Deep learning-based T1-enhanced selection of linear attenuation coefficients (DL-TESLA) for PET/MR attenuation correction in dementia neuroimaging.
PURPOSE
The accuracy of existing PET/MR attenuation correction (AC) has been limited by a lack of correlation between MR signal and tissue electron density. Based on our finding that longitudinal relaxation rate, or R1 , is associated with CT Hounsfield unit in bone and soft tissues in the brain, we propose a deep learning T1 -enhanced selection of linear attenuation coefficients (DL-TESLA) method to incorporate quantitative R1 for PET/MR AC and evaluate its accuracy and longitudinal test-retest repeatability in brain PET/MR imaging.


METHODS
DL-TESLA uses a 3D residual UNet (ResUNet) for pseudo-CT (pCT) estimation. With a total of 174 participants, we compared PET AC accuracy of DL-TESLA to 3 other methods adopting similar 3D ResUNet structures but using UTE  , or Dixon, or T1 -MPRAGE as input. With images from 23 additional participants repeatedly scanned, the test-retest differences and within-subject coefficient of variation of standardized uptake value ratios (SUVR) were compared between PET images reconstructed using either DL-TESLA or CT for AC.


RESULTS
DL-TESLA had (1) significantly lower mean absolute error in pCT, (2) the highest Dice coefficients in both bone and air, (3) significantly lower PET relative absolute error in whole brain and various brain regions, (4) the highest percentage of voxels with a PET relative error within both ±3% and ±5%, (5) similar to CT test-retest differences in SUVRs from the cerebrum and mean cortical (MC) region, and (6) similar to CT within-subject coefficient of variation in cerebrum and MC.


CONCLUSION
DL-TESLA demonstrates excellent PET/MR AC accuracy and test-retest repeatability.
['Chen|Yasheng|Y|', 'Ying|Chunwei|C|0000-0003-1060-3289', 'Binkley|Michael M|MM|0000-0002-7701-7615', 'Juttukonda|Meher R|MR|', 'Flores|Shaney|S|', 'Laforest|Richard|R|', 'Benzinger|Tammie L S|TLS|', 'An|Hongyu|H|0000-0001-6459-2269']
D000077321:Deep Learning D003704:Dementia D006801:Humans D007091:Image Processing, Computer-Assisted D008279:Magnetic Resonance Imaging D064847:Multimodal Imaging D059906:Neuroimaging D049268:Positron-Emission Tomography
PET/MR attenuation correction deep learning UTE Dixon MR/CT conversion