Faculty Bibliography

Myocardial strain is a critical indicator of many cardiac diseases and dysfunctions. The goal of this paper is to extract and use the myocardial strain pattern from tagged magnetic resonance imaging (MRI) to identify and localize regional abnormal cardiac function in human subjects. In order to extract the myocardial strains from the tagged images, we developed a novel nontracking-based strain estimation method for tagged MRI. This method is based on the direct extraction of tag deformation, and therefore avoids some limitations of conventional displacement or tracking-based strain estimators. Based on the extracted spatio-temporal strain patterns, we have also developed a novel tensor-based classification framework that better conserves the spatio-temporal structure of the myocardial strain pattern than conventional vector-based classification 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 methods on 41 human image sequences, and achieved a classification rate of 87.80%. The regional abnormalities recovered from our algorithm agree well with the patient's pathology and clinical image interpretation, and provide a promising avenue for regional cardiac function analysis.

BACKGROUND: . Unique identifier: NCT00798122

PURPOSE: To develop, and validate in vivo, a robust quantitative first-pass perfusion cardiovascular MR (CMR) method with accurate arterial input function (AIF) and myocardial wall enhancement. MATERIALS AND METHODS: A saturation-recovery (SR) pulse sequence was modified to sequentially acquire multiple slices after a single nonselective saturation pulse at 3 Tesla. In each heartbeat, an AIF image is acquired in the aortic root with a short time delay (TD) (50 ms), followed by the acquisition of myocardial images with longer TD values ( approximately 150-400 ms). Longitudinal relaxation rates (R(1) = 1/T(1) ) were calculated using an ideal saturation recovery equation based on the Bloch equation, and corresponding gadolinium contrast concentrations were calculated assuming fast water exchange condition. The proposed method was validated against a reference multi-point SR method by comparing their respective R(1) measurements in the blood and left ventricular myocardium, before and at multiple time-points following contrast injections, in 7 volunteers. RESULTS: R(1) measurements with the proposed method and reference multi-point method were strongly correlated (r > 0.88, P < 10(-5) ) and in good agreement (mean difference +/-1.96 standard deviation 0.131 +/- 0.317 / 0.018 +/- 0.140 s(-1) for blood/myocardium, respectively). CONCLUSION: The proposed quantitative first-pass perfusion CMR method measured accurate R(1) values for quantification of AIF and myocardial wall contrast agent concentrations in 3 cardiac short-axis slices, in a total acquisition time of 523 ms per heartbeat. J. Magn. Reson. Imaging 2011;. (c) 2011 Wiley-Liss, Inc

Cirrhosis is an important and growing public health problem, affecting millions of Americans and many more people internationally. A pathological hallmark of the progression to cirrhosis is the development of liver fibrosis, so that monitoring the appearance and progression of liver fibrosis can be used to guide therapy. Here, we report a method to use magnetization-tagged magnetic resonance imaging to measure the cardiac-induced motion and deformation in the liver, as a means for noninvasively assessing liver stiffness, which is related to fibrosis. The initial results show statistically significant differences between healthy and cirrhotic subjects in the direct comparisons of the maximum displacement (mm), and the maximum (P1) and minimum (P2) two-dimensional strains, through the cardiac cycle (3.514 +/- 0.793, 2.184 +/- 0.611; 0.116 +/- 0.043, 0.048 +/- 0.011; -0.094 +/- 0.020, -0.041 +/- 0.015; healthy, cirrhosis, respectively; P < 0.005 for all). There are also significant differences in the displacement-normalized P1 and P2 strains (mm(-1) ) (0.030 +/- 0.008, 0.017 +/- 0.007; -0.024 +/- 0.006, -0.013 +/- 0.004; healthy, cirrhosis, respectively; P < 0.005 for all). Therefore, this noninvasive imaging-based method is a promising means to assess liver stiffness using clinically available imaging tools. Magn Reson Med, 2011. (c) 2011 Wiley-Liss, Inc

Introduction: Cardiac perfusion MRI requires fast data acquisition to achieve an appropriate combination of temporal resolution, spatial resolution and spatial coverage for clinical studies [1]. We have recently presented a combination of compressed sensing and parallel imaging (k-t SPARSESENSE) to highly accelerate perfusion studies [2]. However, this method is sensitive to respiratory motion, which decreases temporal sparsity and produces temporal blurring in the reconstructed images. In this work, we present a rigid respiratory motion correction method which allows highly-accelerated first-pass cardiac perfusion MRI to be performed without strict breath-holding. Purpose: To develop a respiratory motion correction method for joint compressed sensing and parallel imaging acceleration of first-pass cardiac perfusion MRI. Methods: Free-breathing first-pass cardiac perfusion MRI with 0.1 mmol/kg of Gd-DTPA (Magnevist) was performed using a modified multi-slice TurboFLASH pulse sequence. Healthy volunteers were imaged on a whole-body 3T scanner (Siemens; Tim-Trio) using the standard 12- element body matrix coil array. The relevant imaging parameters include: FOV = 320mmx320mm, image resolution = 1.7mmx1.7mm, slice-thickness = 8mm, TE/ TR = 1.3/2.5ms, repetitions=40. An acceleration factor of 8 was used to acquire 10 slices per heartbeat with temporal resolution of 60ms/slice. Data undersampling was performed using a pseudo-random ky-t pattern [2]. Fully-sampled low-resolution coil sensitivity reference data were acquired in the first heartbeat. Image reconstruction was performed in two-steps using the k-t SPARSE-SENSE algorithm [2] with temporal FFT as sparsifying transform. First, an intermediate k-t SPARSESENSE reconstruction is generated for respiratory motion correction. Rigid motion between frames is detected by computing the displacement of each frame from this intermediate k-t SPARSE-SENSE reconstruction with respect to the coil sensitivity reference using a crosscorrelation approach in the image domain [3]. Second, motion correction is performed by aligning all the frames in the accelerated data. The final k-t SPARSESENSE reconstruction is computed using the aligned accelerated data. Results: Rigid respiratory motion correction significantly increased sparsity in the temporal Fourier domain, which is due to better alignment among frames (Fig. 1). Fig. 2 shows k-t SPARSE-SENSE reconstruction of a representative slice from the free-breathing perfusion scan without and with motion correction. The utilization of motion correction decreased temporal blurring and presented images with higher quality. Conclusions: This work demonstrates feasibility of highly-accelerated first-pass cardiac perfusion MRI without strict breathholding with rigid respiratory motion correction. Future work will explore the use of non-rigid motion correction. The proposed technique may be useful for imaging patients with impaired breath-hold capabilities(figure present)

In this paper, we present a method to simulate and visualize blood flow through the human heart, using the reconstructed 4D motion of the endocardial surface of the left ventricle as boundary conditions. The reconstruction captures the motion of the full 3D surfaces of the complex features, such as the papillary muscles and the ventricular trabeculae. We use visualizations of the flow field to view the interactions between the blood and the trabeculae in far more detail than has been achieved previously, which promises to give a better understanding of cardiac flow. Finally, we use our simulation results to compare the blood flow within one healthy heart and two diseased hearts.

First-pass cardiac perfusion MRI is a natural candidate for compressed sensing acceleration since its representation in the combined temporal Fourier and spatial domain is sparse and the required incoherence can be effectively accomplished by k-t random undersampling. However, the required number of samples in practice (three to five times the number of sparse coefficients) limits the acceleration for compressed sensing alone. Parallel imaging may also be used to accelerate cardiac perfusion MRI, with acceleration factors ultimately limited by noise amplification. In this work, compressed sensing and parallel imaging are combined by merging the k-t SPARSE technique with sensitivity encoding (SENSE) reconstruction to substantially increase the acceleration rate for perfusion imaging. We also present a new theoretical framework for understanding the combination of k-t SPARSE with SENSE based on distributed compressed sensing theory. This framework, which identifies parallel imaging as a distributed multisensor implementation of compressed sensing, enables an estimate of feasible acceleration for the combined approach. We demonstrate feasibility of 8-fold acceleration in vivo with whole-heart coverage and high spatial and temporal resolution using standard coil arrays. The method is relatively insensitive to respiratory motion artifacts and presents similar temporal fidelity and image quality when compared to Generalized autocalibrating partially parallel acquisitions (GRAPPA) with 2-fold acceleration