  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.
