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Electronics, Germany

Wagenknecht G.,Central Institute for Electronics | Kops E.R.,Institute of Neuroscience and Medicine 4 | Kaffanke J.,Institute of Neuroscience and Medicine 4 | Tellmann L.,Institute of Neuroscience and Medicine 4 | And 3 more authors.
IEEE Nuclear Science Symposium Conference Record | Year: 2010

Attenuation correction (AC) is an important prerequisite for quantitative brain PET in MR-BrainPET systems. The new knowledge-based method segments attenuation-differing head regions solely based on the routinely acquired T1-weighted MR data set of the patient's head. The original approach (O) was extended (E1-E3) with regard to the MR image quality at different bandwidth/voxel (130 HZ/voxel, 610 Hz/voxel) obtained at a 3T MR TimTrio system with BrainPET insert installed. Based on the Dice coefficient, the automatically obtained MR-based segmentation results for bone and soft tissue were compared with segmented CT data as gold standard modality data. So far, registered multi-modality data (MR, CT, PET) are available for one female volunteer F and two tumor patients T1, T2 with CT data of different image quality. Best results were obtained for BW130-E3 and BW610-E2. For F, the Dice coefficient for bone is up to 0.776 for BW130-E3 and up to 0.723 for BW610-E2 in the best part of the cranial region. The Dice coefficient for soft tissue is 0.867 for BW130-E3 and 0.868 for BW610-E2 in the whole data set used for AC. The SegMR-(SBA) and CT-based AC (CBA) were compared w.r.t. PET-based AC (PBA) for a HR PET device. AC with SBA yields very similar results to the gold standard CBA. © 2010 IEEE.


Hamo O.,Central Institute for Electronics | Nelles G.,Central Institute for Electronics | Wagenknecht G.,Central Institute for Electronics
CEUR Workshop Proceedings | Year: 2010

In the field of medical image processing, the evaluation of new algorithms is often a difficult task since real data sets do not allow a quantitative evaluation of the algorithms' properties and the correctness of results. Thus, a phantom design toolbox was developed to enable the generation of complex geometries appropriate to simulate anatomical structures as well as realistic image intensity properties and artifacts, such as noise and inhomogeneities. This paper describes the most important features of the new toolbox and shows sample phantoms generated so far.

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