Chen Yasheng, Ying Chunwei, Binkley Michael M, Juttukonda Meher R, Flores Shaney, Laforest Richard, Benzinger Tammie L S, An Hongyu
Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.
Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA.
Magn Reson Med. 2021 Jul;86(1):499-513. doi: 10.1002/mrm.28689. Epub 2021 Feb 8.
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 R , is associated with CT Hounsfield unit in bone and soft tissues in the brain, we propose a deep learning T -enhanced selection of linear attenuation coefficients (DL-TESLA) method to incorporate quantitative R for PET/MR AC and evaluate its accuracy and longitudinal test-retest repeatability in brain PET/MR imaging.
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 T -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.
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.
DL-TESLA demonstrates excellent PET/MR AC accuracy and test-retest repeatability.
现有的正电子发射断层扫描/磁共振成像（PET/MR）衰减校正（AC）的准确性受到磁共振信号与组织电子密度之间缺乏相关性的限制。基于我们的发现，即纵向弛豫率（R1）与脑内骨骼和软组织中的CT亨氏单位相关，我们提出了一种深度学习T1增强线性衰减系数选择（DL-TESLA）方法，将定量R1纳入PET/MR AC，并评估其在脑PET/MR成像中的准确性和纵向重测重复性。
DL-TESLA使用三维残差U-Net（ResUNet）进行伪CT（pCT）估计。我们将174名参与者的DL-TESLA的PET AC准确性与其他3种采用类似三维ResUNet结构但分别使用UTE、Dixon或T1-MPRAGE作为输入的方法进行了比较。利用另外23名参与者的重复扫描图像，比较了使用DL-TESLA或CT进行AC重建的PET图像之间的重测差异和标准化摄取值比率（SUVR）的受试者内变异系数。
DL-TESLA具有以下优势：（1）pCT中的平均绝对误差显著更低；（2）在骨骼和空气区域的骰子系数最高；（3）全脑和各个脑区的PET相对绝对误差显著更低；（4）PET相对误差在±3%和±5%范围内的体素百分比最高；（5）大脑和平均皮质（MC）区域的SUVR重测差异与CT相似；（6）大脑和MC区域的受试者内变异系数与CT相似。
DL-TESLA在PET/MR AC中显示出优异的准确性和重测重复性。
