Optimization and validation of accelerated golden‐angle radial sparse MRI reconstruction with self‐calibrating GRAPPA operator gridding

Benkert, Thomas; Tian, Ye; Huang, Chenchan; DiBella, Edward; Chandarana, Hersh; Feng, Li

doi:10.1002/mrm.27030

Cited by 34 publications

(47 citation statements)

References 36 publications

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“…In this example, the number of dynamic frames, denoted as T , is 175. The low‐resolution GRASP reconstruction using GROG can be performed by solving

{\overset{m}{true}}_{bold-italicL} = \underset{m_{L}}{arg min} \frac{1}{2} {(∥∥, {Em}_{L} - \sqrt{{boldW}_{boldL}} y_{L})}_{2}^{2} + λ {(∥∥, {boldSm}_{boldL})}_{1}

…”

Section: Methodsmentioning

confidence: 99%

“…Equation aims to reconstruct a low‐resolution dynamic image‐series

{boldm}_{boldL} \in {double-struckC}^{T \times L^{2}}

(e.g., L = 96, T = 175) in which each row represents a concatenated dynamic image frame and each column represents the time dimension for a given pixel. y L is the sorted low‐resolution dynamic k‐space shifted to a Cartesian grid using GROG, and W L is corresponding GROG weighting function for compensating varying sampling density .

E = \sqrt{W_{L}} Φ {boldFC}_{bold-italicL}

is the low‐resolution encoding operator incorporating coil sensitivities (

C_{L}

) calculated at this resolution, Fourier transform ( F ), undersampling operator (

normalΦ

) and the GROG weighting function ( W L ).…”

Section: Methodsmentioning

confidence: 99%

“…In a more recent study, GRASP has also been extended to a further optimized framework called RACER‐GRASP (respiratory‐weighted, aortic contrast‐enhancement‐guided and coil unstreaking GRASP) for improved dynamic contrast‐enhanced MRI (DCE‐MRI) of the liver, which enables contrast enhancement curve‐guided data sorting, reduced motion blurring, and less residual streaking artifacts. Moreover, the self‐calibrating generalized autocalibrating partial parallel acquisition operator gridding (GROG), previously proposed as an alternative algorithm to standard gridding in non‐Cartesian MRI reconstruction, has been combined with GRASP (GROG‐GRASP) to eliminate the need of gridding and re‐gridding during iterative reconstruction for faster reconstruction speed …”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the self-calibrating generalized autocalibrating partial parallel acquisition operator gridding (GROG), previously proposed as an alternative algorithm to standard gridding in non-Cartesian MRI reconstruction, 18 has been combined with GRASP (GROG-GRASP) to eliminate the need of gridding and re-gridding during iterative reconstruction for faster reconstruction speed. 19 Despite past efforts for optimizing the GRASP framework, its reconstruction algorithm has largely remained the same since its initial development. In most situations, a first-order finite differences operation is applied along the dynamic dimension as a temporal total variation (TV) constraint.…”

Section: Introductionmentioning

confidence: 99%

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GRASP‐Pro: imProving GRASP DCE‐MRI through self‐calibrating subspace‐modeling and contrast phase automation

Feng

Huang

Tong

et al. 2019

Magnetic Resonance in Med

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Purpose To propose a highly accelerated, high‐resolution dynamic contrast‐enhanced MRI (DCE‐MRI) technique called GRASP‐Pro (golden‐angle radial sparse parallel imaging with imProved performance) through a joint sparsity and self‐calibrating subspace constraint with automated selection of contrast phases. Methods GRASP‐Pro reconstruction enforces a combination of an explicit low‐rank subspace‐constraint and a temporal sparsity constraint. The temporal basis used to construct the subspace is learned from an intermediate reconstruction step using the low‐resolution portion of radial k‐space, which eliminates the need for generating the basis using auxiliary data or a physical signal model. A convolutional neural network was trained to generate the contrast enhancement curve in the artery, from which clinically relevant contrast phases are automatically selected for evaluation. The performance of GRASP‐Pro was demonstrated for high spatiotemporal resolution DCE‐MRI of the prostate and was compared against standard GRASP in terms of overall image quality, image sharpness, and residual streaks and/or noise level. Results Compared to GRASP, GRASP‐Pro reconstructed dynamic images with enhanced sharpness, less residual streaks and/or noise, and finer delineation of the prostate without prolonging reconstruction time. The image quality improvement reached statistical significance (P < 0.05) in all the assessment categories. The neural network successfully generated contrast enhancement curves in the artery, and corresponding peak enhancement indexes correlated well with that from the manual selection. Conclusion GRASP‐Pro is a promising method for rapid and continuous DCE‐MRI. It enables superior reconstruction performance over standard GRASP and allows reliable generation of artery enhancement curve to guide the selection of desired contrast phases for improving the efficiency of GRASP MRI workflow.

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“…In this example, the number of dynamic frames, denoted as T , is 175. The low‐resolution GRASP reconstruction using GROG can be performed by solving

{\overset{m}{true}}_{bold-italicL} = \underset{m_{L}}{arg min} \frac{1}{2} {(∥∥, {Em}_{L} - \sqrt{{boldW}_{boldL}} y_{L})}_{2}^{2} + λ {(∥∥, {boldSm}_{boldL})}_{1}

…”

Section: Methodsmentioning

confidence: 99%

“…Equation aims to reconstruct a low‐resolution dynamic image‐series

{boldm}_{boldL} \in {double-struckC}^{T \times L^{2}}

E = \sqrt{W_{L}} Φ {boldFC}_{bold-italicL}

is the low‐resolution encoding operator incorporating coil sensitivities (

C_{L}

) calculated at this resolution, Fourier transform ( F ), undersampling operator (

normalΦ

) and the GROG weighting function ( W L ).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

GRASP‐Pro: imProving GRASP DCE‐MRI through self‐calibrating subspace‐modeling and contrast phase automation

Feng

Huang

Tong

et al. 2019

Magnetic Resonance in Med

Self Cite

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show abstract

“…Images were reconstructed using a joint multi‐coil temporally and spatially constrained reconstruction technique. Both 2D and 3D datasets were preinterpolated with GRAPPA operating gridding (GROG) . The GROG operator was computed on a slice by slice basis.…”

Section: Methodsmentioning

confidence: 99%

Quantitative 3D myocardial perfusion with an efficient arterial input function

Mendes

Adluru

Likhite

et al. 2019

Magnetic Resonance in Med

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Purpose The purpose of this study was to further develop and combine several innovative sequence designs to achieve quantitative 3D myocardial perfusion. These developments include an optimized 3D stack‐of‐stars readout (150 ms per beat), efficient acquisition of a 2D arterial input function, tailored saturation pulse design, and potential whole heart coverage during quantitative stress perfusion. Theory and Methods All studies were performed free‐breathing on a Prisma 3T MRI scanner. Phantom validation was used to verify sequence accuracy. A total of 21 subjects (3 patients with known disease) were scanned, 12 with a rest only protocol and 9 with both stress (regadenoson) and rest protocols. First pass quantitative perfusion was performed with gadoteridol (0.075 mmol/kg). Results Implementation and quantitative perfusion results are shown for healthy subjects and subjects with known coronary disease. Average rest perfusion for the 15 included healthy subjects was 0.79 ± 0.19 mL/g/min, the average stress perfusion for 6 healthy subject studies was 2.44 ± 0.61 mL/g/min, and the average global myocardial perfusion reserve ratio for 6 healthy subjects was 3.10 ± 0.24. Perfusion deficits for 3 patients with ischemia are shown. Average resting heart rate was 59 ± 7 bpm and the average stress heart rate was 81 ± 10 bpm. Conclusion This work demonstrates that a quantitative 3D myocardial perfusion sequence with the acquisition of a 2D arterial input function is feasible at high stress heart rates such as during stress. T1 values and gadolinium concentrations of the sequence match the reference standard well in a phantom, and myocardial rest and stress perfusion and myocardial perfusion reserve values are consistent with those published in literature.

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Golden‐Angle Radial MRI: Basics, Advances, and Applications

Feng

2022

Magnetic Resonance Imaging

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In recent years, golden‐angle radial sampling has received substantial attention and interest in the magnetic resonance imaging (MRI) community, and it has become a popular sampling trajectory for both research and clinical use. However, although the number of relevant techniques and publications has grown rapidly, there is still a lack of a review paper that provides a comprehensive overview and summary of the basics of golden‐angle rotation, the advantages and challenges/limitations of golden‐angle radial sampling, and recommendations in using different types of golden‐angle radial trajectories for MRI applications. Such a review paper is expected to be helpful both for clinicians who are interested in learning the potential benefits of golden‐angle radial sampling and for MRI physicists who are interested in exploring this research direction. The main purpose of this review paper is thus to present an overview and summary about golden‐angle radial MRI sampling. The review consists of three sections. The first section aims to answer basic questions such as: what is a golden angle; how is the golden angle calculated; why is golden‐angle radial sampling useful, and what are its limitations. The second section aims to review more advanced trajectories of golden‐angle radial sampling, including tiny golden‐angle rotation, stack‐of‐stars golden‐angle radial sampling, and three‐dimensional (3D) kooshball golden‐angle radial sampling. Their respective advantages and limitations and potential solutions to address these limitations are also discussed. Finally, the third section reviews MRI applications that can benefit from golden‐angle radial sampling and provides recommendations to readers who are interested in implementing golden‐angle radial trajectories in their MRI studies. Evidence Level 5 Technical Efficacy Stage 1

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Optimization and validation of accelerated golden‐angle radial sparse MRI reconstruction with self‐calibrating GRAPPA operator gridding

Cited by 34 publications

References 36 publications

GRASP‐Pro: imProving GRASP DCE‐MRI through self‐calibrating subspace‐modeling and contrast phase automation

GRASP‐Pro: imProving GRASP DCE‐MRI through self‐calibrating subspace‐modeling and contrast phase automation

Quantitative 3D myocardial perfusion with an efficient arterial input function

Golden‐Angle Radial MRI: Basics, Advances, and Applications

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