Flow network tracking for spatiotemporal and periodic point matching: Applied to cardiac motion analysis

Abstract

The accurate quantification of left ventricular (LV) deformation/strain shows significant promise for quantitatively assessing cardiac function for use in diagnosis and therapy planning . However, accurate estimation of the displacement of myocardial tissue and hence LV strain has been challenging due to a variety of issues, including those related to deriving tracking tokens from images and following tissue locations over the entire cardiac cycle. In this work, we propose a point matching scheme where correspondences are modeled as flow through a graphical network. Myocardial surface points are set up as nodes in the network and edges define neighborhood relationships temporally. The novelty lies in the constraints that are imposed on the matching scheme, which render the correspondences one-to-one through the entire cardiac cycle, and not just two consecutive frames. The constraints also encourage motion to be cyclic, which is an important characteristic of LV motion. We validate our method by applying it to the estimation of quantitative LV displacement and strain estimation using 8 synthetic and 8 open-chested canine 4D echocardiographic image sequences, the latter with sonomicrometric crystals implanted on the LV wall. We were able to achieve excellent tracking accuracy on the synthetic dataset and observed a good correlation with crystal-based strains on the in-vivo data.

Reliability of Sonography‐based Volume Computer Aided Diagnosis in the Normal Fetal Heart

“…Detection of fetal CHD is extremely challenging, time consuming and highly operator dependent 2,3,6,9 . Three‐dimensional ultrasound has been applied to fetal heart examination, 6,19‐21 detection of CHD 13,15,22 and the quantitative, volumetric evaluation of cardiac function and size 23‐25 . Previous investigations showed SonoVCAD could display diagnostic cardiac planes without much difficulty in both normal fetuses 17 and fetuses with transposition of the great arteries 15 .…”

Artificial intelligence with deep learning in nuclear medicine and radiology

“…17 Breast cancer mammography screening using a convolutional neural network. Image adapted from [161] with permission from Elsevier diagnosis of CVD is motion tracking and segmentation of the main chambers [166][167][168][169][170][171][172][173][174][175]. This allows quantification of cardiac morphology (e.g., ventricle volumes) and cardiac function ejection fraction and wall thickening).…”

A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises

“…Often times, motion tracking algorithms use segmentation results as an anatomical guide to sample points and regions of interest used to generate displacement fields e.g. [150]. In part due to this, there have also been efforts to combine motion tracking and segmentation.…”