Whole-body attenuation correction (AC) is still challenging in combined PET/MR scanners. We describe Dixon-VIBE Deep Learning (DIVIDE), a deep learning network architecture that allows synthesizing pelvis pseudo-CT maps based only on the standard Dixon volumetric interpolated breath-hold examination (Dixon-VIBE) images currently acquired for AC in commercial Siemens scanners. We propose a network that performs a mapping between the four 2D Dixon MRI images (water, fat, in- and out-of-phase) and their corresponding 2D CT image. In contrast to previous methods, we used transposed convolutions to learn the up-sampling parameters, whole 2D slices to provide context information and pretrained the network with brain images. 28 datasets obtained from 19 patients who underwent PET/CT and PET/MR examinations were used to evaluate the proposed method. We assessed the accuracy of the µ-maps and reconstructed PET images by performing voxel- and region-based analysis comparing the standardize uptake values (SUVs, in g/mL) obtained after AC using the Dixon-VIBE (PETDixon), DIVIDE (PETDIVIDE) and CT-based (PETCT) methods. Additionally, the bias in quantification was estimated in synthetic lesions defined in the prostate, rectum, pelvis and spine. Absolute mean relative change (RC) values relative to CT AC were lower than 2% on average for the DIVIDE method in every region of interest (ROI) except for bone tissue where it was lower than 4% and 6.75 times smaller than the RC of the Dixon method. There was an excellent voxel-by-voxel correlation between PETCT and PETDIVIDE (R2=0.9998, p<0.01). The Bland-Altman plot between PETCT and PETDIVIDE showed that the average of the differences and the variability were lower (mean PETCT-PETDIVIDE SUV=0.0003, σ PETCT-PETDIVIDE=0.0094, CI0.95=[-0.0180,0.0188]) than the average of differences between PETCT and PETDixon (mean PETCT-PETDixon SUV=0.0006, σ PETCT-PETDixo = 0.0264, CI0.95=[-0.0510,0.0524]). Statistically significant changes in PET data quantification were observed between the two methods in the synthetic lesions with the largest improvement in femur and spine lesions. The DIVIDE method can accurately synthesize a pelvis pseudo-CT from standard Dixon-VIBE images, allowing for accurate AC in combined PET/MR scanners. Additionally, our implementation allows rapid pseudo-CT synthesis, making it suitable for routine applications and, even allowing the retrospective processing of Dixon-VIBE data.

Purpose The purpose of this study is to safely acquire the first human head images at 10.5T. Methods To ensure safety of subjects, we validated the electromagnetic simulation model of our coil. We obtained quantitative agreement between simulated and experimental B1+ and specific absorption rate (SAR). Using the validated coil model, we calculated radiofrequency power levels to safely image human subjects. We conducted all experiments and imaging sessions in a controlled radiofrequency safety lab and the whole‐body 10.5T scanner in the Center for Magnetic Resonance Research. Results Quantitative agreement between the simulated and experimental results was obtained including S‐parameters, B1+ maps, and SAR. We calculated peak 10 g average SAR using 4 different realistic human body models for a quadrature excitation and demonstrated that the peak 10 g SAR variation between subjects was less than 30%. We calculated safe power limits based on this set and used those limits to acquire T2‐ and T2∗‐weighted images of human subjects at 10.5T. Conclusions In this study, we acquired the first in vivo human head images at 10.5T using an 8‐channel transmit/receive coil. We implemented and expanded a previously proposed workflow to validate the electromagnetic simulation model of the 8‐channel transmit/receive coil. Using the validated coil model, we calculated radiofrequency power levels to safely image human subjects.