Georg Rose

Performance of standard fluoroscopy antiscatter grids in flat-detector-based cone-beam CT

In this paper, the performance of focused lamellar anti-scatter grids, which are currently used in fluoroscopy, is studied in order to determine guidelines of grid usage for flat detector based cone beam CT. The investigation aims at obtaining the signal to noise ratio improvement factor by the use of anti-scatter grids. First, the results of detailed Monte Carlo simulations as well as measurements are presented. From these the general characteristics of the impinging field of scattered and primary photons are derived. Phantoms modeling the head, thorax and pelvis regions have been studied for various imaging geometries with varying phantom size, cone and fan angles and patient-detector distances. Second, simulation results are shown for ideally focused and vacuum spaced grids as best case approach as well as for grids with realistic spacing materials. The grid performance is evaluated by means of the primary and scatter transmission and the signal to noise ratio improvement factor as function of imaging geometry and grid parameters. For a typical flat detector cone beam CT setup, the grid selectivity and thus the performance of anti-scatter grids is much lower compared to setups where the grid is located directly behind the irradiated object. While for small object-to-grid distances a standard grid improves the SNR, the SNR for geometries as used in flat detector based cone beam CT is deteriorated by the use of an anti-scatter grid for many application scenarios. This holds even for the pelvic region. Standard fluoroscopy anti-scatter grids were found to decrease the SNR in many application scenarios of cone beam CT due to the large patient-detector distance and have, therefore, only a limited benefit in flat detector based cone beam CT.

Directional View Interpolation for Compensation of Sparse Angular Sampling in Cone-Beam CT

In flat detector cone-beam computed tomography and related applications, sparse angular sampling frequently leads to characteristic streak artifacts. To overcome this problem, it has been suggested to generate additional views by means of interpolation. The practicality of this approach is investigated in combination with a dedicated method for angular interpolation of 3-D sinogram data. For this purpose, a novel dedicated shape-driven directional interpolation algorithm based on a structure tensor approach is developed. Quantitative evaluation shows that this method clearly outperforms conventional scene-based interpolation schemes. Furthermore, the image quality trade-offs associated with the use of interpolated intermediate views are systematically evaluated for simulated and clinical cone-beam computed tomography data sets of the human head. It is found that utilization of directionally interpolated views significantly reduces streak artifacts and noise, at the expense of small introduced image blur.

Potential of software-based scatter corrections in cone-beam volume CT

This study deals with a systematic assessment of the potential of different schemes for computerized scatter correction in flat detector based cone-beam X-ray computed tomography. The analysis is based on simulated scatter of a CT image of a human head. Using a Monte-Carlo cone-beam CT simulator, the spatial distribution of scattered radiation produced by this object has been calculated with high accuracy for the different projected views of a circular tomographic scan. Using this data and, as a reference, a scatter-free forward projection of the phantom, the potential of different schemes for scatter correction has been evaluated. In particular, the ideally achievable degree of accuracy of schemes based on estimating a constant scatter level in each projection was compared to approaches aiming at estimation of a more complex spatial shape of the scatter distribution. For each scheme, remaining cupping artifacts in the reconstructed volumetric image were quantified and analyzed. It was found that already accurate estimation of a constant scatter level for each projection allows for comparatively accurate compensation of scatter-caused artifacts.

Model based scatter correction for cone-beam computed tomography

Scattered radiation is a major source of image degradation and nonlinearity in flat detector based cone-beam CT. Due to the bigger irradiated volume the amount of scattered radiation in true cone-beam geometry is considerably higher than for fan beam CT. This on the one hand reduces the signal to noise ratio, since the additional scattered photons contribute only to the noise and not to the measured signal, and on the other hand cupping and streak artifacts arise in the reconstructed volume. Anti-scatter grids composed of lead lamellae and interspacing material decrease the SNR for flat detector based CB-CT geometry, because the beneficial scatter attenuating effect is overcompensated by the absorption of primary radiation. Additionally, due to the high amount of scatter that still remains behind the grid, cupping and streak artifacts cannot be reduced sufficiently. Computerized scatter correction schemes are therefore essential for achieving artifact-free reconstructed images in cone-beam CT. In this work, a fast model based scatter correction algorithm is proposed, aiming at accurately estimating the level and spatial distribution of scattered radiation background in each projection. This will allow for effectively reducing streak and cupping artifacts due to scattering in cone-beam CT applications.

Hidden Markov model and support vector machine based decoding of finger movements using electrocorticography

OBJECTIVE Support vector machines (SVM) have developed into a gold standard for accurate classification in brain-computer interfaces (BCI). The choice of the most appropriate classifier for a particular application depends on several characteristics in addition to decoding accuracy. Here we investigate the implementation of hidden Markov models (HMM) for online BCIs and discuss strategies to improve their performance. APPROACH We compare the SVM, serving as a reference, and HMMs for classifying discrete finger movements obtained from electrocorticograms of four subjects performing a finger tapping experiment. The classifier decisions are based on a subset of low-frequency time domain and high gamma oscillation features. MAIN RESULTS We show that decoding optimization between the two approaches is due to the way features are extracted and selected and less dependent on the classifier. An additional gain in HMM performance of up to 6% was obtained by introducing model constraints. Comparable accuracies of up to 90% were achieved with both SVM and HMM with the high gamma cortical response providing the most important decoding information for both techniques. SIGNIFICANCE We discuss technical HMM characteristics and adaptations in the context of the presented data as well as for general BCI applications. Our findings suggest that HMMs and their characteristics are promising for efficient online BCIs.

Advances in confidence measures for large vocabulary

This paper addresses the correct choice and combination of confidence measures in large vocabulary speech recognition tasks. We classify single words within continuous as well as large vocabulary utterances into two categories: utterances within the vocabulary which are recognized correctly, and other utterances, namely misrecognized utterances or (less frequent) out-of-vocabulary (OOV). To this end, we investigate the classification error rate (CER) of several classes of confidence measures and transformations. In particular, we employed data-independent and data-dependent measures. The transformations we investigated include mapping to single confidence measures and linear combinations of these measures. These combinations are computed by means of neural networks trained with Bayes-optimal, and with Gardner-Derrida-optimal criteria. Compared to a recognition system without confidence measures, the selection of (various combinations of) confidence measures, the selection of suitable neural network architectures and training methods, continuously improves the CER.

Discriminative Generalized Hough transform for localization of joints in the lower extremities

A fully automatic iterative training approach for the generation of discriminative shape models for usage in the Generalized Hough Transform (GHT) is presented. The method aims at capturing the shape variability of the target object contained in the training data as well as identifying confusable structures (anti-shapes) and integrating this information into one model. To distinguish shape and anti-shape points and to determine their importance, an individual positive or negative weight is estimated for each model point by means of a discriminative training technique. The model is built from edge points surrounding the target point and the most confusable structure as identified by the GHT. Through an iterative approach, the performance of the model is gradually improved by extending the training dataset with images, where the current model failed to localize the target point. The proposed method is successfully tested on a set of 670 long-leg radiographs, where it achieves a localization rate of 74–97% for the respective tasks.

Performance of image intensifier-equipped X-ray systems for three-dimensional imaging

Abstract The performance of image intensifier (II)-equipped C-arm systems for three-dimensional (3D) imaging was investigated. The three-dimensional image quality was evaluated in terms of spatial resolution (modulation transfer function, MTF), contrast resolution, geometrical accuracy and homogeneity depending on the image intensifier format, focal spot size, number of projections and the angular span covered during the data acquisition. Experiments have been performed on a vascular C-arm system equipped with a 38-cm image intensifier. Several objects, including CT performance, MTF and pelvis phantoms, were scanned under various conditions. It was shown that for reasonable acquisition parameters, a contrast resolution below 100 HU could be obtained with standard acquisition strategies. Focusing on the spatial resolution, an almost isotropic three-dimensional resolution of up to 22 lp/cm at 10% modulation could be obtained when a 17-cm II was used. The homogeneity in the resulting images was limited by the remaining scatter and truncations. The resulting geometrical accuracy was in the order of the voxel size.

Effects of grain shape on packing and dilatancy of sheared granular materials

A granular material exposed to shear shows a variety of unique phenomena: Reynolds dilatancy, positional order and orientational order effects may compete in the shear zone. We study granular packing consisting of macroscopic prolate, oblate and spherical grains and compare their behaviour. X-ray tomography is used to determine the particle positions and orientations in a cylindrical split bottom shear cell. Packing densities and the arrangements of individual particles in the shear zone are evaluated. For anisometric particles, we observe the competition of two opposite effects. On the one hand, the sheared granules are dilated, on the other hand the particles reorient and align with respect to the streamlines. Even though aligned cylinders in principle may achieve higher packing densities, this alignment compensates for the effect of dilatancy only partially. The complex rearrangements lead to a depression of the surface above the well oriented region while neighbouring parts still show the effect of dilation in the form of heaps. For grains with isotropic shapes, the surface remains rather flat. Perfect monodisperse spheres crystallize in the shear zone, whereby positional order partially overcompensates dilatancy effects. However, even slight deviations from the ideal monodisperse sphere shape inhibit crystallization.

Alignment and dynamics of elongated cylinders under shear

When a granular material consisting of macroscopic elongated grains is exposed to shear, the individual grains align. We determine the particle distribution functions and orientational order parameters and study the collective dynamics as well as individual particle motion during shearing. X-ray computed tomography (CT) is used to obtain three-dimensional images of the shear zone. All individual particle positions and orientations are extracted by image processing software and the complete order tensor is determined. We compare the behavior of our ensembles of macroscopic grains with well-known continuum models for shear alignment and director dynamics of anisotropic liquids. Irrespective of the completely different particle interactions and size scales, analogies are found even on a quantitative level. Measurements of the local packing densities inside and outside the shear zone reveal a shear dilatancy, irrespective of the more efficient packing that can be expected for ordered ensembles of cylinders compared to randomly oriented samples.