论文标题
通过深度学习自动重新定位，以产生短轴Spect心肌灌注图像
Automatic reorientation by deep learning to generate short axis SPECT myocardial perfusion images
论文作者
论文摘要
单个光子发射计算机断层扫描（SPECT）心肌灌注图像（MPI）可以在传统的短轴（SA）心脏平面和极性图中显示用于解释和定量。必须将重建的跨轴向SPECT MPI重新定位为标准的SA切片。这项研究旨在开发一种基于学习的方法来自动重新定位MPI。方法：共有254例患者，包括228个应力SPECT MPI和248例REST SPECT MPI。使用180个压力的五倍交叉验证和201个REST MPI用于训练和内部验证；其余图像用于测试。经验丰富的操作员注释了手动重新定位的刚性转换参数（翻译和旋转），并用作地真相。卷积神经网络（CNN）旨在预测转换参数。然后，将派生的变换应用于空间变压器网络（STN）中的网格发生器和采样器，以生成重新定向的图像。采用了包含平均翻译的平均绝对误差的损耗函数，用于旋转的均值误差。采用三阶段优化策略进行模型优化：1）在固定旋转参数时优化翻译参数； 2）在修复翻译参数时优化旋转参数； 3）一起优化翻译和旋转参数。
Single photon emission computed tomography (SPECT) myocardial perfusion images (MPI) can be displayed both in traditional short-axis (SA) cardiac planes and polar maps for interpretation and quantification. It is essential to reorient the reconstructed transaxial SPECT MPI into standard SA slices. This study is aimed to develop a deep-learning-based approach for automatic reorientation of MPI. Methods: A total of 254 patients were enrolled, including 228 stress SPECT MPIs and 248 rest SPECT MPIs. Five-fold cross-validation with 180 stress and 201 rest MPIs was used for training and internal validation; the remaining images were used for testing. The rigid transformation parameters (translation and rotation) from manual reorientation were annotated by an experienced operator and used as the ground truth. A convolutional neural network (CNN) was designed to predict the transformation parameters. Then, the derived transform was applied to the grid generator and sampler in spatial transformer network (STN) to generate the reoriented image. A loss function containing mean absolute errors for translation and mean square errors for rotation was employed. A three-stage optimization strategy was adopted for model optimization: 1) optimize the translation parameters while fixing the rotation parameters; 2) optimize rotation parameters while fixing the translation parameters; 3) optimize both translation and rotation parameters together.
