Guang Jia, Presenter: Nothing to Disclose

Johannes T. Heverhagen MD, PhD, Abstract Co-Author: Nothing to Disclose

Jun Zhang PhD, Abstract Co-Author: Nothing to Disclose

Klaus T Baudendistel PhD, Abstract Co-Author: Nothing to Disclose

Hendrik von Tengg-Kobligk MD, Abstract Co-Author: Nothing to Disclose

Michael V. Knopp MD, PhD, Abstract Co-Author: Nothing to Disclose

To design a software approach for managing offsite-acquired DICOM-based image datasets of cross-sectional imaging devices received at the central assessment facility in multicenter trials, in order to facilitate automated indexing and quality control for verification of protocol adherence.

A software application, named as “DICOM Crawler,” was developed using IDL programming environment that can be run as standalone software using the virtual machine environment. The software approach uses a search engine approach crawling every header of DICOM images and indexing the public and private tag information against the preset labels. The resulting output is then transferred in a table format and readable by Excel/Access standard office software. Matching the crawler's tabulated information against preset expected characteristics (to find study protocol parameters) facilitates an automated quality assessment to verify protocol adherence. In addition, the approach is capable to generate summary report characterizing the received data and their image acquisition parameters. By using modality and/or vender-specific DICOM conformance statements, modality-specific information such as flip angle and TR for MR, kV and reconstruction kernel for CT, and dose of FDG and dose start time for PET images can be determined.

Applying today's popular approaches of search engines to automatically crawl through the DICOM headers of offsite-acquired datasets allows an automated indexing, management, and quality control of received datasets. Such capabilities are important to readily recognize and manage acquisition errors, none-compliance to protocol, transmission errors, incomplete datasets, or other deviation, which can jeopardize the assessment of cases within large multicenter trials. The approach can be combined to confirm HIPAA compliance by verifying de-identification of datasets.

A “DICOM Crawler” approach to search and index the headers of received datasets is an efficient and effective way for compliance and integrity assessment as well as fast management of large collection of DICOM-based image sets especially in multicenter trials.

To design a software approach for managing offsite-acquired DICOM-based image datasets of cross-sectional imaging devices received at the central assessment facility in multicenter trials, in order to facilitate automated indexing and quality control for verification of protocol adherence.

A software application, named as “DICOM Crawler,” was developed using IDL programming environment that can be run as standalone software using the virtual machine environment. The software approach uses a search engine approach crawling every header of DICOM images and indexing the public and private tag information against the preset labels. The resulting output is then transferred in a table format and readable by Excel/Access standard office software. Matching the crawler's tabulated information against preset expected characteristics (to find study protocol parameters) facilitates an automated quality assessment to verify protocol adherence. In addition, the approach is capable to generate summary report characterizing the received data and their image acquisition parameters. By using modality and/or vender-specific DICOM conformance statements, modality-specific information such as flip angle and TR for MR, kV and reconstruction kernel for CT, and dose of FDG and dose start time for PET images can be determined.

Applying today's popular approaches of search engines to automatically crawl through the DICOM headers of offsite-acquired datasets allows an automated indexing, management, and quality control of received datasets. Such capabilities are important to readily recognize and manage acquisition errors, none-compliance to protocol, transmission errors, incomplete datasets, or other deviation, which can jeopardize the assessment of cases within large multicenter trials. The approach can be combined to confirm HIPAA compliance by verifying de-identification of datasets.

A “DICOM Crawler” approach to search and index the headers of received datasets is an efficient and effective way for compliance and integrity assessment as well as fast management of large collection of DICOM-based image sets especially in multicenter trials.

Jia, G, Heverhagen, J, Zhang, J, Baudendistel, K, von Tengg-Kobligk, H, Knopp, M, DICOM Crawler: A Medical Image Management Approach for Cross-sectional (CT/PET/MR) Datasets in Multicenter Trials.  Radiological Society of North America 2005 Scientific Assembly and Annual Meeting, November 27 - December 2, 2005 ,Chicago IL. http://archive.rsna.org/2005/4406795.html