415 research outputs found
Dependent Nonparametric Bayesian Group Dictionary Learning for online reconstruction of Dynamic MR images
In this paper, we introduce a dictionary learning based approach applied to
the problem of real-time reconstruction of MR image sequences that are highly
undersampled in k-space. Unlike traditional dictionary learning, our method
integrates both global and patch-wise (local) sparsity information and
incorporates some priori information into the reconstruction process. Moreover,
we use a Dependent Hierarchical Beta-process as the prior for the group-based
dictionary learning, which adaptively infers the dictionary size and the
sparsity of each patch; and also ensures that similar patches are manifested in
terms of similar dictionary atoms. An efficient numerical algorithm based on
the alternating direction method of multipliers (ADMM) is also presented.
Through extensive experimental results we show that our proposed method
achieves superior reconstruction quality, compared to the other state-of-the-
art DL-based methods
Doctor of Philosophy
dissertationCine phase contrast (PC) magnetic resonance imaging (MRI) is a useful imaging technique that allows for the quantitative measurement of in-vivo blood velocities over the cardiac cycle. Velocity information can be used to diagnose and learn more about the mechanisms of cardio-vascular disease. Compared to other velocity measuring techniques, PC MRI provides high-resolution 2D and 3D spatial velocity information. Unfortunately, as with many other MRI techniques, PC MRI su ers from long acquisition times which places constraints on temporal and spatial resolution. This dissertation outlines the use of temporally constrained reconstruction (TCR) of radial PC data in order to signi cantly reduce the acquisition time so that higher temporal and spatial resolutions can be achieved. A golden angle-based acquisition scheme and a novel self-gating method were used in order to allow for exible selection of temporal resolution and to ameliorate the di culties associated with external electrocardiogram (ECG) gating. Finally, image reconstruction times for TCR are signi cantly reduced by implementation on a high-performance computer cluster. The TCR algorithm is executed in parallel across multiple GPUs achieving a 50 second reconstruction time for a very large cardiac perfusion data set
Fast T2 Mapping with Improved Accuracy Using Undersampled Spin-echo MRI and Model-based Reconstructions with a Generating Function
A model-based reconstruction technique for accelerated T2 mapping with
improved accuracy is proposed using undersampled Cartesian spin-echo MRI data.
The technique employs an advanced signal model for T2 relaxation that accounts
for contributions from indirect echoes in a train of multiple spin echoes. An
iterative solution of the nonlinear inverse reconstruction problem directly
estimates spin-density and T2 maps from undersampled raw data. The algorithm is
validated for simulated data as well as phantom and human brain MRI at 3 T. The
performance of the advanced model is compared to conventional pixel-based
fitting of echo-time images from fully sampled data. The proposed method yields
more accurate T2 values than the mono-exponential model and allows for
undersampling factors of at least 6. Although limitations are observed for very
long T2 relaxation times, respective reconstruction problems may be overcome by
a gradient dampening approach. The analytical gradient of the utilized cost
function is included as Appendix.Comment: 10 pages, 7 figure
Calibrationless Reconstruction of Uniformly-Undersampled Multi-Channel MR Data with Deep Learning Estimated ESPIRiT Maps
Purpose: To develop a truly calibrationless reconstruction method that
derives ESPIRiT maps from uniformly-undersampled multi-channel MR data by deep
learning. Methods: ESPIRiT, one commonly used parallel imaging reconstruction
technique, forms the images from undersampled MR k-space data using ESPIRiT
maps that effectively represents coil sensitivity information. Accurate ESPIRiT
map estimation requires quality coil sensitivity calibration or autocalibration
data. We present a U-Net based deep learning model to estimate the
multi-channel ESPIRiT maps directly from uniformly-undersampled multi-channel
multi-slice MR data. The model is trained using fully-sampled multi-slice axial
brain datasets from the same MR receiving coil system. To utilize subject-coil
geometric parameters available for each dataset, the training imposes a hybrid
loss on ESPIRiT maps at the original locations as well as their corresponding
locations within the standard reference multi-slice axial stack. The
performance of the approach was evaluated using publicly available T1-weighed
brain and cardiac data. Results: The proposed model robustly predicted
multi-channel ESPIRiT maps from uniformly-undersampled k-space data. They were
highly comparable to the reference ESPIRiT maps directly computed from 24
consecutive central k-space lines. Further, they led to excellent ESPIRiT
reconstruction performance even at high acceleration, exhibiting a similar
level of errors and artifacts to that by using reference ESPIRiT maps.
Conclusion: A new deep learning approach is developed to estimate ESPIRiT maps
directly from uniformly-undersampled MR data. It presents a general strategy
for calibrationless parallel imaging reconstruction through learning from coil
and protocol specific data
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