Faculty Bibliography

We propose a novel meshless deformable model for in vivo Left Ventricle (LV) 3D motion estimation and analysis based on tagged MRI (tMRI). The meshless deformable model can capture global deformations such as contraction and torsion with a few parameters, while track local deformations with Laplacian representation. In particular, the model performs well even when the control points (tag intersections) are relatively sparse. We test the performance of the meshless model on a numeric phantom, as well as in vivo heart data of healthy subjects and patients. The experimental results show that the meshless deformable model can fully recover the myocardial motion and strain in 3D

Myocardial deformation is a critical indicator of many cardiac diseases and dysfunctions. The goal of this paper is to use myocardial deformation patterns to identify and localize regional abnormal cardiac function in human subjects. We have developed a novel tensor-based classification framework that better conserves the spatio-temporal structure of the myocardial deformation pattern than conventional vector-based algorithms. In addition, the tensor-based projection function keeps more of the information of the original feature space, so that abnormal tensors in the subspace can be back-projected to reveal the regional cardiac abnormality in a more physically meaningful way. We have tested our novel method on 41 human image sequences, and achieved a classification rate of 87.80%. The recovered regional abnormalities from our algorithm agree well with the patient's pathology and doctor's diagnosis and provide a promising avenue for regional cardiac function analysis

In this paper we introduce a tag separation method for better cardiac boundary segmentation and tag tracking. Our approach is based on two observations in the cardiac tagged MR images: 1) the tag patterns have a regular texture; 2) the cardiac images without tag patterns are piecewise smooth with sparse gradients. These observations motivate us to use two dictionaries, one based on the Discrete Cosine Transform for representing tag patterns and the other based on the Wavelet Transform for representing the underlying cardiac image without tag patterns. The two dictionaries are built such that they can lead to sparse representations of the tag patterns and of the piece-wise smooth regions without tag patterns. With the two dictionaries, a new tag separation approach is proposed to simultaneously optimize w.r.t. the two sparse representations, where optimization is directed by the Total Variation regularization scheme. While previous methods have focused on tag removal, our approach to acquiring both optimally-decomposed tag-only image and the cardiac image without tags simultaneously can be used for better tag tracking and cardiac boundary segmentation. We demonstrate the superior performance of the proposed approach through extensive experiments on large sets of cardiac tagged MR images

Fast tracking of motion is the key step towards tagged MRI-based quantitative cardiac analysis. Existing motion tracking approaches, including the widely used HARP method, are either time consuming or qualitatively inconsistent, or both. We present in this paper a new fast motion tracking method based on a meshless kernel. For MR image sequences containing multiple image frames, tag intersections are automatically detected in all frames and indexed in the first frame. Then a thin plate spline approach is used to establish a point-to-point correspondence between tag intersections in the initial and the current frame. Lastly, we use a meshless registration kernel to generate a dense displacement map that minimizes the residual of sparse motion at intersections. To further improve the motion tracking, we develop a special technique to preserve tangential angles of tags at tag intersections. We tested our new method on both numerical phantoms and in vivo heart data. The motion tracking results are evaluated against the ground truth and manually drawn tags. Clinical application potential is demonstrated by cardiac strain analysis based on the proposed methodology

This paper presents a series of experiments, both in patients and computer models, investigating the transition from acute to chronic hypercapnia in OSA. The data demonstrate that acute hypercapnia during periodic breathing occurs due to either reduction in magnitude of inter-event ventilation and/or reduction in inter-event ventilatory duration relative to duration of the preceding event. The transition between acute hypercapnia during sleep and chronic sustained hypercapnia during wakefulness may be determined by an interaction between respiratory control and renal handling of HCO3-.