Open AccessSpatiotemporal Strategies for Joint Segmentation and Motion Tracking From Cardiac Image Sequences
Author(s) -
Huafeng Liu,
Ting Wang,
Lei Xu,
Pengcheng Shi
Publication year - 2017
Publication title -
ieee journal of translational engineering in health and medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.653
H-Index - 24
ISSN - 2168-2372
DOI - 10.1109/jtehm.2017.2665496
Subject(s) - bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , signal processing and analysis , robotics and control systems , general topics for engineers
Although accurate and robust estimations of the deforming cardiac geometry and kinematics from cine tomographic medical image sequences remain a technical challenge, they have significant clinical value. Traditionally, boundary or volumetric segmentation and motion estimation problems are considered as two sequential steps, even though the order of these processes can be different. In this paper, we present an integrated, spatiotemporal strategy for the simultaneous joint recovery of these two ill-posed problems. We use a mesh-free Galerkin formulation as the representation and computation platform, and adopt iterative procedures to solve the governing equations. Specifically, for each nodal point, the external driving forces are individually constructed through the integration of data-driven edginess measures, prior spatial distributions of myocardial tissues, temporal coherence of image-derived salient features, imaging/image-derived Eulerian velocity information, and cyclic motion model of myocardial behavior. The proposed strategy is accurate and very promising application results are shown from synthetic data, magnetic resonance (MR) phase contrast, tagging image sequences, and gradient echo cine MR image sequences.