PubMed Health⌕ Search

PubMed · 12061126

A resource server for medical training.

Abstract

OBJECTIVES: We have developed a RESOURCE SERVER to collect and store various elements used by a professor during his lecture. METHODS: The server manages four types of objects: ELEMENTS, RESOURCES (set of elements referring to a given topic), INDEXES (to organize the resources for further search and use), and USERS (to identify providers, users, and access rights). If an ELEMENT s modified, the RESOURCE is automatically updated. RESULTS: An example (preparation of an anatomy lecture) explains how the RESOURCE SERVER works in three steps: organization of the training material, indexing, and retrieval. CONCLUSIONS: The RESOURCE SERVER will help instructors develop, update and share pedagogic resources for supporting their training courses, lessons and conferences. Moreover, these techniques, based on Internet technologies for easy handling of and access to these resources, allow local and distant access. Within the general framework of the French-speaking Virtual Medical University, the RESOURCE SERVER will represent an important link between data collection and its use in intelligent pedagogic training.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

J M Brunetaud, S Darmoni, N Souf, E Dufresne, R Beuscart. 2002. A resource server for medical training.. https://pubmed.ncbi.nlm.nih.gov/12061126/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Single-ion dosemeter based on floating gate memories.

Floating Gate (FG) nonvolatile memories are based on a tiny polysilicon layer (the FG) which can be permanently charged with electrons or holes, thus changing the threshold voltage of a MOSFET. Every time a FG is hit by a high energy ion, it experiences a charge loss, depending on the ion linear energy transfer (LET) and on the transistor geometrical and electrical characteristics. This paper discusses the opportunities to use this devices as single an ion dosemeter with sub-micrometer spatial resolution and capable of distinguish the impinging ion LET.

Computer Storage Devices↗

Fast maximum intensity projections of large medical data sets by exploiting hierarchical memory architectures.

Maximum intensity projections (MIPs) are an important visualization technique for angiographic data sets. Efficient data inspection requires frame rates of at least five frames per second at preserved image quality. Despite the advances in computer technology, this task remains a challenge. On the one hand, the sizes of computed tomography and magnetic resonance images are increasing rapidly. On the other hand, rendering algorithms do not automatically benefit from the advances in processor technology, especially for large data sets. This is due to the faster evolving processing power and the slower evolving memory access speed, which is bridged by hierarchical cache memory architectures. In this paper, we investigate memory access optimization methods and use them for generating MIPs on general-purpose central processing units (CPUs) and graphics processing units (GPUs), respectively. These methods can work on any level of the memory hierarchy, and we show that properly combined methods can optimize memory access on multiple levels of the hierarchy at the same time. We present performance measurements to compare different algorithm variants and illustrate the influence of the respective techniques. On current hardware, the efficient handling of the memory hierarchy for CPUs improves the rendering performance by a factor of 3 to 4. On GPUs, we observed that the effect is even larger, especially for large data sets. The methods can easily be adjusted to different hardware specifics, although their impact can vary considerably. They can also be used for other rendering techniques than MIPs, and their use for more general image processing task could be investigated in the future.

Computer Storage Devices↗