Wave‐LORAKS: Combining wave encoding with structured low‐rank matrix modeling for more highly accelerated 3D imaging

Kim, Tae Hyung; Bilgic̦, Berkin; Polak, Daniel; Setsompop, Kawin; Haldar, Justin P.

doi:10.1002/mrm.27511

Cited by 26 publications

(41 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The implicit incorporation of the phase information accelerates the recovery problem. IRLS‐based implementations were previously employed to accelerate SLRMC reconstructions . We note that the exact implementation employing Hankel matrix multiplication is of comparable performance to the approximate FFT‐based implementation.…”

Section: Discussionmentioning

confidence: 99%

“…By exploiting a half circulant approximation of multilevel block Toeplitz/Hankel matrices, this approach reduced the computational burden. This approximation was later incorporated into the LORAKS SLRMC framework also to achieve faster implementations for concatenated Hankel matrices . Inspired by the above works, we seek to achieve faster implementation for the iterative MUSSELS reconstruction.…”

Section: Theorymentioning

confidence: 99%

“…This property can be accommodated into MUSSELS formulation by modifying

{boldH}_{1} false(\overset{m}{true} false)

as:

{boldH}_{1} false(\overset{m}{true} false) = [scriptH (\overset{true^}{m_{1}}) scriptH (\overset{true^}{m_{2}}) \dots scriptH (\overset{true^}{m_{N_{s}}}) scriptH ({\overset{m_{1}}{true}}^{†}) scriptH ({\overset{m_{2}}{true}}^{†}) \dots scriptH ({\overset{m_{N_{s}}}{true}}^{†})],

where † represents the flipped conjugate of the k‐space data. The additional virtual‐shots are concatenated along the columns as in …”

Section: Theorymentioning

confidence: 99%

“…We reformulate the MUSSELS reconstruction to enable a computationally efficient implementation to solve the matrix recovery problem embedded in the recovery scheme. The reformulation is based on the iterative re‐weighted least squares (IRLS) methods that were previously proposed to address the computational complexity in similar reconstruction problems involving matrix completion . It has been shown that nuclear norm minimization involving large matrices can be efficiently solved by reformulating such problems using the IRLS formulation .…”

Section: Introductionmentioning

confidence: 99%

“…The reformulation is based on the iterative reweighted least squares (IRLS) methods that were previously proposed to address the computational complexity in similar reconstruction problems involving matrix completion. [11][12][13][14][15][16] It has been shown that nuclear norm minimization involving large matrices can be efficiently solved by reformulating such problems using the IRLS formulation. 17,18 Specifically, the IRLS formulation reduces the computational complexity associated with the singular value thresholding involved in such reconstructions and also achieve faster convergence.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Improved MUSSELS reconstruction for high‐resolution multi‐shot diffusion weighted imaging

Mani

Aggarwal

Magnotta

et al. 2019

Magnetic Resonance in Med

View full text Add to dashboard Cite

Purpose MUSSELS is a one‐step iterative reconstruction method for multishot diffusion weighted (msDW) imaging. The current work presents an efficient implementation, termed IRLS MUSSELS, that enables faster reconstruction to enhance its utility for high‐resolution diffusion MRI studies. Methods The recently proposed MUSSELS reconstruction belongs to a new class of parallel imaging‐based methods that recover artifact‐free DWIs from msDW data without needing phase compensation. The reconstruction is achieved via structured low‐rank matrix completion algorithms, which are computationally demanding due to the large size of the Hankel matrices and their associated computations involving singular value decompositions. Because of this, computational demands of the MUSSELS reconstruction scales as the matrix size and the number of shots increases, which hinders its practical utility for high‐resolution applications. In this work, we derive a computationally efficient MUSSELS formulation by modifying the iterative reweighted least squares (IRLS) method that were proposed earlier to solve such problems. Using whole‐brain in vivo data, we show the utility of the IRLS MUSSELS for routine high‐resolution studies with reduced computational burden. Results IRLS MUSSELS provides about five times faster reconstruction for matrix sizes 192 × 192 and 256 × 256 compared to the earlier MUSSELS implementation. The widely employed conjugate symmetry priors can also be incorporated into IRLS MUSSELS to reduce blurring of the partial Fourier acquisitions, without incurring much computational burden. Conclusions The proposed method is observed to be computationally efficient to enable routine high‐resolution studies. The computational complexity matches the traditional msDWI reconstruction methods and provides improved reconstruction results with the additional constraints.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Theorymentioning

confidence: 99%

“…This property can be accommodated into MUSSELS formulation by modifying

{boldH}_{1} false(\overset{m}{true} false)

as:

{boldH}_{1} false(\overset{m}{true} false) = [scriptH (\overset{true^}{m_{1}}) scriptH (\overset{true^}{m_{2}}) \dots scriptH (\overset{true^}{m_{N_{s}}}) scriptH ({\overset{m_{1}}{true}}^{†}) scriptH ({\overset{m_{2}}{true}}^{†}) \dots scriptH ({\overset{m_{N_{s}}}{true}}^{†})],

where † represents the flipped conjugate of the k‐space data. The additional virtual‐shots are concatenated along the columns as in …”

Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improved MUSSELS reconstruction for high‐resolution multi‐shot diffusion weighted imaging

Mani

Aggarwal

Magnotta

et al. 2019

Magnetic Resonance in Med

View full text Add to dashboard Cite

show abstract

Accelerated submillimeter wave‐encoded magnetic resonance imaging via deep untrained neural network

et al. 2023

View full text Add to dashboard Cite

BackgroundWave gradient encoding can adequately utilize coil sensitivity profiles to facilitate higher accelerations in parallel magnetic resonance imaging (pMRI). However, there are limitations in mainstream pMRI and a few deep learning (DL) methods for recovering missing data under wave encoding framework: the former is prone to introduce errors from the auto‐calibration signals (ACS) signal acquisition and is time‐consuming, while the latter requires a large amount of training data.PurposeTo tackle the above issues, an untrained neural network (UNN) model incorporating wave‐encoded physical properties and deep generative model, named WDGM, was proposed with additional ACS‐ and training data‐free.MethodsGenerally, the proposed method can provide powerful missing data interpolation capability using the wave physical encoding framework and designed UNN to characterize the MR image (k‐space data) priors. Specifically, the MRI reconstruction combining physical wave encoding and elaborate UNN is modeled as a generalized minimization problem. The designation of UNN is driven by the coil sensitivity maps (CSM) smoothness and k‐space linear predictability. And then, the iterative paradigm to recover the full k‐space signal is determined by the projected gradient descent, and the complex computation is unrolled to the network with optimized parameters by the optimizer. Simulated wave encoding and in vivo experiments are exploited to demonstrate the feasibility of the proposed method. The best quantitative metrics RMSE/SSIM/PSNR of 0.0413, 0.9514, and 37.4862 gave competitive results in all experiments with at least six‐fold acceleration, respectively.ResultsIn vivo experiments of human brains and knees showed that the proposed method can achieve comparable reconstruction quality and even has superiority relative to the comparison, especially at a high resolution of 0.67 mm and fewer ACS. In addition, the proposed method has a higher computational efficiency achieving a computation time of 9.6 s/per slice.ConclusionsThe model proposed in this work addresses two limitations of MRI reconstruction in the wave encoding framework. The first is to eliminate the need for ACS signal acquisition to perform the time‐consuming calibration process and to avoid errors such as motion during the acquisition procedure. Furthermore, the proposed method has clinical application friendly without the need to prepare large training datasets, which is difficult in the clinical. All results of the proposed method demonstrate more confidence in both quantitative and qualitative metrics. In addition, the proposed method can achieve higher computational efficiency.

show abstract

High resolution single‐shot myocardial imaging using bSSFP with wave encoding

Zhu

Che

et al. 2023

Medical Physics

View full text Add to dashboard Cite

BackgroundSingle‐shot balanced steady‐state free precession (bSSFP) sequence is widely used in cardiac imaging. However, the limited scan time in one heartbeat greatly hinders its spatial resolution compared to the segmented acquisition mode. Therefore, a highly accelerated single‐shot bSSFP imaging technology is needed for clinical use.PurposeTo develop and evaluate a wave‐encoded bSSFP sequence with high acceleration rates for single‐shot myocardial imaging.MethodsThe proposed Wave‐bSSFP method is implemented by adding a sinusoidal wave gradient in the phase encoding direction during the readout of bSSFP sequence. Uniform undersampling is used for acceleration. Its performance was first validated via phantom studies by comparison with conventional bSSFP. Then it was evaluated in volunteer studies via anatomical imaging, T2‐prepared bSSFP, and T1 mapping in in‐vivo cardiac imaging. All methods were compared with accelerated conventional bSSFP reconstructed using iterative SENSE and compressed sensing (CS), to demonstrate the advantage of wave encoding in suppressing the noise amplification and artifacts induced by acceleration.ResultsThe proposed Wave‐bSSFP method achieved a high acceleration factor of 4 for single‐shot acquisitions. The proposed method showed lower average g‐factors than bSSFP, and fewer blurring artifacts than CS reconstruction. The Wave‐bSSFP with R = 4 achieved higher spatial and temporal resolutions compared with the conventional bSSFP with R = 2 in several applications such as T2‐prepared bSSFP and T1 mapping, and could be applied in systolic imaging.ConclusionWave encoding can be used to highly accelerate 2D bSSFP imaging with single‐shot acquisitions. Compared with the conventional bSSFP sequence, the proposed Wave‐bSSFP method can effectively reduce the g‐factor and aliasing artifacts in cardiac imaging.

show abstract

Wave‐LORAKS: Combining wave encoding with structured low‐rank matrix modeling for more highly accelerated 3D imaging

Cited by 26 publications

References 41 publications

Improved MUSSELS reconstruction for high‐resolution multi‐shot diffusion weighted imaging

Improved MUSSELS reconstruction for high‐resolution multi‐shot diffusion weighted imaging

Accelerated submillimeter wave‐encoded magnetic resonance imaging via deep untrained neural network

High resolution single‐shot myocardial imaging using bSSFP with wave encoding

Contact Info

Product

Resources

About