Dixon-VIBE Deep Learning (DIVIDE) Pseudo-CT Synthesis for Pelvis PET/MR Attenuation Correction

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. Methods: 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. Results: 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-PETDixon = 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. Conclusion: 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.