Regularized Field Map Estimation in MRI

In fast magnetic resonance (MR) imaging with long readout times, such as echo-planar imaging (EPI) and spiral scans, it is important to correct for the effects of field inhomogeneity to reduce image distortion and blurring. Such corrections require an accurate field map, a map of the off-resonance frequency at each voxel. Standard field map estimation methods yield noisy field maps, particularly in image regions with low spin density. This paper describes regularized methods for field map estimation from two or more MR scans having different echo times. These methods exploit the fact that field maps are generally smooth functions. The methods use algorithms that decrease monotonically a regularized least-squares cost function, even though the problem is highly nonlinear. Results show that the proposed regularized methods significantly improve the quality of field map estimates relative to conventional unregularized methods.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85871/1/Fessler22.pd

Physics-based Reconstruction Methods for Magnetic Resonance Imaging

Conventional Magnetic Resonance Imaging (MRI) is hampered by long scan times and only qualitative image contrasts that prohibit a direct comparison between different systems. To address these limitations, model-based reconstructions explicitly model the physical laws that govern the MRI signal generation. By formulating image reconstruction as an inverse problem, quantitative maps of the underlying physical parameters can then be extracted directly from efficiently acquired k-space signals without intermediate image reconstruction -- addressing both shortcomings of conventional MRI at the same time. This review will discuss basic concepts of model-based reconstructions and report about our experience in developing several model-based methods over the last decade using selected examples that are provided complete with data and code.Comment: 8 figures, review accepted to Philos. Trans. R. Soc.

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

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

Optimizing Magnetic Resonance Imaging for Image-Guided Radiotherapy

Magnetic resonance imaging (MRI) is playing an increasingly important role in image-guided radiotherapy. MRI provides excellent soft tissue contrast, and is flexible in characterizing various tissue properties including relaxation, diffusion and perfusion. This thesis aims at developing new image analysis and reconstruction algorithms to optimize MRI in support of treatment planning, target delineation and treatment response assessment for radiotherapy. First, unlike Computed Tomography (CT) images, MRI cannot provide electron density information necessary for radiation dose calculation. To address this, we developed a synthetic CT generation algorithm that generates pseudo CT images from MRI, based on tissue classification results on MRI for female pelvic patients. To improve tissue classification accuracy, we learnt a pelvic bone shape model from a training dataset, and integrated the shape model into an intensity-based fuzzy c-menas classification scheme. The shape-regularized tissue classification algorithm is capable of differentiating tissues that have significant overlap in MRI intensity distributions. Treatment planning dose calculations using synthetic CT image volumes generated from the tissue classification results show acceptably small variations as compared to CT volumes. As MRI artifacts, such as B1 filed inhomogeneity (bias field) may negatively impact the tissue classification accuracy, we also developed an algorithm that integrates the correction of bias field into the tissue classification scheme. We modified the fuzzy c-means classification by modeling the image intensity as the true intensity corrupted by the multiplicative bias field. A regularization term further ensures the smoothness of the bias field. We solved the optimization problem using a linearized alternating direction method of multipliers (ADMM) method, which is more computational efficient over existing methods. The second part of this thesis looks at a special MR imaging technique, diffusion-weighted MRI (DWI). By acquiring a series of DWI images with a wide range of b-values, high order diffusion analysis can be performed using the DWI image series and new biomarkers for tumor grading, delineation and treatment response evaluation may be extracted. However, DWI suffers from low signal-to-noise ratio at high b-values, and the multi-b-value acquisition makes the total scan time impractical for clinical use. In this thesis, we proposed an accelerated DWI scheme, that sparsely samples k-space and reconstructs images using a model-based algorithm. Specifically, we built a 3D block-Hankel tensor from k-space samples, and modeled both local and global correlations of the high dimensional k-space data as a low-rank property of the tensor. We also added a phase constraint to account for large phase variations across different b-values, and to allow reconstruction from partial Fourier acquisition, which further accelerates the image acquisition. We proposed an ADMM algorithm to solve the constrained image reconstruction problem. Image reconstructions using both simulated and patient data show improved signal-to-noise ratio. As compared to clinically used parallel imaging scheme which achieves a 4-fold acceleration, our method achieves an 8-fold acceleration. Reconstructed images show reduced reconstruction errors as proved on simulated data and similar diffusion parameter mapping results on patient data.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143919/1/llliu_1.pd