Accelerated phase contrast MRI using hybrid one‐ and two‐sided flow encodings only (HOTFEO)

Wang, Da; Chien, Aichi; Shao, Jiaxin; Ali, Fadil; Hu, Peng

doi:10.1002/nbm.3904

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…As a consequence of having to encode velocities in three orthogonal directions and to perform a separate reference scan, the scan time of 4D‐flow MRI is at minimum four times longer than an equivalent non‐flow scan. Methods have been proposed using directionality constraints on the velocity field over time 19 , 20 to reduce the number of velocity encodings; however, these methods can result in a reduction in the velocity‐to‐noise ratio (VNR) and make assumptions regarding the flow field itself.…”

Section: Introductionmentioning

confidence: 99%

Accelerated 4D‐flow MRI with 3‐point encoding enabled by machine learning

Kim

Jen

Eisenmenger

et al. 2022

Magnetic Resonance in Med

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Purpose To investigate the acceleration of 4D‐flow MRI using a convolutional neural network (CNN) that produces three directional velocities from three flow encodings, without requiring a fourth reference scan measuring background phase. Methods A fully 3D CNN using a U‐net architecture was trained in a block‐wise fashion to take complex images from three flow encodings and to produce three real‐valued images for each velocity component. Using neurovascular 4D‐flow scans (n = 144), the CNN was trained to predict velocities computed from four flow encodings by standard reconstruction including correction for residual background phase offsets. Methods to optimize loss functions were investigated, including magnitude, complex difference, and uniform velocity weightings. Subsequently, 3‐point encoding was evaluated using cross validation of pixelwise correlation, flow measurements in major arteries, and in experiments with data at differing acceleration rates than the training data. Results The CNN‐produced 3‐point velocities showed excellent agreements with the 4‐point velocities, both qualitatively in velocity images, in flow rate measures, and quantitatively in regression analysis (slope = 0.96, R2 = 0.992). Optimizing the training to focus on vessel velocities rather than the global velocity error and improved the correlation of velocity within vessels themselves. The lowest error was observed when the loss function used uniform velocity weighting, in which the magnitude‐weighted inverse of the velocity frequency uniformly distributed weighting across all velocity ranges. When applied to highly accelerated data, the 3‐point network maintained a high correlation with ground truth data and demonstrated a denoising effect. Conclusion The 4D‐flow MRI can be accelerated using machine learning requiring only three flow encodings to produce three‐directional velocity maps with small errors.

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Section: Introductionmentioning

confidence: 99%

Accelerated 4D‐flow MRI with 3‐point encoding enabled by machine learning

Kim

Jen

Eisenmenger

et al. 2022

Magnetic Resonance in Med

View full text Add to dashboard Cite

show abstract

“…Because of the time needed to acquire such large data sets, extensive work has focused on accelerating 4D flow acquisitions, which are more typically acquired in 8–20 min as a result of advances in parallel imaging, compressed sensing, and non‐Cartesian acquisitions . Whereas work continues on acquisition acceleration, comparatively little has been done to shorten individual TRs via gradient waveform design. For decades, the primary source of TR optimization has been the work of Bernstein et al who first outlined analytic solutions to optimal trapezoidal gradient waveform design that minimize the duration of the bipolar gradient waveforms needed to encode a specified maximum velocity.…”

Section: Introductionmentioning

confidence: 99%

Time‐optimized 4D phase contrast MRI with real‐time convex optimization of gradient waveforms and fast excitation methods

Loecher

Magrath

Aliotta

et al. 2019

Magnetic Resonance in Med

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Purpose:To shorten 4D flow acquisitions by shortening TRs with fast RF pulses and gradient waveforms. Real-time convex optimization is used to generate these gradients waveforms on the scanner. Theory and Methods: RF and slab-select waveforms were shortened with a minimum phase SLR excitation and the time-optimal variable-rate selective excitation method. Real-time convex optimization was used to shorten bipolar and spoiler gradients by finding the shortest gradient waveforms that satisfied constraints on scan parameters, gradient hardware, M 0 , M 1 , and peripheral nerve stimulation. Waveforms were calculated and TE and/or TR values were compared for a range of scan parameters and compared to a conventional 4D flow sequence. The method was tested in flow phantoms, and in the aorta and neurovasculature of volunteers (N = 10).Additionally, eddy current error was measured in a large phantom. Results: TEs and TRs were shortened by 21-32% and 20-34%, respectively, compared to the conventional sequence over a range of scan parameters. Bland-Altman analysis of 2 flow phantom configurations showed flow rate bias of 0.3 mL/s and limits of agreement (LOA) of [−6.9, 7.5] mL/s for a cardiac phantom and a bias of −0.1 mL/s with LOA = [−0.4, 0.2] mL/s for a neuro phantom. Similar agreement was also seen for flow measurements in volunteers (bias = −1.0 and −0.1 mL/s, LOA = [−34.9, 33.0] and [−0.7, 0.6] mL/s). Measured eddy currents were 39% larger with the CVX + mpVERSE method. Conclusion: The real-time optimized 4D flow gradients and fast slab-selection excitation methods produced up to 34% faster TRs with excellent flow measurement agreement compared to a conventional 4D flow sequence. K E Y W O R D S 4D flow, convex optimization, eddy currents, peripheral nerve stimulation, phase contrast, pulse sequence design 1 | INTRODUCTION Four-dimensional phase-contrast (4D flow) MRI permits the acquisition of volumetric data sets containing time-resolved velocity vector fields enabling both hemodynamic analysis and anatomic examination. These extensive data sets have proven useful for studying and diagnosing a range of cardiovascular diseases, throughout many anatomic territories. 1 214 | LOECHER Et aL.

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Improved acceleration of phase-contrast flow imaging with magnitude difference regularization

Shin¹,

Shin

2020

Magnetic Resonance Imaging

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Accelerated phase contrast MRI using hybrid one‐ and two‐sided flow encodings only (HOTFEO)

Cited by 4 publications

References 21 publications

Accelerated 4D‐flow MRI with 3‐point encoding enabled by machine learning

Accelerated 4D‐flow MRI with 3‐point encoding enabled by machine learning

Time‐optimized 4D phase contrast MRI with real‐time convex optimization of gradient waveforms and fast excitation methods

Improved acceleration of phase-contrast flow imaging with magnitude difference regularization

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