Motion-robust free-running volumetric cardiovascular MRI.

Purpose: To present and assess an outlier mitigation method that makes free-running volumetric cardiovascular MRI (CMR) more robust to motion.

Methods: The proposed method, called compressive recovery with outlier rejection (CORe), models outliers in the measured data as an additive auxiliary variable. We enforce MR physics-guided group sparsity on the auxiliary variable, and jointly estimate it along with the image using an iterative algorithm.

Maximum Likelihood Estimation in Mixed Integer Linear Models.

We consider the maximum likelihood (ML) parameter estimation problem for mixed integer linear models with arbitrary noise covariance. This problem appears in applications such as single frequency estimation, phase contrast imaging, and direction of arrival (DoA) estimation. Parameter estimates are found by solving a closest lattice point problem, which requires a lattice basis.

Alias-Free Arrays.

Nonuniform array geometries provide freedom for increased aperture and reduced mutual coupling. A necessary and sufficient condition is given for an array of isotropic sensor elements to be unambiguous for any specified set of directions of arrival. The set of unambiguous spatial frequencies is shown to be a parallelepiped, admitting simple geometrical interpretation.

Venc Design and Velocity Estimation for Phase Contrast MRI.

In phase-contrast magnetic resonance imaging (PC-MRI), spin velocity contributes to the phase measured at each voxel. Therefore, estimating velocity from potentially wrapped phase measurements is the task of solving a system of noisy congruence equations. We propose Phase Recovery from Multiple Wrapped Measurements (PRoM) as a fast, approximate maximum likelihood estimator of velocity from multi-coil data with possible amplitude attenuation due to dephasing.

MAXIMIZING UNAMBIGUOUS VELOCITY RANGE IN PHASE-CONTRAST MRI WITH MULTIPOINT ENCODING.

In phase-contrast magnetic resonance imaging (PC-MRI), the velocity of spins at a voxel is encoded in the image phase. The strength of the velocity encoding gradient offers a trade-off between the velocity-to-noise ratio (VNR) and the extent of phase aliasing. Phase differences provide invariance to an unknown background phase.

CALIBRATIONLESS MRI RECONSTRUCTION WITH A PLUG-IN DENOISER.

Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique that provides excellent soft-tissue contrast without using ionizing radiation. MRI's clinical application may be limited by long data acquisition time; therefore, MR image reconstruction from highly under-sampled k-space data has been an active research area. Calibrationless MRI not only enables a higher acceleration rate but also increases flexibility for sampling pattern design.

High-dimensional fast convolutional framework (HICU) for calibrationless MRI.

Purpose: To present a computational procedure for accelerated, calibrationless magnetic resonance image (Cl-MRI) reconstruction that is fast, memory efficient, and scales to high-dimensional imaging.

Theory And Methods: Cl-MRI methods can enable high acceleration rates and flexible sampling patterns, but their clinical application is limited by computational complexity and large memory footprint. The proposed computational procedure, HIgh-dimensional fast convolutional framework (HICU), provides fast, memory-efficient recovery of unsampled k-space points.

Patient-Adaptive Magnetic Resonance Oximetry: Comparison With Invasive Catheter Measurement of Blood Oxygen Saturation in Patients With Cardiovascular Disease.

Background: The current standard method to measure intracardiac oxygen (O ) saturation is by invasive catheterization. Accurate noninvasive blood O saturation by MRI could potentially reduce the duration and risk of invasive diagnostic procedures.

Purpose: To noninvasively determine blood oxygen saturation in the heart with MRI and compare the accuracy with catheter measurements.

CONVOLUTIONAL FRAMEWORK FOR ACCELERATED MAGNETIC RESONANCE IMAGING.

Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique that provides exquisite soft-tissue contrast without using ionizing radiation. The clinical application of MRI may be limited by long data acquisition times; therefore, MR image reconstruction from highly undersampled k-space data has been an active area of research. Many works exploit rank deficiency in a Hankel data matrix to recover unobserved k-space samples; the resulting problem is non-convex, so the choice of numerical algorithm can significantly affect performance, computation, and memory.

A Bayesian approach for 4D flow imaging of aortic valve in a single breath-hold.

Purpose: To develop and validate a data processing technique that allows phase-contrast MRI-based 4D flow imaging of the aortic valve in a single breath-hold.

Theory And Methods: To regularize the ill-posed inverse problem, we extend a recently proposed 2D phase-contrast MRI method to 4D flow imaging. Adopting an empirical Bayes approach, spatial and temporal redundancies are exploited via sparsity in the wavelet domain, and the voxel-wise magnitude and phase structure across encodings is captured in a conditional mixture prior that applies regularizing constraints based on the presence of flow.

A bayesian method for accelerated magnetic resonance elastography of the liver.

Purpose: Magnetic resonance elastography (MRE) is a noninvasive tool for quantifying soft tissue stiffness. MRE has been adopted as a clinical method for staging liver fibrosis. The application of liver MRE, however, requires multiple lengthy breath holds.

CMR-based blood oximetry via multi-parametric estimation using multiple T2 measurements.

Background: Measurement of blood oxygen saturation (O2 saturation) is of great importance for evaluation of patients with many cardiovascular diseases, but currently there are no established non-invasive methods to measure blood O2 saturation in the heart. While T2-based CMR oximetry methods have been previously described, these approaches rely on technique-specific calibration factors that may not generalize across patient populations and are impractical to obtain in individual patients. We present a solution that utilizes multiple T2 measurements made using different inter-echo pulse spacings.

Quantification of aortic stenosis diagnostic parameters: comparison of fast 3 direction and 1 direction phase contrast CMR and transthoracic echocardiography.

Background: Aortic stenosis (AS) is a common valvular disorder, and disease severity is currently assessed by transthoracic echocardiography (TTE). However, TTE results can be inconsistent in some patients, thus other diagnostic modalities such as cardiovascular magnetic resonance (CMR) are demanded. While traditional unidirectional phase-contrast CMR (1Dir PC-CMR) underestimates velocity if the imaging plane is misaligned to the flow direction, multi-directional acquisitions are expected to improve velocity measurement accuracy.

A Bayesian model for highly accelerated phase-contrast MRI.

Purpose: Phase-contrast magnetic resonance imaging is a noninvasive tool to assess cardiovascular disease by quantifying blood flow; however, low data acquisition efficiency limits the spatial and temporal resolutions, real-time application, and extensions to four-dimensional flow imaging in clinical settings. We propose a new data processing approach called Reconstructing Velocity Encoded MRI with Approximate message passing aLgorithms (ReVEAL) that accelerates the acquisition by exploiting data structure unique to phase-contrast magnetic resonance imaging.

Theory And Methods: The proposed approach models physical correlations across space, time, and velocity encodings.

Pitfalls and possibilities of radar compressive sensing.

In this paper, we consider the application of compressive sensing (CS) to radar remote sensing applications. We survey a suite of practical system-level issues related to the compression of radar measurements, and we advocate the consideration of these issues by researchers exploring potential gains of CS in radar applications. We also give abbreviated examples of decades-old radio-frequency (RF) practices that already embody elements of CS for relevant applications.

A subspace-based coil combination method for phased-array magnetic resonance imaging.

Purpose: Coil-by-coil reconstruction methods are followed by coil combination to obtain a single image representing a spin density map. Typical coil combination methods, such as square-root sum-of-squares and adaptive coil combining, yield images that exhibit spatially varying modulation of image intensity. Existing practice is to first combine coils according to a signal-to-noise criterion, then postprocess to correct intensity inhomogeneity.

Ambiguity and regularization in parallel MRI.

In this paper, we formulate the parallel magnetic resonance imaging(pMRI) as a multichannel blind deconvolution problem with subsampling. First, the model allows formal characterization of image solutions consistent with data obtained by uniform subsampling of k-space. Second, the model allows analysis of the minimum set of required calibration data.

EPR oximetry in three spatial dimensions using sparse spin distribution.

A method is presented to use continuous wave electron paramagnetic resonance imaging for rapid measurement of oxygen partial pressure in three spatial dimensions. A particulate paramagnetic probe is employed to create a sparse distribution of spins in a volume of interest. Information encoding location and spectral linewidth is collected by varying the spatial orientation and strength of an applied magnetic gradient field.

Estimation of mean and median pO2 values for a composite EPR spectrum.

Electron paramagnetic resonance (EPR)-based oximetry is capable of quantifying oxygen content in samples. However, for a heterogeneous environment with multiple pO2 values, peak-to-peak linewidth of the composite EPR lineshape does not provide a reliable estimate of the overall pO2 in the sample. The estimate, depending on the heterogeneity, can be severely biased towards narrow components.

Multivariate MIMO FIR inverses.

The problem of computing exact finite impulse response (FIR) inverses for multivariate multiple-input multiple-output (MIMO) FIR systems is considered. Necessary and sufficient conditions for invertibility are given, along with computation techniques. Random systems and structured systems are defined.

Uniform distribution of projection data for improved reconstruction quality of 4D EPR imaging.

In continuous wave (CW) electron paramagnetic resonance imaging (EPRI), high quality of reconstruction in a limited acquisition time is a high priority. It has been shown for the case of 3D EPRI, that a uniform distribution of the projection data generally enhances reconstruction quality. In this work, we have suggested two data acquisition techniques for which the gradient orientations are more evenly distributed over the 4D acquisition space as compared to the existing methods.

A parametric approach to spectral-spatial EPR imaging.

Continuous wave electron paramagnetic resonance imaging for in vivo mapping of spin distribution and spectral shape requires rapid data acquisition. A spectral-spatial imaging technique is presented that provides an order of magnitude reduction in acquisition time, compared to iterative tomographic reprojection. The proposed approach assumes that spectral shapes in the sample are well-approximated by members from a parametric family of functions.