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Lean body mass correction of standardized uptake value in simultaneous whole-body positron emission tomography and magnetic resonance imaging


Schulz,  Jessica
Department of Nuclear Medicine, University of Leipzig, Germany;
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Jochimsen, T. H., Schulz, J., Busse, H., Werner, P., Schaudinn, A., Zeisig, V., et al. (2015). Lean body mass correction of standardized uptake value in simultaneous whole-body positron emission tomography and magnetic resonance imaging. Physics in Medicine and Biology, 60(12), 4651-4664. doi:10.1088/0031-9155/60/12/4651.

This study explores the possibility of using simultaneous positron emission tomography—magnetic resonance imaging (PET-MRI) to estimate the lean body mass (LBM) in order to obtain a standardized uptake value (SUV) which is less dependent on the patients' adiposity. This approach is compared to (1) the commonly-used method based on a predictive equation for LBM, and (2) to using an LBM derived from PET-CT data. It is hypothesized that an MRI-based correction of SUV provides a robust method due to the high soft-tissue contrast of MRI. A straightforward approach to calculate an MRI-derived LBM is presented. It is based on the fat and water images computed from the two-point Dixon MRI primarily used for attenuation correction in PET-MRI. From these images, a water fraction was obtained for each voxel. Averaging over the whole body yielded the weight-normalized LBM. Performance of the new approach in terms of reducing variations of 18F-Fludeoxyglucose SUVs in brain and liver across 19 subjects was compared with results using predictive methods and PET-CT data to estimate the LBM. The MRI-based method reduced the coefficient of variation of SUVs in the brain by 41  ± 10% which is comparable to the reduction by the PET-CT method (35  ± 10%). The reduction of the predictive LBM method was 29  ± 8%. In the liver, the reduction was less clear, presumably due to other sources of variation. In conclusion, employing the Dixon data in simultaneous PET-MRI for calculation of lean body mass provides a brain SUV which is less dependent on patient adiposity. The reduced dependency is comparable to that obtained by CT and predictive equations. Therefore, it is more comparable across patients. The technique does not impose an overhead in measurement time and is straightforward to implement.