  Our anatomy is in constant motion. With modern MR imaging it is possible to
record this motion in real-time during an ongoing radiation therapy session. In
this paper we present an image registration method that exploits the sequential
nature of 2D MR images to estimate the corresponding displacement field. The
method employs several discriminative correlation filters that independently
track specific points. Together with a sparse-to-dense interpolation scheme we
can then estimate of the displacement field. The discriminative correlation
filters are trained online, and our method is modality agnostic. For the
interpolation scheme we use a neural network with normalized convolutions that
is trained using synthetic diffeomorphic displacement fields. The method is
evaluated on a segmented cardiac dataset and when compared to two conventional
methods we observe an improved performance. This improvement is especially
pronounced when it comes to the detection of larger motions of small objects.
