Simon (Signal Interpretation and Monitoring) is an ongoing research and development effort at Vanderbilt University. Simon's overall goal is to provide effective computerized medical decision support in critical care through novel collection, analysis, and presentation of physiologic data from bedside medical devices.
1) The visualization (2D and 3D) of images. Its main area of application is thevisualization of tomographic medical images data sets2) the interaction with these data sets. The software permits the manual alignment of datasets, the delineation of regions of interest (both in 2D and in 3D), the import and export of these regions of interest, the display of these regions both as contours or as surfaces, as well as the processing of the images (filtering, image enhancement, etc.)3) The registration of medical images. This software has been designed to permit therealignment of multiple data sets. This realignment can be performed manually and interactively or it can be performed automatically. In the latter case, transformations that permit the realignment of the images can be imported in the software that uses these transformations to realign the images. The software is designed to accept a wide variety of transformations ranging from rigid transformations to non-rigid transformations. These transformations can be modified interactively and re-exported.The software runs on any computer on which the IDL virtual machine runs (currently Windows, Unix, and Mac OS). The software is designed to be user friendly but it does not currently include a user's manual.
The invention relates to an improved method and system for synchronizing signals in a particle accelerator system. In one embodiment, a method and system is disclosed whereby a phase of laser pulses are monitored, and a high-frequency signal is adjusted as necessary to be substantially in-phase with the laser pulses. In another embodiment, a method and system is disclosed whereby a phase of an electromagnetic field in an electron gun is monitored, and a high-frequency signal is adjusted as necessary to be substantially in-phase with the electromagnetic field.
Currently practical (as opposed to didactic) training is performed by trainees practicing on live patients and then learning disease processes from mentors. The training is serendipitous by necessity. This technology would potentially shorten, standardize, and broaden the training for technicians as well as radiologists and surgeons.
Gene Expression Model Selector (GEMS) is a system that constructs, in a supervised fashion, diagnostic and outcome prediction models from array gene expression data. Examples of such models are: (a) models that detect cancer, (b) models that determine the correct subtype of cancer or (c) models that predict survival after treatment. Models that support such complex decision making are widely recognized as having the potential to revolutionize medicine in the years to come. In addition to the decision support models, GEMS can be used to select a small number of genes that are as good or better than the full gene set for diagnosis and/or outcome prediction. These biomarkers (genes) are also useful for discovery purposes (e.g., they suggest plausible causes and treatments of various types of cancer). Finally, GEMS provides estimates of the models' performance (e.g., accuracy) in future applications (i.e., when applied on patients not used to build the models but who come from the same patient population as the ones used to build the models), and allows users to run the models for individual patients.
A method of determining a local causal neighborhood of a target variable from a data set can include identifying variables of the data set as candidates of the local causal neighborhood using statistical characteristics, and including the identified variables within a candidate set. False positive variables can be removed from the candidate set according to further statistical characteristics applied to each variable of the candidate set. The remaining variables of the candidate set can be identified as the local causal neighborhood of the target variable.
This technology enables continuum robots (aka snake robots) to precisely navigate the intricate structures of deep anatomical passages during minimally invasive or natural orifice surgery. Collateral surgical damage is minimized by the force sensing capabilities of the algorithms used.
This technology enhances the capabilities of continuum robots by not only detecting contact during movement but also estimating the position of the contact during the movements executed by the robot. An algorithmic feedback loop can then constrain the movement of the robot to avoid damage to its robot arm, damage to another robot arm or damage to surrounding structure. Applications for this technology include enhanced safe telemanipulation for multi-arm continuum robots in surgery, micro-assembly in confined spaces, and exploration in unknown environments.