Ultrashort echo time (UTE) magnetic resonance imaging of myelin: technical developments and challenges

Ma, Yajun; Jang, Hyungseok; Chang, Eric Y.; Hiniker, Annie; Head, Brian P.; Lee, Roland R.; Corey–Bloom, Jody; Bydder, Graeme M.; Du, Jiang

doi:10.21037/qims-20-541

Cited by 23 publications

(10 citation statements)

References 54 publications

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“…Certain standard MRI techniques (such as spin‐echo‐MRI) are unable to image plant tissues in which the signal decays rapidly as a result of a short T2 relaxation time. A recently introduced innovation designed to overcome this limitation is ultrashort echo time (UTE)‐MRI (Ma et al ., 2020). Ultrashort echo time techniques may be highly suited for the analysis of either structures displaying substantial susceptibility‐induced magnetic field inhomogeneity (e.g.…”

Section: Mri As a Noninvasive Approachmentioning

confidence: 99%

Seeing plants as never before

et al. 2023

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Summary Imaging has long supported our ability to understand the inner life of plants, their development, and response to a dynamic environment. While optical microscopy remains the core tool for imaging, a suite of novel technologies is now beginning to make a significant contribution to visualize plant metabolism. The purpose of this review was to provide the scientific community with an overview of current imaging methods, which rely variously on either nuclear magnetic resonance (NMR), mass spectrometry (MS) or infrared (IR) spectroscopy, and to present some examples of their application in order to illustrate their utility. In addition to providing a description of the basic principles underlying these technologies, the review discusses their various advantages and limitations, reveals the current state of the art, and suggests their potential application to experimental practice. Finally, a view is presented as to how the technologies will likely develop, how these developments may encourage the formulation of novel experimental strategies, and how the enormous potential of these technologies can contribute to progress in plant science.

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Section: Mri As a Noninvasive Approachmentioning

confidence: 99%

Seeing plants as never before

et al. 2023

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“…After the magnitudes of each myelin component, i.e., A MW , A AW , A EW , in Eq. [5] were estimated, the MWF can be calculated as the fraction of MW over the total water signal amplitude:…”

Section: Signal Synthetizationmentioning

confidence: 99%

“…Myelin water fraction (MWF) derived from quantitative myelin water imaging (MWI), is a powerful and important magnetic resonance imaging (MRI) method for studying and diagnosing neurological and psychiatric diseases such as multiple sclerosis (1), schizophrenia (2), Alzheimer disease (3) and stroke (4,5). MWF maps are commonly calculated based on T 2 /T 2 * imaging data acquired using a Carr-Purcell-Meiboom-Gill (CPMG) sequence or its accelerated version, i.e., the gradient and spin echo (GRASE) sequence, in which the T 2 value of the multicomponent water is analyzed (6).…”

Section: Introductionmentioning

confidence: 99%

Improved magnetic resonance myelin water imaging using multi-channel denoising convolutional neural networks (MCDnCNN)

Xu¹,

He²,

Yu³

et al. 2022

Quant Imaging Med Surg

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Background: Myelin water imaging (MWI) is powerful and important for studying and diagnosing neurological and psychiatric diseases. In particular, myelin water fraction (MWF) is derived from MWI data for quantifying myelination. However, MWF estimation is typically sensitive to noise. Improving the accuracy of MWF estimation based on WMI data acquired using a magnetic resonance (MR) multiple gradient recalled echo (mGRE) imaging sequence is desired. Methods:The proposed method employs a recently introduced the multi-channel denoising convolutional neural networks (MCDnCNN). Five different MCDnCNN models, denoted as Delevel1, Delevel2, Delevel3, Delevel4 and DelevelMix corresponding to five noise levels (Level1, Level2, Level3, Level4 and LevelMix), were trained using the data of the first echo of the mGRE brain images acquired from 15 healthy human subjects. Using simulated noisy data that employed a hollow cylinder model, we first evaluated the improvement in estimating MWF based on data denoised by the five different MCDnCNNs, by comparing the MWF maps calculated from the denoised data with ground truth. Next, we again evaluated the improvement using real-world in vivo datasets of 11 human participants acquired using the mGRE sequence.The datasets were first denoised by five different MCDnCNNs (Delevel1, 2, 3, 4 and DelevelMix), and subsequently their MWF maps were calculated and compared with the MWF maps directly calculated from the raw mGRE images without being denoised.Results: Experiments using the simulation data denoised by the appropriate MCDnCNN models showed that the standard deviation (SD) of the absolute error (AE) of the derived MWF results was significantly reduced (maximal reduction =15.5%, Level3 simulated noisy data, orientation angle =0, all the five MCDnCNN models). In the test using in vivo data, estimating MWF based on data particularly denoised by the appropriate MCDnCNN models was found to be the best, compared to otherwise not using the appropriate models. The results demonstrated that the appropriate MCDnCNN models may permit highquality MWF mapping, i.e., substantial reduction of random variation in estimating MWF-maps while preserving accuracy and structural details. Conclusions: Appropriate MCDnCNN models as proposed may improve both the accuracy and precision 2 Xu et al. Improved MWI using MCDnCNN

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“…Although MWF is myelin-specific, it is still not directly imaging the myelin. In this regard, UTE sequences have the potential to image the myelin proton (very short T2) in the SC WM, but this approach is still largely not investigated in the MS population [ 83 ].…”

Section: Spinal Cord Mri In Multiple Sclerosismentioning

confidence: 99%

Imaging of the Spinal Cord in Multiple Sclerosis: Past, Present, Future

2020

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Spinal cord imaging in multiple sclerosis (MS) plays a significant role in diagnosing and tracking disease progression. The spinal cord is one of four key areas of the central nervous system where documenting the dissemination in space in the McDonald criteria for diagnosing MS. Spinal cord lesion load and the severity of cord atrophy are believed to be more relevant to disability than white matter lesions in the brain in different phenotypes of MS. Axonal loss contributes to spinal cord atrophy in MS and its degree correlates with disease severity and prognosis. Therefore, measures of axonal loss are often reliable biomarkers for monitoring disease progression. With recent technical advances, more and more qualitative and quantitative MRI techniques have been investigated in an attempt to provide objective and reliable diagnostic and monitoring biomarkers in MS. In this article, we discuss the role of spinal cord imaging in the diagnosis and prognosis of MS and, additionally, we review various techniques that may improve our understanding of the disease.

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Ultrashort echo time (UTE) magnetic resonance imaging of myelin: technical developments and challenges

Cited by 23 publications

References 54 publications

Seeing plants as never before

Seeing plants as never before

Improved magnetic resonance myelin water imaging using multi-channel denoising convolutional neural networks (MCDnCNN)

Imaging of the Spinal Cord in Multiple Sclerosis: Past, Present, Future

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