Model-based reconstruction of magnetic resonance spectroscopic imaging

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 77-80).Magnetic resonance imaging (MRI) is a medical imaging technique that is used to obtain images of soft tissue throughout the body. Since its development in the 1970s, MRI has gained tremendous importance in clinical practice because it can produce high quality images of diagnostic value in an ever expanding range of applications from neuroimaging to body imaging to cancer. By far the dominant signal source in MRI is hydrogen nuclei in water. The presence of water at high concentration (-50M) in body tissue, combined with signal contrast modulation induced by the local environment of water molecules, accounts for the success of MRI as a medical imaging modality. As opposed to conventional MRI, which derives its signal from the water component, magnetic resonance spectroscopy (MRS) acquires the magnetic resonance signal from other chemical components, most frequently various metabolites in the brain, but also signals from tumors in breast and prostate. The spectroscopic signal arises from low concentration (-1 - 10mM) compounds, but in spite of the challenges posed by the resulting low signal-to-noise ratio (SNR), the development of MRS is motivated by the desire to directly observe signal sources other than water. The combination of MRS with spatial encoding is called magnetic resonance spectroscopic imaging (MRSI). MRSI captures not only the relative intensities of metabolite signals at each voxel, but also their spatial distributions. While MRSI has been proven to be clinically useful, it suffers from fundamental tradeoffs due to the inherently low SNR, such as long acquisition time and low spatial resolution. In this thesis, techniques that combine benefits from both model-based reconstruction methods and regularized reconstructions with prior knowledge are proposed and demonstrated for MRSI. These methods address constraints on acquisition time in MRSI by undersampling data during acquisition in combination with improved image reconstruction methods.by Itthi Chatnuntawech.S.M

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

Organ-targeted high-throughput in vivo biologics screen identifies materials for RNA delivery

Therapies based on biologics involving delivery of proteins, DNA, and RNA are currently among the most promising approaches. However, although large combinatorial libraries of biologics and delivery vehicles can be readily synthesized, there are currently no means to rapidly characterize them in vivo using animal models. Here, we demonstrate high-throughput in vivo screening of biologics and delivery vehicles by automated delivery into target tissues of small vertebrates with developed organs. Individual zebrafish larvae are automatically oriented and immobilized within hydrogel droplets in an array format using a microfluidic system, and delivery vehicles are automatically microinjected to target organs with high repeatability and precision. We screened a library of lipid-like delivery vehicles for their ability to facilitate the expression of protein-encoding RNAs in the central nervous system. We discovered delivery vehicles that are effective in both larval zebrafish and rats. Our results showed that the in vivo zebrafish model can be significantly more predictive of both false positives and false negatives in mammals than in vitro mammalian cell culture assays. Our screening results also suggest certain structure–activity relationships, which can potentially be applied to design novel delivery vehicles.National Institutes of Health (U.S.) (Transformative Research Award R01 NS073127)National Institutes of Health (U.S.) (Director's Innovator Award DP2 OD002989)David & Lucile Packard Foundation (Award in Science and Engineering)Sanofi Aventis (Firm)Foxconn International Holdings Ltd.Hertz Foundation (Fellowship)University Grants Committee (Hong Kong, China) (Early Career Award 125012)National Natural Science Foundation (China) (81201164)ITC (ITS/376/13)Chinese University of Hong Kong (Grant 9610215)Chinese University of Hong Kong (Grant 7200269

Blip-Up Blip-Down Circular EPI (BUDA-cEPI) for Distortion-Free dMRI with Rapid Unrolled Deep Learning Reconstruction

Purpose: We implemented the blip-up, blip-down circular echo planar imaging (BUDA-cEPI) sequence with readout and phase partial Fourier to reduced off-resonance effect and T2* blurring. BUDA-cEPI reconstruction with S-based low-rank modeling of local k-space neighborhoods (S-LORAKS) is shown to be effective at reconstructing the highly under-sampled BUDA-cEPI data, but it is computationally intensive. Thus, we developed an ML-based reconstruction technique termed "BUDA-cEPI RUN-UP" to enable fast reconstruction. Methods: BUDA-cEPI RUN-UP - a model-based framework that incorporates off-resonance and eddy current effects was unrolled through an artificial neural network with only six gradient updates. The unrolled network alternates between data consistency (i.e., forward BUDA-cEPI and its adjoint) and regularization steps where U-Net plays a role as the regularizer. To handle the partial Fourier effect, the virtual coil concept was also incorporated into the reconstruction to effectively take advantage of the smooth phase prior, and trained to predict the ground-truth images obtained by BUDA-cEPI with S-LORAKS. Results: BUDA-cEPI with S-LORAKS reconstruction enabled the management of off-resonance, partial Fourier, and residual aliasing artifacts. However, the reconstruction time is approximately 225 seconds per slice, which may not be practical in a clinical setting. In contrast, the proposed BUDA-cEPI RUN-UP yielded similar results to BUDA-cEPI with S-LORAKS, with less than a 5% normalized root mean square error detected, while the reconstruction time is approximately 3 seconds. Conclusion: BUDA-cEPI RUN-UP was shown to reduce the reconstruction time by ~88x when compared to the state-of-the-art technique, while preserving imaging details as demonstrated through DTI application.Comment: Number: Figures: 8 Tables: 3 References: 7

Organ-targeted high-throughput in vivo biologics screen identifies materials for RNA delivery

Therapies based on biologics involving delivery of proteins, DNA, and RNA are currently among the most promising approaches. However, although large combinatorial libraries of biologics and delivery vehicles can be readily synthesized, there are currently no means to rapidly characterize them in vivo using animal models. Here, we demonstrate high-throughput in vivo screening of biologics and delivery vehicles by automated delivery into target tissues of small vertebrates with developed organs. Individual zebrafish larvae are automatically oriented and immobilized within hydrogel droplets in an array format using a microfluidic system, and delivery vehicles are automatically microinjected to target organs with high repeatability and precision. We screened a library of lipid-like delivery vehicles for their ability to facilitate the expression of protein-encoding RNAs in the central nervous system. We discovered delivery vehicles that are effective in both larval zebrafish and rats. Our results showed that the in vivo zebrafish model can be significantly more predictive of both false positives and false negatives in mammals than in vitro mammalian cell culture assays. Our screening results also suggest certain structure–activity relationships, which can potentially be applied to design novel delivery vehicles.National Institutes of Health (U.S.) (Transformative Research Award R01 NS073127)National Institutes of Health (U.S.) (Director's Innovator Award DP2 OD002989)David & Lucile Packard Foundation (Award in Science and Engineering)Sanofi Aventis (Firm)Foxconn International Holdings Ltd.Hertz Foundation (Fellowship)University Grants Committee (Hong Kong, China) (Early Career Award 125012)National Natural Science Foundation (China) (81201164)ITC (ITS/376/13)Chinese University of Hong Kong (Grant 9610215)Chinese University of Hong Kong (Grant 7200269

Acquisition and reconstruction methods for magnetic resonance imaging

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 119-138).Magnetic Resonance Imaging (MRI) is a non-invasive medical imaging modality that has a wide range of applications in both diagnostic clinical imaging and medical research. MRI has progressively gained in importance in clinical use because of its ability to produce high quality images of soft tissue throughout the body without subjecting the patient to any ionizing radiation. In addition to exquisite anatomical detail obtained from the conventional MRI, complementary physiological information is also available through emerging specialized applications of MRI such as magnetic resonance spectroscopic imaging, quantitative susceptibility mapping, functional MRI, and diffusion MRI. Despite its great versatility, MRI is limited by the long time required to acquire the data needed to form an image. Since a typical MRI protocol consists of multiple scans of the same patient, the total scan time is commonly extended beyond half an hour. During the session, the patient must remain perfectly still within a tight and closed environment, raising difficulties for certain populations such as children and patients with claustrophobia. The long acquisition time of MRI not only reduces the availability of the MRI scanner, but also results in patient discomfort, which often leads to motion that degrades image quality. Therefore, reducing the acquisition time of MRI is a well-motivated problem. This thesis proposes acquisition and reconstruction methods that increase the imaging efficiency of MRI and two of its emerging specialized applications, magnetic resonance spectroscopic imaging and quantitative susceptibility mapping. In particular, each of the proposed methods increases the imaging efficiency by achieving at least one of two aims: reduction of total scan time and improved image quality by mitigating image artifacts, while minimizing reconstruction time.by Itthi Chatnuntawech.Ph. D

Wave-Encoded Model-Based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction

A recently introduced model-based deep learning (MoDL) technique successfully incorporates convolutional neural network (CNN)-based regularizers into physics-based parallel imaging reconstruction using a small number of network parameters. Wave-controlled aliasing in parallel imaging (CAIPI) is an emerging parallel imaging method that accelerates imaging acquisition by employing sinusoidal gradients in the phase- and slice/partition-encoding directions during the readout to take better advantage of 3D coil sensitivity profiles. We propose wave-encoded MoDL (wave-MoDL) combining the wave-encoding strategy with unrolled network constraints for highly accelerated 3D imaging while enforcing data consistency. We extend wave-MoDL to reconstruct multicontrast data with CAIPI sampling patterns to leverage similarity between multiple images to improve the reconstruction quality. We further exploit this to enable rapid quantitative imaging using an interleaved look-locker acquisition sequence with T2 preparation pulse (3D-QALAS). Wave-MoDL enables a 40 s MPRAGE acquisition at 1 mm resolution at 16-fold acceleration. For quantitative imaging, wave-MoDL permits a 1:50 min acquisition for T1, T2, and proton density mapping at 1 mm resolution at 12-fold acceleration, from which contrast-weighted images can be synthesized as well. In conclusion, wave-MoDL allows rapid MR acquisition and high-fidelity image reconstruction and may facilitate clinical and neuroscientific applications by incorporating unrolled neural networks into wave-CAIPI reconstruction