Challenges in Image Acquisition and Distribution for Clinical Image Service

Melson, David L.; Moore, Stephen; Blaine, James G.; Clark, Kenneth W.; Sadhra, Sarbjit; Blume, Hartwig R.

doi:10.1007/s10278-002-5010-4

Cited by 3 publications

(3 citation statements)

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“…Issues related to modification, analysis and communication of DICOM objects, community wide sharing of images have been addressed [27][28][29][30][31][32][33][34][35]. There are DICOM standards based toolkits such as PixelMed available for developers to handle PACS systems programmatically [36] .…”

Section: Background and Related Workmentioning

confidence: 99%

Flexible patient information search and retrieval framework: pilot implementation

et al. 2007

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Medical centers collect and store significant amount of valuable data pertaining to patients' visit in the form of medical free-text. In addition, standardized diagnosis codes (International Classification of Diseases, Ninth Revision, Clinical Modification: ICD9-CM) related to those dictated reports are usually available. In this work, we have created a framework where image searches could be initiated through a combination of free-text reports as well as ICD9 codes. This framework enables more comprehensive search on existing large sets of patient data in a systematic way. The free text search is enriched by computer-aided inclusion of additional search terms enhanced by a thesaurus. This combination of enriched search allows users to access to a larger set of relevant results from a patient-centric PACS in a simpler way. Therefore, such framework is of particular use in tasks such as gathering images for desired patient populations, building disease models, and so on. As the motivating application of our framework, we implemented a search engine. This search engine processed two years of patient data from the OSU Medical Center's Information Warehouse and identified lung nodule location information using a combination of UMLS Meta-Thesaurus enhanced text report searches along with ICD9 code searches on patients that have been discharged. Five different queries with various ICD9 codes involving lung cancer were carried out on 172552 cases. Each search was completed under a minute on average per ICD9 code and the inclusion of UMLS thesaurus increased the number of relevant cases by 45% on average.

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Section: Background and Related Workmentioning

confidence: 99%

Flexible patient information search and retrieval framework: pilot implementation

et al. 2007

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“…Assuming an average mix of images [13], an effective UC of 4.0 GB/h or 96 GB per day appears to be a careful and realistic approximation. The average daily image production in PACS installations strongly varies: 3.4 GB/day [1]; 6.2 GB/day [14]; 8 GB/day [15]; 9 GB/day [16]; 19 GB/day [11]; 20-30 GB/day [17]; and even 34 GB/day [18] in a quite large installation (values provided in Terabyte per year were divided by 365 for the ease of comparison, although this is not fully correct). Even when those amounts do not include the prior examinations, which are estimated to be one-third of the daily production [16], and the fact that the data production is likely to rise with the installation of multislice CT [19], it is concluded that a UC of 96 GB/day is sufficient.…”

Section: Upload Performancementioning

confidence: 99%

Upload capacity and time-to-display of an image Web system during simultaneous up- and download processes

et al. 2004

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The aim of this study was to assess the up- and download performance of an image Web system (IWS) during simultaneous up- and download procedures. Following preparatory tests, the upload capacity (UC) with and without simultaneous download and the time-to-display (TTD) with and without simultaneous upload were determined for different image, server and compression types, and with up to 16 concurrent clients. The UC varied between 1.7-5.5 Gigabyte per hour (GB/h) and was slightly influenced by a simultaneous download; however, the TTDs were substantially prolonged during an upload and only below 5 s with up to four to six concurrent clients. The choice of image and server type had a strong impact on UC and TTD. Lossy primary compression proved slightly superior. An effective UC of approximately 4.0 GB/h or 96 Gigabyte per day (GB/day) can be achieved, which appears suitable even for large institutions. Because of its substantial influence on UC and TTD during simultaneous up- and download, the server hardware should be equipped with two processors and 1 GB RAM. Lossy primary compression may be used with slight performance benefits when full-resolution images are not required. The upload is a time-demanding process, and it is possible that during peak hours the waiting times are unacceptable for clinicians.

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“…Our Philips PACS cannot be configured to route inbound studies to multiple destinations; studies destined for the Philips PACS are sent from their modalities to the IRN pool, which can simultaneously route to the Philips PACS and to the Imageweb server. 3 …”

Section: Image Flowmentioning

confidence: 99%