Minimum Field Strength Simulator for Proton Density Weighted MRI

Wu, Ziyue; Chen, Weiyi; Nayak, Krishna S.

doi:10.1371/journal.pone.0154711

Cited by 13 publications

(16 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…90–92 The use of iterative constrained reconstruction algorithms have been shown to improve the SNR from low field simulated measurements, and these hold great potential in future use of upright low field scanners for RT-MRI of the upper airway. 93 …”

Section: Discussionmentioning

confidence: 99%

“…[90][91][92] The use of iterative constrained reconstruction algorithms have been shown to improve the SNR from low field simulated measurements, and these hold great potential in future use of upright low field scanners for RT-MRI of the upper airway. 93 The clinical role of RT-MRI in the assessment of abnormal vocal tract and upper airway dynamics has not yet been determined, and remains an exciting area for investigation. VPI, swallowing-aspiration, and obstructive apnea are among the most compelling initial applications due to the prevalence of these conditions, and the lack of safe nonionizing alternatives.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Recommendations for real-time speech MRI

Lingala

Sutton

Miquel

et al. 2015

J. Magn. Reson. Imaging

Self Cite

127

View full text Add to dashboard Cite

Real-time magnetic resonance imaging (RT-MRI) is being increasingly used for speech and vocal production research studies. Several imaging protocols have emerged based on advances in RT-MRI acquisition, reconstruction, and audio-processing methods. This review summarizes the state-of-the-art, discusses technical considerations, and provides specific guidance for new groups entering this field. We provide recommendations for performing RT-MRI of the upper airway. This is a consensus statement stemming from the ISMRM-endorsed Speech MRI summit held in Los Angeles, February 2014. A major unmet need identified at the summit was the need for consensus on protocols that can be easily adapted by researchers equipped with conventional MRI systems. To this end, we provide a discussion of tradeoffs in RT-MRI in terms of acquisition requirements, a priori assumptions, artifacts, computational load, and performance for different speech tasks. We provide four recommended protocols and identify appropriate acquisition and reconstruction tools. We list pointers to open-source software that facilitate implementation. We conclude by discussing current open challenges in the methodological aspects of RT-MRI of speech.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Recommendations for real-time speech MRI

Lingala

Sutton

Miquel

et al. 2015

J. Magn. Reson. Imaging

Self Cite

127

View full text Add to dashboard Cite

show abstract

“…2 As discussed in the accelerated readouts section, these techniques can be used to reduce the motion sensitivity. In simulations, it has been demonstrated that such techniques can be used, even at low field, to image upper airway displacement in real time …”

Section: New Acquisition Strategiesmentioning

confidence: 99%

“…In simulations, it has been demonstrated that such techniques can be used, even at low field, to image upper airway displacement in real time. 73…”

Section: New Acquisition Strategiesmentioning

confidence: 99%

Low‐field MRI: An MR physics perspective

Marques

Simonis

Webb

2019

Magnetic Resonance Imaging

246

266

View full text Add to dashboard Cite

Historically, clinical MRI started with main magnetic field strengths in the ∼0.05–0.35T range. In the past 40 years there have been considerable developments in MRI hardware, with one of the primary ones being the trend to higher magnetic fields. While resulting in large improvements in data quality and diagnostic value, such developments have meant that conventional systems at 1.5 and 3T remain relatively expensive pieces of medical imaging equipment, and are out of the financial reach for much of the world. In this review we describe the current state‐of‐the‐art of low‐field systems (defined as 0.25–1T), both with respect to its low cost, low foot‐print, and subject accessibility. Furthermore, we discuss how low field could potentially benefit from many of the developments that have occurred in higher‐field MRI. In the first section, the signal‐to‐noise ratio (SNR) dependence on the static magnetic field and its impact on the achievable contrast, resolution, and acquisition times are discussed from a theoretical perspective. In the second section, developments in hardware (eg, magnet, gradient, and RF coils) used both in experimental low‐field scanners and also those that are currently in the market are reviewed. In the final section the potential roles of new acquisition readouts, motion tracking, and image reconstruction strategies, currently being developed primarily at higher fields, are presented. Level of Evidence : 5 Technical Efficacy Stage : 1 J. Magn. Reson. Imaging 2019.

show abstract

“…The dataset in [8], [24] is however not publicly available. To circumvent this, we simulate low field MR images for the ADNI dataset using a recently developed technique in [47]. Certain assumptions are made by the authors of [47]: 1.)…”

Section: E Enhancing Low Field Mr Imagesmentioning

confidence: 99%

Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors

Cherukuri

Guo

Schiff

et al. 2020

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

High resolution Magnetic Resonance (MR) images are desired for accurate diagnostics. In practice, image resolution is restricted by factors like hardware and processing constraints. Recently, deep learning methods have been shown to produce compelling state-of-the-art results for image enhancement/superresolution. Paying particular attention to desired hi-resolution MR image structure, we propose a new regularized network that exploits image priors, namely a low-rank structure and a sharpness prior to enhance deep MR image super-resolution (SR). Our contributions are then incorporating these priors in an analytically tractable fashion as well as towards a novel prior guided network architecture that accomplishes the superresolution task. This is particularly challenging for the low rank prior since the rank is not a differentiable function of the image matrix (and hence the network parameters), an issue we address by pursuing differentiable approximations of the rank. Sharpness is emphasized by the variance of the Laplacian which we show can be implemented by a fixed feedback layer at the output of the network. As a key extension, we modify the fixed feedback (Laplacian) layer by learning a new set of training data driven filters that are optimized for enhanced sharpness. Experiments performed on publicly available MR brain image databases and comparisons against existing state-of-the-art methods show that the proposed prior guided network offers significant practical gains in terms of improved SNR/image quality measures. Because our priors are on output images, the proposed method is versatile and can be combined with a wide variety of existing network architectures to further enhance their performance.

show abstract

Minimum Field Strength Simulator for Proton Density Weighted MRI

Cited by 13 publications

References 31 publications

Recommendations for real-time speech MRI

Recommendations for real-time speech MRI

Low‐field MRI: An MR physics perspective

Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors

Contact Info

Product

Resources

About