Theoretical determination of surface roughness during high-speed milling and grinding

Аналитически установлено, что высокоскоростное фрезерование располагает значительными технологическими возможностями с точки зрения уменьшения шероховатости поверхности при одновременном увеличении производительности обработки. Установлено также, что при шлифовании уменьшение шероховатости поверхности связано с уменьшением производительности. Наиболее прогрессивным методом шлифования, обеспечивающим одновременно увеличение производительности и уменьшение шероховатости поверхности, является глубинное шлифование с небольшой скоростью детали, которое характеризуется меньшей производительностью по сравнению с высокоскоростным фрезерованием.The paper presents the results of theoretical studies of the surface roughness during milling and grinding. It is shown that high-speed milling has significant technological capabilities in terms of reducing surface roughness, because cutting data parameters are included in the calculated dependencies obtained to determine surface roughness with higher degrees than during grinding. This applies in particular to the speed of rotation of the cutter. Therefore, with its increase, it becomes possible to significantly reduce the surface roughness while increasing the processing capacity, which opens up broad prospects for the practical use of high-speed milling. It is established that during grinding, a decrease in surface roughness is associated with a decrease in productivity, and this reduces the efficiency of processing. The most progressive method of grinding, providing b oth an increase in productivity and a reduction in surface roughness, is deep-grinding at a low speed of the part. However, it is characterized by lower productivity in comparison with high-speed milling

Анализ эффективности использования ремонтной конструкции дефектного участка нефтепровода с применением программного комплекса ANSYS

To investigate the performance of semi-automated measurements (RECIST, volume) of hepatic metastases in multidetector-row computed tomography (MDCT) under normal-dose- and simulated low-dose-protocols.Thirty-five patients (67 +/- 13 years) with a total of 79 hepatic metastases underwent 16-MDCT (120 kv, 160 mAseff, pitch 1, 3 mm slice thickness, 2 mm reconstruction increment, B30f standard soft tissue kernel) for either initial staging or therapy monitoring. Corresponding raw data from these standard-dose scans were simulated at lower radiation doses of 80/60/40 mAseff (Somatom Noise Vers.6.1 beta, Siemens Healthcare, Forchheim, Germany). A semi-automated software tool (SyngoCT Oncology, Siemens Healthcare, Forchheim, Germany) was applied to each dose setting to evaluate size parameters (RECIST, volume). These measurements were compared by applying repeated-measures analysis of variance and displayed graphically.For RECIST measurements no statistically significant differences were found between standard dose (Mean RECIST diameter: 20.46 +/- 8.37 mm) and different simulated low radiation doses (80 mAseff: 20.95 +/- 8.20 mm/60 mAseff: 20.50 +/- 8.35 mm/40 mAseff: 19.95 +/- 8.16 mm): P = 0.0774.Statistically significant differences of volume quantification (P 0.05) between 160 mAseff- and either 80 mAseff-(3.46 +/- 4.31 mL) or 60 mAseff-protocols (3.44 +/- 4.35 mL).Software-assisted assessment of RECIST criteria and volume demonstrated valid performances under different dose-settings in MDCT; therefore, substantial radiation dose reduction could be possible with the use of semi-automated measurements in follow-up studies

Registrierung und Simulation zur Analyse von intraoperativen Gehirndeformationen

In recent years, the quality of surgical procedures has rapidly advanced in a large part due to the development of navigation systems. In particular, neurosurgical interventions have enormously benefited from the possibility of being able to track surgical tools simultaneously in the physical space associated with the operating room and in the virtual space related to volumetric images giving insight into patient's body. Navigation systems combined with intraoperative imaging allow precise removal of diseased tissue, thus reducing postoperative traumatological deficits. At the same time, there is an immense demand for new methods establishing a correspondence between time-shifted data such as pre- and intraoperative images. This would further improve surgical interventions by a more extensive integration of preoperative planning data into the operating environment. In this doctoral thesis different algorithms for registration of medical tomographic images are presented and implemented. The introduced methods focus on application within an intraoperative environment. In neurosurgery restrictive time requirements are an extremely important issue. Thus, the presented methods focus on acceleration techniques and at the same time on a high accuracy and reliability. In this respect, a rigid registration strategy exploiting graphics hardware in order to accelerate the interpolation operations has been optimized and extended by a hierarchical technique. This implementation can be used for an initial alignment of pre- and intraoperative data. However, the deformation of soft brain tissue occurring after craniotomy referred to as brain shift) necessitates non-linear data registration. This has been achieved with a volumetric image deformation based on a flexible hexahedral model. This model is designed to allow acceleration of the registration with graphics hardware. In order to improve the quality of the algorithm, an extension has been proposed where the free-form deformation (FFD) concept is applied to conduct a non-linear registration. The developed system has proved its value in experiments with real clinical data acquired before and during open-skull surgery. It has also been successfully applied to correct the distortions in diffusion tensor image (DTI) data, thus showing the techniques flexibility and strength. Furthermore, this thesis introduces a fundamental simulation framework for physically-based modeling of the intraoperative brain deformation. In this concept the brain is described as a linearly elastic medium saturated with a viscous fluid. Implementing this system, prediction of the extent and the direction of the brain shift is possible, thus enabling a better surgery planning. Here, the important problem is establishing the correct elastic parameters for the brain tissue. This has been addressed with a novel approach combining simulation and registration. In an automatic optimization procedure the intraoperative data is compared to the image reconstructed after simulation, thus finding the best elastic constants. Furthermore, a parallel implementation of the simulation model has led to a significant reduction in computation times. Additionally, huge data structures which do not fit in the memory of a single-processor machine can also be processed with this approach.In den letzten Jahren hat sich die Qualität der chirurgischen Verfahren hauptsächlich infolge der Anwendung von Navigationssystemen deutlich verbessert. Insbesondere neurochirurgische Interventionen haben enorm von der Möglichkeit profitiert, chirurgische Werkzeuge gleichzeitig im physikalischen Raum, der mit dem Operationssaal verbunden ist und im virtuellen Raum der volumetrischen Bilder, die Einblick in den Körper des Patienten geben, zu verfolgen. Navigationssysteme, kombiniert mit intraoperativer Bildgebung, erlauben eine präzisere Resektion des kranken Gewebes und verringern damit postoperative traumatologische Defizite. Gleichzeitig gibt es einen erheblichen Bedarf an neue Methoden, die eine Korrespondenz zwischen zeitlich versetzt aufgenommenen Daten, wie prä- und intraoperativen Bildern, herstellen. Das wird chirurgische Eingriffe durch eine umfangreichere Integration präoperativer Planungsdaten in die intraoperative Umgebung weiter verbessern. In dieser Doktorarbeit werden verschiedene Algorithmen zur Registrierung von medizinischen tomographischen Bilddaten präsentiert. Die eingeführten Methoden beziehen sich auf die Anwendung in einer intraoperativen Umgebung. In der Neurochirurgie sind strenge Zeitanforderungen ein extrem wichtiger Faktor. Aus diesem Grund konzentrieren sich die dargestellten Methoden auf Beschleunigungstechniken und gleichzeitig auf hohe Genauigkeit und Zuverlässigkeit. In dieser Hinsicht ist eine starre Registrierungsstrategie, die Grafikhardware zur Beschleunigung der Interpolationsoperationen ausnutzt, optimiert und durch eine hierarchische Technik erweitert worden. Diese Implementierung kann für eine Ausgangsregistrierung der prä- und intraoperativen Daten verwendet werden. Jedoch erfordert die Deformation des Gehirngewebes, die nach der Craniotomie auftritt (im Englischen als Brain Shift bekannt), eine nichtlineare Registrierung der Daten. Dies wird mit einer volumetrischen Bilddeformation erzielt, die auf einem flexiblen Hexaedermodell basiert. Dieses Modell wurde entworfen, um eine Beschleunigung der Registrierung mit Grafikkarten zu erlauben. Um die Qualität des Algorithmus zu verbessern, ist eine Erweiterung vorgeschlagen worden, bei der das Konzept der Freiformdeformationen (FFD) zur Realisierung einer nichtlinearen Registrierung angewendet wurde. Das entwickelte System hat sich in Experimenten mit realen klinischen Daten bewährt, die vor und während der Operation am offenen Schädel gewonnen wurden. Das System ist auch erfolgreich angewendet worden, um die Verzerrungen in Diffusions-Tensor (DT) Daten zu kompensieren. Damit wurde die Flexibilität und Stärke der Techniken demonstriert. Ferner präsentiert diese Arbeit eine grundlegende Simulationsumgebung zur physikalisch basierten Modellierung intraoperativer Gehirndeformationen. In diesem Konzept wird das Gehirn als ein linear elastisches Medium beschrieben, das mit einer viskosen Flüssigkeit gesättigt ist. Die Implementierung des Systems ermöglicht eine Vorhersage des Umfanges und der Richtung des Brain Shifts. Damit ist eine bessere Chirurgieplanung möglich, wobei die Bestimmung der korrekten elastischen Parameter für das Hirngewebe ein wichtiges Problem ist. Dies ist mit einem neuen Ansatz gelöst worden, der Simulation und Registrierung kombiniert. In einem automatischen Optimierungsverfahren wurden die intraoperative Daten mit dem Bild verglichen, das nach der Simulation rekonstruiert wurde. Auf diese Weise wurden die besten elastischen Konstanten gefunden. Eine signifikante Reduktion der Rechenzeit wurde durch eine parallele Implementierung des Simulationsmodells erzielt. Mit diesem Ansatz konnten auch sehr große Datenmengen, die nicht in den Speicher einer Einprozessor-Maschine passten, erfolgreich bearbeitet werden

Non-linear integration of DTI-based fiber tracts into standard 3D MR data

Diffusion tensor imaging (DTI) provides information about the location of white matter tracts within the human brain. This information is essential for preoperative neurosurgical planning to achieve maximal tumor resection while avoiding postoperative neurological deficits. Due to the anatomical distortion of echo planar imaging, DT images- and as a result the fiber tracts computed from them-are distorted. In this paper, we present a novel approach to account for those distortions. All voxels containing fibers within the distorted DT dataset were marked. Subsequently, a non-linear registration with standard 3D MR data was performed. The marked voxels were re-extracted from the registered DT dataset and displayed within the 3D MR dataset. The strategy introduced in this paper is an essential prerequisite for the integration of fiber tract data into 3D MR datasets. The fused data is of high value for neuronavigation and thereby a benefit for neurosurgery.

Correction of susceptibility artifacts in diffusion tensor data using non-linear registration

Diffusion tensor imaging can be used to localize major white matter tracts within the human brain. For surgery of tumors near eloquent brain areas such as the pyramidal tract this information is of importance to achieve an optimal resection while avoiding post-operative neurological deficits. However, due to the small bandwidth of echo planar imaging, diffusion tensor images suffer from susceptibility artifacts resulting in positional shifts and distortion. As a consequence, the fiber tracts computed from echo planar imaging data are spatially distorted. We present an approach based on non-linear registration using B´ezier functions to efficiently correct distortions due to susceptibility artifacts. The approach makes extensive use of graphics hardware to accelerate the non-linear registration procedure. An improvement presented in this paper is a more robust and efficient optimization strategy based on simultaneous perturbation stochastic approximation (SPSA). Since the accuracy of non-linear registration is crucial for the value of the presented correction method, two techniques were applied in order to prove the quality of the proposed framework. First, the registration accuracy was evaluated by recovering a known transformation with non-linear registration. Second, landmark-based evaluation of the registration method for anatomical and diffusion tensor data was performed. The registration was then applied to patients with lesions adjacent to the pyramidal tract in order to compensate for susceptibility artifacts. The effect of the correction on the pyramidal tract was then quantified by measuring the position of the tract before and after registration. As a result, the distortions observed in phase encoding direction were most prominent at the cortex and the brainstem. The presented approach allows correcting fiber tract distortions which is an important prerequisite when tractography data are integrated into a stereotactic setup for intra-operative guidance

Non-rigid Registration with Use of Hardware-Based 3D Bézier Functions

Abstract. In this paper we introduce a new method for non-rigid voxelbased registration. In many medical applications there is a need to establish an alignment between two image datasets. Often a registration of a time-shifted medical image sequence with appearing deformation of soft tissue (e.g. pre- and intraoperative data) has to be conducted. Soft tissue deformations are usually highly non-linear. For the handling of this phenomenon and for obtaining an optimal non-linear alignment of respective datasets we transform one of them using 3D Bézier functions, which provides some inherent smoothness as well as elasticity. In order to find the optimal transformation, many evaluations of this Bézier function are necessary. In order to make the method more efficient, graphics hardware is extensively used. We applied our non-rigid algorithm successfully to MR brain images in several clinical cases and showed its value.