Shiri Isaac, Arabi Hossein, Geramifar Parham, Hajianfar Ghasem, Ghafarian Pardis, Rahmim Arman, Ay Mohammad Reza, Zaidi Habib
Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Eur J Nucl Med Mol Imaging. 2020 Oct;47(11):2533-2548. doi: 10.1007/s00259-020-04852-5. Epub 2020 May 15.
We demonstrate the feasibility of direct generation of attenuation and scatter-corrected images from uncorrected images (PET-nonASC) using deep residual networks in whole-body F-FDG PET imaging.
Two- and three-dimensional deep residual networks using 2D successive slices (DL-2DS), 3D slices (DL-3DS) and 3D patches (DL-3DP) as input were constructed to perform joint attenuation and scatter correction on uncorrected whole-body images in an end-to-end fashion. We included 1150 clinical whole-body F-FDG PET/CT studies, among which 900, 100 and 150 patients were randomly partitioned into training, validation and independent validation sets, respectively. The images generated by the proposed approach were assessed using various evaluation metrics, including the root-mean-squared-error (RMSE) and absolute relative error (ARE %) using CT-based attenuation and scatter-corrected (CTAC) PET images as reference. PET image quantification variability was also assessed through voxel-wise standardized uptake value (SUV) bias calculation in different regions of the body (head, neck, chest, liver-lung, abdomen and pelvis).
Our proposed attenuation and scatter correction (Deep-JASC) algorithm provided good image quality, comparable with those produced by CTAC. Across the 150 patients of the independent external validation set, the voxel-wise REs (%) were - 1.72 ± 4.22%, 3.75 ± 6.91% and - 3.08 ± 5.64 for DL-2DS, DL-3DS and DL-3DP, respectively. Overall, the DL-2DS approach led to superior performance compared with the other two 3D approaches. The brain and neck regions had the highest and lowest RMSE values between Deep-JASC and CTAC images, respectively. However, the largest ARE was observed in the chest (15.16 ± 3.96%) and liver/lung (11.18 ± 3.23%) regions for DL-2DS. DL-3DS and DL-3DP performed slightly better in the chest region, leading to AREs of 11.16 ± 3.42% and 11.69 ± 2.71%, respectively (p value < 0.05). The joint histogram analysis resulted in correlation coefficients of 0.985, 0.980 and 0.981 for DL-2DS, DL-3DS and DL-3DP approaches, respectively.
This work demonstrated the feasibility of direct attenuation and scatter correction of whole-body F-FDG PET images using emission-only data via a deep residual network. The proposed approach achieved accurate attenuation and scatter correction without the need for anatomical images, such as CT and MRI. The technique is applicable in a clinical setting on standalone PET or PET/MRI systems. Nevertheless, Deep-JASC showing promising quantitative accuracy, vulnerability to noise was observed, leading to pseudo hot/cold spots and/or poor organ boundary definition in the resulting PET images.
我们展示了在全身F-FDG PET成像中使用深度残差网络直接从未校正图像（PET-nonASC）生成衰减和散射校正图像的可行性。
构建了以二维连续切片（DL-2DS）、三维切片（DL-3DS）和三维小块（DL-3DP）为输入的二维和三维深度残差网络，以端到端的方式对未校正的全身图像进行联合衰减和散射校正。我们纳入了1150例临床全身F-FDG PET/CT研究，其中900例、100例和150例患者分别被随机分为训练集、验证集和独立验证集。使用各种评估指标对所提出方法生成的图像进行评估，包括均方根误差（RMSE）和绝对相对误差（ARE%），以基于CT的衰减和散射校正（CTAC）PET图像作为参考。还通过在身体不同区域（头部、颈部、胸部、肝肺、腹部和骨盆）进行体素级标准化摄取值（SUV）偏差计算来评估PET图像定量变异性。
我们提出的衰减和散射校正（Deep-JASC）算法提供了良好的图像质量，与CTAC生成的图像相当。在独立外部验证集的150例患者中，DL-2DS、DL-3DS和DL-3DP的体素级相对误差（%）分别为-1.72±4.22%、3.75±6.91%和-3.08±5.64%。总体而言，与其他两种三维方法相比，DL-2DS方法表现更优。Deep-JASC和CTAC图像之间，大脑和颈部区域的RMSE值分别最高和最低。然而，DL-2DS在胸部（15.16±3.96%）和肝/肺（11.18±3.23%）区域观察到最大的ARE。DL-3DS和DL-3DP在胸部区域表现稍好，ARE分别为11.16±3.42%和11.69±2.71%（p值<0.05）。联合直方图分析得出DL-2DS、DL-3DS和DL-3DP方法的相关系数分别为0.985、0.980和0.981。
这项工作证明了使用仅发射数据通过深度残差网络对全身F-FDG PET图像进行直接衰减和散射校正的可行性。所提出的方法无需CT和MRI等解剖图像即可实现准确的衰减和散射校正。该技术适用于独立PET或PET/MRI系统的临床环境。尽管如此，Deep-JASC显示出有前景的定量准确性，但观察到其对噪声敏感，导致所得PET图像中出现伪热点/冷点和/或器官边界定义不佳。
