Magnetic Resonance-Based Attenuation Correction for PET/MR Hybrid Imaging Using Continuous Valued Attenuation Maps

Objectives: Attenuation correction of positron emission tomographic (PET) data is critical in providing accurate and quantitative PET volumes. Deriving an attenuation map (mu-map) from magnetic resonance (MR) volumes is a challenge in PET/MR hybrid imaging. The difficulty lies in differentiating cortical bone from air from standard MR sequences because both these classes yield little to no MR signal and thus shows no distinguishable information. The objective of this contribution is 2-fold: (1) to generate and evaluate a continuous valued computed tomography (CT)-like attenuation map (mu-map) with continuous density values from dedicated MR sequences and (2) to compare its PET quantification accuracy with respect to a CT-based attenuation map as the criterion standard and other segmentation-based attenuation maps for studies of the head. Materials and Methods: Three-dimensional Dixon-volume interpolated breath-hold examination and ultrashort echo time sequences were acquired for each patient on a Siemens 3-T Biograph mMR PET/MR hybrid system and the corresponding patient CT on a Siemens Biograph 64. A pseudo-CT training was done using the epsilon-insensitive support vector regression (epsilon-SVR) technique on 5 patients who had CT/MR/PET triplets, and the generated model was evaluated on 5 additional patients who were not included in the training process. Four mu-maps were compared, and 3 of them derived from CT: scaled CT (mu-map(CT)), 3-class segmented CT without cortical bone (mu-map(nobone)), 4-class segmented CT with cortical bone (mu-map(bone)), and 1 from MR sequences via epsilon-SVR technique previously mentioned (ie, MR predicted [mu-map(MR)]). Positron emission tomographic volumes with each of the previously mentioned mu-maps were reconstructed, and relative difference images were calculated with respect to mu-map(CT) as the criterion standard. Results: For PET quantification, the proposed method yields a mean (SD) absolute error of 2.40% (3.69%) and 2.16% (1.77%) for the complete brain and the regions close to the cortical bone, respectively. In contrast, PET using mu-map(nobone) yielded 10.15% (3.31%) and 11.03 (2.26%) for the same, although PET using mu-map(bone) resulted in errors of 3.96% (3.71%) and 4.22% (3.91%). Furthermore, it is shown that the model can be extended to predict pseudo-CTs for other anatomical regions on the basis of only MR information. Conclusions: In this study, the generation of continuous valued attenuation maps from MR sequences is demonstrated and its effect on PET quantification is evaluated in comparison with segmentation-based mu-maps. A less-than-2-minute acquisition time makes the proposed approach promising for a clinical application for studies of the head. However, further experiments are required to validate and evaluate this technique for attenuation correction in other regions of the body.