  Motion estimation is a fundamental step in dynamic medical image processing
for the assessment of target organ anatomy and function. However, existing
image-based motion estimation methods, which optimize the motion field by
evaluating the local image similarity, are prone to produce implausible
estimation, especially in the presence of large motion. In this study, we
provide a novel motion estimation framework of Dense-Sparse-Dense (DSD), which
comprises two stages. In the first stage, we process the raw dense image to
extract sparse landmarks to represent the target organ anatomical topology and
discard the redundant information that is unnecessary for motion estimation.
For this purpose, we introduce an unsupervised 3D landmark detection network to
extract spatially sparse but representative landmarks for the target organ
motion estimation. In the second stage, we derive the sparse motion
displacement from the extracted sparse landmarks of two images of different
time points. Then, we present a motion reconstruction network to construct the
motion field by projecting the sparse landmarks displacement back into the
dense image domain. Furthermore, we employ the estimated motion field from our
two-stage DSD framework as initialization and boost the motion estimation
quality in light-weight yet effective iterative optimization. We evaluate our
method on two dynamic medical imaging tasks to model cardiac motion and lung
respiratory motion, respectively. Our method has produced superior motion
estimation accuracy compared to existing comparative methods. Besides, the
extensive experimental results demonstrate that our solution can extract well
representative anatomical landmarks without any requirement of manual
annotation. Our code is publicly available online.
