Kai-Pin Tung

A Comprehensive Cardiac Motion Estimation Framework Using Both Untagged and 3-D Tagged MR Images Based on Nonrigid Registration

In this paper, we present a novel technique based on nonrigid image registration for myocardial motion estimation using both untagged and 3-D tagged MR images. The novel aspect of our technique is its simultaneous usage of complementary information from both untagged and 3-D tagged MR images. To estimate the motion within the myocardium, we register a sequence of tagged and untagged MR images during the cardiac cycle to a set of reference tagged and untagged MR images at end-diastole. The similarity measure is spatially weighted to maximize the utility of information from both images. In addition, the proposed approach integrates a valve plane tracker and adaptive incompressibility into the framework. We have evaluated the proposed approach on 12 subjects. Our results show a clear improvement in terms of accuracy compared to approaches that use either 3-D tagged or untagged MR image information alone. The relative error compared to manually tracked landmarks is less than 15% throughout the cardiac cycle. Finally, we demonstrate the automatic analysis of cardiac function from the myocardial deformation fields.

Registration using sparse free-form deformations

Non-rigid image registration using free-form deformations (FFD) is a widely used technique in medical image registration. The balance between robustness and accuracy is controlled by the control point grid spacing and the amount of regularization. In this paper, we revisit the classic FFD registration approach and propose a sparse representation for FFDs using the principles of compressed sensing. The sparse free-form deformation model (SFFD) can capture fine local details such as motion discontinuities without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate smooth as well as discontinuous deformations in 2D and 3D image sequences. Compared to the classic FFD approach, a significant increase in registration accuracy can be observed in natural images (61%) as well as in cardiac MR images (53%) with discontinuous motions.

Automatical vessel wall detection in intravascular coronary OCT

The aim of this study is to automatically detect the boundary of vessel walls in optical coherence tomography (OCT) sequences. We developed a new method to eliminate guide-wire shadow artifacts and accurately estimate the vessel wall. The estimation of the position of the guide-wire is the key concept for the elimination of guide-wire shadow artifacts. After identification of the artifacts we propose a geometrically-based method which can be applied to OCT cross-section images to remove the artifacts. The segmentation approach is based on a novel combination of expectation maximization (EM) based segmentation and graph cut (GC) based segmentation. Validation is performed using simulated data and 4 typical in vivo OCT sequences. The comparison against manual expert segmentation demonstrates that the proposed vessel wall identification is robust and accurate.

A multi-image graph cut approach for cardiac image segmentation and uncertainty estimation

Registration and segmentation uncertainty may be important information to convey to a user when automatic image analysis is performed. Uncertainty information may be used to provide additional diagnostic information to traditional analysis of cardiac function. In this paper, we develop a framework for the automatic segmentation of the cardiac anatomy from multiple MR images. We also define the registration and segmentation uncertainty and explore its use for diagnostic purposes. Our framework uses cardiac MR image sequences that are widely available in clinical practice. We improve the performance of the cardiac segmentation algorithms by combining information from multiple MR images using a graph-cut based segmentation. We evaluate this framework on images from 32 subjects: 13 patients with ischemic cardiomyopathy, 14 patients with dilated cardiomyopathy and 5 normal volunteers. Our results indicate that the proposed method is capable of producing segmentation results with very high robustness and high accuracy with minimal user interaction across all subject groups. We also show that registration and segmentation uncertainties are good indicators for segmentation failures as well as good predictors for the functional abnormality of the subject.

Multi-atlas based neointima segmentation in intravascular coronary OCT

Neointima thickening plays a decisive role in coronary restenosis after stenting. The aim of this study is to detect neointima tissue in intravascular optical coherence tomography (IVOCT) sequences. We developed a multi-atlas based segmentation method to detect neointima without stent struts locations. The atlases are selected by measurements of stenosis and a similarity metric. The probability map is then used to estimate neointima label in the unseen image. To account for the registration errors, a patch-based label fusion approach is applied. Validation is performed using 18 typical in-vivo IVOCT sequences. The comparison against manual expert segmentation and other fusion approaches demonstrates that the proposed neointima identification is robust and accurate.

Landmark detection and coupled patch registration for cardiac motion tracking

Increasing attention has been focused on the estimation of the deformation of the endocardium to aid the diagnosis of cardiac malfunction. Landmark tracking can provide sparse, anatomically relevant constraints to help establish correspondences between images being tracked or registered. However, landmarks on the endocardium are often characterized by ambiguous appearance in cardiac MR images which makes the extraction and tracking of these landmarks problematic. In this paper we propose an automatic framework to select and track a sparse set of distinctive landmarks in the presence of relatively large deformations in order to capture the endocardial motion in cardiac MR sequences. To achieve this a sparse set of the landmarks is identified using an entropy-based approach. In particular we use singular value decomposition (SVD) to reduce the search space and localize the landmarks with relatively large deformation across the cardiac cycle. The tracking of the sparse set of landmarks is performed simultaneously by optimizing a two-stage Markov Random Field (MRF) model. The tracking result is further used to initialize registration based dense motion tracking. We have applied this framework to extract a set of landmarks at the endocardial border of the left ventricle in MR image sequences from 51 subjects. Although the left ventricle undergoes a number of different deformations, we show how the radial, longitudinal motion and twisting of the endocardial surface can be captured by the proposed approach. Our experiments demonstrate that motion tracking using sparse landmarks can outperform conventional motion tracking by a substantial amount, with improvements in terms of tracking accuracy of 20:8% and 19:4% respectively.

Automatic cardiac motion tracking using both untagged and 3d tagged MR images

We present a fully automatic framework for cardiac motion tracking based on non-rigid image registration for the analysis of myocardial motion using both untagged and 3D tagged MR images. We detect and track anatomical landmarks in the heart and combine this with intensity-based motion tracking to allow accurately model cardiac motion while significantly reduce the computational complexity. A collaborative similarity measure simultaneously computed in three LA views is employed to register a sequence of images taken during the cardiac cycle to a reference image taken at end-diastole. We then integrate a valve plane tracker into the framework which uses short-axis and long-axis untagged MR images as well as 3D tagged images to estimate a fully four-dimensional motion field of the left ventricle.