Predictive cardiac motion modeling and correction with partial least squares regression

• Published in: IEEE transactions on medical imaging Vol. 23; no. 10; pp. 1315 - 1324
• Main Authors: Ablitt, N.A., Jianxin Gao, Keegan, J., Stegger, L., Firmin, D.N., Guang-Zhong Yang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2004; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abdomen; Algorithms; Computer Simulation; Deformable models; Heart - anatomy & histology; Heart - physiology; High-resolution imaging; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image reconstruction; Imaging, Three-Dimensional - methods; Information Storage and Retrieval - methods; Least squares methods; Least-Squares Analysis; Magnetic resonance; Magnetic Resonance Imaging - methods; Models, Cardiovascular; Motion measurement; Movement - physiology; Pattern Recognition, Automated - methods; Predictive models; Reproducibility of Results; Respiratory Mechanics; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Studies; Subtraction Technique; Surface reconstruction; Tracking
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2004.834622

Respiratory-induced cardiac deformation is a major problem for high-resolution cardiac imaging. This paper presents a new technique for predictive cardiac motion modeling and correction, which uses partial least squares regression to extract intrinsic relationships between three-dimensional (3-D) cardiac deformation due to respiration and multiple one-dimensional real-time measurable surface intensity traces at chest or abdomen. Despite the fact that these surface intensity traces can be strongly coupled with each other but poorly correlated with respiratory-induced cardiac deformation, we demonstrate how they can be used to accurately predict cardiac motion through the extraction of latent variables of both the input and output of the model. The proposed method allows cross-modality reconstruction of patient specific models for dense motion field prediction, which after initial modeling can be used for real-time prospective motion tracking or correction. Detailed numerical issues related to the technique are discussed and the effectiveness of the motion and deformation modeling is validated with 3-D magnetic resonance data sets acquired from ten asymptomatic subjects covering the entire respiratory range.