Compressed Sensing Accelerated Magnetic Resonance Spectroscopic Imaging

abstract: Magnetic resonance spectroscopic imaging (MRSI) is a valuable technique for assessing the in vivo spatial profiles of metabolites like N-acetylaspartate (NAA), creatine, choline, and lactate. Changes in metabolite concentrations can help identify tissue heterogeneity, providing prognostic and diagnostic information to the clinician. The increased uptake of glucose by solid tumors as compared to normal tissues and its conversion to lactate can be exploited for tumor diagnostics, anti-cancer therapy, and in the detection of metastasis. Lactate levels in cancer cells are suggestive of altered metabolism, tumor recurrence, and poor outcome. A dedicated technique like MRSI could contribute to an improved assessment of metabolic abnormalities in the clinical setting, and introduce the possibility of employing non-invasive lactate imaging as a powerful prognostic marker. However, the long acquisition time in MRSI is a deterrent to its inclusion in clinical protocols due to associated costs, patient discomfort (especially in pediatric patients under anesthesia), and higher susceptibility to motion artifacts. Acceleration strategies like compressed sensing (CS) permit faithful reconstructions even when the k-space is undersampled well below the Nyquist limit. CS is apt for MRSI as spectroscopic data are inherently sparse in multiple dimensions of space and frequency in an appropriate transform domain, for e.g. the wavelet domain. The objective of this research was three-fold: firstly on the preclinical front, to prospectively speed-up spectrally-edited MRSI using CS for rapid mapping of lactate and capture associated changes in response to therapy. Secondly, to retrospectively evaluate CS-MRSI in pediatric patients scanned for various brain-related concerns. Thirdly, to implement prospective CS-MRSI acquisitions on a clinical magnetic resonance imaging (MRI) scanner for fast spectroscopic imaging studies. Both phantom and in vivo results demonstrated a reduction in the scan time by up to 80%, with the accelerated CS-MRSI reconstructions maintaining high spectral fidelity and statistically insignificant errors as compared to the fully sampled reference dataset. Optimization of CS parameters involved identifying an optimal sampling mask for CS-MRSI at each acceleration factor. It is envisioned that time-efficient MRSI realized with optimized CS acceleration would facilitate the clinical acceptance of routine MRSI exams for a quantitative mapping of important biomarkers.Dissertation/ThesisDoctoral Dissertation Bioengineering 201

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

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

Simultaneous fMRI and metabolic imaging of the brain using spice

In this thesis, we propose a novel approach to achieve simultaneous acquisition of high resolution MRSI and fMRI in a fast scan. The proposed acquisition scheme adds an EVI-based sequence module into a subspace-based imaging technique called SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation). With the features of ultrashort TE/short TR, no water and lipid suppression, extended k-space coverage by prolonged EPSI readout and highly sparse sampling, the data acquisition captures both the spatiospectral information of brain metabolites and the dynamic information of brain functional activation. The data processing and reconstruction are based on the subspace modeling and involve pre-trained basis functions and spatial prior information. Moreover, the complementary information between fMRI and MRSI is utilized to further improve the quality of both fMRI and metabolic imaging. The in vivo experimental results demonstrate that the proposed method can achieve whole brain covered, simultaneous fMRI at spatial resolution of 3.0 × 3.0 × 1.8 mm, temporal resolution 3 seconds, along with metabolic imaging at nominal spatial resolution of 1.9 × 2.3 × 3.0 mm in a single 6-minute scan. The high-quality metabolic maps, spatially resolved spectra, resting-state functional networks and task time courses corresponding to the task events can all be obtained in the in vivo scans. This technique, when fully developed, will become a powerful tool to study the brain metabolism and function activities

New Advances in Fast Methods of 2D NMR Experiments

Although nuclear magnetic resonance spectroscopy is a potent analytical tool for identification, quantification, and structural elucidation, it suffers from inherently low sensitivity limitations. This chapter focuses on recently reported methods that enable quick acquisition of NMR spectra, as well as new methods of faster, efficient, and informative two-dimensional (2D) NMR methods. Fast and efficient data acquisition has risen in response to an increasing need to investigate chemical and biological processes in real time. Several new techniques have been successfully introduced. One example of this is band-selective optimized-flip-angle short-transient (SOFAST) NMR, which has opened the door to studying the kinetics of biological processes such as the phosphorylation of proteins. The fast recording of NMR spectra allows researchers to investigate time sensitive molecules that have limited stability under experimental conditions. The increasing awareness that molecular structures are dynamic, rather than static, has pushed some researchers to find alternatives to standard, time-consuming methods of 15N relaxation observables acquisition

Advanced parallel magnetic resonance imaging methods with applications to MR spectroscopic imaging

Parallel magnetic resonance imaging offers a framework for acceleration of conventional MRI encoding using an array of receiver coils with spatially-varying sensitivities. Novel encoding and reconstruction techniques for parallel MRI are investigated in this dissertation. The main goal is to improve the actual reconstruction methods and to develop new approaches for massively parallel MRI systems that take advantage of the higher information content provided by the large number of small receivers. A generalized forward model and inverse reconstruction with regularization for parallel MRI with arbitrary k-space sub-sampling is developed. Regularization methods using the singular value decomposition of the encoding matrix and pre-conditioning of the forward model are proposed to desensitize the solution from data noise and model errors. Variable density k-space sub-sampling is presented to improve the reconstruction with the common uniform sub-sampling. A novel method for massively parallel MRI systems named Superresolution Sensitivity Encoding (SURE-SENSE) is proposed where acceleration is performed by acquiring the low spatial resolution representation of the object being imaged and the stronger sensitivity variation from small receiver coils is used to perform intra-pixel reconstruction. SURE-SENSE compares favorably the performance of standard SENSE reconstruction for low spatial resolution imaging such as spectroscopic imaging. The methods developed in this dissertation are applied to Proton Echo Planar Spectroscopic Imaging (PEPSI) for metabolic imaging in human brain with high spatial and spectral resolution in clinically feasible acquisition times. The contributions presented in this dissertation are expected to provide methods that substantially enhance the utility of parallel MRI for clinical research and to offer a framework for fast MRSI of human brain with high spatial and spectral resolution

Investigation of compressed-sensing for acceleration of magnetic resonance spectroscopic imaging

Magnetic Resonance Spectroscopic Imaging (MRSI) is a functional MRI technique allowing non-invasive biochemical mapping of the brain. MRSI is advantageous for characterising many neurological conditions; however, its clinical application is limited by lengthy scan time and low spatial resolution, which are intrinsically linked. This research investigated the potential of Compressed Sensing (CS) to speed-up MRSI or enhance spatial resolution. CS allows accelerated acquisition by reducing the data sampling requirements, whilst preserving image quality. The focus of this work was the effect of CS-MRSI at different acceleration factors upon spatial integrity. CS reconstruction software was developed and applied to retrospective MRSI data. Imaging test objects and software simulations were developed to assess MRSI spatial resolution via metabolite edge response measurements. CS-MRSI was also investigated in realistic scenarios using data from healthy volunteers and a child with Optic Pathway Glioma (OPG). The potential of CS-MRSI to enable high-resolution MRSI in feasible scan times was investigated using simulations of focal and infiltrative OPG. Results suggest that CS-MRSI can reduce scan duration by up to a factor of 5 whilst simultaneously eliminating ringing artefacts and increasing spatial resolution compared with conventionally filtered MRSI. Therefore, CS could greatly increase the clinical utility of MRSI

Nonuniform sampling and maximum entropy reconstruction in multidimensional NMR

NMR spectroscopy is one of the most powerful and versatile analytic tools available to chemists. The discrete Fourier transform (DFT) played a seminal role in the development of modern NMR, including the multidimensional methods that are essential for characterizing complex biomolecules. However, it suffers from well-known limitations: chiefly the difficulty in obtaining high-resolution spectral estimates from short data records. Because the time required to perform an experiment is proportional to the number of data samples, this problem imposes a sampling burden for multidimensional NMR experiments. At high magnetic field, where spectral dispersion is greatest, the problem becomes particularly acute. Consequently multidimensional NMR experiments that rely on the DFT must either sacrifice resolution in order to be completed in reasonable time or use inordinate amounts of time to achieve the potential resolution afforded by high-field magnets.Maximum entropy (MaxEnt) reconstruction is a non-Fourier method of spectrum analysis that can provide high-resolution spectral estimates from short data records. It can also be used with nonuniformly sampled data sets. Since resolution is substantially determined by the largest evolution time sampled, nonuniform sampling enables high resolution while avoiding the need to uniformly sample at large numbers of evolution times. The Nyquist sampling theorem does not apply to nonuniformly sampled data, and artifacts that occur with the use of nonuniform sampling can be viewed as frequency-aliased signals. Strategies for suppressing nonuniform sampling artifacts include the careful design of the sampling scheme and special methods for computing the spectrum. Researchers now routinely report that they can complete an N-dimensional NMR experiment 3 times faster (a 3D experiment in one ninth of the time). As a result, high-resolution three- and four-dimensional experiments that were prohibitively time consuming are now practical. Conversely, tailored sampling in the indirect dimensions has led to improved sensitivity.Further advances in nonuniform sampling strategies could enable further reductions in sampling requirements for high resolution NMR spectra, and the combination of these strategies with robust non-Fourier methods of spectrum analysis (such as MaxEnt) represent a profound change in the way researchers conduct multidimensional experiments. The potential benefits will enable more advanced applications of multidimensional NMR spectroscopy to study biological macromolecules, metabolomics, natural products, dynamic systems, and other areas where resolution, sensitivity, or experiment time are limiting. Just as the development of multidimensional NMR methods presaged multidimensional methods in other areas of spectroscopy, we anticipate that nonuniform sampling approaches will find applications in other forms of spectroscopy

Development of pulse sequences for hyperpolarized 13C magnetic resonance spectroscopic imaging of tumour metabolism

Metabolic imaging with hyperpolarized 13C-labeled cell substrates is a promising technique for imaging tissue metabolism in vivo. However, the transient nature of the hyperpolarization - and its depletion following excitation - limits the imaging time and the number of excitation pulses that can be used. A single-shot 3D imaging sequence has been developed and it is shown in this thesis to generate 13C MR images in tumour-bearing mice injected with hyperpolarized [1-13C]pyruvate. The pulse sequence acquires a stack-of-spirals at two spin echoes after a single excitation pulse and encodes the kz-dimension in an interleaved manner to enhance robustness to B0 inhomogeneity. Spectral-spatial pulses are used to acquire dynamic 3D images from selected hyperpolarized 13C-labeled metabolites. A nominal spatial/temporal resolution of 1.25 x 1.25 x 2.5 $mm^3$ x 2 s was achieved in tumour images of hyperpolarized [1-13C]pyruvate and [1-13C]lactate acquired in vivo. An advanced sequence is also described in this thesis in a later study to acquire higher resolution images with isotropic voxels (1.25 x 1.25 x 1.25 $mm^3$) at no cost of temporal resolution. EPI is a sequence widely used in hyperpolarized 13C MRI because images can be acquired rapidly with limited RF exposure. However, EPI suffers from Nyquist ghosting, which is normally corrected for by acquiring a reference scan. In this thesis a workflow for hyperpolarized 13C EPI is proposed that requires no reference scan and, therefore, that does not sacrifice a time point in the dynamic monitoring of tissue metabolism. To date, most of the hyperpolarized MRI on metabolism are based on 13C imaging, while 1H is a better imaging target for its 4 times higher gyromagnetic ratio and hence 16 times signal. In this thesis the world’s first dynamic 1H imaging in vivo of hyperpolarized [1-13C]lactate is presented, via a novel double-dual-spin-echo INEPT sequence that transfers the hyperpolarization from 13C to 1H, achieving a spatial resolution of 1.25 x 1.25 $mm^2$