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Generation of a Four-Class Attenuation Map for MRI-Based Attenuation Correction of PET Data in the Head Area Using a Novel Combination of STE/Dixon-MRI and FCM Clustering.

Molecular imaging and biology : MIB : the official publication of the Academy of Molecular Imaging (2015-04-29)
Parisa Khateri, Hamidreza Saligheh Rad, Amir Homayoun Jafari, Anahita Fathi Kazerooni, Afshin Akbarzadeh, Mohsen Shojae Moghadam, Arvin Aryan, Pardis Ghafarian, Mohammad Reza Ay
ABSTRACT

The aim of this study is to generate a four-class magnetic resonance imaging (MRI)-based attenuation map (μ-map) for attenuation correction of positron emission tomography (PET) data of the head area using a novel combination of short echo time (STE)/Dixon-MRI and a dedicated image segmentation method. MR images of the head area were acquired using STE and two-point Dixon sequences. μ-maps were derived from MRI images based on a fuzzy C-means (FCM) clustering method along with morphologic operations. Quantitative assessment was performed to evaluate generated MRI-based μ-maps compared to X-ray computed tomography (CT)-based μ-maps. The voxel-by-voxel comparison of MR-based and CT-based segmentation results yielded an average of more than 95 % for accuracy and specificity in the cortical bone, soft tissue, and air region. MRI-based μ-maps show a high correlation with those derived from CT scans (R (2) > 0.95). Results indicate that STE/Dixon-MRI data in combination with FCM-based segmentation yields precise MR-based μ-maps for PET attenuation correction in hybrid PET/MRI systems.

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Tantalum(V) ethoxide, 99.98% trace metals basis