Bagnall G Cody, Koonjoo Neha, Altobelli Stephen A, Conradi Mark S, Fukushima Eiichi, Kuethe Dean O, Mullet John E, Neely Haly, Rooney William L, Stupic Karl F, Weers Brock, Zhu Bo, Rosen Matthew S, Morgan Cristine L S
Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX, USA.
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA.
Geoderma. 2020 Jul;370. doi: 10.1016/j.geoderma.2020.114356.
The development of a robust method to non-invasively visualize root morphology in natural soils has been hampered by the opaque, physical, and structural properties of soils. In this work we describe a novel technology, low field magnetic resonance imaging (LF-MRI), for imaging energy sorghum ( (L.) Moench) root morphology and architecture in intact soils. The use of magnetic fields much weaker than those used with traditional MRI experiments reduces the distortion due to magnetic material naturally present in agricultural soils. A laboratory based LF-MRI operating at 47 mT magnetic field strength was evaluated using two sets of soil cores: 1) soil/root cores of Weswood silt loam (Udifluventic Haplustept) and a Belk clay (Entic Hapluderts) from a conventionally tilled field, and 2) soil/root cores from rhizotrons filled with either a Houston Black (Udic Haplusterts) clay or a sandy loam purchased from a turf company. The maximum soil water nuclear magnetic resonance (NMR) relaxation time T (4 ms) and the typical root water relaxation time T (100 ms) are far enough apart to provide a unique contrast mechanism such that the soil water signal has decayed to the point of no longer being detectable during the data collection time period. 2-D MRI projection images were produced of roots with a diameter range of 1.5-2.0 mm using an image acquisition time of 15 min with a pixel resolution of 1.74 mm in four soil types. Additionally, we demonstrate the use of a data-driven machine learning reconstruction approach, Automated Transform by Manifold Approximation (AUTOMAP) to reconstruct raw data and improve the quality of the final images. The application of AUTOMAP showed a SNR (Signal to Noise Ratio) improvement of two fold on average. The use of low field MRI presented here demonstrates the possibility of applying low field MRI through intact soils to root phenotyping and agronomy to aid in understanding of root morphology and the spatial arrangement of roots .
土壤不透明的物理和结构特性阻碍了一种能够在自然土壤中对根系形态进行非侵入式可视化的强大方法的发展。在这项工作中，我们描述了一种新技术，即低场磁共振成像（LF-MRI），用于对完整土壤中的能源高粱（(L.) Moench）根系形态和结构进行成像。与传统MRI实验相比，使用强度弱得多的磁场可以减少农业土壤中天然存在的磁性物质造成的失真。使用两组土芯对一个基于实验室的、磁场强度为47 mT的LF-MRI进行了评估：1）来自传统耕作田地的韦兹伍德粉质壤土（Udifluventic Haplustept）和贝尔克黏土（Entic Hapluderts）的土壤/根系土芯，以及2）来自装有休斯顿黑黏土（Udic Haplusterts）或从草坪公司购买的沙壤土的根际箱的土壤/根系土芯。土壤水的最大核磁共振（NMR）弛豫时间T（4毫秒）与典型的根水弛豫时间T（100毫秒）相差足够大，从而提供了一种独特的对比机制，使得在数据采集时间段内土壤水信号衰减到不再可检测的程度。在四种土壤类型中，使用15分钟的图像采集时间和1.74毫米的像素分辨率，生成了直径范围为1.5 - 2.0毫米的根系的二维MRI投影图像。此外，我们展示了使用一种数据驱动的机器学习重建方法——通过流形逼近自动变换（AUTOMAP）来重建原始数据并提高最终图像的质量。AUTOMAP的应用平均使信噪比（SNR）提高了两倍。本文介绍的低场MRI的应用证明了通过完整土壤将低场MRI应用于根系表型分析和农艺学以帮助理解根系形态和根系空间排列的可能性。
