We quantified the effect of seed orientation deviations on five prostate seed implant cases at our institution. While keeping their positions fixed, the iodine-125 seeds were assigned orientations sampled from a realistic probability distribution derived from the post-implant radiographs of ten patients. Dose distributions were calculated with both a model that explicitly includes anisotropy (TG43 anisotropy function) and a point source model (TG43 anisotropy factor). Orientation deviations had only a small influence on prostate dose-volume histograms: the 95% confidence intervals on the volumes receiving 100%, 150% and 200% dose were at most +/-0.8%, +/-1.1% and +/-0.6% of the prostate volume, respectively. The dose-volume histograms of anisotropic seed distributions were marginally better than those with isotropic point-source seeds. Anisotropy caused a displacement of cold spots (regions receiving <100% of the prescribed dose) in <1% of the prostate volume. Our results indicate no net benefit to prostate dosimetry in using more isotropic seeds. Furthermore, we propose a new 'weighted anisotropy function' to better account for the effects of anisotropy when seed orientation is unknown. Conceptually, the TG43 anisotropy factor described in AAPM TG43 averages the effect of anisotropy over all solid angles, with the implicit assumption that all seed orientations are equally probable. In prostate implants, however, seeds are preferentially oriented parallel to the needle axis. The proposed weighted anisotropy function incorporates this non-uniform probability.

ABSTR4CTWe are presently constructing ''ANIPET", a new high spatial resolution PET scanner for imaging small animals. This instrument will be used to investigate new tracers and as a substitute for autoradi0graph.v. The instrument uses two pixellated BGO crystal arrays coupled to position-sensitive PA4T.s. Animals can be imaged in two modes. One is similar to a "whole-body" PETscan in which the detectors are stationary and the animal support couch moves longitudinally between the detectors. This mode is used for initial characterization of the bio-distribution of new tracers, In the second mode the animal support is first rotated through 90" in the horizontal plane, allowing the detectors to rotate about the animal's head. Thismode resembles a conventional 3-0 PETscan using a partial detector ring. FuUv reconstructed, quantitative images can be obtained. Continuous motion of either the bed, or detectors (via computercontrolled translation stages), and list-mode data collection are used. The field of view is 65 mm (lateral) by 55 mm (axial). To image larger species, the detectors can be oflset by up to 25 mm allowing the lateral FOV to extend to 90 mm.

Cumulative dose-volume histograms ͑DVH͒ are crucial in evaluating the quality of radioactive seed prostate implants. When calculating DVHs, the choice of voxel size is a compromise between computational speed ͑larger voxels͒ and accuracy ͑smaller voxels͒. We quantified the effect of voxel size on the accuracy of DVHs using an in-house computer program. The program was validated by comparison with a hand-calculated DVH for a single 0.4-U iodine-125 model 6711 seed. We used the program to find the voxel size required to obtain accurate DVHs of five iodine-125 prostate implant patients at our institution. One-millimeter cubes were sufficient to obtain DVHs that are accurate within 5% up to 200% of the prescription dose. For the five patient plans, we obtained good agreement with the VariSeed ͑version 6.7, Varian, USA͒ treatment planning software's DVH algorithm by using voxels with a sup-inf dimension equal to the spacing between successive transverse seed implant planes ͑5 mm͒. The volume that receives at least 200% of the target dose, V 200 , calculated by VariSeed was 30% to 43% larger than that calculated by our program with small voxels. The single-seed DVH calculated by VariSeed fell below the hand calculation by up to 50% at low doses ͑30 Gy͒, and above it by over 50% at high doses ͑Ͼ250 Gy͒.

Microscale flow models used in the wind energy industry commonly assume statically neutral conditions. These models can provide reasonable wind speed predictions for statically unstable and neutral flows; however, they do not provide reliable predictions for stably stratified flows, which can represent a substantial fraction of the available energy at a given site. With the objective of improving wind speed predictions and in turn reducing uncertainty in energy production estimates, we developed a Reynolds-Averaged Navier-Stokes (RANS)-based model of the stable boundary layer. We then applied this model to eight prospective wind farms and compared the results with on-site wind speed measurements classified using proxies for stability; the comparison also included results from linear and RANS wind flow models that assume neutral stratification. This validation demonstrates that a RANS-based model of the stable boundary layer can significantly and consistently improve wind speed predictions.In order to better predict statically stable flows, we developed a RANS model of the stable boundary layer; we refer to this model as the stable RANS model. The stable RANS model is implemented in STAR-CCM+ (CD-adapco Melville, NY USA), a commercial computational fluid dynamics (CFD) package from CD-adapco. 16 We used STAR-CCM+ as a 2 nd -order, implicit, incompressible RANS solver with k-ε turbulence closure to produce the results presented herein. The following describes the stable RANS model equations with a focus on how they differ from the equations solved in a typical neutral RANS wind flow analysis. The boundary conditions for the stable RANS calculations are also discussed in detail. * The term 'speedup' is used throughout this paper. A speedup refers to the average ratio of wind speed at a target meteorological mast to wind speed at a reference meteorological mast.† This finding is based on an analysis of 82 meteorological masts at 14 sites located in the US Great Plains. The mast heights ranged from 50 to 100 m; each mast had temperature sensors and anemometers at multiple heights.Modeling stable thermal stratification J. Bleeg et al.

A linear model for neutral surface-layer flow over orography is presented. The Reynolds-Averaged Navier-Stokes and E − ε turbulence closure equations are expressed in a terrain-following coordinate system created from a simple analytical expression in the Fourier domain. The perturbation equations are solved spectrally horizontally and by numerical integration vertically. Non-dimensional solutions are stored in look-up tables for quick re-use. Model results are compared to measurements, as well as other authors' flow models, in three test cases. The model is implemented and tested in two-dimensional space; the equations for a full three-dimensional version are presented.