Magnetic resonance imaging (MRI) has long been recognized as a powerful tool for cardiovascular imaging because of its unique potential to measure blood flow, cardiac wall motion, and tissue properties jointly. However, many clinical applications of cardiac MRI have been limited by low imaging speed. In this paper, we present a novel method to accelerate cardiovascular MRI through the integration of parallel imaging, low-rank modeling, and sparse modeling. This method consists of a novel image model and specialized data acquisition. Of particular novelty is the proposed low-rank model component, which is specially adapted to the particular low-rank structure of cardiovascular signals. Simulations and in vivo experiments were performed to evaluate the method, as well as an analysis of the low-rank structure of a numerical cardiovascular phantom. Cardiac imaging experiments were carried out on both human and rat subjects without the use of ECG or respiratory gating and without breath holds. The proposed method reconstructed 2-D human cardiac images up to 22 fps and 1.0 mm × 1.0 mm spatial resolution and 3-D rat cardiac images at 67 fps and 0.65 mm × 0.65 mm × 0.31 mm spatial resolution. These capabilities will enhance the practical utility of cardiovascular MRI.

Non-invasive in-vivo tracking of T-cells by magnetic resonance imaging (MRI) can lead to a better understanding of many pathophysiological situations, including AIDS, cancer, diabetes, graft rejection, etc. However, an efficient MRI contrast agent and a reliable technique to track non-phagocytic T-cells are needed. We report a novel superparamagnetic nano-sized iron-oxide particle, IOPC-NH2 series particles, coated with polyethylene glycol (PEG), with high transverse relaxivity (250 s−1mM−1), thus useful for MRI studies. IOPC-NH2 particles are the first reported magnetic particles that can label rat and human T-cells with over 90% efficiency, without using transfection agents, HIV-1 transactivator peptide, or electroporation. IOPC-NH2 particles do not cause any measurable effects on T-cell properties. Infiltration of IOPC-NH2-labeled-T-cells can be detected in a rat model of heart-lung transplantation by in-vivo MRI. IOPC-NH2 is potentially valuable contrast agents for labeling a variety of cells for basic and clinical cellular MRI studies, e.g., cellular therapy.

Purpose-In this study, we investigated the labeling efficiency and magnetic resonance imaging (MRI) signal sensitivity of a newly synthesized, nano-sized iron oxide particle (IOP) coated with polyethylene glycol (PEG), designed by Industrial Technology Research Institute (ITRI).Procedures-Macrophages, bone-marrow-derived dendritic cells, and mesenchymal stem cells (MSCs) were isolated from rats and labeled by incubating with ITRI-IOP, along with three other iron oxide particles in different sizes and coatings as reference. These labeled cells were characterized with transmission electron microscopy (TEM), light and fluorescence microscopy, phantom MRI, and finally in vivo MRI and ex vivo magnetic resonance microscopy (MRM) of transplanted hearts in rats infused with labeled macrophages.Results-The longitudinal (r 1 ) and transverse (r 2 ) relaxivities of ITRI-IOP are 22.71 and 319.2 s −1 mM −1 , respectively. TEM and microscopic images indicate the uptake of multiple ITRI-IOP particles per cell for all cell types. ITRI-IOP provides sensitivity comparable or higher than the other three particles shown in phantom MRI. In vivo MRI and ex vivo MRM detect punctate spots of hypointensity in rejecting hearts, most likely caused by the accumulation of macrophages labeled by ITRI-IOP.Conclusion-ITRI-IOP, the nano-sized iron oxide particle, shows high efficiency in cell labeling, including both phagocytic and non-phagocytic cells. Furthermore, it provides excellent sensitivity in T 2 *-weighted MRI, and thus can serve as a promising contrast agent for in vivo cellular MRI.

In this study, we investigated a method for accurately measuring myocardial T(1) for the quantification of myocardial blood flow (MBF) with arterial spin labeling (ASL). A single-shot gradient-echo (GE)-based ASL sequence with an adiabatic hyperbolic secant inversion recovery pulse was modified to acquire a pair of myocardial T(1)'s within a breath-hold. A multivariable regression algorithm that accounted for the magnetization saturation effects was developed to calculate T(1). The MBF was then determined with a well-developed model. The accuracy of our T(1) calculation was first evaluated in a phantom, and then in six dogs for the MBF calculation, with (N = 4) and without (N = 2) coronary artery stenosis. In the phantom study, the accuracy of T(1) measured with a slice-selective inversion prepared pulse was within 2.5% of error. In healthy dogs, the MBF increased 2-5 times during vasodilation. In contrast, regional differences of MBF were well visualized in the stenotic dogs during vasodilation (perfusion reserve of 2.75 +/- 0.83 in normal myocardium, and 1.46 +/- 0.75 in the stenotic area). A correlation analysis revealed a close agreement in MBF between the ASL and microsphere (MS) in both healthy and stenotic dogs. In summary, the modified ASL technique and T(1) regression algorithm proposed here provide an accurate measurement of myocardial T(1) and demonstrate potential for reliably assessing MBF at steady state.

Accurate and fast quantification of myocardial blood flow (MBF) with MR first-pass perfusion imaging techniques on a pixel-bypixel basis remains difficult due to relatively long calculation times and noise-sensitive algorithms. In this study, Zierler's central volume principle was used to develop an algorithm for the calculation of MBF with few assumptions on the shapes of residue curves. Simulation was performed to evaluate the accuracy of this algorithm in the determination of MBF. To examine our algorithm in vivo, studies were performed in nine normal dogs. Two first-pass perfusion imaging sessions were performed with the administration of the intravascular contrast agent Gadomer at rest and during dipyridamole-induced vasodilation. Radiolabeled microspheres were injected to measure MBF at the same time. MBF measurements in dogs using MR methods correlated well with the microsphere measurements (R 2 ‫؍‬ 0.96, slope ‫؍‬ 0.9), demonstrating a fair accuracy in the perfusion measurements at rest and during the vasodilation stress. In addition to its accuracy, this method can also be optimized to run relatively fast, providing potential for fast and Quantification of myocardial blood flow (MBF) has been shown to be an effective tool for diagnosing blood flow defects (regional or global myocardium) and monitoring the effectiveness of therapeutic treatment (1-5). In particular, the application of first-pass techniques to each pixel of an image to produce an accurate blood flow map allows visualization of regional differences in blood flow with relatively high resolution, and is a noninvasive approach of assessing the severity of coronary artery blockage (6 -9).MBF is quantified by deconvolving tissue residue curves measured by dynamic first-pass images and by finding the peak of the resulting impulse response. Consequently, the accuracy of a first-pass algorithm depends largely on its ability to represent a wide variety of impulse response curves. For this reason, model-independent algorithms (10,11), which make few assumptions about the shape of the impulse response, are advantageous. However, because model-independent algorithms require curve-fitting of many parameters, they require special techniques to control noise susceptibility (10). One common way of stabilizing these methods is to introduce a set of smoothing constraints with a weight that depends on the noise level of the data. This "regularization method" is similar to applying a low-pass filter (10). While this technique substantially improves the conditioning of the deconvolution, it increases computation time and strong regularization can lead to underestimation of parameters to be measured.In this study we investigated a new model-independent technique that can be performed with relatively few parameters and does not require regularization. Like other quantification methods it utilizes a deconvolution based on Zierler's central volume principle. However, by choosing a simple representation of the impulse response curve we can achieve low noise sensiti...