This article describes a distributed classroom experiment carried out by five universities in the US and Europe at the beginning of 2007. This experiment was motivated by the emergence of new digital media technology supporting uncompressed high-definition video capture, transport and display as well as the networking services required for its deployment across wide distances. The participating institutes have designed a distributed collaborative environment centered around the new technology and applied it to join the five sites into a single virtual classroom where a real course has been offered to the registered students.Here we are presenting the technologies utilized in the experiment, the results of a technology evaluation done with the help of the participating students and we identify areas of future improvements of the system. While there are a few hurdles in the path of successfully deploying this technology on a large scale, our experiment shows that the new technology is sustainable and the significant quality improvements brought by it can help build an effective distributed and collaborative classroom environment.
Motion is a serious artifact in Cardiac nuclear imaging because the scanning operation takes a long time. Since reconstruction algorithms assume consistent or stationary data the quality of resulting image is affected by motion, sometimes significantly. Even after adoption of the gold standard MoCo(R) algorithm from Cedars-Sinai by most vendors, heart motion remains a significant challenge. Also, any serious study in quantitative analysis necessitates correction for motion artifacts. It is generally recognized that human eye is a very sensitive tool for detecting motion. However, two reasons prevent such manual correction: (1) it is costly in terms of specialist's time, and (2) no such tool for manual correction is available currently. Previously, at SPIE-MIC'11, we presented a simple tool (SinoCor) that allows sinograms to be corrected manually or automatically. SinoCor performs correction of sinograms containing inter-frame patient or respiratory motions using rigid-body dynamics. The software is capable of detecting the patient motion and estimating the body-motion vector using scanning geometry parameters. SinoCor applies appropriate geometrical correction to all the frames subsequent to the frame when the movement has occurredin a manual or automated mode. For respiratory motion, it is capable of automatically smoothing small oscillatory (frame-wise local) movements. Lower order image moments are used to represent a frame and the required rigid body movement compensation is computed accordingly. Our current focus is on enhancement of SinoCor with the capability to automatically detect and compensate for intra-frame motion that causes motion blur on the respective frame. Intra-frame movements are expected in both patient and respiratory motions. For a controlled study we also have developed a motion simulator. A stable version of SinoCor is available under license
We are developing SinoCor, a body-motion detection and correction tool for nuclear imaging. It is currently provided as a JAVA-based plug-in for ImageJ [1] for use with data acquired via Single Photon Emission Computed Tomography (SPECT) scans. SinoCor works only in the sinogram space without going through any back-projection operation. It provides automatic correction for both inter frame and intra-frame motion. Inter-frame motion occurs if the subject shifts location in-between two frame acquisitions during the tomographic imaging. The intra-frame motion happens when the shift occurs when a particular frame is being acquired.Independent from SinoCor, we have also developed a motion simulator to generate motion-induced sinogram from any given 3D image and acquisition geometry by the appropriate forward-projection operation. II. MOTION GENERATIONThe motion simulation tool is capable of injecting inter frame or intra-frame motion into phantom volumes during the forward-projection process (i.e. sinogram generation), similar to that found in [2], for a given motion model. Inter frame motion is accomplished by applying a simple linear shift across the volume prior to projecting specific frames. Geometrically appropriate shifts are applied for all subsequent projections/frames. Intra-frame motion generation convolves the volwne with a kernel (P) prior to forward-projecting the affected sinogram frame. P is derived from an input motion vector .E, the body-motion experienced by the volume. The kernel, defined below, acts as a 3D point-spread function (PSF), and its values (Pijd are biased toward the line described by -.E.N is the sum of values in p.Where:The specified convolved frame will suffer a motion blur for the introduced intra-frame motion, and the subsequent projections will be given linear shifts, but no motion-blur, as in inter-frame motion.
Although distance learning has a history that spans many decades, the full opportunity that is implicit in its exploitation has not been fully realized due to combination of factors including disparate experience between it and its classroom counterpart. However current and emerging technologies are helping overcome this barrier by providing significantly better interaction among the individual participants, thereby opening new avenues for knowledge dissemination. LSU in collaboration with five other institutions has developed effective methods that greatly extend the educational opportunities through combination of advanced technologies and educational methodologies. LSU and its partners have tested these technologies in realtime over the last two years. While further improvements are needed, this activity represents the current state of the art in technologies utilized and the quality of content and experience delivered. The distance learning initiative undertaken by LSU and its partners is driven by a vision for education, which aims to deliver expert & top-quality educational content to locations irrespective of their economic or technological limitations.
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