Automatic Spatial Calibration of Ultra-Low-Field MRI for High-Accuracy Hybrid MEG--MRI

With a hybrid MEG--MRI device that uses the same sensors for both modalities, the co-registration of MRI and MEG data can be replaced by an automatic calibration step. Based on the highly accurate signal model of ultra-low-field (ULF) MRI, we introduce a calibration method that eliminates the error sources of traditional co-registration. The signal model includes complex sensitivity profiles of the superconducting pickup coils. In ULF MRI, the profiles are independent of the sample and therefore well-defined. In the most basic form, the spatial information of the profiles, captured in parallel ULF-MR acquisitions, is used to find the exact coordinate transformation required. We assessed our calibration method by simulations assuming a helmet-shaped pickup-coil-array geometry. Using a carefully constructed objective function and sufficient approximations, even with low-SNR images, sub-voxel and sub-millimeter calibration accuracy was achieved. After the calibration, distortion-free MRI and high spatial accuracy for MEG source localization can be achieved. For an accurate sensor-array geometry, the co-registration and associated errors are eliminated, and the positional error can be reduced to a negligible level.Comment: 11 pages, 8 figures. This work is part of the BREAKBEN project and has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 68686

Image Encoding and Reconstruction for Portable Magnetic Resonance Imaging

Magnetic Resonance Imaging (MRI) is a successful imaging tool, but due to high cost, high weight and complexity of equipment, MRI is not currently as easily accessible clinically as desired. By making MRI cheaper, lighter, less complex and therefore potentially portable, it can become more widely accessible. A portable MRI system can be used in primary health care, operating and emergency rooms, car and air ambulances, sport and war facilities and remote regions (including outer space). The objective of this study was to show the feasibility of reconstructing MRI signals generated by two different portable MRI systems. The first portable MRI, known as radiofrequency (RF) phase encoded MRI, encodes spatial information through the use of a non-linear spatially varying RF transmit ($B_1$) phase. The second portable MRI, known as rotating field MRI, encodes information through non-uniform radially varying main magnet field ($B_0$). The fact that there is no need for gradient coils in both systems, leads to a smaller, lighter and more affordable MRI than most conventional systems. In RF phase encoded MRI, since the $B_1$ phase spatially varies non-linearly, using Fourier transform (FT) to reconstruct images results in distorted images. Therefore, regularized least squares inversion was used in place of the usual FT. The RF phase encoding coil generates an inhomogeneous $B_1$ field that leads to RF pulse imperfection in terms of flip angles produced versus flip angles intended. Composite pulses were therefore used to minimize the effect of RF transmit field inhomogeneity on tip angles. In rotating field MRI, a Halbach magnet was used to generate a non-uniform radially varying $B_0$ field to encode information in the radial direction. For encoding information in the angular direction two separate Saddle RF receiver coils were used. The main magnet and receiver coils are fixed relative to each other, but both rotate around the object. A regularized least squares inversion (LS) method followed by total variation (TV) techniques were used to reconstruct the images. MRI simulation signals encoded in RF transmit field with non-linearly varying spatial phase may be accurately reconstructed using regularized LS method thus pointing the way to the use of simple RF coil designs for RF encoded MRI. Also, my results from simulation and experimental data, indicated the feasibility of reconstructing images from rotating field MRI. I have made progress in the realization of a novel approach to different MRI systems that do not rely on active magnetic gradient fields. These two methods can be combined to encode information in 3 dimensions (3D) in the future, for example inhomogeneous $B_0$ field can be used for slice selection and RF phase encoding can be used to encode information in the plane

Conjugate Phase MRI Reconstruction With Spatially Variant Sample Density Correction

A new image reconstruction method to correct for the effects of magnetic field inhomogeneity in non-Cartesian sampled magnetic resonance imaging (MRI) is proposed. The conjugate phase reconstruction method, which corrects for phase accumulation due to applied gradients and magnetic field inhomogeneity, has been commonly used for this case. This can lead to incomplete correction, in part, due to the presence of gradients in the field inhomogeneity function. Based on local distortions to the k-space trajectory from these gradients, a spatially variant sample density compensation function is introduced as part of the conjugate phase reconstruction. This method was applied to both simulated and experimental spiral imaging data and shown to produce more accurate image reconstructions. Two approaches for fast implementation that allow the use of fast Fourier transforms are also described. The proposed method is shown to produce fast and accurate image reconstructions for spiral sampled MRI.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85978/1/Fessler52.pd

Advancing Magnetic Resonance Spectroscopy and Endoscopy with Prior Knowledge

Reconstruction is key to the generation of anatomic, functional and biochemical information in the field of Magnetic Resonance (MR) in medicine. Here, prior knowledge based on various conditions is utilized through reconstruction to accelerate current MR techniques and reduce artifacts. First, prior knowledge from Magnetic Resonance Imaging (MRI) is exploited to accelerate spatial localization in Magnetic Resonance Spectroscopy (MRS). The MRS information is contained in one extra chemical shift dimension, beyond the three spatial dimensions of MRI, and can provide valuable in vivo metabolic information for the study of numerous diseases. However, its research and clinical applications are often compromised by long scan times. Here, a new method of localized Spectroscopy with Linear Algebraic Modeling (SLAM) is proposed for accelerating MRS scans. The method assumes pre-conditions that the MRS scan is preceded by a scout MRI scan and that a compartment-averaged MRS measurement will suffice for the assessment of metabolic status. SLAM builds a priori MRI-based segmentation information into the standard Fourier-encoded MRS model of chemical shift imaging (CSI), to directly reconstruct compartmental spectra. Second, SLAM is extended to higher dimensions and to incorporate parallel imaging techniques that deploy pre-acquired sensitivity information based on the use of separate multiple receive-coil elements, to further accelerate scan speed. In addition, eddy current-induced phase effects are incorporated into the SLAM model, and a modified reconstruction algorithm provides improved suppression of signal leakage due to heterogeneity in the MRS signal, especially when employing sensitivity encoding. Third, prior information from MRI is also used to reduce the problem of lipid artifacts in 1H brain CSI. CSI is routinely used for human brain MRS studies, and low spatial resolution in CSI causes partial volume error and signal ‘bleed’ that is especially deleterious to voxels near the scalp. A standard solution is to apply spatial apodization, which adversely affects spatial resolution. Here, a novel automated strategy for partial volume correction that employs grid shifting (‘PANGS’) is presented, which minimizes lipid signal bleed without compromising spatial resolution. PANGS shifts the reconstruction coordinate in a designated region of image space—the scalp, identified by MRI—to match the tissue center of mass instead of the geometric center of each voxel. Last, prior knowledge of the spatially sparse nature of endoscopic MRI images acquired with tiny internal MRI antennae, and that of the null signal location of the endoscopic probe, are used to accelerate MR endoscopy and reduce motion artifacts. High-resolution endoscopic MRI is susceptible to degradation from physiological motion, which can necessitate time-consuming cardiac gating techniques. Here, we develop acceleration techniques based on the compressed sensing theory, and un-gated motion compensation strategies using projection shifting, to effectively produce faster motion-suppressed MRI endoscopy

TRIPLE QUANTUM IMAGING OF SODIUM IN INHOMOGENEOUS FIELDS

Triple quantum filtered sodium MRI techniques have been recently demonstrated in vivo. These techniques have been previously advocated as a means to separate the sodium NMR signal from different physiological compartments based on the differences between their relaxation rates. Among the different triple quantum coherence transfer filters, the three-pulse coherence transfer filter has been demonstrated to be better suited for human imaging than the traditional four-pulse implementation. While the three-pulse structure has distinct advantages in terms of the radiofrequency power efficiency, it is characterized, also, by an increased dependence on the main magnetic field inhomogeneities. In this thesis, we characterize these dependences and introduce a method for their compensation through the acquisition of a field map and the use of a modified phase cycling scheme.We analyze the dynamics of spin 3/2 systems using the density matrix theory of relaxation. We show that by using the superoperator formalism, we can obtain an algebraic formulation of the density matrix's evolution, in which the contributions from relaxation and radio frequency application are factored out. To achieve this goal, we derive an exact form for the propagator of the density matrix, in the presence of both static quadrupolar couplings and magnetic field inhomogeneities.Using the algebraic formulation, we derive exact expressions for the behavior of the density matrix in the classical one-, two- and three-pulse NMR experiments. These theoretical formulas are then used to illustrate the bias introduced on the measured relaxation parameters by the presence of large spatial variations in the B0 and B1 fields. This approach is proved useful for the characterization of the spatial variations of the signal intensity in multiple quantum-filtered sodium MRI experiments.On the imaging applications side, we demonstrate that the conventional on-the-fly triple quantum filtered schemes are affected by the presence of B0 inhomogeneities in a severe way, which can be described as destructive interference among several coherence pathways. A new class of robust filtering schemes is introduced to avoid the destructive interference. The usefulness of the introduced technique is experimentally illustrated for twisted projection imaging, for human brain imaging

Acquisition and Reconstruction Techniques for Fat Quantification Using Magnetic Resonance Imaging

Quantifying the tissue fat concentration is important for several diseases in various organs including liver, heart, skeletal muscle and kidney. Uniquely, MRI can separate the signal from water and fat in-vivo, rendering it the most suitable imaging modality for non-invasive fat quantification. Chemical-shift-encoded MRI is commonly used for quantitative fat measurement due to its unique ability to generate a separate image for water and fat. The tissue fat concentration can be consequently estimated from the two images. However, several confounding factors can hinder the water/fat separation process, leading to incorrect estimation of fat concentration. The inhomogeneities of the main magnetic field represent the main obstacle to water/fat separation. Most existing techniques rely mainly on imposing spatial smoothness constraints to address this problem; however, these often fail to resolve large and abrupt variations in the magnetic field. A novel convex relaxation approach to water/fat separation is proposed. The technique is compared to existing methods, demonstrating its robustness to resolve abrupt magnetic field inhomogeneities. Water/fat separation requires the acquisition of multiple images with different echo-times, which prolongs the acquisition time. Bipolar acquisitions can efficiently acquire the required data in shorter time. However, they induce phase errors that significantly distort the fat measurements. A new bipolar acquisition strategy that overcomes the phase errors and provides accurate fat measurements is proposed. The technique is compared to the current clinical sequence, demonstrating its efficiency in phantoms and in-vivo experiments. The proposed acquisition technique is also applied on animal models to achieve higher spatial resolution than the current sequence. In conclusion, this dissertation describes a complete framework for accurate and precise MRI fat quantification. Novel acquisitions and reconstruction techniques that address the current challenges for fat quantification are proposed

Toeplitz-Based Iterative Image Reconstruction for MRI With Correction for Magnetic Field Inhomogeneity

In some types of magnetic resonance (MR) imaging, particularly functional brain scans, the conventional Fourier model for the measurements is inaccurate. Magnetic field inhomogeneities, which are caused by imperfect main fields and by magnetic susceptibility variations, induce distortions in images that are reconstructed by conventional Fourier methods. These artifacts hamper the use of functional MR imaging (fMRI) in brain regions near air/tissue interfaces. Recently, iterative methods that combine the conjugate gradient (CG) algorithm with nonuniform FFT (NUFFT) operations have been shown to provide considerably improved image quality relative to the conjugate-phase method. However, for non-Cartesian k-space trajectories, each CG-NUFFT iteration requires numerous k-space interpolations; these are operations that are computationally expensive and poorly suited to fast hardware implementations. This paper proposes a faster iterative approach to field-corrected MR image reconstruction based on the CG algorithm and certain Toeplitz matrices. This CG-Toeplitz approach requires k-space interpolations only for the initial iteration; thereafter, only fast Fourier transforms (FFTs) are required. Simulation results show that the proposed CG-Toeplitz approach produces equivalent image quality as the CG-NUFFT method with significantly reduced computation time.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85903/1/Fessler50.pd