Faculty Bibliography

The tracking and reconstruction of myocardial motion is critical to the diagnosis and treatment of heart disease. Currently, little has been done for the analysis of motion in long axis (LA) cardiac images. We propose a new fully automated motion reconstruction method for grid- tagged MRI that combines Gabor filters and deformable models. First, we use a Gabor filter bank to generate the corresponding phase map in the myocardium and estimate the location of grid tag intersections. Second, we use a non-rigid registration module driven by thin plate splines (TPS) to generate a transformation function between tag intersections in two consecutive images. Third, deformable spline models are initialized using Fourier domain analysis and tracked during the cardiac cycle using the TPS generated transformation function. The splines will then locally deform under the influence of gradient flow and image phase information. The final motion is decomposed into tangential and normal components corresponding to the local orientation of the heart wall. The new method has been tested on LA phantoms and in vivo heart data, and its performance has been quantitatively validated. The results show that our method can reconstruct the motion field in LA cardiac tagged MR images accurately and efficiently

Ultrasound Myocardial Elastography (UME) and Tagged Magnetic Resonance Imaging (tMRI) are two imaging modalities that were developed in the recent years to quantitatively estimate the myocardial deformations. Tagged MRI is currently considered as the gold standard for myocardial strain mapping in vivo. However, despite the low SNR nature of ultrasound signals, echocardiography enjoys the widespread availability in the clinic, as well as its low cost and high temporal resolution. Comparing the strain estimation performances of the two techniques has been of great interests to the community. In order to assess the cardiac deformation across different imaging modalities, in this paper, we developed a semi-automatic intensity and gradient based registration framework that rigidly registers the 3D tagged MRIs with the 2D ultrasound images. Based on the two registered modalities, we conducted spatially and temporally more detailed quantitative strain comparison of the RF-based UME technique and tagged MRI. From the experimental results, we conclude that qualitatively the two modalities share similar overall trends. But error and variations in UME accumulate over time. Quantitatively tMRI is more robust and accurate than UME

In this paper, we introduce an adaptive model-based segmentation framework, in which edge and region information are integrated and used adaptively while a solid model deforms toward the object boundary. Our 3D segmentation method stems from Metamorphs deformable models. The main novelty of our work is in that, instead of performing segmentation in an entire 3D volume, we propose model-based segmentation in an adaptively changing subvolume of interest. The subvolume is determined based on appearance statistics of the evolving object model, and within the subvolume, more accurate and object-specific edge and region information can be obtained. This local and adaptive scheme for computing edges and object region information makes our segmentation solution more efficient and more robust to image noise, artifacts and intensity inhomogeneity. External forces for model deformation are derived in a variational framework that consists of both edge-based and region-based energy terms, taking into account the adaptively changing environment. We demonstrate the performance of our method through extensive experiments using cardiac MR and liver CT images

RATIONALE AND OBJECTIVES: The aim of the study is to develop a theory-based signal calibration approach to be used for the conversion of signal-time curves to absolute contrast concentration-time curves for first-pass contrast-enhanced quantitative myocardial perfusion studies. MATERIALS AND METHODS: A normalization procedure was used to obtain a theoretical relationship between image signal and T1 and perform rapid single-point T1 measurements. T1 measurements were compared with reference T1 measurements. The method also was used in preliminary in vivo contrast-enhanced first-pass perfusion studies, and its applicability for dual-delay-time acquisitions was shown. A theory-based error sensitivity analysis was used to characterize the robustness of the method. RESULTS: The normalization procedure was implemented with minimal noise enhancement and insensitivity to small misregistrations through postprocessing techniques. The rapid T1 measurements are in excellent agreement with the reference measurements (R = 0.99, slope = 1.05, bias = -5.96 milliseconds). For in vivo studies, it is possible to simultaneously calibrate the arterial input function and myocardial enhancement curves acquired with different effective trigger delays through appropriate use of the theory-based signal calibration model. With this method, errors of in vivo baseline T1 estimates are large, but the effect of these large errors on the accuracy of contrast agent concentration estimates is limited. CONCLUSION: This theory-based signal calibration approach can be used to perform rapid T1 mapping and provides flexibility for in vivo calibration of signal-time curves resulting from dual-delay-time first-pass contrast-enhanced acquisitions

MRI of the heart with magnetization tagging provides a potentially useful new way to assess cardiac mechanical function, through revealing the local motion of otherwise indistinguishable portions of the heart wall. Although still an evolving area, tagged cardiac MRI is already able to provide novel quantitative information on cardiac function. Exploiting this potential requires developing tailored methods for both imaging and image analysis. In this article, we review some of the progress that has been made in developing imaging methods for tagged cardiac MRI