  Subject motion in whole-body dynamic PET introduces inter-frame mismatch and
seriously impacts parametric imaging. Traditional non-rigid registration
methods are generally computationally intense and time-consuming. Deep learning
approaches are promising in achieving high accuracy with fast speed, but have
yet been investigated with consideration for tracer distribution changes or in
the whole-body scope. In this work, we developed an unsupervised automatic deep
learning-based framework to correct inter-frame body motion. The motion
estimation network is a convolutional neural network with a combined
convolutional long short-term memory layer, fully utilizing dynamic temporal
features and spatial information. Our dataset contains 27 subjects each under a
90-min FDG whole-body dynamic PET scan. With 9-fold cross-validation, compared
with both traditional and deep learning baselines, we demonstrated that the
proposed network obtained superior performance in enhanced qualitative and
quantitative spatial alignment between parametric $K_{i}$ and $V_{b}$ images
and in significantly reduced parametric fitting error. We also showed the
potential of the proposed motion correction method for impacting downstream
analysis of the estimated parametric images, improving the ability to
distinguish malignant from benign hypermetabolic regions of interest. Once
trained, the motion estimation inference time of our proposed network was
around 460 times faster than the conventional registration baseline, showing
its potential to be easily applied in clinical settings.
