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The paper is concerned with simulation of the periodontal ligament response to force in the initial phase of orthodontic tooth movement. This is based on two previous investigations, a in vitro experiment with specimens of porcine mandibular premolars and a in vivo experiment on human upper first incisors. For the curve fit of the in vitro experiment a model function, assuming viscoelasticity, was introduced. The viscoelastic model function was augmented by a ramp rise time term, to account for observed dependence of the response on actuator velocity, and a previous load history term, to account for the effect of the previous tests on the current test. The correlation coefficient of a curve fit for all tests grouped together was R 2 = 0.98 . Next, a curve fit of the in vivo experiment was done. Good correlation was found for a simplified model function, without viscoelastic term ( R 2 = 0.96 ). For both tests, in vitro and in vivo, the ramp rise time term improved correlation. A finite element model of the specimen of the in vitro experiment was created. For the PDL a hyperelastic constitutive model for compressible material was used and model parameters were identified. The present work indicates that the macroscopic response of the periodontal ligament to an external load can be simulated with a poro-visco-hyperelastic model. The simulation showed that poroelastic behaviour will gradually cease when viscoelastic relaxation progresses. This followed also from dimensionless analysis. As a consequence, for slow loading, or if initial response to fast loading is not of interest, a visco-hyperelastic model may suffice. To identify parameters of the finite element model several optimisation problems were solved. A model function, which can be regarded as a reduced order model, allowed a full factorial experiment (analysis) at low cost, to identify initial parameters. The thus found parameters were further refined with an optimum interpolation meta-model. That is, for limited number of parameter combinations the response was simulated with the finite element model and a refined parameter study was conducted by means of optimal interpolation. The thus found optimal parameters were verified by simulation with the finite element model. Optimal interpolation is computationally cheap, which allowed full factorial experiments at low cost.

This paper describes a novel application of statistical learning theory (SLT) to single motion estimation and tracking. The problem of motion estimation can be related to statistical model selection, where the goal is to select one (correct) motion model from several possible motion models, given finite noisy samples. SLT, also known as Vapnik-Chervonenkis (VC), theory provides analytic generalization bounds for model selection, which have been used successfully for practical model selection. This paper describes a successful application of an SLT-based model selection approach to the challenging problem of estimating optimal motion models from small data sets of image measurements (flow). We present results of experiments on both synthetic and real image sequences for motion interpolation and extrapolation; these results demonstrate the feasibility and strength of our approach. Our experimental results show that for motion estimation applications, SLT-based model selection compares favorably against alternative model selection methods, such as the Akaike's fpe, Schwartz' criterion (sc), Generalized Cross-Validation (gcv), and Shibata's Model Selector (sms). The paper also shows how to address the aperture problem using SLT-based model selection for penalized linear (ridge regression) formulation.

Under mass-action kinetics, biochemical reaction networks give rise to polynomial autonomous dynamical systems whose parameters are often difficult to estimate. We deal in this paper with the problem of identifying the kinetic parameters of a class of biochemical networks which are abundant, such as multisite phosphorylation systems and phosphorylation cascades (for example, MAPK cascades). For any system of this class, we explicitly exhibit a single species for each connected component of the associated digraph such that the successive total derivatives of its concentration allow us to identify all the parameters occurring in the component. The number of derivatives needed is bounded essentially by the length of the corresponding connected component of the digraph. Moreover, in the particular case of the cascades, we show that the parameters can be identified from a bounded number of successive derivatives of the last product of the last layer. This theoretical result induces also a heuristic interpolation-based identifiability procedure to recover the values of the rate constants from exact measurements. Fil: Jeronimo, Gabriela Tali. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina Fil: Pérez Millán, Mercedes Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina Fil: Solernó, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina

The paper presents a new open-source visualization system, named ReVisE, aimed to provide interactive visualization of large datasets, which are results of complex numerical simulations. These datasets are hosted on a remote server or a supercomputer. The design of the system is briefly described. Dataset representation, proposed for interactive visualization and implemented in the system, is discussed. The effectiveness of our approach is confirmed by results of performance measurements on test and real-life large datasets. A comparison with other visualization systems is presented. Future plans of system development are outlined.

A new Interpolation based Reversible Data Hiding (IRDH) scheme is reported in this paper. For different applications of an IRDH scheme to the digital image, video, multimedia, big-data and biological data, the embedding capacity requirement usually varies. Disregarding this important consideration, existing IRDH schemes do not offer a better embedding rate-distortion performance for varying size payloads. To attain this varying capacity requirement with our proposed adaptive embedding, we formulate a capacity control parameter and propose to utilize it to determine a minimum set of embeddable bits in a pixel. Additionally, we use a logical (or bit-wise) correlation between the embeddable pixel and estimated versions of an embedded pixel. Thereby, while a higher range between an upper and lower limit of the embedding capacity is maintained, a given capacity requirement within that limit is also attained with a better-embedded image quality. Computational modeling of all new processes of the scheme is presented, and performance of the scheme is evaluated with a set of popular test-images. Experimental results of our proposed scheme compared to the prominent IRDH schemes have recorded a significantly better-embedding rate-distortion performance.

Dynamic solar shading has the potential to dramatically reduce the energy consumption in buildings while at the same time improving the thermal and visual comfort of its occupants. Many new typologies of shading systems that have appeared recently, but it is difficult to compare those new systems to existing typologies due to control algorithm being rule-based as opposed to performance driven. Since solar shading is a design problem, there is no single right answer. What is the metric to determine if a system has reached its optimal kinematic design? Shading solutions should come from a thorough iterative and comparative process. This paper provides an original and flexible framework for the design and performance optimization of dynamic shading systems based on interpolation of simulations and global minimization. The methodology departs from existing rule-based strategies and applies to existing and to complex shading systems with multiple degree-of-freedom mobility. The strategy for control is centered on meeting comfort targets for work plane illuminance while minimizing the energy needed to operate space. The energy demand for thermal comfort and work plane daylight quantity (illuminance) are evaluated with Radiance and EnergyPlus based on local weather data. Applied to a case study of three typologies of dynamic shading, the results of the methodology inform the usefulness and quality of each degree-of-freedom of the kinematic systems. The case study exemplifies the iterative benefits of the methodology by providing detailed analytics on the behavior of the shades. Designers of shading systems can use this framework to evaluate their design and compare them to existing shading systems. This allows creativity to be guided so that eventually building occupants benefit from the innovation in the field.

Analytical CT reconstruction is popular in practice because of its computational efficiency, but it suffers from low reconstruction quality when an insufficient number of projections are used. To address this issue, this paper presents a new analytical method of backprojection Wiener deconvolution (BPWD). BPWD executes backprojection first, and then applies a Wiener deconvolution to the whole backprojected image. The Wiener filter is derived from a ramp filter, enabling the proposed approach to perform reconstruction and denoising simultaneously. The use of a filter after backprojection does not differentiate between real sampled projections and interpolated ones, introducing reconstruction errors. Therefore a weighted ramp filter was applied to increase the contribution of real sampled projections in the reconstruction, thus improving reconstruction quality. Experiments on synthetic data and real phase-contrast x-ray images showed that the proposed approach yields better reconstruction quality compared to the classical filtered backprojection (FBP) method, with comparable reconstruction speed Refereed/Peer-reviewed

The availability of digital X-ray detectors, together with advances in reconstruction algorithms, creates an opportunity for bringing 3D capabilities to conventional radiology systems. The downside is that reconstruction algorithms for non-standard acquisition protocols are generally based on iterative approaches that involve a high computational burden. The development of new flexible X-ray systems could benefit from computer simulations, which may enable performance to be checked before expensive real systems are implemented. The development of simulation/reconstruction algorithms in this context poses three main difficulties. First, the algorithms deal with large data volumes and are computationally expensive, thus leading to the need for hardware and software optimizations. Second, these optimizations are limited by the high flexibility required to explore new scanning geometries, including fully configurable positioning of source and detector elements. And third, the evolution of the various hardware setups increases the effort required for maintaining and adapting the implementations to current and future programming models. Previous works lack support for completely flexible geometries and/or compatibility with multiple programming models and platforms. In this paper, we present FUX-Sim, a novel X-ray simulation/reconstruction framework that was designed to be flexible and fast. Optimized implementation for different families of GPUs (CUDA and OpenCL) and multi-core CPUs was achieved thanks to a modularized approach based on a layered architecture and parallel implementation of the algorithms for both architectures. A detailed performance evaluation demonstrates that for different system configurations and hardware platforms, FUX-Sim maximizes performance with the CUDA programming model (5 times faster than other state-of-the-art implementations). Furthermore, the CPU and OpenCL programming models allow FUX-Sim to be executed over a wide range of hardware platforms.

The scientific demand for more accurate modeling of the climate system calls for more computing power to support higher resolutions, inclusion of more component models, more complicated physics schemes, and larger ensembles. As the recent improvements in computing power mostly come from the increasing number of nodes in a system and the integration of heterogeneous accelerators, how to scale the computing problems onto more nodes and various kinds of accelerators has become a challenge for the model development. This paper describes our efforts on developing a highly scalable framework for performing global atmospheric modeling on heterogeneous supercomputers equipped with various accelerators, such as GPU (Graphic Processing Unit), MIC (Many Integrated Core), and FPGA (Field Programmable Gate Arrays) cards. We propose a generalized partition scheme of the problem domain, so as to keep a balanced utilization of both CPU resources and accelerator resources. With optimizations on both computing and memory access patterns, we manage to achieve around 8 to 20 times speedup when comparing one hybrid GPU or MIC node with one CPU node with 12 cores. Using a customized FPGA-based data-flow engines, we see the potential to gain another 5 to 8 times improvement on performance. On heterogeneous supercomputers, such as Tianhe-1A and Tianhe-2, our framework is capable of achieving ideally linear scaling efficiency, and sustained double-precision performances of 581 Tflops on Tianhe-1A (using 3750 nodes) and 3.74 Pflops on Tianhe-2 (using 8644 nodes). Our study also provides an evaluation on the programming paradigm of various accelerator architectures (GPU, MIC, FPGA) for performing global atmospheric simulation, to form a picture about both the potential performance benefits and the programming efforts involved.

Dual-energy X-ray CT (DECT) has the potential to improve contrast and reduce artifacts as compared to traditional CT. Moreover, by applying model-based iterative reconstruction (MBIR) to dual-energy data, one might also expect to reduce noise and improve resolution. However, the direct implementation of dual-energy MBIR requires the use of a nonlinear forward model, which increases both complexity and computation. Alternatively, simplified forward models have been used which treat the material-decomposed channels separately, but these approaches do not fully account for the statistical dependencies in the channels. In this paper, we present a method for joint dual-energy MBIR (JDE-MBIR), which simplifies the forward model while still accounting for the complete statistical dependency in the material-decomposed sinogram components. The JDE-MBIR approach works by using a quadratic approximation to the polychromatic log-likelihood and a simple but exact nonnegativity constraint in the image domain. We demonstrate that our method is particularly effective when the DECT system uses fast kVp switching, since in this case the model accounts for the inaccuracy of interpolated sinogram entries. Both phantom and clinical results show that the proposed model produces images that compare favorably in quality to previous decomposition-based methods, including FBP and other statistical iterative approaches.

Measuring the three-dimensional motion of muscular tissues, e.g., the heart or the tongue, using magnetic resonance (MR) tagging is typically carried out by interpolating the two-dimensional motion information measured on orthogonal stacks of images. The incompressibility of muscle tissue is an important constraint on the reconstructed motion field and can significantly help to counter the sparsity and incompleteness of the available motion information. Previous methods utilizing this fact produced incompressible motions with limited accuracy. In this paper, we present an incompressible deformation estimation algorithm (IDEA) that reconstructs a dense representation of the three-dimensional displacement field from tagged MR images and the estimated motion field is incompressible to high precision. At each imaged time frame, the tagged images are first processed to determine components of the displacement vector at each pixel relative to the reference time. IDEA then applies a smoothing, divergence-free, vector spline to interpolate velocity fields at intermediate discrete times such that the collection of velocity fields integrate over time to match the observed displacement components. Through this process, IDEA yields a dense estimate of a three-dimensional displacement field that matches our observations and also corresponds to an incompressible motion. The method was validated with both numerical simulation and in vivo human experiments on the heart and the tongue.

In this paper, we propose a new metal artifacts reduction algorithm based on fractional-order total-variation sinogram inpainting model for X-ray computed tomography (CT). The numerical algorithm for our fractional-order framework is also analyzed. Simulations show that, both quantitatively and qualitatively, our method is superior to conditional interpolation methods and the classic integral-order total variation model.

This paper proposes a new morphology-based approach for the interslice interpolation of current transformer (CT) and MRI datasets composed of parallel slices. Our approach is object based and accepts as input data binary slices belonging to the same anatomical structure. Such slices may contain one or more regions, since topological changes between two adjacent slices may occur. Our approach handles explicitly interslice topology changes by decomposing a many-to-many correspondence into three fundamental cases: one-to-one, one-to-many, and zero-to-one correspondences. The proposed interpolation process is iterative. One iteration of this process computes a transition sequence between a pair of corresponding input slices, and selects the element located at equal distance from the input slices. This algorithmic design yields a gradual, smooth change of shape between the input slices. Therefore, the main contribution of our approach is its ability to interpolate between two anatomic shapes by creating a smooth, gradual change of shape, and without generating over-smoothed interpolated shapes.

Asynchronous level crossing sampling analog-to-digital converters (ADCs) are known to be more energy efficient and produce fewer samples than their equidistantly sampling counterparts. However, as the required threshold voltage is lowered, the number of samples and, in turn, the data rate and the energy consumed by the overall system increases. In this paper, we present a cubic Hermitian vector-based technique for online compression of asynchronously sampled electrocardiogram signals. The proposed method is computationally efficient data compression. The algorithm has complexity $O(n)$ , thus well suited for asynchronous ADCs. Our algorithm requires no data buffering, maintaining the energy advantage of asynchronous ADCs. The proposed method of compression has a compression ratio of up to 90% with achievable percentage root-mean-square difference ratios as a low as 0.97. The algorithm preserves the superior feature-to-feature timing accuracy of asynchronously sampled signals. These advantages are achieved in a computationally efficient manner since algorithm boundary parameters for the signals are extracted a priori .

A method is described here that uses a modified Monte-Carlo method to provide an improved estimate of the confidence bounds of concentration estimates. This method accommodates even strongly nonlinear curve models, such as the five parameter logistic model, in contrast to the common but often poor approach of linearizing the regression problem and using linear theory to obtain the confidence bounds. The method uses an interpolation technique to reduce artifacts in the precision profile due to small simulation sample sizes and proximity to horizontal asymptotes in the curve model. The paper also describes how to define and calculate the minimum and maximum acceptable concentrations of dose-response curves by locating the concentrations where the size of the error, defined in terms of the size of the concentration confidence interval, exceeds the threshold of acceptability determined for the application.

This paper addresses the problem of human gait classification from a robust model (in)validation perspective. The main idea is to associate to each class of gaits a nominal model, subject to bounded uncertainty and measurement noise. In this context, the problem of recognizing an activity from a sequence of frames can be formulated as the problem of determining whether this sequence could have been generated by a given (model, uncertainty, and noise) triple. By exploiting interpolation theory, this problem can be recast into a nonconvex optimization. In order to efficiently solve it, we propose two convex relaxations, one deterministic and one stochastic. As we illustrate experimentally, these relaxations achieve over 83 percent and 86 percent success rates, respectively, even in the face of noisy data.

Groupwise nonrigid registrations of medical images define dense correspondences across a set of images, defined by a continuous deformation field that relates each target image in the group to some reference image. These registrations can be automatic, or based on the interpolation of a set of user-defined landmarks, but in both cases, quantifying the normal and abnormal structural variation across the group of imaged structures implies analysis of the set of deformation fields. We contend that the choice of representation of the deformation fields is an integral part of this analysis. This paper presents methods for constructing a general class of multi-dimensional diffeomorphic representations of deformations. We demonstrate, for the particular case of the polyharmonic clamped-plate splines, that these representations are suitable for the description of deformations of medical images in both two and three dimensions, using a set of two-dimensional annotated MRI brain slices and a set of three-dimensional segmented hippocampi with optimized correspondences. The class of diffeomorphic representations also defines a non-Euclidean metric on the space of patterns, and, for the case of compactly supported deformations, on the corresponding diffeomorphism group. In an experimental study, we show that this non-Euclidean metric is superior to the usual ad hoc Euclidean metrics in that it enables more accurate classification of legal and illegal variations.

In this paper, we propose an interpolation algorithm using a mathematical morphology morphing approach. The aim of this algorithm is to reconstruct the n-dimensional object from a group of (n - 1)-dimensional sets representing sections of that object. The morphing transformation modifies pairs of consecutive sets such that they approach in shape and size. The interpolated set is achieved when the two consecutive sets are made idempotent by the morphing transformation. We prove the convergence of the morphological morphing. The entire object is modeled by successively interpolating a certain number of intermediary sets between each two consecutive given sets. We apply the interpolation algorithm for three-dimensional tooth reconstruction.

Parametric methods for modeling sinusoidal signals with line spectra have been studied for decades. In general, these methods start by representing each sinusoidal component by means of two complex exponential functions, thereby doubling the number of unknown parameters. Recently, a Hankel-plus-Toeplitz matrix pencil method was proposed which directly models sinusoidal signals with discrete spectral content. Compared to its counterpart, which uses a Hankel matrix pencil, it halves the required number of time-domain samples and reduces the size of the involved linear systems. The aim of this paper is twofold. Firstly, to show that this Hankel-plus-Toeplitz matrix pencil also applies to continuous spectra. Secondly, to explore its use in the reconstruction of real-life signals. Promising preliminary results in the reconstruction of correlated multichannel electroencephalographic (EEG) activity are presented. A principal component analysis preprocessing step is carried out to exploit the redundancy in the channel domain. Then the reduced signal representation is successfully reconstructed from fewer samples using the Hankel-plus-Toeplitz matrix pencil. The obtained results encourage the future development of this matrix pencil method along the lines of well-established spectral analysis methods. ispartof: pages:3897-3900 ispartof: Proc. of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society vol:2013 pages:3897-3900 ispartof: 35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC) location:Osaka, Japan date:3 Jul - 7 Jul 2013 status: published

Q-ball imaging was presented as a model free, linear and multimodal diffusion sensitive approach to reconstruct diffusion orientation distribution function (ODF) using diffusion weighted MRI data. The ODFs are widely used to estimate the fiber orientations. However, the smoothness constraint was proposed to achieve a balance between the angular resolution and noise stability for ODF constructs. Different regularization methods were proposed for this purpose. However, these methods are not robust and quite sensitive to the global regularization parameter. Although, numerical methods such as L-curve test are used to define a globally appropriate regularization parameter, it cannot serve as a universal value suitable for all regions of interest. This may result in over smoothing and potentially end up in neglecting an existing fiber population. In this paper, we propose to include an interpolation step prior to the spherical harmonic decomposition. This interpolation based approach is based on Delaunay triangulation provides a reliable, robust and accurate smoothing approach. This method is easy to implement and does not require other numerical methods to define the required parameters. Also, the fiber orientations estimated using this approach are more accurate compared to other common approaches.

This paper presents an edge-directed image interpolation algorithm. In the proposed algorithm, the edge directions are implicitly estimated with a statistical-based approach. In opposite to explicit edge directions, the local edge directions are indicated by length-16 weighting vectors. Implicitly, the weighting vectors are used to formulate geometric regularity (GR) constraint (smoothness along edges and sharpness across edges) and the GR constraint is imposed on the interpolated image through the Markov random field (MRF) model. Furthermore, under the maximum a posteriori-MRF framework, the desired interpolated image corresponds to the minimal energy state of a 2-D random field given the low-resolution image. Simulated annealing methods are used to search for the minimal energy state from the state space. To lower the computational complexity of MRF, a single-pass implementation is designed, which performs nearly as well as the iterative optimization. Simulation results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity. Compared to traditional methods and other edge-directed interpolation methods, the proposed method improves the subjective quality of the interpolated edges while maintaining a high PSNR level.

In this paper, a motion-compensated de-interlacing algorithm using the Markov random field (MRF) model is proposed. The de-interlacing problem is formulated as a maximum a posteriori (MAP) MRF problem. The MAP solution is the one that minimizes an energy function, which imposes discontinuity-adaptive smoothness (DAS) spatial constraint on the de-interlaced frame. The edge direction information, which is used to formulate the DAS constraint, is implicitly indicated by weight vectors (weights for 16 digitized directions). Generally, large weights are assigned to along-edge directions and relatively small weights are assigned to across-edge directions. As a local statistical-based method, the proposed weighting method should be more robust than traditional edge-directed interpolation methods in deciding local edge directions. The proposed algorithm is implemented by an iterative optimization process, which guarantees convergence. However, a global optimal solution is not guaranteed due to computational complexity concern. Simulation results compare the proposed algorithm to other motion compensated de-interlacing algorithms. Significant improvements of de-interlaced edges are observed.

In this paper, we propose a maximum a posteriori framework for the super-resolution problem, i.e., reconstructing high-resolution images from shifted, rotated, low-resolution degraded observations. The main contributions of this work are two; first, the use of a new locally adaptive edge preserving prior for the super-resolution problem. Second an efficient two-step reconstruction methodology that includes first an initial registration using only the low-resolution degraded observations. This is followed by a fast iterative algorithm implemented in the discrete Fourier transform domain in which the restoration, interpolation and the registration subtasks of this problem are preformed simultaneously. We present examples with both synthetic and real data that demonstrate the advantages of the proposed framework.

International audience; Modeling the variability of brain structures is a fundamental problem in the neurosciences. In this paper, we start from a dataset of precisely delineated anatomical structures in the cerebral cortex: a set of 72 sulcal lines in each of 98 healthy human subjects. We propose an original method to compute the average sulcal curves, which constitute the mean anatomy in this context. The second order moment of the sulcal distribution is modeled as a sparse field of covariance tensors (symmetric, positive definite matrices). To extrapolate this information to the full brain, one has to overcome the limitations of the standard Euclidean matrix calculus. We propose an affine-invariant Riemannian framework to perform computations with tensors. In particular, we generalize radial basis function (RBF) interpolation and harmonic diffusion PDEs to tensor fields. As a result, we obtain a dense 3D variability map which proves to be in accordance with previously published results on smaller samples subjects. Moreover, leave one (sulcus) out tests show that our model is globally able to recover the missing information when there is a consistent neighboring variability. Last but not least, we propose innovative methods to analyze the asymmetry of brain variability. As expected, the greatest asymmetries are found in regions that includes the primary language areas. Interestingly, such an asymmetry in anatomical variance could explain why there may be greater power to detect group activation in one hemisphere than the other in fMRI studies.

In a traditional cone-beam computed tomography (CT) system, the cost of product and computation is very high. In this paper, we develop a transversely truncated cone-beam X-ray CT system with a reduced-size detector positioned off-center, in which X-ray beams only cover half of the object. The existing filtered backprojection (FBP) or backprojection-filtration (BPF) algorithms are not directly applicable in this new system. Hence, we develop a BPF-type direct backprojection algorithm. Different from the traditional rebinning methods, our algorithm directly backprojects the pretreated projection data without rebinning. This makes the algorithm compact and computationally more efficient. Because of avoiding interpolation errors of rebinning process, higher spatial resolution is obtained. Finally, some numerical simulations and practical experiments are done to validate the proposed algorithm and compare with rebinning algorithm.

Contains fulltext : 51265.pdf (Publisher’s version ) (Closed access) In inverse treatment planning for intensity-modulated radiation therapy (IMRT), beamlet intensity levels in fluence maps of high-energy photon beams are optimized. Treatment plan evaluation criteria are used as objective functions to steer the optimization process. Fluence map optimization can be considered a multi-objective optimization problem, for which a set of Pareto optimal solutions exists: the Pareto efficient frontier (PEF). In this paper, a constrained optimization method is pursued to iteratively estimate the PEF up to some predefined error. We use the property that the PEF is convex for a convex optimization problem to construct piecewise-linear upper and lower bounds to approximate the PEF from a small initial set of Pareto optimal plans. A derivative-free Sandwich algorithm is presented in which these bounds are used with three strategies to determine the location of the next Pareto optimal solution such that the uncertainty in the estimated PEF is maximally reduced. We show that an intelligent initial solution for a new Pareto optimal plan can be obtained by interpolation of fluence maps from neighbouring Pareto optimal plans. The method has been applied to a simplified clinical test case using two convex objective functions to map the trade-off between tumour dose heterogeneity and critical organ sparing. All three strategies produce representative estimates of the PEF. The new algorithm is particularly suitable for dynamic generation of Pareto optimal plans in interactive treatment planning.

This paper presents a practical algorithm for resolving superimposed action potentials encountered during the decomposition of electromyographic signals. The problem is posed as an optimization problem: to align a set of templates with a given waveform to minimize the euclidean distance between them. The algorithm uses a recursive approach to search all possible discrete-time alignments, starting with the most likely ones and stopping once it can be verified that the optimal alignment has been found. Each candidate solution is aligned to finer-than-sampling-interval resolution using interpolation and continuous-time optimization. Both the cases in which the identities of the involved templates are known and not known are considered. Simulations are presented to show that the proposed algorithm is very accurate even for complex superpositions involving three or more similarly shaped templates, destructive interference, and added noise.