Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China.
Department of Radiology, University of Massachusetts Medical School, Worcester, MA, USA.
Eur J Nucl Med Mol Imaging. 2021 Oct;48(11):3457-3468. doi: 10.1007/s00259-021-05319-x. Epub 2021 Apr 2.
Reconstructed transaxial cardiac SPECT images need to be reoriented into standard short-axis slices for subsequent accurate processing and analysis. We proposed a novel deep-learning-based method for fully automatic reorientation of cardiac SPECT images and evaluated its performance on data from two clinical centers.
We used a convolutional neural network to predict the 6 rigid-body transformation parameters and a spatial transformation network was then implemented to apply these parameters on the input images for image reorientation. A novel compound loss function which balanced the parametric similarity and penalized discrepancy of the prediction and training dataset was utilized in the training stage. Data from a set of 322 patients underwent data augmentation to 6440 groups of images for the network training, and a dataset of 52 patients from the same center and 23 patients from another center were used for evaluation. Similarity of the 6 parameters was analyzed between the proposed and the manual methods. Polar maps were generated from the output images and the averaged count values of the 17 segments were computed from polar maps to evaluate the quantitative accuracy of the proposed method.
All the testing patients achieved automatic reorientation successfully. Linear regression results showed the 6 predicted rigid parameters and the average count value of the 17 segments having good agreement with the reference manual method. No significant difference by paired t-test was noticed between the rigid parameters of our method and the manual method (p > 0.05). Average count values of the 17 segments show a smaller difference of the proposed and manual methods than those between the existing and manual methods.
The results strongly indicate the feasibility of our method in accurate automatic cardiac SPECT reorientation. This deep-learning-based reorientation method has great promise for clinical application and warrants further investigation.
重建的心脏横断面 SPECT 图像需要重新定向到标准短轴切片，以便后续进行准确的处理和分析。我们提出了一种新的基于深度学习的方法，用于全自动定向心脏 SPECT 图像，并在来自两个临床中心的数据上评估其性能。
我们使用卷积神经网络来预测 6 个刚体变换参数，然后实现空间变换网络将这些参数应用于输入图像，以进行图像重定向。在训练阶段，我们使用了一种新的复合损失函数，该函数平衡了参数的相似性和预测与训练数据集的差异。从一组 322 名患者的数据中进行了数据扩充，得到了 6440 组图像用于网络训练，还使用了来自同一中心的 52 名患者和另一个中心的 23 名患者的数据集进行评估。分析了提出的方法和手动方法之间 6 个参数的相似性。从输出图像生成极图，并从极图计算 17 个节段的平均计数值，以评估所提出方法的定量准确性。
所有测试患者均成功实现了自动重定向。线性回归结果表明，6 个预测的刚体参数和 17 个节段的平均计数值与参考手动方法具有良好的一致性。配对 t 检验未发现我们的方法和手动方法之间的刚体参数有显著差异（p>0.05）。17 个节段的平均计数值显示，提出的方法和手动方法之间的差异小于现有方法和手动方法之间的差异。
结果强烈表明我们的方法在准确的自动心脏 SPECT 重定向中的可行性。这种基于深度学习的重定向方法具有很大的临床应用前景，值得进一步研究。
