An SVM Based Approach for Cardiac View Planning
This addresses the issue of time-consuming and variable-quality manual plane prescription in cardiac imaging for clinicians and technologists, but it is incremental as it applies existing methods (SVM and genetic algorithms) to a specific medical domain.
The paper tackles the problem of automatically prescribing oblique planes in Cardiac MRI to improve quality and reduce examination time, achieving an angular deviation of less than 15 degrees on 90% of cases across oblique planes.
We consider the problem of automatically prescribing oblique planes (short axis, 4 chamber and 2 chamber views) in Cardiac Magnetic Resonance Imaging (MRI). A concern with technologist-driven acquisitions of these planes is the quality and time taken for the total examination. We propose an automated solution incorporating anatomical features external to the cardiac region. The solution uses support vector machine regression models wherein complexity and feature selection are optimized using multi-objective genetic algorithms. Additionally, we examine the robustness of our approach by training our models on images with additive Rician-Gaussian mixtures at varying Signal to Noise (SNR) levels. Our approach has shown promising results, with an angular deviation of less than 15 degrees on 90% cases across oblique planes, measured in terms of average 6-fold cross validation performance -- this is generally within acceptable bounds of variation as specified by clinicians.