865 research outputs found
Accelerated partial separable model using dimension-reduced optimization technique for ultra-fast cardiac MRI
Objective. Imaging dynamic object with high temporal resolution is
challenging in magnetic resonance imaging (MRI). Partial separable (PS) model
was proposed to improve the imaging quality by reducing the degrees of freedom
of the inverse problem. However, PS model still suffers from long acquisition
time and even longer reconstruction time. The main objective of this study is
to accelerate the PS model, shorten the time required for acquisition and
reconstruction, and maintain good image quality simultaneously. Approach. We
proposed to fully exploit the dimension reduction property of the PS model,
which means implementing the optimization algorithm in subspace. We optimized
the data consistency term, and used a Tikhonov regularization term based on the
Frobenius norm of temporal difference. The proposed dimension-reduced
optimization technique was validated in free-running cardiac MRI. We have
performed both retrospective experiments on public dataset and prospective
experiments on in-vivo data. The proposed method was compared with four
competing algorithms based on PS model, and two non-PS model methods. Main
results. The proposed method has robust performance against shortened
acquisition time or suboptimal hyper-parameter settings, and achieves superior
image quality over all other competing algorithms. The proposed method is
20-fold faster than the widely accepted PS+Sparse method, enabling image
reconstruction to be finished in just a few seconds. Significance. Accelerated
PS model has the potential to save much time for clinical dynamic MRI
examination, and is promising for real-time MRI applications.Comment: 23 pages, 11 figures. Accepted as manuscript on Physics in Medicine &
Biolog
Accelerating Magnetic Resonance Parametric Mapping Using Simultaneously Spatial Patch-based and Parametric Group-based Low-rank Tensors (SMART)
Quantitative magnetic resonance (MR) parametric mapping is a promising
approach for characterizing intrinsic tissue-dependent information. However,
long scan time significantly hinders its widespread applications. Recently,
low-rank tensor has been employed and demonstrated good performance in
accelerating MR parametricmapping. In this study, we propose a novel method
that uses spatial patch-based and parametric group-based low rank tensors
simultaneously (SMART) to reconstruct images from highly undersampled k-space
data. The spatial patch-based low-rank tensor exploits the high local and
nonlocal redundancies and similarities between the contrast images in
parametric mapping. The parametric group based low-rank tensor, which
integrates similar exponential behavior of the image signals, is jointly used
to enforce the multidimensional low-rankness in the reconstruction process. In
vivo brain datasets were used to demonstrate the validity of the proposed
method. Experimental results have demonstrated that the proposed method
achieves 11.7-fold and 13.21-fold accelerations in two-dimensional and
three-dimensional acquisitions, respectively, with more accurate reconstructed
images and maps than several state-of-the-art methods. Prospective
reconstruction results further demonstrate the capability of the SMART method
in accelerating MR quantitative imaging.Comment: 15 pages, 12 figure
Recommended from our members
MR Shuffling: Accelerated Single-Scan Multi-Contrast Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) is an attractive medical imaging modality as it is non-invasive and does not involve ionizing radiation. Routine clinical MRI exams obtain MR images corresponding to different soft tissue contrast by performing multiple scans. When two-dimensional (2D) imaging is used, these scans are often repeated in other scanning planes. As a result, the number of scans comprising an MRI exam leads to prohibitively long exam times as compared to other medical imaging modalities such as computed tomography. Many approaches have been designed to accelerate the MRI acquisition while maintaining diagnostic quality.One approach is to collect multiple measurements while the MRI signal is evolving due to relaxation. This enables a reduction in scan time, as fewer acquisition windows are needed to collect the same number of measurements. However, when the temporal aspect of the acquisition is left unmodeled, artifacts are likely to appear in the reconstruction. Most often, these artifacts manifest as image blurring. The effect depends on the acquisition parameters as well as the tissue relaxation itself, resulting in spatially varying blurring. The severity of the artifacts is directly related to the level of acceleration, and thus presents a tradeoff with scan time. The effect is amplified when imaging in three dimensions, severely limiting scan efficiency. Volumetric variants would be used if not for the blurring, as they are able to reconstruct images at isotropic resolution and support mutli-planar reformatting.Another established acceleration technique, called parallel imaging, takes advantage of spatially sensitive receive coil arrays to collect multiple MRI measurements in parallel. Thus, the acquisition is shortened, and the reconstruction uses the spatial sensitivity information to recover the image. More recently, methods have been developed that leverage image structure such as sparsity and low rank to reduce the required number of samples for a well-posed reconstruction. Compressed sensing and its low rank extensions use these concepts to acquire incoherent measurements below the Nyquist rate. These techniques are especially suited to MRI, as incoherent measurements can be easily achieved through pseudo-random under-sampling. As the mechanisms behind parallel imaging and compressed sensing are fundamentally different, they can be combined to achieve even higher acceleration.This dissertation proposes accelerated MRI acquisition and reconstruction techniques that account for the temporal dynamics of the MR signal. The methods build off of parallel imaging and compressed sensing to reduce scan time and flexibly model the temporal relaxation behavior. By randomly shuffling the sampling in the acquisition stage and imposing low rank constraints in the reconstruction stage, intrinsic physical parameters are modeled and their dynamics are recovered as multiple images of varying tissue contrast. Additionally, blurring artifacts are significantly reduced, as the temporal dynamics are accounted for in the reconstruction.This dissertation first introduces T2 Shuffling, a volumetric technique that reduces blurring and reconstructs multiple T2-weighted image contrasts from a single acquisition. The method is integrated into a clinical hospital environment and evaluated on patients. Next, this dissertation develops a fast and distributed reconstruction for T2 Shuffling that achieves clinically relevant processing time latency. Clinical validation results are shown comparing T2 Shuffling as a single-sequence alternative to conventional pediatric knee MRI. Based off the compelling results, a fast targeted knee MRI using T2 Shuffling is implemented, enabling same-day access to MRI at one-third the cost compared to the conventional exam. To date, over 2,400 T2 Shuffling patient scans have been performed.Continuing the theme of accelerated multi-contrast imaging, this dissertation extends the temporal signal model with T1-T2 Shuffling. Building off of T2 Shuffling, the new method additionally samples multiple points along the saturation recovery curve by varying the repetition time durations during the scan. Since the signal dynamics are governed by both T1 recovery and T2 relaxation, the reconstruction captures information about both intrinsic tissue parameters. As a result, multiple target synthetic contrast images are reconstructed, all from a single scan. Approaches for selecting the sequence parameters are provided, and the method is evaluated on in vivo brain imaging of a volunteer.Altogether, these methods comprise the theme of MR Shuffling, and may open new pathways toward fast clinical MRI
An untrained deep learning method for reconstructing dynamic magnetic resonance images from accelerated model-based data
The purpose of this work is to implement physics-based regularization as a
stopping condition in tuning an untrained deep neural network for
reconstructing MR images from accelerated data. The ConvDecoder neural network
was trained with a physics-based regularization term incorporating the spoiled
gradient echo equation that describes variable-flip angle (VFA) data.
Fully-sampled VFA k-space data were retrospectively accelerated by factors of
R={8,12,18,36} and reconstructed with ConvDecoder (CD), ConvDecoder with the
proposed regularization (CD+r), locally low-rank (LR) reconstruction, and
compressed sensing with L1-wavelet regularization (L1). Final images from CD+r
training were evaluated at the \emph{argmin} of the regularization loss;
whereas the CD, LR, and L1 reconstructions were chosen optimally based on
ground truth data. The performance measures used were the normalized root-mean
square error, the concordance correlation coefficient (CCC), and the structural
similarity index (SSIM). The CD+r reconstructions, chosen using the stopping
condition, yielded SSIMs that were similar to the CD (p=0.47) and LR SSIMs
(p=0.95) across R and that were significantly higher than the L1 SSIMs
(p=0.04). The CCC values for the CD+r T1 maps across all R and subjects were
greater than those corresponding to the L1 (p=0.15) and LR (p=0.13) T1 maps,
respectively. For R > 12 (<4.2 minutes scan time), L1 and LR T1 maps exhibit a
loss of spatially refined details compared to CD+r. We conclude that the use of
an untrained neural network together with a physics-based regularization loss
shows promise as a measure for determining the optimal stopping point in
training without relying on fully-sampled ground truth data.Comment: 45 pages, 7 figures, 2 Tables, supplementary material included (10
figures, 4 tables
Robust Cardiac Motion Estimation using Ultrafast Ultrasound Data: A Low-Rank-Topology-Preserving Approach
Cardiac motion estimation is an important diagnostic tool to detect heart
diseases and it has been explored with modalities such as MRI and conventional
ultrasound (US) sequences. US cardiac motion estimation still presents
challenges because of the complex motion patterns and the presence of noise. In
this work, we propose a novel approach to estimate the cardiac motion using
ultrafast ultrasound data. -- Our solution is based on a variational
formulation characterized by the L2-regularized class. The displacement is
represented by a lattice of b-splines and we ensure robustness by applying a
maximum likelihood type estimator. While this is an important part of our
solution, the main highlight of this paper is to combine a low-rank data
representation with topology preservation. Low-rank data representation
(achieved by finding the k-dominant singular values of a Casorati Matrix
arranged from the data sequence) speeds up the global solution and achieves
noise reduction. On the other hand, topology preservation (achieved by
monitoring the Jacobian determinant) allows to radically rule out distortions
while carefully controlling the size of allowed expansions and contractions.
Our variational approach is carried out on a realistic dataset as well as on a
simulated one. We demonstrate how our proposed variational solution deals with
complex deformations through careful numerical experiments. While maintaining
the accuracy of the solution, the low-rank preprocessing is shown to speed up
the convergence of the variational problem. Beyond cardiac motion estimation,
our approach is promising for the analysis of other organs that experience
motion.Comment: 15 pages, 10 figures, Physics in Medicine and Biology, 201
Zero-DeepSub: Zero-Shot Deep Subspace Reconstruction for Rapid Multiparametric Quantitative MRI Using 3D-QALAS
Purpose: To develop and evaluate methods for 1) reconstructing
3D-quantification using an interleaved Look-Locker acquisition sequence with T2
preparation pulse (3D-QALAS) time-series images using a low-rank subspace
method, which enables accurate and rapid T1 and T2 mapping, and 2) improving
the fidelity of subspace QALAS by combining scan-specific deep-learning-based
reconstruction and subspace modeling. Methods: A low-rank subspace method for
3D-QALAS (i.e., subspace QALAS) and zero-shot deep-learning subspace method
(i.e., Zero-DeepSub) were proposed for rapid and high fidelity T1 and T2
mapping and time-resolved imaging using 3D-QALAS. Using an ISMRM/NIST system
phantom, the accuracy of the T1 and T2 maps estimated using the proposed
methods was evaluated by comparing them with reference techniques. The
reconstruction performance of the proposed subspace QALAS using Zero-DeepSub
was evaluated in vivo and compared with conventional QALAS at high reduction
factors of up to 9-fold. Results: Phantom experiments showed that subspace
QALAS had good linearity with respect to the reference methods while reducing
biases compared to conventional QALAS, especially for T2 maps. Moreover, in
vivo results demonstrated that subspace QALAS had better g-factor maps and
could reduce voxel blurring, noise, and artifacts compared to conventional
QALAS and showed robust performance at up to 9-fold acceleration with
Zero-DeepSub, which enabled whole-brain T1, T2, and PD mapping at 1 mm
isotropic resolution within 2 min of scan time. Conclusion: The proposed
subspace QALAS along with Zero-DeepSub enabled high fidelity and rapid
whole-brain multiparametric quantification and time-resolved imaging.Comment: 17 figures, 3 table
- …