A Study of Nonlinear Approaches to Parallel Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) has revolutionized radiology in the past four decades by its ability to visualize not only the detailed anatomical structures, but also function and metabolism information. A major limitation with MRI is its low imaging speed, which makes it difficult to image the moving objects. Parallel MRI (pMRI) is an emerging technique to increase the speed of MRI. It acquires the MRI data from multiple coils simultaneously such that fast imaging can be achieved by reducing the amount of data acquired in each coil. Several methods have developed to reconstruct the original image using the reduced data from multiple coils based on their distinct spatial sensitivities. Among the existing methods, Sensitivity Encoding (SENSE) and GeneRally Autocalibrating Partially Parallel Acquisition (GRAPPA) are commercially used reconstruction methods for parallel MRI. Both methods use linear approaches for image reconstruction. GRAPPA is known to outperform SENSE because no coil sensitivities are needed in reconstruction. However, GRAPPA can only accelerate the speed by a factor of 2-3. The objective of this dissertation is to develop novel techniques to significantly improve the acceleration factor upon the existing GRAPPA methods. Motivated by the success of recent study in our group which has demonstrated the benefit of nonlinear approaches for SENSE, in this dissertation, nonlinear approaches are studied for GRAPPA. Based on the fact that GRAPPA needs a calibration step before reconstruction, nonlinear models are investigated in both calibration and reconstruction using a kernel method widely used in machine learning. In addition, compressed sensing (CS), a nonlinear optimization technique will also be incorporated for even higher accelerations. In order to reduce the computation time, a nonlinear approach is proposed to reduce the effective number of coils in reconstruction. The imaging speed is expected to improve by a factor of 4-6 using the proposed nonlinear techniques. These new techniques will find many applications in accurate brain imaging, dynamic cardiac imaging, functional imaging, and so forth

High Impedance Detector Arrays for Magnetic Resonance

Resonant inductive coupling is commonly seen as an undesired fundamental phenomenon emergent in densely packed resonant structures, such as nuclear magnetic resonance phased array detectors. The need to mitigate coupling imposes rigid constraints on the detector design, impeding performance and limiting the scope of magnetic resonance experiments. Here we introduce a high impedance detector design, which can cloak itself from electrodynamic interactions with neighboring elements. We verify experimentally that the high impedance detectors do not suffer from signal-to-noise degradation mechanisms observed with traditional low impedance elements. Using this new-found robustness, we demonstrate an adaptive wearable detector array for magnetic resonance imaging of the hand. The unique properties of the detector glove reveal new pathways to study the biomechanics of soft tissues, and exemplify the enabling potential of high-impedance detectors for a wide range of demanding applications that are not well suited to traditional coil designs.Comment: 16 pages, 12 figures, videos available upon reques

A Fourier Description of Covariance, and Separation of Simultaneously Encoded Slices with In-Plane Acceleration in fMRI

Functional magnetic resonance imaging (fMRI) studies aim to identify localized neural regions associated with a cognitive task performed by the subject. An indirect measure of the brain activity is the blood oxygenation level dependent (BOLD) signal fluctuations observed within the complex-valued spatial frequencies measured over time. The standard practice in fMRI is to discard the phase information after image reconstruction, even with evidence of biological task-related change in the phase time-series. In the first aim of this dissertation, a complex-valued time-series covariance is derived as a linear combination of second order temporal Fourier frequency coefficients. As opposed to magnitude-only analysis, the complex-valued covariance increases the sensitivity and specificity in fMRI correlation analysis, which is particularly advantageous for low contrast-to-noise ratio (CNR) fMRI time-series. In the remaining aims, increased statistical significance is achieved through a higher sampling rate of the fMRI time-course, by simultaneously magnetizing multiple slice images. With multi-frequency band excitations, a single k-space readout reconstructs to an image of composite aliased slice images. To disentangle the signal, or aliased voxels, phase and coil encoding techniques are incorporated into the data acquisition and image reconstruction. Inter-slice signal leakage, which also manifests as improper placement of the BOLD signal, presents in the separated slice images from induced correlations as a result of suboptimal simultaneous multi-slice (SMS) reconstruction methods. In the second aim of this dissertation, the Multi-coil Separation of Parallel Encoded Complex-valued Slices (mSPECS) reconstruction method is proposed as a solution to preserve the activation statistics in the separated slice images through a Bayesian approach of sampling calibration images. In the third aim of this dissertation, the mSPECS reconstruction is extended to include In-Plane Acceleration (mSPECS-IPA), to reconstruct aliased slice images with additional in-plane subsampling using a two-dimensional orthogonal phase encoding derivation of Hadamard encoding. Mitigating induced correlations with mSPECS(-IPA), results in accurately placed functional activation in the previously aliased complex-valued slice images. The development of novel complex-valued analysis and reconstruction methods in fMRI strengthens the significance of the activation statistics and precludes inter-slice signal leakage, so the true underlying neural dynamics are modeled in complex-valued fMRI data analysis

Simulated design strategies for SPECT collimators to reduce the eddy currents induced by MRI gradient fields

Combining single photon emission computed tomography (SPECT) with magnetic resonance imaging (MRI) requires the insertion of highly conductive SPECT collimators inside the MRI scanner, resulting in an induced eddy current disturbing the combined system. We reduced the eddy currents due to the insert of a novel tungsten collimator inside transverse and longitudinal gradient coils. The collimator was produced with metal additive manufacturing, that is part of a microSPECT insert for a preclinical SPECT/MRI scanner. We characterized the induced magnetic field due to the gradient field and adapted the collimators to reduce the induced eddy currents. We modeled the x-, y-, and z-gradient coil and the different collimator designs and simulated them with FEKO, a three-dimensional method of moments / finite element methods (MoM/FEM) full-wave simulation tool. We used a time analysis approach to generate the pulsed magnetic field gradient. Simulation results show that the maximum induced field can be reduced by 50.82% in the final design bringing the maximum induced magnetic field to less than 2% of the applied gradient for all the gradient coils. The numerical model was validated with measurements and was proposed as a tool for studying the effect of a SPECT collimator within the MRI gradient coils

Structured low-rank methods for robust 3D multi-shot EPI

Magnetic resonance imaging (MRI) has inherently slow acquisition speed, and Echo-Planar Imaging (EPI), as an efficient acquisition scheme, has been widely used in functional magnetic resonance imaging (fMRI) where an image series with high temporal resolution is needed to measure neuronal activity. Recently, 3D multi-shot EPI which samples data from an entire 3D volume with repeated shots has been drawing growing interest for fMRI with its high isotropic spatial resolution, particularly at ultra-high fields. However, compared to single-shot EPI, multi-shot EPI is sensitive to any inter-shot instabilities, e.g., subject movement and even physiologically induced field fluctuations. These inter-shot inconsistencies can greatly negate the theoretical benefits of 3D multi-shot EPI over conventional 2D multi-slice acquisitions. Structured low-rank image reconstruction which regularises under-sampled image reconstruction by exploiting the linear dependencies in MRI data has been successfully demonstrated in a variety of applications. In this thesis, a structured low-rank reconstruction method is optimised for 3D multi-shot EPI imaging together with a dedicated sampling pattern termed seg-CAIPI, in order to enhance the robustness to physiological fluctuations and improve the temporal stability of 3D multi-shot EPI for fMRI at 7T. Moreover, a motion compensated structured low-rank reconstruction framework is also presented for robust 3D multi-shot EPI which further takes into account inter-shot instabilities due to bulk motion. Lastly, this thesis also investigates into the improvement of structured low-rank reconstruction from an algorithmic perspective and presents the locally structured low-rank reconstruction scheme

Hybrid Parallel Imaging and Compressed Sensing MRI Reconstruction with GRAPPA Integrated Multi-loss Supervised GAN

Objective: Parallel imaging accelerates the acquisition of magnetic resonance imaging (MRI) data by acquiring additional sensitivity information with an array of receiver coils resulting in reduced phase encoding steps. Compressed sensing magnetic resonance imaging (CS-MRI) has achieved popularity in the field of medical imaging because of its less data requirement than parallel imaging. Parallel imaging and compressed sensing (CS) both speed up traditional MRI acquisition by minimizing the amount of data captured in the k-space. As acquisition time is inversely proportional to the number of samples, the inverse formation of an image from reduced k-space samples leads to faster acquisition but with aliasing artifacts. This paper proposes a novel Generative Adversarial Network (GAN) namely RECGAN-GR supervised with multi-modal losses for de-aliasing the reconstructed image. Methods: In contrast to existing GAN networks, our proposed method introduces a novel generator network namely RemU-Net integrated with dual-domain loss functions including weighted magnitude and phase loss functions along with parallel imaging-based loss i.e., GRAPPA consistency loss. A k-space correction block is proposed as refinement learning to make the GAN network self-resistant to generating unnecessary data which drives the convergence of the reconstruction process faster. Results: Comprehensive results show that the proposed RECGAN-GR achieves a 4 dB improvement in the PSNR among the GAN-based methods and a 2 dB improvement among conventional state-of-the-art CNN methods available in the literature. Conclusion and significance: The proposed work contributes to significant improvement in the image quality for low retained data leading to 5x or 10x faster acquisition.Comment: 12 pages, 11 figure

Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI

Parallel MRI is a fast imaging technique that enables the acquisition of highly resolved images in space or/and in time. The performance of parallel imaging strongly depends on the reconstruction algorithm, which can proceed either in the original k-space (GRAPPA, SMASH) or in the image domain (SENSE-like methods). To improve the performance of the widely used SENSE algorithm, 2D- or slice-specific regularization in the wavelet domain has been deeply investigated. In this paper, we extend this approach using 3D-wavelet representations in order to handle all slices together and address reconstruction artifacts which propagate across adjacent slices. The gain induced by such extension (3D-Unconstrained Wavelet Regularized -SENSE: 3D-UWR-SENSE) is validated on anatomical image reconstruction where no temporal acquisition is considered. Another important extension accounts for temporal correlations that exist between successive scans in functional MRI (fMRI). In addition to the case of 2D+t acquisition schemes addressed by some other methods like kt-FOCUSS, our approach allows us to deal with 3D+t acquisition schemes which are widely used in neuroimaging. The resulting 3D-UWR-SENSE and 4D-UWR-SENSE reconstruction schemes are fully unsupervised in the sense that all regularization parameters are estimated in the maximum likelihood sense on a reference scan. The gain induced by such extensions is illustrated on both anatomical and functional image reconstruction, and also measured in terms of statistical sensitivity for the 4D-UWR-SENSE approach during a fast event-related fMRI protocol. Our 4D-UWR-SENSE algorithm outperforms the SENSE reconstruction at the subject and group levels (15 subjects) for different contrasts of interest (eg, motor or computation tasks) and using different parallel acceleration factors (R=2 and R=4) on 2x2x3mm3 EPI images.Comment: arXiv admin note: substantial text overlap with arXiv:1103.353

Advanced methods for mapping the radiofrequency magnetic fields in MRI

As MRI systems have increased in static magnetic field strength, the radiofrequency (RF) fields that are used for magnetisation excitation and signal reception have become significantly less uniform. This can lead to image artifacts and errors when performing quantitative MRI. A further complication arises if the RF fields vary substantially in time. In the first part of this investigation temporal variations caused by respiration were explored on a 3T scanner. It was found that fractional changes in transmit field amplitude between inhalation and expiration ranged from 1% to 14% in the region of the liver in a small group of normal subjects. This observation motivated the development of a pulse sequence and reconstruction method to allow dynamic observation of the transmit field throughout the respiratory cycle. However, the proposed method was unsuccessful due to the inherently time-consuming nature of transmit field mapping sequences. This prompted the development of a novel data reconstruction method to allow the acceleration of transmit field mapping sequences. The proposed technique posed the RF field reconstruction as a nonlinear least-squares optimisation problem, exploiting the fact that the fields vary smoothly. It was shown that this approach was superior to standard reconstruction approaches. The final component of this thesis presents a unified approach to RF field calibration. The proposed method uses all measured data to estimate both transmit and receive sensitivities, whilst simultaneously insisting that they are smooth functions of space. The resulting maps are robust to both noise and imperfections in regions of low signal

Accelerated Imaging Techniques for Chemical Shift Magnetic Resonance Imaging

Chemical shift imaging is a method for the separation two or more chemical species. The cost of chemical shift encoding is increased acquisition time as multiple acquisitions are acquired at different echo times. Image acceleration techniques, typically parallel imaging, are often used to improve the spatial coverage and resolution. This thesis describes a new technique for estimating the signal to noise ratio for parallel imaging reconstructions and proposes new image reconstructions for accelerated chemical shift imaging using compressed sensing and/or parallel imaging for two applications: water-fat separation and metabolic imaging of hyperpolarized [1-13C] pyruvate. Spatially varying noise in parallel imaging reconstructions makes measurements of the signal to noise ratio, a commonly used metric for image for image quality, difficult. Existing approaches have limitations such as they are not applicable to all reconstructions, require significant computation time, or rely on repeated image acquisitions. A SNR estimation technique is proposed that does not exhibit these limitations. Water-fat imaging of highly undersampled datasets from the liver, calf, knee, and abdominal cavity are demonstrated using a customized IDEAL-SPGR pulse sequence and an integrated compressed sensing, parallel imaging, water-fat reconstruction. This method is shown to offer comparable image quality relative to fully sampled reference images for a range of acceleration factors. At high acceleration factors, this technique is shown to offer improved image quality when compared to the current standard of parallel imaging. Accelerated chemical shift imaging was demonstrated for metabolic of hyperpolarized [1-13C] pyruvate. Pyruvate, lactate, alanine, and bicarbonate images were reconstructed from undersampled datasets. Phantoms were used to validate this technique while retrospectively and prospectively accelerated 3D in vivo datasets were used to demonstrate. Alternatively, acceleration was also achieved through the use of a high performance magnetic field gradient set. This thesis addresses the inherently slow acquisition times of chemical shift imaging by examining the role compressed sensing and parallel imaging can be play in chemical shift imaging. An approach to SNR assessment for parallel imaging reconstruction is proposed and approaches to accelerated chemical shift imaging are described for applications in water-fat imaging and metabolic imaging of hyperpolarized [1-13C] pyruvate