670 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
Implicit Representation of GRAPPA Kernels for Fast MRI Reconstruction
MRI data is acquired in Fourier space/k-space. Data acquisition is typically
performed on a Cartesian grid in this space to enable the use of a fast Fourier
transform algorithm to achieve fast and efficient reconstruction. However, it
has been shown that for multiple applications, non-Cartesian data acquisition
can improve the performance of MR imaging by providing fast and more efficient
data acquisition, and improving motion robustness. Nonetheless, the image
reconstruction process of non-Cartesian data is more involved and can be
time-consuming, even through the use of efficient algorithms such as
non-uniform FFT (NUFFT). Reconstruction complexity is further exacerbated when
imaging in the presence of field imperfections. This work (implicit GROG)
provides an efficient approach to transform the field corrupted non-Cartesian
data into clean Cartesian data, to achieve simpler and faster reconstruction
which should help enable non-Cartesian data sampling to be performed more
widely in MRI
Sequence adaptive field-imperfection estimation (SAFE): retrospective estimation and correction of and inhomogeneities for enhanced MRF quantification
and field-inhomogeneities can significantly reduce accuracy and
robustness of MRF's quantitative parameter estimates. Additional and
calibration scans can mitigate this but add scan time and cannot be
applied retrospectively to previously collected data. Here, we proposed a
calibration-free sequence-adaptive deep-learning framework, to estimate and
correct for and effects of any MRF sequence. We demonstrate its
capability on arbitrary MRF sequences at 3T, where no training data were
previously obtained. Such approach can be applied to any previously-acquired
and future MRF-scans. The flexibility in directly applying this framework to
other quantitative sequences is also highlighted.Comment: 12 pages, 5 figures, submitted to International Society for Magnetic
Resonance in Medicine 31th Scientific Meeting, 202
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
Four dimensional spectral‐spatial fat saturation pulse design
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109603/1/mrm25076.pd
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
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