We present an approach for head MR-based attenuation correction (MR-AC) based on the Statistical Parametric Mapping (SPM8) software that combines segmentation- and atlas-based features to provide a robust technique to generate attenuation maps (µ-maps) from MR data in integrated PET/MR scanners. Methods Coregistered anatomical MR and CT images acquired in 15 glioblastoma subjects were used to generate the templates. The MR images from these subjects were first segmented into 6 tissue classes (gray and white matter, cerebro-spinal fluid, bone and soft tissue, and air), which were then non-rigidly coregistered using a diffeomorphic approach. A similar procedure was used to coregister the anatomical MR data for a new subject to the template. Finally, the CT-like images obtained by applying the inverse transformations were converted to linear attenuation coefficients (LACs) to be used for AC of PET data. The method was validated on sixteen new subjects with brain tumors (N=12) or mild cognitive impairment (N=4) who underwent CT and PET/MR scans. The µ-maps and corresponding reconstructed PET images were compared to those obtained using the gold standard CT-based approach and the Dixon-based method available on the Siemens Biograph mMR scanner. Relative change (RC) images were generated in each case and voxel- and region of interest (ROI)-based analyses were performed. Results The leave-one-out cross-validation analysis of the data from the 15 atlas-generation subjects showed small errors in brain LACs (RC=1.38%±4.52%) compared to the gold standard. Similar results (RC=1.86±4.06%) were obtained from the analysis of the atlas-validation datasets. The voxel- and ROI-based analysis of the corresponding reconstructed PET images revealed quantification errors of 3.87±5.0% and 2.74±2.28%, respectively. The Dixon-based method performed substantially worse (the mean RC values were 13.0±10.25% and 9.38±4.97%, respectively). Areas closer to skull showed the largest improvement. Conclusion We have presented an SPM8-based approach for deriving the head µ-map from MR data to be used for PET AC in integrated PET/MR scanners. Its implementation is straightforward and only requires the morphological data acquired with a single MR sequence. The method is very accurate and robust, combining the strengths of both segmentation- and atlas-based approaches while minimizing their drawbacks.

oncurrent acquisition of morphologic and functional imaging information for the diagnosis of neurologic disorders has fueled interest in simultaneous PET and MRI (PET/MRI) (1). PET/MRI allows spatial and temporal registration of the two imaging data sets; therefore, the information derived from one modality can be used to improve the other (2,3). For dementia evaluation, PET/ MRI enables a single combined imaging examination (4). Amyloid PET has become useful as an adjunct to the diagnosis of Alzheimer disease-amyloid plaque accumulation is a hallmark pathologic finding of Alzheimer disease and can precede the onset of frank dementia by 10 to 20 years (5)-as well as for screening younger populations at high risk of Alzheimer disease in clinical trials of Alzheimer disease pharmaceuticals (6,7). PET image quality depends on collecting a sufficient number of coincidence events from annihilation photon pairs. However, the injection of radiotracers will subject patients who are scanned to radiation dose; motion during the prolonged data acquisition period results in a misplacement of the events in space, leading to inaccuracies in PET radiotracer uptake quantification (8,9). Thus, reducing collected PET counts either through radiotracer dose reduction (the focus of this work) or shortening scan time (ie, limiting the time for possible motion) while maintaining image quality would be valuable for increased use of PET/MRI. Convolutional neural networks (CNNs) have the ability to learn translation-invariant representations of objects (10). This has led to remarkable performance increases for image identification (11) and generation (12-14).