Motion Robust Magnetic Susceptibility and Field Inhomogeneity Estimation Using Regularized Image Restoration Techniques for fMRI

In functional MRI, head motion may cause dynamic nonlinear field-inhomogeneity changes, especially with large out-of-plane rotations. This may lead to dynamic geometric distortion or blurring in the time series, which may reduce activation detection accuracy. The use of image registration to estimate dynamic field inhomogeneity maps from a static field map is not sufficient in the presence of such rotations. This paper introduces a retrospective approach to estimate magnetic susceptibility induced field maps of an object in motion, given a static susceptibility induced field map and the associated object motion parameters. It estimates a susceptibility map from a static field map using regularized image restoration techniques, and applies rigid body motion to the former. The dynamic field map is then computed using susceptibility voxel convolution. The method addresses field map changes due to out-of-plane rotations during time series acquisition and does not involve real time field map acquisitions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85944/1/Fessler233.pd

Advances in Concurrent Motion and Field-Inhomogeneity Correction in Functional MRI.

Head motion and static magnetic field (B0) inhomogeneity are two important sources of image intensity variability in functional MRI (fMRI). Ideally, in MRI, any deviation in B0 homogeneity in an object occurs only by design. However, due to imperfections in the main magnet and gradient coils, and, magnetic susceptibility differences in the object, undesired B0 deviations may occur. This causes geometric distortion in Cartesian EPI images. In addition to spatial shifts and rotations of images, head motion during an fMRI experiment may induce time-varying field-inhomogeneity changes in the brain. As a result, correcting for motion and field-inhomogeneity effects independently of each other with a static field map may be insufficient, especially in the presence of large out-of-plane rotations. Our primary concern is the correction of the combined effects of motion and field-inhomogeneity induced geometric distortion in Cartesian EPI fMRI images. We formulate a concurrent field-inhomogeneity with map-slice-to-volume motion correction, and develop a motion-robust dual-echo bipolar gradient echo static field map estimation method. We also propose and evaluate a penalized weighted least squares approach to dynamic field map estimation using the susceptibility voxel convolution method. This technique accounts for field changes due to out-of-plane rotations, and estimates dynamic field maps from a high resolution static field map without requiring accurate image segmentation, or the use of literature susceptibility values. Experiments with simulated data suggest that the technique is promising, and the method will be applied to real data in future work. In a separate clinical fMRI project, which is independent of the above work, we also formulate a current density weighted index to quantify correspondence between electrocortical stimulation and fMRI maps for brain presurgical planning. The proposed index is formulated with the broader goal of defining safe limits for lesion resection, and is characterized extensively with simulated data. The index is also computed for real human datasets.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60787/1/tbyeo_1.pd

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

Efficient Model-Based Reconstruction for Dynamic MRI

Dynamic magnetic resonance imaging (MRI) has important clinical and neuro- science applications (e.g., cardiac disease diagnosis, neurological behavior studies). It captures an object in motion by acquiring data across time, then reconstructing a sequence of images from them. This dissertation considers efficient dynamic MRI reconstruction using handcrafted models, to achieve fast imaging with high spatial and temporal resolution. Our modeling framework considers data acquisition process, image properties, and artifact correction. The reconstruction model expressed as a large-scale inverse problem requires optimization algorithms to solve, and we consider efficient implementations that make use of underlying problem structures. In the context of dynamic MRI reconstruction, we investigate efficient updates in two frameworks of algorithms for solving a nonsmooth composite convex optimization problem for the low-rank plus sparse (L+S) model. In the proximal gradient framework, current algorithms for the L+S model involve the classical iterative soft thresholding algorithm (ISTA); we consider two accelerated alternatives, one based on the fast iterative shrinkage-thresholding algorithm (FISTA), and the other with the recent proximal optimized gradient method (POGM). In the augmented Lagrangian (AL) framework, we propose an efficient variable splitting scheme based on the form of the data acquisition operator, leading to simpler computation than the conjugate gradient (CG) approach required by existing AL methods. Numerical results suggest faster convergence of our efficient implementations in both frameworks, with POGM providing the fastest convergence overall and the practical benefit of being free of algorithm tuning parameters. In the context of magnetic field inhomogeneity correction, we present an efficient algorithm for a regularized field inhomogeneity estimation problem. Most existing minimization techniques are computationally or memory intensive for 3D datasets, and are designed for single-coil MRI. We consider 3D MRI with optional consideration of coil sensitivity and a generalized expression that addresses both multi-echo field map estimation and water-fat imaging. Our efficient algorithm uses a preconditioned nonlinear conjugate gradient method based on an incomplete Cholesky factorization of the Hessian of the cost function, along with a monotonic line search. Numerical experiments show the computational advantage of the proposed algorithm over state- of-the-art methods with similar memory requirements. In the context of task-based functional MRI (fMRI) reconstruction, we introduce a space-time model that represents an fMRI timeseries as a sum of task-correlated signal and non-task background. Our model consists of a spatiotemporal decomposition based on assumptions of the activation waveform shape, with spatial and temporal smoothness regularization on the magnitude and phase of the timeseries. Compared with two contemporary task fMRI decomposition models, our proposed model yields better timeseries and activation maps on simulated and human subject fMRI datasets with multiple tasks. The above examples are part of a larger framework for model-based dynamic MRI reconstruction. This dissertation concludes by presenting a general framework with flexibility on model assumptions and artifact compensation options (e.g., field inhomogeneity, head motion), and proposing future work ideas on both the framework and its connection to data acquisition.PHDApplied and Interdisciplinary MathematicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168081/1/yilinlin_1.pd

Time-optimized high-resolution readout-segmented diffusion tensor imaging

Readout-segmented echo planar imaging with 2D navigator-based reacquisition is an uprising technique enabling the sampling of high-resolution diffusion images with reduced susceptibility artifacts. However, low signal from the small voxels and long scan times hamper the clinical applicability. Therefore, we introduce a regularization algorithm based on total variation that is applied directly on the entire diffusion tensor. The spatially varying regularization parameter is determined automatically dependent on spatial variations in signal-to-noise ratio thus, avoiding over- or under-regularization. Information about the noise distribution in the diffusion tensor is extracted from the diffusion weighted images by means of complex independent component analysis. Moreover, the combination of those features enables processing of the diffusion data absolutely user independent. Tractography from in vivo data and from a software phantom demonstrate the advantage of the spatially varying regularization compared to un-regularized data with respect to parameters relevant for fiber-tracking such as Mean Fiber Length, Track Count, Volume and Voxel Count. Specifically, for in vivo data findings suggest that tractography results from the regularized diffusion tensor based on one measurement (16 min) generates results comparable to the un-regularized data with three averages (48 min). This significant reduction in scan time renders high resolution (1×1×2.5 mm3) diffusion tensor imaging of the entire brain applicable in a clinical context

Deconvolution‐based distortion correction of EPI using analytic single‐voxel point‐spread functions

Purpose To develop a postprocessing algorithm that corrects geometric distortions due to spatial variations of the static magnetic field amplitude, B0, and effects from relaxation during signal acquisition in EPI. Theory and Methods An analytic, complex point‐spread function is deduced for k‐space trajectories of EPI variants and applied to corresponding acquisitions in a resolution phantom and in human volunteers at 3 T. With the analytic point‐spread function and experimental maps of B0 (and, optionally, the effective transverse relaxation time, urn:x-wiley:07403194:media:mrm28591:mrm28591-math-0004) as input, a point‐spread function matrix operator is devised for distortion correction by a Thikonov‐regularized deconvolution in image space. The point‐spread function operator provides additional information for an appropriate correction of the signal intensity distribution. A previous image combination algorithm for acquisitions with opposite phase blip polarities is adapted to the proposed method to recover destructively interfering signal contributions. Results Applications of the proposed deconvolution‐based distortion correction (“DecoDisCo”) algorithm demonstrate excellent distortion corrections and superior performance regarding the recovery of an undistorted intensity distribution in comparison to a multifrequency reconstruction. Examples include full and partial Fourier standard EPI scans as well as double‐shot center‐out trajectories. Compared with other distortion‐correction approaches, DecoDisCo permits additional deblurring to obtain sharper images in cases of significant urn:x-wiley:07403194:media:mrm28591:mrm28591-math-0005 effects. Conclusion Robust distortion corrections in EPI acquisitions are feasible with high quality by regularized deconvolution with an analytic point‐spread function. The general algorithm, which is publicly released on GitHub, can be straightforwardly adapted for specific EPI variants or other acquisition schemes

Spatiotemporal brain imaging and modeling

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, February 2004.Includes bibliographical references.This thesis integrates hardware development, data analysis, and mathematical modeling to facilitate our understanding of brain cognition. Exploration of these brain mechanisms requires both structural and functional knowledge to (i) reconstruct the spatial distribution of the activity, (ii) to estimate when these areas are activated and what is the temporal sequence of activations, and (iii)to determine how the information flows in the large-scale neural network during the execution of cognitive and/or behavioral tasks. Advanced noninvasive medical imaging modalities are able to locate brain activities at high spatial and temporal resolutions. Quantitative modeling of these data is needed to understand how large-scale distributed neuronal interactions underlying perceptual, cognitive, and behavioral functions emerge and change over time. This thesis explores hardware enhancement and novel analytical approaches to improve the spatiotemporal resolution of single (MRI) or combined (MRI/fMRI and MEG/EEG) imaging modalities. In addition, mathematical approaches for identifying large-scale neural networks and their correlation to behavioral measurements are investigated. Part I of the thesis investigates parallel MRI. New hardware and image reconstruction techniques are introduced to improve spatiotemporal resolution and to reduce image distortion in structural and functional MRI. Part II discusses the localization of MEG/EEG signals on the cortical surface using anatomical information from AMTRI, and takes advantage of the high temporal resolution of MEG/EEG measurements to study cortical oscillations in the human auditory system. Part III introduces a multivariate modeling technique to identify "nodes" and "connectivity" in a(cont.) large-scale neural network and its correlation to behavior measurements in the human motor system.by Fa-Hsuan Lin.Ph.D

Dynamic Image and Fieldmap Joint Estimation Methods for MRI Using Single-Shot Trajectories.

In susceptibility-weighted MRI, ignoring the magnetic field inhomogeneity can lead to severe reconstruction artifacts. Correcting for the effects of magnetic field inhomogeneity requires accurate fieldmaps. Especially in functional MRI, dynamic updates are desirable, since the fieldmap may change in time. Also, susceptibility effects that induce field inhomogeneity often have non-zero through-plane gradients, which, if uncorrected, can cause signal loss in the reconstructed images. Most image reconstruction methods that compensate for field inhomogeneity, even using dynamic fieldmap updates, ignore through-plane fieldmap gradients. Furthermore, standard optimization methods, like CG-based algorithms, may be slow to converge and recently proposed algorithms based on the Augmented Lagrangian (AL) framework have shown the potential to lead to more efficient optimization algorithms, especially in MRI reconstruction problems with non-quadratic regularization. In this work, we propose a computationally efficient, model-based iterative method for joint reconstruction of dynamic images and fieldmaps in single coil and parallel MRI, using single-shot trajectories. We first exploit the fieldmap smoothness to perform joint estimation using less than two full data sets and then we exploit the sensitivity encoding from parallel imaging to reduce the acquisition length and perform joint reconstruction using just one full k-space dataset. Subsequently, we extend the proposed method to account for the through-plane gradients of the field inhomogeneity. To improve the efficiency of the reconstruction algorithm we use a linearization technique for fieldmap estimation, which allows the use of the conjugate gradient algorithm. The resulting method allows for efficient reconstruction by applying fast approximations that allow the use of the conjugate gradient algorithm along with FFTs. Our proposed method can be computationally efficient for quadratic regularizers, but the CG-based algorithm is not directly applicable to non-quadratic regularization. To improve the efficiency of our method for non-quadratic regularization we propose an algorithm based on the augmented Lagrangian (AL) framework with variable splitting. This new algorithm can also be used for the non-linear optimization problem of fieldmap estimation without the need for the linearization approximation.PhDElectrical Engineering-SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102449/1/amatakos_1.pd

Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge

PURPOSE: The aim of the 2016 quantitative susceptibility mapping (QSM) reconstruction challenge was to test the ability of various QSM algorithms to recover the underlying susceptibility from phase data faithfully. METHODS: Gradient-echo images of a healthy volunteer acquired at 3T in a single orientation with 1.06 mm isotropic resolution. A reference susceptibility map was provided, which was computed using the susceptibility tensor imaging algorithm on data acquired at 12 head orientations. Susceptibility maps calculated from the single orientation data were compared against the reference susceptibility map. Deviations were quantified using the following metrics: root mean squared error (RMSE), structure similarity index (SSIM), high-frequency error norm (HFEN), and the error in selected white and gray matter regions. RESULTS: Twenty-seven submissions were evaluated. Most of the best scoring approaches estimated the spatial frequency content in the ill-conditioned domain of the dipole kernel using compressed sensing strategies. The top 10 maps in each category had similar error metrics but substantially different visual appearance. CONCLUSION: Because QSM algorithms were optimized to minimize error metrics, the resulting susceptibility maps suffered from over-smoothing and conspicuity loss in fine features such as vessels. As such, the challenge highlighted the need for better numerical image quality criteria

Doctor of Philosophy

dissertationDynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is a powerful tool to detect cardiac diseases and tumors, and both spatial resolution and temporal resolution are important for disease detection. Sampling less in each time frame and applying sophisticated reconstruction methods to overcome image degradations is a common strategy in the literature. In this thesis, temporal TV constrained reconstruction that was successfully applied to DCE myocardial perfusion imaging by our group was extended to three-dimensional (3D) DCE breast and 3D myocardial perfusion imaging, and the extension includes different forms of constraint terms and various sampling patterns. We also explored some other popular reconstruction algorithms from a theoretical level and showed that they can be included in a unified framework. Current 3D Cartesian DCE breast tumor imaging is limited in spatiotemporal resolution as high temporal resolution is desired to track the contrast enhancement curves, and high spatial resolution is desired to discern tumor morphology. Here temporal TV constrained reconstruction was extended and different forms of temporal TV constraints were compared on 3D Cartesian DCE breast tumor data with simulated undersampling. Kinetic parameters analysis was used to validate the methods