Three-dimensional, in-silico breast phantom for multimodality image simulations

We report on the construction of anatomically realistic three-dimensional in-silico breast phantoms with adjustable sizes, shapes and morphologic features. The concept of multiscale spatial resolution is implemented for generating breast tissue images from multiple modalities. Breast epidermal boundary and subcutaneous fat layer is generated by fitting an ellipsoid and 2nd degree polynomials to reconstructive surgical data and ultrasound imaging data. Intraglandular fat is simulated by randomly distributing and orienting adipose ellipsoids within a fibrous region immediately within the dermal layer. Cooper’s ligaments are simulated as fibrous ellipsoidal shells distributed within the subcutaneous fat layer. Individual ductal lobes are simulated following a random binary tree model which is generated based upon probabilistic branching conditions described by ramification matrices, as originally proposed by Bakic et al [3, 4]. The complete ductal structure of the breast is simulated from multiple lobes that extend from the base of the nipple and branch towards the chest wall. As lobe branching progresses, branches are reduced in height and radius and terminal branches are capped with spherical lobular clusters. Biophysical parameters are mapped onto the complete anatomical model and synthetic multimodal images (Mammography, Ultrasound, CT) are generated for phantoms of different adipose percentages (40%, 50%, 60%, and 70%) and are analytically compared with clinical examples. Results demonstrate that the in-silico breast phantom has applications in imaging performance evaluation and, specifically, great utility for solving image registration issues in multimodality imaging.

Characterization of the effect of nitrogen deficiency on above-ground plant phenotypes in relation to root complexity in maize

Nitrogen (N) is an essential plant nutrient in maize production, and if considering only natural sources, is often the limiting factor world-wide in terms of a plant’s grain yield. For this reason, many farmers around the world supplement available soil N with synthetic man-made forms. Years of over-application of N fertilizer have led to increased N in groundwater and streams due to leaching and run-off from agricultural sites. In the Midwest Corn Belt much of this excess N eventually makes its way to the Gulf of Mexico leading to eutrophication (increase of phytoplankton) and a hypoxic (reduced oxygen) dead zone. Growing concerns about these types of problems and desire for greater input use efficiency have led to demand for crops with improved N use efficiency (NUE) to allow reduced N fertilizer application rates and subsequently lower N pollution. It is well known that roots are responsible for N uptake by plants, but it is relatively unknown how root architecture affects this ability. This research was conducted to better understand the influence of root complexity (RC) in maize on a plant’s response to N stress as well as the influence of RC on other above-ground plant traits. Thirty-one above-ground plant traits were measured for 64 recombinant inbred lines (RILs) from the intermated B73 & Mo17 (IBM) population and their backcrosses (BCs) to either parent, B73 and Mo17, under normal (182 kg N ha-1) and N deficient (0 kg N ha-1) conditions. The RILs were selected based on results from an earlier experiment by Novais et al. (2011) which screened 232 RILs from the IBM to obtain their root complexity measurements. The 64 selected RILs were comprised of 31 of the lowest complexity RILs (RC1) and 33 of the highest complexity RILs (RC2) in terms of root architecture (characterized as fractal dimensions). The use of the parental BCs classifies the experiment as Design III, an experimental design developed by Comstock and Robinson (1952) which allows for estimation of dominance significance and level. Of the 31 traits measured, 12 were whole plant traits chosen due to their documented response to N stress. The other 19 traits were ear traits commonly measured for their influence on yield. Results showed that genotypes from RC1 and RC2 significantly differ for several above-ground phenotypes. We also observed a difference in the number and magnitude of N treatment responses between the two RC classes. Differences in phenotypic trait correlations and their change in response to N were also observed between the RC classes. RC did not seem to have a strong correlation with calculated NUE (ΔYield/ΔN). Quantitative genetic analysis utilizing the Design III experimental design revealed significant dominance effects acting on several traits as well as changes in significance and dominance level between N treatments. Several QTL were mapped for 26 of the 31 traits and significant N effects were observed across the majority of the genome for some N stress indicative traits (e.g. stay-green). This research and related projects are essential to a better understanding of plant N uptake and metabolism. Understanding these processes is a necessary step in the progress towards the goal of breeding for better NUE crops.