Congenital heart disease is the leading cause of birth defect related deaths. The modified myocardial performance index of the right ventricle (R-MPI) is a sensitive and early clinical indicator of fetal cardiac health. Objective repeatable measurement of R-MPI is an important deciding factor for the clinical adaptation of the R-MPI. In this work, we describe a novel method for automatic computation of R-MPI from the Pulsed Wave Doppler (PWD) images. Our method involves a Fourier series based cardiac cycle detection followed by an adaptive windowed energy based valve click localization and weighted gradient based refinement. Using this method, we have been able to measure R-MPI reliably with a mean difference of 0.0075 ± 0.034 from 170 expert annotations on 68 fetal PWD images with an Intra-Class Correlation (ICC) of 0.9380. Furthermore, we have introduced novel methods for normalization and synchronization of PWD images acquired at two different time intervals for the assessment of iso-volume time intervals and an accurate measurement of R-MPI.
The Aortic Valve (AV) is an important anatomical structure which lies on the left side of the human heart. The AV
regulates the flow of oxygenated blood from the Left Ventricle (LV) to the rest of the body through aorta. Pathologies
associated with the AV manifest themselves in structural and functional abnormalities of the valve. Clinical management
of pathologies often requires repair, reconstruction or even replacement of the valve through surgical intervention.
Assessment of these pathologies as well as determination of specific intervention procedure requires quantitative
evaluation of the valvular anatomy. 4D (3D + t) Transesophageal Echocardiography (TEE) is a widely used imaging
technique that clinicians use for quantitative assessment of cardiac structures. However, manual quantification of 3D
structures is complex, time consuming and suffers from inter-observer variability. Towards this goal, we present a semiautomated
approach for segmentation of the aortic root (AR) structure. Our approach requires user-initialized landmarks
in two reference frames to provide AR segmentation for full cardiac cycle. We use ‘coarse-to-fine’ B-spline Explicit
Active Surface (BEAS) for AR segmentation and Masked Normalized Cross Correlation (NCC) method for AR tracking.
Our method results in approximately 0.51 mm average localization error in comparison with ground truth annotation
performed by clinical experts on 10 real patient cases (139 3D volumes).
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