Simultaneous use of Individual and Joint Regularization Terms in Compressive Sensing: Joint Reconstruction of Multi-Channel Multi-Contrast MRI Acquisitions

Purpose: A time-efficient strategy to acquire high-quality multi-contrast images is to reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause leakage of uncommon features among contrasts, compromising diagnostic utility. The goal of this study is to develop a compressive sensing method for multi-channel multi-contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Theory: Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi-channel multi-contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. Methods: The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single-channel simulated and multi-channel in-vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. Results: The proposed method demonstrates rapid convergence and improved image quality for both simulated and in-vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage-of-features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms. Conclusion: The proposed compressive sensing method performs fast reconstruction of multi-channel multi-contrast MRI data with improved image quality. It offers reliability against feature leakage in joint reconstructions, thereby holding great promise for clinical use.Comment: 13 pages, 13 figures. Submitted for possible publicatio

Reconstruction algorithms for Magnetic Resonance Imaging

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 135-142).This dissertation presents image reconstruction algorithms for Magnetic Resonance Imaging (MRI) that aims to increase the imaging efficiency. Algorithms that reduce imaging time without sacrificing the image quality and mitigate image artifacts are proposed. The goal of increasing the MR efficiency is investigated across multiple imaging techniques: structural imaging with multiple contrasts preparations, Diffusion Spectrum Imaging (DSI), Chemical Shift Imaging (CSI), and Quantitative Susceptibility Mapping (QSM). The main theme connecting the proposed methods is the utilization of prior knowledge on the reconstructed signal. This prior often presents itself in the form of sparsity with respect to either a prespecified or learned signal transformation.by Berkin Bilgic.Ph.D

Fast image reconstruction with L2-regularization

Purpose We introduce L2-regularized reconstruction algorithms with closed-form solutions that achieve dramatic computational speed-up relative to state of the art L1- and L2-based iterative algorithms while maintaining similar image quality for various applications in MRI reconstruction. Materials and Methods We compare fast L2-based methods to state of the art algorithms employing iterative L1- and L2-regularization in numerical phantom and in vivo data in three applications; (i) Fast Quantitative Susceptibility Mapping (QSM), (ii) Lipid artifact suppression in Magnetic Resonance Spectroscopic Imaging (MRSI), and (iii) Diffusion Spectrum Imaging (DSI). In all cases, proposed L2-based methods are compared with the state of the art algorithms, and two to three orders of magnitude speed up is demonstrated with similar reconstruction quality. Results The closed-form solution developed for regularized QSM allows processing of a three-dimensional volume under 5 s, the proposed lipid suppression algorithm takes under 1 s to reconstruct single-slice MRSI data, while the PCA based DSI algorithm estimates diffusion propagators from undersampled q-space for a single slice under 30 s, all running in Matlab using a standard workstation. Conclusion For the applications considered herein, closed-form L2-regularization can be a faster alternative to its iterative counterpart or L1-based iterative algorithms, without compromising image quality.National Institute for Biomedical Imaging and Bioengineering (U.S.) (Grant NIBIB K99EB012107)National Institutes of Health (U.S.) (Grant NIH R01 EB007942)National Institute for Biomedical Imaging and Bioengineering (U.S.) (Grant NIBIB R01EB006847)Grant K99/R00 EB008129National Center for Research Resources (U.S.) (Grant NCRR P41RR14075)National Institutes of Health (U.S.) (Blueprint for Neuroscience Research U01MH093765)Siemens CorporationSiemens-MIT AllianceMIT-Center for Integration of Medicine and Innovative Technology (Medical Engineering Fellowship

Fast dynamic magnetic resonance imaging using sparse recovery methods and novel signal encoding formulations

Magnetic resonance imaging (MRI) is a non-invasive imaging modality that provides excellent soft tissue contrast without using ionizing radiations. These qualities/properties make MRI the preferred imaging modality for critical organs like heart and brain. Over the past decade, the advancement in hardware and image reconstruction algorithms has led to substantial improvements in MRI in terms of imaging speeds, quality and reliability. However, MRI speeds need to be further improved while retaining/maintaining the image quality given that the emerging medical diagnostic procedures are increasingly relying on detailed characterization of physiological functions that evolve on time scales too fast to be captured using conventional MRI methods. This dissertation starts with presenting a sparse signal recovery based fast MRI method. This method synergistically combines a data redundancy scheme for high frequency details with a novel and physically realizable MR signal encoding formulation. The new signal encoding formulation uses clinically deployed tagging radio frequency pulses to mix information in the spatial frequency domain prior to acquisition. Thus, the new formulation leads to a more uniform coverage of spatial frequency information even at high accelerations. The synergistic combination of image-detail redundancy encoding with tagging based signal encoding allows recovery of edges and fine structures with unprecedented quality. Next, this dissertation evaluates the use of fast spiral trajectories for high spatial resolution functional imaging of human superior colliculus. Gradient efficient and motion-robust spiral trajectories are used to keep fMRI scan durations short. . However, high resolution imaging of human subcortical structures using these trajectories is limited due to the weak functional responses of SC structures and also low signal-to-noise ratio associated with small voxels. To improve the functional sensitivity of spiral trajectories, dual echo variants are used. Combination of two echoes of the dual-echo variants reduces noise and thereby improves the functional sensitivity of high resolution fMRI. Lastly, this dissertation presents a novel formulation for fast dynamic MRI which combines the generic linear dynamical system model with sparse recovery techniques. Specifically, the formulation uses a known prior spatio-temporal model to predict the underlying image and uses sparse recovery techniques to recover the residual image. The spatio-temporal evolution model inherently encodes for coupled data redundancies in the spatial- and temporal-dimensions. Also, the generalizability of the formulation in choosing the evolution model allows it to be applicable to various physiological functions.Electrical and Computer Engineerin

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

Advanced sparse sampling techniques for accelerating structural and quantitative MRI

Magnetic Resonance Imaging (MRI) has become a routine clinical procedure for the screening, diagnosis and treatment monitoring of various clinical conditions. Although MRI has highly desirable properties such as being completely non-ionizing and providing excellent soft tissue contrast which has resulted in its widespread usage across the gamut of clinical applications, it is limited by a slow data acquisition process. Several techniques have been developed over the years that have considerably improved the speed of MRI but there is still a clinical need to further accelerate MRI for many clinical applications. This thesis focuses on two recent advances in MRI acceleration to reduce the overall patient scan time. The first part of the thesis describes the development of a fast 3D neuroimaging methodology that has been implemented in a clinical Magnetic Resonance (MR) sequence which was accelerated using a combination of compressed sensing and sampling order optimization of acquired measurements. This methodology reduced the overall scan time by more than 60% compared to the normal scan time while also producing images of acceptable quality for clinical diagnosis. The clinical utility of accelerated neuroimaging is demonstrated by conducting a healthy volunteer study on eight subjects using this fast 3D MRI method. The results of the radiological diagnostic quality assessments that were carried out on the accelerated human brain MR images by four experienced neuroradiologists are presented. The results show that accelerated MR neuroimaging retained sufficient clinical diagnostic value for certain clinical applications. The second part of the thesis describes the development of an accelerated Cartesian sampling scheme for a rapid quantitative MR method called Magnetic Resonance Fingerprinting (MRF). This method was able to simultaneously generate quantitative multi-parametric maps such as T1, T2 and proton density (PD) maps in a very short scan duration that is clinically acceptable. The developed Cartesian sampling method using Echo Planar Imaging (EPI) is compared with conventional spiral sampling that is generally used for MR fingerprinting. The ability of novel iterative reconstruction techniques to improve the multi-parametric estimation accuracy is also demonstrated. The results show that accelerated Cartesian MR fingerprinting can be an alternative to conventional spiral MR fingerprinting