas-Patch database helped to improve prediction accuracy, although the size of the database was reduced by one half. Significant improvement in prediction speed can be gained at a cost of only slightly reduced accuracy, while further optimizations are still possible. },
volume = {2011},
number = {MIC18.M-96},
month = oct,
year = {2011},
author = {Mantlik, F. and Hofmann, M. and Bezrukov, I. and Schmidt, H. and Kolb, A. and Beyer, T. and Reimold, M. and Sch{\"o}lkopf, B. and Pichler, BJ.},
month_numeric = {10}
}