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BACKGROUND: In patients with advanced stage cancer of the uterine cervix who undergo irradiation with curative intent, there is the necessity to treat all suspicious nodes on imaging. Our hypothesis was that adding fluorodeoxyglucose positron emission computer tomography/computer tomography (FDG-PET/CT) to the imaging workup would alter the external beam radiotherapy (EBRT) treatment plan, either resulting in an extended external beam radiotherapy (EBRT) field to the para-aortal region or an additional boost to suspicious nodes. Since extended field radiotherapy or additional boost can cause toxicity, our secondary aim was to assess the incidence of severe late bowel toxicity in patients treated with extended para-aortal EBRT-field and boost compared to elective pelvic radiotherapy.METHODS: Eighty-eight patients were enrolled. First, the optimal radiation treatment plan (EBRT and boost) was retrospectively determined based on magnetic resonance imaging (MRI) or FDG-PET/CT. Second, the severe bowel toxicity caused by the extended para-aortal field was assessed, based on the executed radiotherapy.RESULTS: Based on MRI 8/88 patients would receive EBRT with para-aortic extension, this was 21/88 for FDG-PET/CT. Based on MRI 47/704 lymph node regions would receive additional boost, while based on PET/CT 91/704. Late severe bowel toxicity was seen in 12/84 patients, 6/65 in the group who received elective pelvic irradiation and 6/19 with para-aortal EBRT and boost at common iliac and/or para-aortal lymph nodes. Significant worse overall survival was seen of patients who needed para-aortal irradiation.CONCLUSIONS: Addition of FDG-PET/CT leads to an extension of the elective EBRT volume and more suspicious lymph nodes receive a boost. However, when deciding to intensify radiation therapy, late severe bowel toxicity has to be taken into account.

Adaptive optics (AO) is used in ground-based astronomical telescopes to improve the resolution by counteracting the effects of atmospheric turbulence. Most AO systems are based on a simple control law that neglects the temporal evolution of the distortions introduced by the atmosphere. This paper presents a data-driven control design approach that is able to exploit the spatio- temporal correlation in the wavefront, without assuming any form of decoupling. The approach consists of a dedicated subspace-identification algorithm to identify an atmospheric disturbance model from open-loop wavefront sensor data, followed by H2-optimal control design. It is shown that in the case that the deformable mirror and wavefront sensor dynamics can be represented by a delay and a two taps impulse response, it is possible to derive an analytical expression for the H2-optimal controller. Numerical simulations on AO test bench data demonstrate a performance improvement with respect to the common AO control approach.

This paper presents a subspace-based identification algorithm for estimating the state-space quadruple [A,B,C,D] of a minimum-phase spectral factor from matrix-valued power spectrum data. For a given pair [A,C] with A stable, the minimum-phase property is guaranteed via the solution of a conic linear programming (CLP) problem. In comparison with the classical LMI-based solution, this results in a more efficient way to minimize the weighted 2-norm of the error between the estimated and given power spectrum. The conic linear programming problem can be solved in a globally optimal sense. This property is exploited in the derivation of a separable least-squares procedure for the (local) minimization of the above 2-norm with respect to the parameters of the minimal phase spectral factor. The advantages of the derived subspace algorithm and the iterative local minimization procedure are illustrated in a brief simulation study. In this study, the effect of dealing with short length data sets for computing the power spectrum, on the estimated spectral factor, is illustrated.

Disparity estimation has been investigated for decades. Fully automatic methods have problems in texture-less regions, around object boundaries and in occlusions regions. In this paper, we exploit user input to address these problematic areas interactively. By drawing contours and polygons, we achieve sharp disparity discontinuities and smooth disparity planes in the disparity maps. Annotations are tracked quite accurately over a number of frames. Experimental results on Middlebury data set and our own stereo video suggest that the accuracy of disparity maps can be improved significantly with limited user input.

Detailed image structures and visual textures (of stochastic nature) in digital video are difficult to compress efficiently. At medium to low bit rates, texture flattening and blocking artifacts appear, even when using advanced video coding standards such as H.264/MPEG-4 AVC. In this paper, we propose video compression systems to compress stochastic textures by exploiting rank-reduction techniques. In this work, rank reduction is implemented by applying a singular value decomposition and selective transmission of the primary signal components as in principal component analysis. In the low bit-rate range, our implementation shows encouraging results compared to H.264/MPEG-4 AVC, not only in rate-distortion performance, but also in the improved visual quality of the reconstructed videos.

A recently proposed data-driven H2-optimal control approach is demonstrated on a laboratory setup. Most adaptive optics (AO) systems are based on a control law that neglects the temporal evolution of the wavefront. The proposed control approach is able to exploit the spatiotemporal correlation in the wavefront without assuming any form of decoupling. By analyzing the dynamic behavior of the wavefront sensor (WFS), it is shown that if the wavefront correction device can be considered static, the transfer function from control input to WFS output reduces to a two-tap impulse response and an integer number of samples delay. Considering this model structure, a data-driven identification procedure is developed to estimate the relevant parameters from measurement data. The specific structure allows for an analytical expression of the optimal controller in terms of the system matrices of the minimum-phase spectral factor of the atmospheric disturbance model. The performance of the optimal controller is compared with that of the standard AO control law. An analysis of the dominant error sources shows that optimal control may reduce the temporal error. © 2007 Optical Society of America.

Even though the wavefront distortion introduced by atmospheric turbulence is a dynamic process, its temporal evolution is usually neglected in the adaptive optics (AO) control design. Most AO control systems consider only the spatial correlation in a separate wavefront reconstruction step. By accounting for the temporal evolution of the wavefront it should be possible to further reduce the residual phase error and enable the use of fainter guide stars. Designing a controller that takes full advantage of the spatio-temporal correlation in the wavefront requires a detailed model of the wavefront distortion. In this paper we present a dedicated subspace identification algorithm that is able to provide the required prior knowledge. On the basis of open-loop wavefront slope data it estimates a multi-variable state-space model of the wavefront disturbance. The model provides a full description of the spatio-temporal statistics in a form that is suitable for control. The algorithm is demonstrated on open-loop wavefront data.

The problem of finding the closed-loop optimal controller is formulated in an Η 2 -optimal control framework. This provides a natural way to account for the fact that in many AO systems the wavefront phase cannot be measured directly. Given a multi-variable disturbance model of both wavefront slopes and wavefront phases, this provides a general procedure to compute the closed-loop controller. If the wavefront sensor and deformable mirror are static and the only dynamics in the system is a unit-sample delay between measurement and correction, an analytical expression for the optimal controller can be derived. This results in a control approach, in which both identification and computation of the optimal controller are exclusively based on standard matrix operations. No Riccati equation needs to be solved to compute the optimal controller. The proposed Η 2 -control approach is numerically validated on open-loop wavefront sensor data and its performance is compared with the common approach. Also the sensitivity to measurement noise is considered.

This research considers the control of a Thermal Deformable Mirror (TDM), which is used to correct a static aberration in a light beam. The TDM is a cheap, but relatively slow, deformable mirror suited for the correction of static and thermal aberrations. Correction of quasi-static disturbances can, for example, be used to enhance microscope images, or to improve the quality of femto-second light wave packages. The control objective is to maximize the light power that drops through a pinhole after focusing of the beam in a minimal time span. Only the light power behind the pinhole is available for feedback. The slow dynamics of the mirror and the lack of measurement information complicate the control task. The optimization algorithm (OA) used to maximize the light power is based on consecutive line searches in the direction of several Zernike modes. The OA operates on the surface shape of the mirror, rather than the mirror input voltages. To speed up and to improve the performance of the algorithm, model predictive control (MPC) is used to adjust the mirror shape to the shape provided by the OA. The effectiveness of using MPC has been experimentally validated. The disturbance in the experiments consists of a focus and tilt misalignment, and the zero-input shape of the TDM. Without MPC, the light power reaches 40% of the maximum light power within 3000 iterations. When applying the MPC controller, 40% of the maximum intensity is obtained within 700 iterations, and 50% within 2200 iterations.

An Adaptive Optics (AO) system for astronomy is analysed from a control point of view. The focus is put on the temporal error. The AO controller is identified as a feedback regulator system, operating in closed-loop with the aim of rejecting wavefront disturbances. Limitations on the performance of feedback regulator systems are discussed. The concept of optimal control is proposed to minimise the temporal error. The issue of closed-loop feedback controller design is made transparent by using the principle of Internal Model Control. The central issue in reducing the temporal wavefront is the design of a feedforward prediction filter. In three separate tests - a numerical simulation example, measured data from an AO test bench and open-loop telescope data - the advantage of optimal control over the common approach of integral control is demonstrated. Optimal control of the temporal error yields a smaller temporal error, enables a longer integration time in the wavefront sensor, or the use of fainter natural guide stars.