Evaluation and Optimization of MR-Based Attenuation Correction Methods in Combined Brain PET/MR

2011

Poster

ei

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.

@poster{MantlikHBSKBRSP2011,
  title = {Evaluation and Optimization of MR-Based Attenuation Correction Methods in Combined Brain PET/MR},
  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.},
  volume = {2011},
  number = {MIC18.M-96},
  month = oct,
  year = {2011},
  doi = {},
  month_numeric = {10}
}