Low-field magnetic resonance imaging of roots in intact clayey and silty soils

Bagnall, G. Cody; Koonjoo, Néha; Altobelli, Stephen A.; Conradi, Mark S.; Fukushima, Eiichi; Kuethe, Dean O.; Mullet, John E.; Neely, Haly L.; Rooney, William L.; Stupic, Karl F.; Weers, Brock; Zhu, Bin; Rosen, Matthew S.; Morgan, Cristine L.S.

doi:10.1016/j.geoderma.2020.114356

Cited by 24 publications

(31 citation statements)

References 24 publications

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“…Besides training on T 1 -weighted brain images, we investigated domain-speci c training on plant root MRI datasets acquired on a 47 mT magnet 20 . Reconstruction of the plant roots datasets is feasible with a T 1weighted brain trained model; however, the low-dimensional representation of plant root images is different than that of brain images.…”

Section: Discussionmentioning

confidence: 99%

“…Sorghum root imaging at 47 mT 2D projection images were acquired from rhizotron cores 20 . The 2D projection images were acquired using a 2D spin-warp pulse sequence.…”

Section: In Vivo Brain -Data Acquisition At 65 Mtmentioning

confidence: 99%

“…Recent prior work has demonstrated the feasibility of studying the root phenotype of sorghum growing in natural clay soils with low eld MRI 20 . With the aim of further improving root images acquired from the 47 mT cylindrical electromagnet-based scanner described therein, raw k-space from several 2D root projection imaging experiments were reconstructed with AUTOMAP and the images were compared with the conventional IFFT reconstruction.…”

Section: Plant Root Mr Imaging At 45 Mtmentioning

confidence: 99%

“…Raw data was acquired from two different MRI systems operating at low magnetic eld: a 6.5 mT human brain scanner operating in our laboratory 3 and a 47 mT plant root imager 20 designed to operate outdoors underground in clay soil. The 6.5 mT ULF scanner was used to acquire image data of both phantoms and human heads.…”

Section: Introductionmentioning

confidence: 99%

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Boosting the Signal-to-Noise of Low-Field MRI With Deep Learning Image Reconstruction

Koonjoo

Zhu

Bagnall

et al. 2020

Preprint

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Recent years have seen a resurgence of interest in inexpensive low-field (<0.3 T) MRI systems mainly due to advances in magnet, coil and gradient set designs. However, most of these advances are focused on hardware development and signal acquisition while far less attention has been given to how advanced image reconstruction can improve image quality at low field. We describe here the use of our end-to-end deep neural network approach (AUTOMAP) to improve the image quality of highly noise-corrupted low-field MRI data. We compare the performance of this approach to two additional state-of-the-art denoising pipelines. We find that AUTOMAP improves image reconstruction of data acquired on two very different low-field MRI systems: human brain data acquired at 6.5 mT, and plant root data acquired at 47 mT, demonstrating SNR gains above Fourier reconstruction by factors of 1.5- to 4.5-fold, and 3-fold, respectively. In these applications, AUTOMAP outperformed both contemporary denoising algorithms and suppressed noise-like spike artifacts in reconstructed images. The impact of domain-specific training corpora on the reconstruction performance is discussed. The AUTOMAP approach to image reconstruction will enable significant image quality improvements at low-field, especially in highly noise-corrupted environments.

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Section: Discussionmentioning

confidence: 99%

“…Sorghum root imaging at 47 mT 2D projection images were acquired from rhizotron cores 20 . The 2D projection images were acquired using a 2D spin-warp pulse sequence.…”

Section: In Vivo Brain -Data Acquisition At 65 Mtmentioning

confidence: 99%

Section: Plant Root Mr Imaging At 45 Mtmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Boosting the Signal-to-Noise of Low-Field MRI With Deep Learning Image Reconstruction

Koonjoo

Zhu

Bagnall

et al. 2020

Preprint

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“…One of the awarded projects is co-led by Cristine Morgan at Texas A&M University (now Chief Scientific Officer at the Soil Health Institute) and Matthew Rosen of Massachusetts General Hospital (MGH) Martinos Imaging Center with support by ABQMR, Inc. and NIST-Boulder. The goal of the ROOTS project is to connect the phenotype of the root structure, while it is in the ground to the genes necessary to control how far the roots penetrate into the earth [ 124 ]. This information would in turn be used to direct roots to grow deeper into the ground, whereby carbon dioxide from the air could be more efficiently sequestered into the earth.…”

Section: How Far Is Everywhere? Examples From Low-field Nmrmentioning

confidence: 99%

EPR Everywhere

Biller

McPeak

2021

Appl Magn Reson

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This review is inspired by the contributions from the University of Denver group to low-field EPR, in honor of Professor Gareth Eaton's 80th birthday. The goal is to capture the spirit of innovation behind the body of work, especially as it pertains to development of new EPR techniques. The spirit of the DU EPR laboratory is one that never sought to limit what an EPR experiment could be, or how it could be applied. The most well-known example of this is the development and recent commercialization of rapid-scan EPR. Both of the Eatons have made it a point to remain knowledgeable on the newest developments in electronics and instrument design. To that end, our review touches on the use of miniaturized electronics and applications of single-board spectrometers based on software-defined radio (SDR) implementations and single-chip voltage-controlled oscillator (VCO) arrays. We also highlight several non-traditional approaches to the EPR experiment such as an EPR spectrometer with a "wand" form factor for analysis of the OxyChip, the EPR-MOUSE which enables non-destructive in situ analysis of many non-conforming samples, and interferometric EPR and frequency swept EPR as alternatives to classical high Q resonant structures.

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LEADER (Leaf Element Accumulation from DEep Roots): A nondestructive phenotyping platform to estimate rooting depth in the field

Hanlon,

Brown,

Lynch

2023

Crop Science

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Deeper rooted crops are an avenue to increase plant water and nitrogen uptake under limiting conditions and increase long‐term soil carbon storage. Measuring rooting depth, however, is challenging due to the destructive, laborious, or imprecise methods that are currently available. Here, we present LEADER (Leaf Element Accumulation from DEep Roots) as a method to estimate in‐field root depth of maize plants. We use both X‐ray fluorescence (XRF) spectroscopy and ICP‐OES (inductively coupled plasma optical emission spectroscopy) to measure leaf elemental content and relate this to metrics of root depth. Principal components of leaf elemental content correlate with measures of root length in four genotypes (R2 = 0.8 for total root length), and we use linear discriminant analysis to classify plants as having different metrics related to root depth across four field sites in the United States. We can correctly classify the plots with the longest root length at depth (deeper than 30 or 40 cm) with high accuracy (accuracy >0.6) at two of our field sites (Hancock, WI and Rock Spring, PA). We also use strontium (Sr) as a tracer element in both greenhouse and field studies, showing that elemental accumulation of Sr in leaf tissue can be measured with XRF and can estimate root depth. We propose the adoption of LEADER as a tool for measuring root depth in different plant species and soils. LEADER is faster and easier than any other methods that currently exist and could allow for extensive study and understanding of deep rooting.

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Low-field magnetic resonance imaging of roots in intact clayey and silty soils

Cited by 24 publications

References 24 publications

Boosting the Signal-to-Noise of Low-Field MRI With Deep Learning Image Reconstruction

Boosting the Signal-to-Noise of Low-Field MRI With Deep Learning Image Reconstruction

EPR Everywhere

LEADER (Leaf Element Accumulation from DEep Roots): A nondestructive phenotyping platform to estimate rooting depth in the field

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Low-field magnetic resonance imaging of roots in intact clayey and silty soils

Cited by 24 publications

References 24 publications

Boosting the Signal-to-Noise of Low-Field MRI&nbsp;With Deep Learning Image Reconstruction

Boosting the Signal-to-Noise of Low-Field MRI&nbsp;With Deep Learning Image Reconstruction

EPR Everywhere

LEADER (Leaf Element Accumulation from DEep Roots): A nondestructive phenotyping platform to estimate rooting depth in the field

Contact Info

Product

Resources

About

Boosting the Signal-to-Noise of Low-Field MRI With Deep Learning Image Reconstruction

Boosting the Signal-to-Noise of Low-Field MRI With Deep Learning Image Reconstruction