Volume 2 Issue 1
Authors: Chander Bhan Mehta; Mahinder Singh
Abstract: The instability of plane interface between two superposed Rivlin-Ericksen elastico-viscous fluids in porous medium has been studied to include the suspended (dust) particles effect. It is found that for potentially stable arrangement Rivlin-Ericksen elastico-viscous fluid of different permeabilities in the presence of suspended particles in porous medium is stable, whereas in potentially unstable case instability of the system occurs in the system. In the presence of magnetic field for potentially stable arrangement system is always stable and for the potentially unstable arrangement, the magnetic field succeeds in stabilizing certain wave-number band which was unstable in the absence of magnetic field.
Keywords: Porous Medium; Different Permeability; Suspended Particles; Rivlin-Ericksen Fluid and Magnetic Field
Authors: Mark Gainey; Kostyantyn Holubyev; Klaus Bratengeier
Abstract: The purpose of this work is to describe the automation of 2-Step IMRT/IMAT segmentation. 2-Step IMRT/IMAT employs analysis of the PTV-OAR topology to determine the initial segment shapes for each gantry angle. Previous retrospective planning studies by the authors have shown that the quality of 2-Step IMRT/IMAT plans is at least as good as that of plans calculated based solely upon the optimization module in commercial treatment planning system (TPS) software. Hitherto the generation of the 2-Step segment shapes was performed manually employing the TPS, a time consuming process requiring approximately 5 minutes per PTV-OAR pair for each gantry angle. The software was written in C++ programing language and tested to process the structure set files to generate the 2-Step segments. Six clinical prostate cases incorporating a simultaneously integrated boost were employed to test segment generation for a typical seven field IMRT using the Synergy linac and BeamModulator MLC commissioned in the Pinnacle TPS available at the University Hospital Wuerzburg. Additionally, two clinical paraspinal tumour cases were also used to test segment generation for IMAT. The C++ classes were designed to be extensible for other MLCs and TPSs, which permit seeding of the optimiser with segment forms. For each PTV-OAR pair, a conformal (zeroth order), OAR blocked (first order) and “dose saturation” (second order) segment was automatically generated employing the BEV projection of the PTV and OAR. Subsequently these segment shapes were sequenced into deliverable MLC segments and exported to the TPS as IMRT or VMAT plans. For the prostate cases the mean time per generated segment was 36 ± 6 ms (total 70 segments). For the paraspinal tumour cases, three complete arcs were generated with 2˚ spacing per control point (179 segments for a complete rotation). The total time taken was 13.1 and 12.0 s (537 segments) or 24 ms and 22 ms per segment respectively. These exported plans may be further optimized by the TPS. Alternatively the software may be used as the basis for adaption of treatment plans on the basis of daily cone beam CT (CBCT) studies. This work demonstrates the automatic generation of 2-Step IMRT and IMAT cases for the Pinnacle TPS. It enables more comprehensive planning studies to be performed.
Keywords: Radiotherapy; Treatment Plannung; IMRT; VMAT; Adaption
Authors: Jonathan Becedas; Rodrigo Quian Quiroga
Abstract: There is substantial evidence that it is possible to predict movement intentions from single cell recordings in monkeys and since more recently, in humans. Such predictions, using decoding algorithms, have a large potential for clinical applications in order to drive robotic devices to be used by paralyzed patients or amputees. In spite of these advances, it is still not clear how accurate and practical the movements obtained from real neuronal devices could be. In this work, an original decoding method to perform movements to different locations was proposed and studied in realistic simulations. The method provides a high level control command to a Brain-Machine Interface device, which is a precise estimation of the target location as a function of the number of recorded neurons. Finally the method was applied to a 7 Degrees of Freedom (DOF) anthropomorphic robotic arm for reaching and grasping an object.
Keywords: Brain-Machine Interface; Neuroprosthetic Devices; Neural Decoding; Parietal Cortex
Authors: Jiang Fan; Fangqi Liu; Huining Gu; Ye Xu; Peng Nan; Binbin Cai
Abstract: Loss of mismatch repair (MMR) function predisposes to a mutator cell phenotype, microsatellite instability (MSI) and cancer, especially hereditary non-polyposis colorectal cancer (HNPCC). Plenty of studies of MMR gene MLH1 and MSH2 have been conducted and many mutations have been reported. However, there are few studies of other MMR genes, such as MSH6, PMS2 and MLH3, which are also important especially from statistical aspect. In this study, we collected all the related literatures and extracted the useful information to build a database and performed further analysis. The study focused on the germline mutation characteristics of these three genes and proposed different strategies when dealing with patients of different area, genders and ages. The results showed that (1) each gene has its own hot spots, exons and mutation pattern, which we might pay special attention to when processing the clinical detection and diagnosis; (2) Although Asian people account for the minority of the total patients, there are still some important hot spots and exons that are different from the total sample and require special attention; (3) For the patients with mutations of MSH6, the average age of patients (around 50) is older than usual age (45), and many of them would get more than one attack. And it is worth mentioning that most women (51.3%) got both colorectal cancer and endometrial cancer, which implies that when diagnosing colorectal cancer, it is necessary to check the female reproductive systems such as the uterus and ovary for women.
Keywords: HNPCC; MSH6; PMS2; MLH3; Data Analysis
Authors: Mamoru MINAMI; Shintaro ISHIYAMA
Abstract: A new control scheme “Move on Sensing” technology was proposed and demonstrated its great potential as an autonomous control system to respond to interaction with growing simulation environment of differentiated Induced Pluripotent Stem (iPS) cell and the following results were derived; (1) Remove of the targeted cell models was completed by 20 times selection process from original cell group model composed of 20 red and 20 blue colored beads within 16sec per cell. (2) High-accuracy sorting operation of iPS cell model was achieved within 16sec per cell. (3)Retrievability of the discrete cell models isolated from group model was ensured by use of the MOS control.
Keywords: Visual Servoing; Move on Sensing Control; iPS Cell; Genetic Algorithm
An Innovative Soft Computing Framework to Measure and Classify Solid Pulmonary Tumors from CT Images
Authors: Vitoantonio Bevilacqua; Daniele Ranieri; Gaetano Nacci; Gioacchino Brunetti; Piero Larizza; Francescomaria Marino
Abstract: Assessing the shrinking of lung nodules in response to the treatment is an important task to evaluate the effectiveness of the therapy, so guidelines to minimize variability and make this task objective and observer-independent are strongly needed. The Response Evaluation Criteria in Solid Tumors (RECIST) is a set of standardized rules that defines when cancer patients improve, stay the same, or deteriorate during the treatments. An accurate evaluation of the growth rate can be performed only by means of a high resolute segmentation, followed by volumetric techniques, although the higher the resolution, the more time is consumed in the process. In this work we present a framework that carries out a fully automatic 3D segmentation of the human respiratory system in Computer Tomography images. Usually, an analysis of the lungs requires manual segmentation of nodules that is always subjective and often error-prone. So the framework encompasses a second step, which semi-automatically segments the lung nodules, extracting them from the surrounding tissues and providing an accurate data set for estimating several volumetric features. These features can be used for the classification of the malignancy of the nodule.
Keywords: 3D segmentation; CT Images; Lung Nodules; Region Growing; Level Set; Metaball; GENOCOP; Neural Network