A fast Edge-preserving Bayesian reconstruction method for Parallel Imaging applications in cardiac MRI.

Among recent parallel MR imaging reconstruction advances, a Bayesian method called Edge-preserving Parallel Imaging reconstructions with GRAph cuts Minimization (EPIGRAM) has been demonstrated to significantly improve signal-to-noise ratio when compared with conventional regularized sensitivity encoding method. However, EPIGRAM requires a large number of iterations in proportion to the number of intensity labels in the image, making it computationally expensive for high dynamic range images. The objective of this study is to develop a Fast EPIGRAM reconstruction based on the efficient binary jump move algorithm that provides a logarithmic reduction in reconstruction time while maintaining image quality. Preliminary in vivo validation of the proposed algorithm is presented for two-dimensional cardiac cine MR imaging and three-dimensional coronary MR angiography at acceleration factors of 2-4. Fast EPIGRAM was found to provide similar image quality to EPIGRAM and maintain the previously reported signal-to-noise ratio improvement over regularized sensitivity encoding method, while reducing EPIGRAM reconstruction time by 25-50 times.

Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: validation and application to brain imaging.

The diagnosis of many neurologic diseases benefits from the ability to quantitatively assess iron in the brain. Paramagnetic iron modifies the magnetic susceptibility causing magnetic field inhomogeneity in MRI. The local field can be mapped using the MR signal phase, which is discarded in a typical image reconstruction. The calculation of the susceptibility from the measured magnetic field is an ill-posed inverse problem. In this work, a bayesian regularization approach that adds spatial priors from the MR magnitude image is formulated for susceptibility imaging. Priors include background regions of known zero susceptibility and edge information from the magnitude image. Simulation and phantom validation experiments demonstrated accurate susceptibility maps free of artifacts. The ability to characterize iron content in brain hemorrhage was demonstrated on patients with cavernous hemangioma. Additionally, multiple structures within the brain can be clearly visualized and characterized. The technique introduces a new quantitative contrast in MRI that is directly linked to iron in the brain.

Calculation of susceptibility through multiple orientation sampling (COSMOS): a method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI.

Magnetic susceptibility differs among tissues based on their contents of iron, calcium, contrast agent, and other molecular compositions. Susceptibility modifies the magnetic field detected in the MR signal phase. The determination of an arbitrary susceptibility distribution from the induced field shifts is a challenging, ill-posed inverse problem. A method called "calculation of susceptibility through multiple orientation sampling" (COSMOS) is proposed to stabilize this inverse problem. The field created by the susceptibility distribution is sampled at multiple orientations with respect to the polarization field, B(0), and the susceptibility map is reconstructed by weighted linear least squares to account for field noise and the signal void region. Numerical simulations and phantom and in vitro imaging validations demonstrated that COSMOS is a stable and precise approach to quantify a susceptibility distribution using MRI.

On measuring the change in size of pulmonary nodules.

The pulmonary nodule is the most common manifestation of lung cancer, the most deadly of all cancers. Most small pulmonary nodules are benign, however, and currently the growth rate of the nodule provides for one of the most accurate noninvasive methods of determining malignancy. In this paper, we present methods for measuring the change in nodule size from two computed tomography image scans recorded at different times; from this size change the growth rate may be established. The impact of partial voxels for small nodules is evaluated and isotropic resampling is shown to improve measurement accuracy. Methods for nodule location and sizing, pleural segmentation, adaptive thresholding, image registration, and knowledge-based shape matching are presented. The latter three techniques provide for a significant improvement in volume change measurement accuracy by considering both image scans simultaneously. Improvements in segmentation are evaluated by measuring volume changes in benign or slow growing nodules. In the analysis of 50 nodules, the variance in percent volume change was reduced from 11.54% to 9.35% (p = 0.03) through the use of registration, adaptive thresholding, and knowledge-based shape matching.

Computer-aided diagnostics.

The computer can be used in a number of ways to aid the physician to interpret CT lung images. Commercial tools are becoming available to assist the radiologist in growth rate determination, hence cancer diagnosis. Computer algorithms are in development that will permit lung health evaluation, including nodule detection. Finally, the results of such efforts will probably produce more detailed visualizations of the lung region, including depictions of the location and state of lung abnormalities. While computer methods have found a first application with the radiologist, these methods should also provide a valuable aid to surgery and pathology.

Nonlinear regularization for per voxel estimation of magnetic susceptibility distributions from MRI field maps.

Magnetic susceptibility is an important physical property of tissues, and can be used as a contrast mechanism in magnetic resonance imaging (MRI). Recently, targeting contrast agents by conjugation with signaling molecules and labeling stem cells with contrast agents have become feasible. These contrast agents are strongly paramagnetic, and the ability to quantify magnetic susceptibility could allow accurate measurement of signaling and cell localization. Presented here is a technique to estimate arbitrary magnetic susceptibility distributions by solving an ill-posed inversion problem from field maps obtained in an MRI scanner. Two regularization strategies are considered: conventional Tikhonov regularization and a sparsity promoting nonlinear regularization using the l(1) norm. Proof of concept is demonstrated using numerical simulations, phantoms, and in a stroke model in a rat. Initial experience indicates that the nonlinear regularization better suppresses noise and streaking artifacts common in susceptibility estimation.

Magnetic source MRI: a new quantitative imaging of magnetic biomarkers.

A new approach to generating MRI contrast by solving the magnetic field to susceptibility source inverse problem is presented to address the quantification difficulties associated with traditional T1/T2 relaxation and susceptibility weighted T2* methods. The forward problem from source to field is reviewed. Its inverse field to source problem is ill posed. Accurate solutions are found by conditioning the data acquisition or regularizing the solution. Preclinical and clinical applications using this magnetic source MRI are discussed for quantitative mapping magnetic biomarkers such as contrast agents in molecular MRI and iron deposits in diseases.

Block circulant quasi-band matrix property for the SENSE unfolding in k-space and justification for GRAPPA.

The k-space unfolding matrix for parallel imaging in MRI was examined and was found to have the structure of block circulant quasi-band, providing a rigorous mathematical justification for the interpolation kernel in the commonly used GRAPPA method. The optimal GRAPPA kernel size and dimension were closely related to sensitivity spectrum.

Three-dimensional cine imaging using variable-density spiral trajectories and SSFP with application to coronary artery angiography.

A single breath-hold 3D cardiac phase resolved steady-state free precession (SSFP) sequence was developed, allowing 3D visualization of the moving coronary arteries. A 3D stack of spirals was acquired continuously throughout the cardiac cycle, and a sliding window reconstruction was used to achieve high temporal resolution. A coil specific field of view reconstruction technique was combined with Parallel Imaging with Localized Sensitivities (PILS) to allow acquisition of a reduced field of view. A view ordering incorporating fat suppression was employed to allow use of sliding window reconstruction. The technique was evaluated on healthy volunteers (n=8), yielding images with 102 ms temporal resolution and 1.35 mm in-plane resolution, and reasonable visualization of the left and right coronary arteries was achieved.

Bayesian parallel imaging with edge-preserving priors.

Existing parallel MRI methods are limited by a fundamental trade-off in that suppressing noise introduces aliasing artifacts. Bayesian methods with an appropriately chosen image prior offer a promising alternative; however, previous methods with spatial priors assume that intensities vary smoothly over the entire image, resulting in blurred edges. Here we introduce an edge-preserving prior (EPP) that instead assumes that intensities are piecewise smooth, and propose a new approach to efficiently compute its Bayesian estimate. The estimation task is formulated as an optimization problem that requires a nonconvex objective function to be minimized in a space with thousands of dimensions. As a result, traditional continuous minimization methods cannot be applied. This optimization task is closely related to some problems in the field of computer vision for which discrete optimization methods have been developed in the last few years. We adapt these algorithms, which are based on graph cuts, to address our optimization problem. The results of several parallel imaging experiments on brain and torso regions performed under challenging conditions with high acceleration factors are shown and compared with the results of conventional sensitivity encoding (SENSE) methods. An empirical analysis indicates that the proposed method visually improves overall quality compared to conventional methods.

Reduction of reconstruction time for time-resolved spiral 3D contrast-enhanced magnetic resonance angiography using parallel computing.

Time-resolved 3D MRI with high spatial and temporal resolution can be achieved using spiral sampling and sliding-window reconstruction. Image reconstruction is computationally intensive because of the need for data regridding, a large number of temporal phases, and multiple RF receiver coils. Inhomogeneity blurring correction for spiral sampling further increases the computational work load by an order of magnitude, hindering the clinical utility of spiral trajectories. In this work the reconstruction time is reduced by a factor of >40 compared to reconstruction using a single processor. This is achieved by using a cluster of 32 commercial off-the-shelf computers, commodity networking hardware, and readily available software. The reconstruction system is demonstrated for time-resolved spiral contrast-enhanced (CE) peripheral MR angiography (MRA), and a reduction of reconstruction time from 80 min to 1.8 min is achieved.

Statistical aspects of parallel imaging reconstruction.

A statistical interpretation of existing parallel magnetic resonance imaging methods reveals that the underlying noise model is of additive independent Gaussian noise. In reality MR imaging processes suffer from a variety of noise, errors and other uncertainties. A careful statistical analysis of these uncertainties can potentially allow significant improvement of the reconstruction process. In this paper we present such an analysis and describe a few very recent approaches to handle these statistical models. We show examples of simulation and in vivo reconstructed data which demonstrate the potential of the statistical approach.

Improved signal-to-noise ratio in parallel coronary artery magnetic resonance angiography using graph cuts based Bayesian reconstruction.

High resolution 3D coronary artery MR angiography is time-consuming and can benefit from accelerated data acquisition provided by parallel imaging techniques without sacrificing spatial resolution. Currently, popular maximum likelihood based parallel imaging reconstruction techniques such as the SENSE algorithm offer this advantage at the cost of reduced signal-to-noise ratio (SNR). Maximum a posteriori (MAP) reconstruction techniques that incorporate globally smooth priors have been developed to recover this SNR loss, but they tend to blur sharp edges in the target image. The objective of this study is to demonstrate the feasibility of employing edge-preserving Markov random field priors in a MAP reconstruction framework, which can be solved efficiently using a graph cuts based optimization algorithm. The preliminary human study shows that our reconstruction provides significantly better SNR than the SENSE reconstruction performed by a commercially available scanner for navigator gated steady state free precession 3D coronary magnetic resonance angiography images (n = 4).