TensorFlow MRI: A Library for Modern Computational MRI on Heterogenous Systems

Montalt-Tordera, Javier; Steeden, Jennifer A.; Muthurangu, Vivek

doi:10.58530/2022/2769

Cited by 3 publications

(5 citation statements)

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“…The optimized spiral raw data were also retrospectively reconstructed using (1) a simple gridded reconstruction (the equivalent to the input to the network); (2) navigator‐less spiral SToRM, 27 which is a state‐of‐the‐art compressed‐sensing reconstruction; and (3) spiral VarNet 28 reconstruction, which is an unrolled ML network architecture including data consistency. The gridded, SToRM, and VarNet reconstructions were performed offline using open‐source codes 27–29 . VarNet was retrained on the same data set and same optimized trajectory as the proposed HyperSLICE network.…”

Section: Methodsmentioning

confidence: 99%

“…The gridded, SToRM, and VarNet reconstructions were performed offline using open‐source codes. 27 , 28 , 29 VarNet was retrained on the same data set and same optimized trajectory as the proposed HyperSLICE network. The Cartesian data sets used for comparison were reconstructed on the scanner platform using the scanner software reconstructions (including GRAPPA).…”

Section: Methodsmentioning

confidence: 99%

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HyperSLICE: HyperBand optimized spiral for low‐latency interactive cardiac examination

Jaubert,

Montalt‐Tordera,

Knight

et al. 2023

Magnetic Resonance in Med

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PurposeInteractive cardiac MRI is used for fast scan planning and MR‐guided interventions. However, the requirement for real‐time acquisition and near‐real‐time visualization constrains the achievable spatio‐temporal resolution. This study aims to improve interactive imaging resolution through optimization of undersampled spiral sampling and leveraging of deep learning for low‐latency reconstruction (deep artifact suppression).MethodsA variable density spiral trajectory was parametrized and optimized via HyperBand to provide the best candidate trajectory for rapid deep artifact suppression. Training data consisted of 692 breath‐held CINEs. The developed interactive sequence was tested in simulations and prospectively in 13 subjects (10 for image evaluation, 2 during catheterization, 1 during exercise). In the prospective study, the optimized framework—HyperSLICE— was compared with conventional Cartesian real‐time and breath‐hold CINE imaging in terms quantitative and qualitative image metrics. Statistical differences were tested using Friedman chi‐squared tests with post hoc Nemenyi test (p < 0.05).ResultsIn simulations the normalized RMS error, peak SNR, structural similarity, and Laplacian energy were all statistically significantly higher using optimized spiral compared to radial and uniform spiral sampling, particularly after scan plan changes (structural similarity: 0.71 vs. 0.45 and 0.43). Prospectively, HyperSLICE enabled a higher spatial and temporal resolution than conventional Cartesian real‐time imaging. The pipeline was demonstrated in patients during catheter pull back, showing sufficiently fast reconstruction for interactive imaging.ConclusionHyperSLICE enables high spatial and temporal resolution interactive imaging. Optimizing the spiral sampling enabled better overall image quality and superior handling of image transitions compared with radial and uniform spiral trajectories.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

HyperSLICE: HyperBand optimized spiral for low‐latency interactive cardiac examination

Jaubert,

Montalt‐Tordera,

Knight

et al. 2023

Magnetic Resonance in Med

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“…Conventional reconstruction for 2D and 3D acquisitions consisted of nonuniform fast Fourier transform (NUFFT) with density compensation 23,24 …”

Section: Methodsmentioning

confidence: 99%

“…All CS reconstructions were developed in‐house using Python (version 3.8.10) and TensorFlow MRI (version 0.21.0) 24 …”

Section: Methodsmentioning

confidence: 99%

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2D sodium MRI of the human calf using half‐sinc excitation pulses and compressed sensing

Baker,

Muthurangu,

Rega

et al. 2023

Magnetic Resonance in Med

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PurposeSodium MRI can be used to quantify tissue sodium concentration (TSC) in vivo; however, UTE sequences are required to capture the rapidly decaying signal. 2D MRI enables high in‐plane resolution but typically has long TEs. Half‐sinc excitation may enable UTE; however, twice as many readouts are necessary. Scan time can be minimized by reducing the number of signal averages (NSAs), but at a cost to SNR. We propose using compressed sensing (CS) to accelerate 2D half‐sinc acquisitions while maintaining SNR and TSC.MethodsEx vivo and in vivo TSC were compared between 2D spiral sequences with full‐sinc (TE = 0.73 ms, scan time ≈ 5 min) and half‐sinc excitation (TE = 0.23 ms, scan time ≈ 10 min), with 150 NSAs. Ex vivo, these were compared to a reference 3D sequence (TE = 0.22 ms, scan time ≈ 24 min). To investigate shortening 2D scan times, half‐sinc data was retrospectively reconstructed with fewer NSAs, comparing a nonuniform fast Fourier transform to CS. Resultant TSC and image quality were compared to reference 150 NSAs nonuniform fast Fourier transform images.ResultsTSC was significantly higher from half‐sinc than from full‐sinc acquisitions, ex vivo and in vivo. Ex vivo, half‐sinc data more closely matched the reference 3D sequence, indicating improved accuracy. In silico modeling confirmed this was due to shorter TEs minimizing bias caused by relaxation differences between phantoms and tissue. CS was successfully applied to in vivo, half‐sinc data, maintaining TSC and image quality (estimated SNR, edge sharpness, and qualitative metrics) with ≥50 NSAs.Conclusion2D sodium MRI with half‐sinc excitation and CS was validated, enabling TSC quantification with 2.25 × 2.25 mm2 resolution and scan times of ≤5 mins.

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