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In this paper, we consider combined TV denoising and diffusion tensor fitting in DTI using the affine-invariant Riemannian metric on the space of diffusion tensors. Instead of first fitting the diffusion tensors, and then denoising them, we define a suitable TV type energy functional which incorporates the measured DWIs (using an inverse problem setup) and which measures the nearness of neighboring tensors in the manifold. To approach this functional, we propose generalized forward- backward splitting algorithms which combine an explicit and several implicit steps performed on a decomposition of the functional. We validate the performance of the derived algorithms on synthetic and real DTI data. In particular, we work on real 3D data. To our knowledge, the present paper describes the first approach to TV regularization in a combined manifold and inverse problem setup.

Fluoroscopy-guided percutaneous biopsy interventions are mostly performed with traditional free-hand technique. The practical experience of the surgeon influences the duration of the intervention and the radiation exposure for patients and him-/herself. Especially when the placement of heavy and long instruments in double oblique angles is required, manual techniques reach their technical limitations very fast. The system presented herein automatizes the needle positioning using only two 2D scans while the robotic platform guides the intervention. These two images were used to plan the needle pathway and to estimate the pose of the robot using a custom-made end-effector with embedded registration fiducials. The estimated pose was subsequently used to transfer the planed needle path to the robot’s coordinate system and finally to compute the movement parameters in order to align the robot with this plan. To evaluate the system, two phantoms with 11 different targets on it were developed. The targets were punctured, and the application accuracy was measured quantitatively. The solution achieved sub-millimetric accuracy for needle placement (min. 0.23, max. 1.04 in mm). Our approach combines the advantages of fluoroscopic imaging and ensures automatic needle alignment with highly reduced X-ray radiation. The proposed system shows promising potential to be a guidance platform that is easy to combine with available fluoroscopic imaging systems and provides valuable help to the physician in more difficult interventions.

Purpose: Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography. Methods: In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization. Results: Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method’s strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection. Conclusions: The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directionalmore » features.« less

The x-ray dark-field signal measured in grating interferometers is anisotropic, depending on both the beam direction and the grating orientation with respect to the sample. We present a novel general closed-form, continuous forward model of the anisotropic dark-field signal. Furthermore, we derive a discretization using spherical harmonics, leading to a large-scale linear inverse problem. We present first experimental results of a wooden sample, demonstrating marked advantages over previous results, in particular, the resolution of multiple scattering directions in one volume element.

X-ray Tensor Tomography (XTT) is a recently developed imaging modality that provides reconstruction of X-ray scattering tensors from dark-field projections obtained in a grating interferometry setup. In this work we present a novel component-based total variation (TV) regularized reconstruction technique for XTT data. First results show promising qualitative improvements of the reconstructed tensors as well as reduced noise and reduced streak artifacts.

Eine weltweit wachsende Zahl von Fahrzeugen verfugt uber ein Automatikgetriebe. Da die zugehorigen Getriebesteuerungen kundenspezifisch entwickelt werden, steigt damit die Vielfalt der Steuergerate. Gleichzeitig nimmt der Anteil an komplexer Mechatronik mit immer hoheren Anforderungen zu. Vor allem integrierte Steuergerate zeichnen sich als Trend ab. Continental berichtet uber aktuelle technische Losungen, uber Strategien zur Beherrschung der Komplexitat und Vielfalt sowie uber die weitere Entwicklung.

A growing number of vehicles worldwide are equipped with an automatic transmission. As the related transmission controls are the result of customer specific development, the diversity of control units is getting greater. At the same time the share of complex mechatronics grows along with the ever tougher requirements. Integrated control units are the most visible resulting trend. Continental reports on current technological solutions, on strategies to master complexity and diversity and on future developments.

Attenuation based X-ray micro computed tomography (XCT) provides three-dimensional images with micrometer resolution. However, there is a trade-off between the smallest size of the structures that can be resolved and the measurable sample size. In this letter, we present an imaging method using a compact laboratory setup that reveals information about micrometer-sized structures within samples that are several orders of magnitudes larger. We combine the anisotropic dark-field signal obtained in a grating interferometer and advanced tomographic reconstruction methods to reconstruct a six dimensional scattering tensor at every spatial location in three dimensions. The scattering tensor, thus obtained, encodes information about the orientation of micron-sized structures such as fibres in composite materials or dentinal tubules in human teeth. The sparse acquisition schemes presented in this letter enable the measurement of the full scattering tensor at every spatial location and can be easily incorporated in a practical, commercially feasible laboratory setup using conventional X-ray tubes, thus allowing for widespread industrial applications.

Quite recently, a method has been presented to reconstruct X-ray scattering tensors from projections obtained in a grating interferometry setup. The original publications present a rather specialised approach, for instance by suggesting a single SART-based solver. In this work, we propose a novel approach to solving the inverse problem, allowing the use of other algorithms than SART (like conjugate gradient), a faster tensor recovery, and an intuitive visualisation. Furthermore, we introduce constraint enforcement for X-ray tensor tomography (cXTT) and demonstrate that this yields visually smoother results in comparison to the state-of-art approach, similar to regularisation.

Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography.In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization.Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method's strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection.The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.

Die Umweltanforderungen an Motor- und Getriebesteuerungen werden hauptsachlich durch folgende Parameter bestimmt

This report describes a flexible, object-oriented software framework for modelling linear inverse problems, including solvers for analytic and least-squares solutions with optional regularization. Real-world linear problems can be composed at runtime, making the library suitable for a broad variety of applications. We demonstrate this flexibility with examples from X-ray Computed Tomography for linear attenuation and differential phase contrast.

In this paper, we present an interactive X-Ray perceptual visualization technique (IXPV) to improve 3D perception in standard single-view X-Ray images. Based on a priori knowledge from CT data, we re-introduce lost depth information into the original single-view X-Ray image without jeopardizing information of the original X-Ray. We propose a novel approach that is suitable for correct fusion of intraoperative X-Ray and ultrasound, co-visualization of X-Ray and surgical tools, and for improving the 3D perception of standard radiographs. Phantom and animal cadaver datasets were used during experimentation to demonstrate the impact of our technique. Results from a questionnaire completed by 11 clinicians and computer scientists demonstrate the added value of introduced depth cues directly in an X-Ray image. In conclusion, we propose IXPV as a futuristic alternative to the standard radiographic image found in today's clinical setting.

Visual discomfort is a major problem for head-mounted displays and other stereo displays. One effect that is known to reduce visual comfort is double vision, which can occur due to high disparities. Previous studies suggest that adding artificial out-of-focus blur increases the fusional limits, where the left and right image can be fused without double vision. We investigate the effect of adding artificial out-of-focus blur on visual discomfort using two different setups. One uses a stereo monitor and an eye tracker to change the depth of focus based on the gaze of the user. The other one uses a video-see through head mounted display. A study involving 18 subjects showed that the viewing comfort when using blur is significantly higher in both setups for virtual scenes. However we can not confirm without doubt that the higher viewing comfort is only related to an increase of the fusional limits, as many subjects reported that double vision did not occur during the experiment. Results for additional photographed images that have been shown to the subjects were less significant. A first prototype of an AR system extracting a depth map from stereo images and adding artificial out-of-focus blur is presented.