Tagged magnetic resonance imaging (MRI) has been used for decades to observe
and quantify the detailed motion of deforming tissue. However, this technique
faces several challenges such as tag fading, large motion, long computation
times, and difficulties in obtaining diffeomorphic incompressible flow fields.
To address these issues, this paper presents a novel unsupervised phase-based
3D motion estimation technique for tagged MRI. We introduce two key
innovations. First, we apply a sinusoidal transformation to the harmonic phase
input, which enables end-to-end training and avoids the need for phase
interpolation. Second, we propose a Jacobian determinant-based learning
objective to encourage incompressible flow fields for deforming biological
tissues. Our method efficiently estimates 3D motion fields that are accurate,
dense, and approximately diffeomorphic and incompressible. The efficacy of the
method is assessed using human tongue motion during speech, and includes both
healthy controls and patients that have undergone glossectomy. We show that the
method outperforms existing approaches, and also exhibits improvements in
speed, robustness to tag fading, and large tongue motion.Comment: Accepted to MIDL 2023 (full paper