Daniel Pflugfelder

High-performance computing MRI simulations

A new open‐source software project is presented, JEMRIS, the Jülich Extensible MRI Simulator, which provides an MRI sequence development and simulation environment for the MRI community. The development was driven by the desire to achieve generality of simulated three‐dimensional MRI experiments reflecting modern MRI systems hardware. The accompanying computational burden is overcome by means of parallel computing. Many aspects are covered that have not hitherto been simultaneously investigated in general MRI simulations such as parallel transmit and receive, important off‐resonance effects, nonlinear gradients, and arbitrary spatiotemporal parameter variations at different levels. The latter can be used to simulate various types of motion, for instance. The JEMRIS user interface is very simple to use, but nevertheless it presents few limitations. MRI sequences with arbitrary waveforms and complex interdependent modules are modeled in a graphical user interface–based environment requiring no further programming. This manuscript describes the concepts, methods, and performance of the software. Examples of novel simulation results in active fields of MRI research are given. Magn Reson Med 64:186–193, 2010.

Quantitative 3D Analysis of Plant Roots Growing in Soil Using Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) enables nondestructive 3D imaging and quantification of roots or root system architecture in soil and is suited for automated and routine measurements of root development. Precise measurements of root system architecture traits are an important requirement for plant phenotyping. Most of the current methods for analyzing root growth require either artificial growing conditions (e.g. hydroponics), are severely restricted in the fraction of roots detectable (e.g. rhizotrons), or are destructive (e.g. soil coring). On the other hand, modalities such as magnetic resonance imaging (MRI) are noninvasive and allow high-quality three-dimensional imaging of roots in soil. Here, we present a plant root imaging and analysis pipeline using MRI together with an advanced image visualization and analysis software toolbox named NMRooting. Pots up to 117 mm in diameter and 800 mm in height can be measured with the 4.7 T MRI instrument used here. For 1.5 l pots (81 mm diameter, 300 mm high), a fully automated system was developed enabling measurement of up to 18 pots per day. The most important root traits that can be nondestructively monitored over time are root mass, length, diameter, tip number, and growth angles (in two-dimensional polar coordinates) and spatial distribution. Various validation measurements for these traits were performed, showing that roots down to a diameter range between 200 μm and 300 μm can be quantitatively measured. Root fresh weight correlates linearly with root mass determined by MRI. We demonstrate the capabilities of MRI and the dedicated imaging pipeline in experimental series performed on soil-grown maize (Zea mays) and barley (Hordeum vulgare) plants.

On the numerically predicted spatial BOLD fMRI specificity for spin echo sequences

This work utilises general numerical magnetic resonance imaging MRI simulations to predict the spatial specificity of the blood oxygenation level-dependent (BOLD) functional MRI (fMRI) signal. A Monte Carlo simulation approach was utilized on a microvascular structure consisting of randomly oriented cylinders representing blood vessels. This framework was employed to numerically investigate the spatial specificity, defined as ratio of pial vessel to microvascular signal, of the spin echo BOLD fMRI signal as a function of field strength, echo time and tissue types [grey matter (GM) and cerebrospinal fluid (CSF), respectively]. Spatial specificity of spin echo BOLD fMRI signal was determined to increase with field strength up to 16 T and with maximal specificity for echo time shorter than tissue T(2). In addition, it was found that, for large pial vessels, the extravascular signal decay could not be described using the oversimplified but nevertheless commonly employed mono-exponential signal decay approximation (MEA). Consequently, a recently proposed model relying on the MEA deviates substantially from our results on the spatial specificity. A refinement of this model is proposed based on an available, more detailed signal description. Finally, the effect of CSF on the spatial specificity was investigated. While a large spatial specificity of the spin echo BOLD fMRI signal is observed if a pial vessel is surrounded by grey matter, this is greatly reduced for a pial vessel situated on a GM/CSF interface, rendering the suppression of pial vessels on the cortex surface unlikely.

phenoVein—A Tool for Leaf Vein Segmentation and Analysis

phenoVein is a user-friendly software tool designed for automated leaf vein segmentation and analysis of leaf vein traits, including a model-based vein width determination. Precise measurements of leaf vein traits are an important aspect of plant phenotyping for ecological and genetic research. Here, we present a powerful and user-friendly image analysis tool named phenoVein. It is dedicated to automated segmenting and analyzing of leaf veins in images acquired with different imaging modalities (microscope, macrophotography, etc.), including options for comfortable manual correction. Advanced image filtering emphasizes veins from the background and compensates for local brightness inhomogeneities. The most important traits being calculated are total vein length, vein density, piecewise vein lengths and widths, areole area, and skeleton graph statistics, like the number of branching or ending points. For the determination of vein widths, a model-based vein edge estimation approach has been implemented. Validation was performed for the measurement of vein length, vein width, and vein density of Arabidopsis (Arabidopsis thaliana), proving the reliability of phenoVein. We demonstrate the power of phenoVein on a set of previously described vein structure mutants of Arabidopsis (hemivenata, ondulata3, and asymmetric leaves2-101) compared with wild-type accessions Columbia-0 and Landsberg erecta-0. phenoVein is freely available as open-source software.

Physical rupture of the xylem in developing sweet cherry fruit causes progressive decline in xylem sap inflow rate

AbstractMain conclusionXylem flow is progressively shut down during maturation beginning with minor veins at the stylar end and progressing to major veins and finally to bundles at the stem end. This study investigates the functionality of the xylem vascular system in developing sweet cherry fruit (Prunus avium L.). The tracers acid fuchsin and gadoteric acid were fed to the pedicel of detached fruit. The tracer distribution was studied using light microscopy and magnetic resonance imaging. The vasculature of the sweet cherry comprises five major bundles. Three of these supply the flesh; two enter the pit to supply the ovules. All vascular bundles branch into major and minor veins that interconnect via numerous anastomoses. The flow in the xylem as indexed by the tracer distribution decreases continuously during development. The decrease is first evident at the stylar (distal) end of the fruit during pit hardening and progresses basipetally towards the pedicel (proximal) end of the fruit at maturity. That growth strains are the cause of the decreased conductance is indicated by: elastic strain relaxation after tissue excision, the presence of ruptured vessels in vivo, the presence of intrafascicular cavities, and the absence of tyloses.