Spatial planning for adapting to climate change.

During the past decades human interference in regional hydrologic systems has intensified. These systems act as an integrating medium. They link climate, human activities and ecologic processes through groundwater and surface water interactions. For simulating these linkages an integrated regional hydrologic model has been coupled to an ecologic evaluation model. The simulated ecologic effects of climate change on mesotrophic riverine grasslands are clearly positive. Simulation results also indicate a high sensitivity of the peak discharges to the precipitation. For modelling the long-term development of land use and water management an integrated 'bio-economic' model has been constructed. It includes a model for the development of agriculture. Results for the autonomous development in reaction to climate change indicate a strong increase of field drainage by agriculture. This development would substantially reduce the predicted positive effects of climate change on riverine grasslands. The challenge is to guide regional developments in such a manner that opportunities for improving nature are not lost, but that at the same time the peak discharges are kept under control. Flow retardation in the 'fine arteries' of the upstream areas appear to be a viable option for the latter. The bio-economic model can provide help in anticipating on climate change through spatial planning.     

Standardized evaluation methodology for 2-D-3-D registration

In the past few years, a number of two-dimensional (2-D) to three-dimensional (3-D) (2-D-3-D) registration algorithms have been introduced. However, these methods have been developed and evaluated for specific applications, and have not been directly compared. Understanding and evaluating their performance is therefore an open and important issue. To address this challenge we introduce a standardized evaluation methodology, which can be used for all types of 2-D-3-D registration methods and for different applications and anatomies. Our evaluation methodology uses the calibrated geometry of a 3-D rotational X-ray (3DRX) imaging system (Philips Medical Systems, Best, The Netherlands) in combination with image-based 3-D-3-D registration for attaining a highly accurate gold standard for 2-D X-ray to 3-D MR/CT/3DRX registration. Furthermore, we propose standardized starting positions and failure criteria to allow future researchers to directly compare their methods. As an illustration, the proposed methodology has been used to evaluate the performance of two 2-D-3-D registration techniques, viz. a gradient-based and an intensity-based method, for images of the spine. The data and gold standard transformations are available on the internet (http://www.isi.uu.nl/Research/Databases/).     

Robust shape regression for supervised vessel segmentation and its application to coronary segmentation in CTA

This paper presents a vessel segmentation method which learns the geometry and appearance of vessels in medical images from annotated data and uses this knowledge to segment vessels in unseen images. Vessels are segmented in a coarse-to-fine fashion. First, the vessel boundaries are estimated with multivariate linear regression using image intensities sampled in a region of interest around an initialization curve. Subsequently, the position of the vessel boundary is refined with a robust nonlinear regression technique using intensity profiles sampled across the boundary of the rough segmentation and using information about plausible cross-sectional vessel shapes. The method was evaluated by quantitatively comparing segmentation results to manual annotations of 229 coronary arteries. On average the difference between the automatically obtained segmentations and manual contours was smaller than the inter-observer variability, which is an indicator that the method outperforms manual annotation. The method was also evaluated by using it for centerline refinement on 24 publicly available datasets of the Rotterdam Coronary Artery Evaluation Framework. Centerlines are extracted with an existing method and refined with the proposed method. This combination is currently ranked second out of 10 evaluated interactive centerline extraction methods. An additional qualitative expert evaluation in which 250 automatic segmentations were compared to manual segmentations showed that the automatically obtained contours were rated on average better than manual contours.     

Guide wire reconstruction and visualization in 3DRA using monoplane fluoroscopic imaging

A method has been developed that, based on the guide wire position in monoplane fluoroscopic images, visualizes the approximate guide wire position in the three-dimensional (3-D) vasculature, that is obtained prior to the intervention with 3-D rotational X-ray angiography (3DRA). The method assumes the position of the guide wire in the fluoroscopic images is known. A two-dimensional feature image is determined from the 3DRA data. In this feature image, the guide wire position is determined in a two-step approach: a mincost algorithm is used to determine a suitable position for the guide wire, and subsequently a snake optimization technique is applied to move the guide wire to a better position. The resulting guide wire can then be visualized in 3-D in combination with the 3DRA dataset. The reconstruction accuracy of the method has been evaluated using a 3DRA image of a vascular phantom filled with contrast, and monoplane fluoroscopic images of the same phantom without contrast and with a guide wire inserted. The evaluation has been performed for different projection angles, and with different parameters for the method. The final result does not appear to be very sensitive to the parameters of the method. The average mean error of the estimated 3-D guide wire position is 1.5 mm, and the average tip distance is 2.3 mm. The effect of inaccurate C-arm geometry information is also investigated. Small errors in geometry information (up to 1 degrees) will slightly decrease the 3-D reconstruction accuracies, with an error of at most 1 mm. The feasibility of this approach on clinical data is demonstrated.     

Hydrolysis of lactose in whey permeate for subsequent fermentation to ethanol

Fermentation of lactose in whey permeate directly into ethanol has had only limited commercial success, as the yields and alcohol tolerances of the organisms capable of directly fermenting lactose are low. This study proposes an alternative strategy: treat the permeate with acid to liberate monomeric sugars that are readily fermented into ethanol. We identified optimum hydrolysis conditions that yield mostly monomeric sugars and limit formation of fermentation inhibitors such as hydroxymethyl furfural by caramelization reactions. Both lactose solutions and commercial whey permeates were hydrolyzed using inorganic acids and carbonic acid. In all cases, more glucose was consumed by secondary reactions than galactose. Galactose was recovered in approximately stoichiometric proportions. Whey permeate has substantial buffering capacity-even at high partial pressures (>5500 kPa[g]), carbon dioxide had little effect on the pH in whey permeate solutions. The elevated temperatures required for hydrolysis with CO2-generated inhibitory compounds through caramelization reactions. For these reasons, carbon dioxide was not a feasible acidulant. With mineral acids reversion reactions dominated, resulting in a stable amount of glucose released. However, the Maillard browning reactions also appeared to be involved. By applying Hammet's acidity function, kinetic data from all experiments were described by a single line. With concentrated inorganic acids, low reaction temperatures allowed lactose hydrolysis with minimal by-product formation and generated a hexose-rich solution amenable to fermentation.     

Feature extraction and iconic visualization

We present a conceptual framework and a process model for feature extraction and iconic visualization. The features are regions of interest extracted from a dataset. They are represented by attribute sets, which play a key role in the visualization process. These attribute sets are mapped to icons, or symbolic parametric objects, for visualization. The features provide a compact abstraction of the original data, and the icons are a natural way to visualize them. We present generic techniques to extract features and to calculate attribute sets, and describe a simple but powerful modeling language which was developed to create icons and to link the attributes to the icon parameters. We present illustrative examples of iconic visualization created with the techniques described, showing the effectiveness of this approach     

Statistical shape model-based femur kinematics from biplane fluoroscopy

Studying joint kinematics is of interest to improve prosthesis design and to characterize postoperative motion. State of the art techniques register bones segmented from prior computed tomography or magnetic resonance scans with X-ray fluoroscopic sequences. Elimination of the prior 3D acquisition could potentially lower costs and radiation dose. Therefore, we propose to substitute the segmented bone surface with a statistical shape model based estimate. A dedicated dynamic reconstruction and tracking algorithm was developed estimating the shape based on all frames, and pose per frame. The algorithm minimizes the difference between the projected bone contour and image edges. To increase robustness, we employ a dynamic prior, image features, and prior knowledge about bone edge appearances. This enables tracking and reconstruction from a single initial pose per sequence. We evaluated our method on the distal femur using eight biplane fluoroscopic drop-landing sequences. The proposed dynamic prior and features increased the convergence rate of the reconstruction from 71% to 91%, using a convergence limit of 3 mm. The achieved root mean square point-to-surface accuracy at the converged frames was 1.48 ± 0.41 mm. The resulting tracking precision was 1-1.5 mm, with the largest errors occurring in the rotation around the femoral shaft (about 2.5° precision).     

Predictive value of the hip flexion test for fractures of the pelvis

In 39 patients with pelvic injury, four clinical tests were performed during physical examination. Of these four tests, the positive and negative hip flexion test (active flexion of the hip with extension of the knee) had the highest predictive value for the presence (95 per cent) or the absence of pelvic fractures (90 per cent), respectively. The predictive values of the other three tests (downward compression on the pubic bone, compression on the iliac rings, and distraction pressure on the iliac crests) were considerably lower.     

Regression-based cardiac motion prediction from single-phase CTA

State of the art cardiac computed tomography (CT) enables the acquisition of imaging data of the heart over the entire cardiac cycle at concurrent high spatial and temporal resolution. However, in clinical practice, acquisition is increasingly limited to 3-D images. Estimating the shape of the cardiac structures throughout the entire cardiac cycle from a 3-D image is therefore useful in applications such as the alignment of preoperative computed tomography angiography (CTA) to intra-operative X-ray images for improved guidance in coronary interventions. We hypothesize that the motion of the heart is partially explained by its shape and therefore investigate the use of three regression methods for motion estimation from single-phase shape information. Quantitative evaluation on 150 4-D CTA images showed a small, but statistically significant, increase in the accuracy of the predicted shape sequences when using any of the regression methods, compared to shape-independent motion prediction by application of the mean motion. The best results were achieved using principal component regression resulting in point-to-point errors of 2.3±0.5 mm, compared to values of 2.7±0.6 mm for shape-independent motion estimation. Finally, we showed that this significant difference withstands small variations in important parameter settings of the landmarking procedure.     

Three-dimensional guide-wire reconstruction from biplane image sequences for integrated display in 3-D vasculature

Using three-dimensional rotational X-ray angiography (3DRA), three-dimensional (3-D) information of the vasculature can be obtained prior to endovascular interventions. However, during interventions, the radiologist has to rely on fluoroscopy images to manipulate the guide wire. In order to take full advantage of the 3-D information from 3DRA data during endovascular interventions, a method is presented that yields an integrated display of the position of the guide wire and vasculature in 3-D. The method relies on an automated method that tracks the guide wire simultaneously in biplane fluoroscopy images. Based on the calibrated geometry of the C-arm, the 3-D guide-wire position is determined and visualized in the 3-D coordinate system of the vasculature. The method is evaluated in an intracranial anthropomorphic vascular phantom. The influence of the angle between projections, distortion correction of the projection images, and accuracy of geometry knowledge on the accuracy of 3-D guide-wire reconstruction from biplane images is determined. If the calibrated geometry information is used and the images are corrected for distortion, a mean distance to the reference standard of 0.42 mm and a tip distance of 0.65 mm is found, which means that accurate guide-wire reconstruction from biplane images can be performed.