2021
DOI: 10.48550/arxiv.2110.03417
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Prepolarized MRI of Hard Tissues and Solid-State Matter

J. M. González,
J. Borreguero,
E. Pallás
et al.

Abstract: Prepolarized Magnetic Resonance Imaging (PMRI) is a long-established technique conceived to counteract the loss in signal-to-noise ratio (SNR) inherent to low-field MRI systems.When it comes to hard biological tissues and solid-state matter, PMRI is severely restricted by their ultra-short characteristic relaxation times. Here we demonstrate that efficient hard tissue prepolarization is within reach with a special-purpose 0.26 T scanner designed for dental MRI and equipped with suitable high-power electronics.… Show more

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Cited by 4 publications
(5 citation statements)
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“…[1][2][3] LF-MRI is developing as a customizable and affordable complement to standard high-field MRI (>1 T), which is an expensive medical imaging modality in terms of purchase cost, maintenance, siting, and training, and consequently is concentrated in large hospitals in the economically developed world. [4][5][6][7][8] In the last few years, LF-MRI has demonstrated its value for point-of-care imaging, [9][10][11][12][13][14][15] home healthcare, 16 quantitative MRI and fingerprinting, 17,18 hard-tissue imaging, [19][20][21] and artifact-free imaging of metallic implants, 16,22 as well as educational purposes, 23 among others. These achievements are enabled by a new generation of scanners combining refined hardware engineering with powerful computational algorithms, including machine learning architectures.…”
Section: Introductionmentioning
confidence: 99%
“…[1][2][3] LF-MRI is developing as a customizable and affordable complement to standard high-field MRI (>1 T), which is an expensive medical imaging modality in terms of purchase cost, maintenance, siting, and training, and consequently is concentrated in large hospitals in the economically developed world. [4][5][6][7][8] In the last few years, LF-MRI has demonstrated its value for point-of-care imaging, [9][10][11][12][13][14][15] home healthcare, 16 quantitative MRI and fingerprinting, 17,18 hard-tissue imaging, [19][20][21] and artifact-free imaging of metallic implants, 16,22 as well as educational purposes, 23 among others. These achievements are enabled by a new generation of scanners combining refined hardware engineering with powerful computational algorithms, including machine learning architectures.…”
Section: Introductionmentioning
confidence: 99%
“…At the marcos client level, a sequence is specified by supplying pairs of (time, value) arrays for each hardware output and control, such as the TX envelope, gradient waveforms, etc. 15 . The library scales and rounds the arrays to machine units, applies time offsets to cancel out latencies in the gradient serialisers and other hardware, and compiles these values and times into binary instructions.…”
Section: Desktop Software Platform a Marcos Client Librarymentioning
confidence: 99%
“…This will be used for multi-device synchronization in the future 15. For example, to specify two pulses of 70% full-scale amplitude starting at 20 µs and 100 µs from the beginning of the sequence, each with a duration of 30 µs, one would supply the array pair ([20, 50, 100, 130], [0.7, 0, 0.7, 0]).…”
mentioning
confidence: 99%
“…Zero Echo Time pulse sequences, otherwise known as Zero TE or ZTE, are designed to capture the weak and short-lived signal emitted by hard biological tissues and solid-state matter in Magnetic Resonance Imaging (MRI) scanners 1,2 . ZTE sequences are particularly suitable for ultra-short (submillisecond) T 2 or T * 2 MRI 3 , and have been successfully used for a wide variety of applications 4 , including imaging of tendons and bones 5,6 , teeth [7][8][9] , myelin 10 or lungs 11 . Two other families of sequences used for MR imaging of short T 2 samples are SWeep Imaging with Fourier Transformation (SWIFT, 12 ) and Ultra-short Echo Time (UTE, 13 ), although the latter is not suitable when the sample T 2 is comparable to or shorter than the time it takes to switch on the encoding magnetic gradient fields.…”
Section: Introductionmentioning
confidence: 99%