72 research outputs found
Design algorithms for parallel transmission in magnetic resonance imaging
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 153-158).The focus of this dissertation is on the algorithm design, implementation, and validation of parallel transmission technology in Magnetic Resonance Imaging (MRI). Novel algorithms are proposed which yield excellent excitation control, low RF power requirements, methods that extend to non-linear large-flip-angle excitation, as well as a new algorithm for simultaneous spectral and spatial excitation critical to quantification of low-SNR brain metabolites in MR spectroscopic imaging. For testing and validation, these methods were implemented on a newly developed parallel transmission platform on both 3 T and 7 T MRI scanners to demonstrate the ability of these methods for highfidelity B1+ mitigation, first by excitation of phantoms and then by human imaging. Further, spatially tailored RF pulses were demonstrated beyond conventional slice- or slab-selective excitation.by Kawin Setsompop.Ph.D
Echo Planar Time-Resolved Imaging (EPTI) with Subspace Reconstruction and Optimized Spatiotemporal Encoding
Purpose: To develop new encoding and reconstruction techniques for fast
multi-contrast quantitative imaging. Methods: The recently proposed Echo Planar
Time-resolved Imaging (EPTI) technique can achieve fast distortion- and
blurring-free multi-contrast quantitative imaging. In this work, a subspace
reconstruction framework is developed to improve the reconstruction accuracy of
EPTI at high encoding accelerations. The number of unknowns in the
reconstruction is significantly reduced by modeling the temporal signal
evolutions using low-rank subspace. As part of the proposed reconstruction
approach, a B0-update algorithm and a shot-to-shot B0 variation correction
method are developed to enable the reconstruction of high-resolution tissue
phase images and to mitigate artifacts from shot-to-shot phase variations.
Moreover, the EPTI concept is extended to 3D k-space for 3D GE-EPTI, where a
new temporal-variant of CAIPI encoding is proposed to further improve
performance. Results: The effectiveness of the proposed subspace reconstruction
was demonstrated first in 2D GESE EPTI, where the reconstruction achieved
higher accuracy when compared to conventional B0-informed GRAPPA. For 3D
GE-EPTI, a retrospective undersampling experiment demonstrates that the new
temporal-variant CAIPI encoding can achieve up to 72x acceleration with close
to 2x reduction in reconstruction error when compared to conventional
spatiotemporal-CAIPI encoding. In a prospective undersampling experiment,
high-quality whole-brain T2* and QSM maps at 1 mm isotropic resolution was
acquired in 52 seconds at 3T using 3D GE-EPTI with temporal-variant CAIPI
encoding. Conclusion: The proposed subspace reconstruction and optimized
temporal-variant CAIPI encoding can further improve the performance of EPTI for
fast quantitative mapping
Joint multi-contrast Variational Network reconstruction (jVN) with application to rapid 2D and 3D imaging
Purpose: To improve the image quality of highly accelerated multi-channel MRI
data by learning a joint variational network that reconstructs multiple
clinical contrasts jointly.
Methods: Data from our multi-contrast acquisition was embedded into the
variational network architecture where shared anatomical information is
exchanged by mixing the input contrasts. Complementary k-space sampling across
imaging contrasts and Bunch-Phase/Wave-Encoding were used for data acquisition
to improve the reconstruction at high accelerations. At 3T, our joint
variational network approach across T1w, T2w and T2-FLAIR-weighted brain scans
was tested for retrospective under-sampling at R=6 (2D) and R=4x4 (3D)
acceleration. Prospective acceleration was also performed for 3D data where the
combined acquisition time for whole brain coverage at 1 mm isotropic resolution
across three contrasts was less than three minutes.
Results: Across all test datasets, our joint multi-contrast network better
preserved fine anatomical details with reduced image-blurring when compared to
the corresponding single-contrast reconstructions. Improvement in image quality
was also obtained through complementary k-space sampling and
Bunch-Phase/Wave-Encoding where the synergistic combination yielded the overall
best performance as evidenced by exemplarily slices and quantitative error
metrics.
Conclusion: By leveraging shared anatomical structures across the jointly
reconstructed scans, our joint multi-contrast approach learnt more efficient
regularizers which helped to retain natural image appearance and avoid
over-smoothing. When synergistically combined with advanced encoding
techniques, the performance was further improved, enabling up to R=16-fold
acceleration with good image quality. This should help pave the way to very
rapid high-resolution brain exams
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
- …