Three-dimensional (3D) vessel data from CTA or MRA are not always available prior to or during endovascular interventional procedures, whereas multiple 2D projection angiograms often are. Unfortunately, patient moveme...
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ISBN:
(纸本)9780819466297
Three-dimensional (3D) vessel data from CTA or MRA are not always available prior to or during endovascular interventional procedures, whereas multiple 2D projection angiograms often are. Unfortunately, patient movement, table movement, and gantry sag during angiographic procedures can lead to large errors in gantry-based imaging geometries and thereby incorrect 3D. Therefore, we are developing methods for combining vessel data from multiple 2D angiographic views obtained during interventional procedures to provide 3D vessel data during these procedures. Multiple 2D projection views of carotid vessels are obtained, and the vessel centerlines are indicated. For each pair of views, endpoints of the 3D centerlines are reconstructed using triangulation based on the provided gantry geometry. Previous investigations indicated that translation errors were the primary source of error in the reconstructed 3D. Therefore, the errors in the translations relating the imaging systems are corrected by minimizing the L1 distance between the reconstructed endpoints, after which the 3D centerlines are reconstructed using epipolar constraints for every pair of views. Evaluations were performed using simulations, phantom data, and clinical cases. In simulation and phantom studies, the RMS error decreased from 6.0 nun obtained with biplane approaches to 0.5 mm with our technique. Centerlines in clinical cases are smoother and more consistent than those calculated from individual biplane pairs. The 3D centerlines are calculated in about 2 seconds. These results indicate that reliable 3D vessel data can be generated for treatment planning or revision during interventional procedures.
Python offers basic facilities for interactive work and a comprehensive library on top of which more sophisticated systems can be built. The IPython project provides an enhanced interactive environment that includes, ...
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Python offers basic facilities for interactive work and a comprehensive library on top of which more sophisticated systems can be built. The IPython project provides an enhanced interactive environment that includes, among other features, support for data visualization and facilities for distributed and parallel computation.
Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.
Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.
By itself, Python is an excellent "steering" language for scientific codes written in other languages. However, with additional basic tools, Python transforms into a high-level language suited for scientific...
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By itself, Python is an excellent "steering" language for scientific codes written in other languages. However, with additional basic tools, Python transforms into a high-level language suited for scientific and engineering code that's often fast enough to be immediately useful but also flexible enough to be sped up with additional extensions.
My coeditors (Paul Dubois and Konstantin Läufer) and I are among a growing number of individuals who install and maintain a home computer network. When Paul and I first discussed the idea of writing an article ab...
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My coeditors (Paul Dubois and Konstantin Läufer) and I are among a growing number of individuals who install and maintain a home computer network. When Paul and I first discussed the idea of writing an article about the subject several months ago, my first thought was that you--our cherished readers-might consider this whole notion so trivial as to not be worthy of a column that focuses mostly on scientific programming and software development topics. But based on the realization that we three computer scientists devote a significant part of our time to network and system administration, it almost goes without saying that networking is becoming more commonplace at home than at work.
Yes Jack, like Patton in France, I've broken out (of the scientific programming department) and am rolling like a juggernaut. This column will now come to you each issue and will be somewhat more wide-ranging than...
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Yes Jack, like Patton in France, I've broken out (of the scientific programming department) and am rolling like a juggernaut. This column will now come to you each issue and will be somewhat more wide-ranging than before. Meanwhile, this gives George Thiruvathukal and Konstantin Laufer more chances to express themselves, too, in their own sidebars. I hope you enjoy the column and continue to point me at new ideas that you think your fellow readers should learn about.
In scientific computing, we sometimes begin with a clean slate; We're given anew problem to solve, and we write software to accomplish the task. in this situation, we're theinventors. Other times, we work on a...
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In scientific computing, we sometimes begin with a clean slate; We're given anew problem to solve, and we write software to accomplish the task. in this situation, we're theinventors. Other times, we work on a problem for which considerable software development has beendone, often over a period of many years. The existing software might have had many authors, some ofwhom have moved on to other positions. In this situation, our job is more akin to detective work. Westudy the existing code, run examples to see how it behaves, and come to understand both what itdoes and how it does it.
I peaked as a computational scientist in 1986. At that time, two years from finishing my PhD, I was trying to account for an aspect of the x-ray emission from a pulsar known as Hercules X-1.
I peaked as a computational scientist in 1986. At that time, two years from finishing my PhD, I was trying to account for an aspect of the x-ray emission from a pulsar known as Hercules X-1.
Parallel computing is increasingly a requirement of Computational Science and Engineering (CSE) software because problems of interest are very large and hardware systems are becoming parallel through multicore technol...
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ISBN:
(纸本)9781595936035
Parallel computing is increasingly a requirement of Computational Science and Engineering (CSE) software because problems of interest are very large and hardware systems are becoming parallel through multicore technologies. Inexperienced programmers-non-computer scientists with one semester to a few years of programming experience-are crucial to CSE software development because academic research teams rely on them as application developers. For this group, the basic concepts of parallelism can be explained by analogies rather than through exploring the specifics of various technologies. These analogies are also useful for computer scientists who are new to parallel computing.
Combine a high rate of change (which makes correctness hard to maintain) withan increased sensitivity to failure to maintain correctness and you have a big problem. Solvingthis problem must be the focus of our methodo...
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Combine a high rate of change (which makes correctness hard to maintain) withan increased sensitivity to failure to maintain correctness and you have a big problem. Solvingthis problem must be the focus of our methodology. In this paper, the author describes the layeredapproach that he found to be the most successful in maintaining correctness in the face of rapidchange.
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