DICOM metadata-mining in PACS for computed radiography X-Ray exposure analysis: a mammography multisite study

Transcription

1 DICOM metadata-mining in PACS for computed radiography X-Ray exposure analysis: a mammography multisite study Poster No.: B-0276 Congress: ECR 2014 Type: Authors: Keywords: DOI: Scientific Paper M. R. D. Santos, P. Sá-Couto, A. Silva, N. Rocha; Aveiro/PT Radioprotection / Radiation dose, Computer applications, Breast, Digital radiography, PACS, Plain radiographic studies, Computer Applications-General, Dosimetry, Radiation safety /ecr2014/B-0276 Any information contained in this pdf file is automatically generated from digital material submitted to EPOS by third parties in the form of scientific presentations. References to any names, marks, products, or services of third parties or hypertext links to thirdparty sites or information are provided solely as a convenience to you and do not in any way constitute or imply ECR's endorsement, sponsorship or recommendation of the third party, information, product or service. ECR is not responsible for the content of these pages and does not make any representations regarding the content or accuracy of material in this file. As per copyright regulations, any unauthorised use of the material or parts thereof as well as commercial reproduction or multiple distribution by any traditional or electronically based reproduction/publication method ist strictly prohibited. You agree to defend, indemnify, and hold ECR harmless from and against any and all claims, damages, costs, and expenses, including attorneys' fees, arising from or related to your use of these pages. Please note: Links to movies, ppt slideshows and any other multimedia files are not available in the pdf version of presentations. Page 1 of 7

2 Purpose The examination data produced by digital modalities can contain information useful in different scenarios, for example, to monitor patient dosimetry, radiographic procedures and image quality. There are important parameters such as image processing parameters, exposure index, patient dose and geometric information that are generated by the modality and transferred to the Picture Archiving and Communication System (PACS) database as DICOM metadata. As a consequence of the recent technological developments occurring within Radiology and information technologies, it is now possible to use tools that enable the collection and analysis of information related to the whole medical imaging process. These tools have been used for different purposes, namely to support x-ray population exposure programs [1-4] or to be included in productivity and professional performance analysis software [5-7]. However, the development of strategies for information access, analysis and storage, independently of equipment manufacturers and information systems, still pose key challenges that need to be addressed. An open source alternative solution that allows the realization of flexible queries over DICOM metadata is the Dicoogle system [8, 9]. Dicoogle provides two types of data indexing: a hierarchical content indexing of the DICOM metadata (patient, study, series, image) and a text content indexing (free text query) [9]. The purpose of our study is to extract DICOM Metadata from the PACS of two different institutions using the Dicoogle system for the x-ray exposure variation analysis in Computed Radiography Mammographic studies. We have the dual objective of not only assessing the exposure variation data but also to highlight with practical workflow evidence the cost-effectiveness of the Dicoogle system for DICOM metadata mining. Methods and materials In the present work we carried out a retrospective study on DICOM Metadata stored in two PACS archives of two different health institutions (Institution A and Institution B). It was requested authorization to the Hospital Board of Directors and to the Ethics Commission, making sure that would be guaranteed the confidentiality of the data collected. DICOM metadata were indexed and extracted after Dicoogle user validation. Retrieved tag-value data elements were used to objectively assess X-ray exposure. Page 2 of 7

3 Due to its generalized availability, the Sensitivity DICOM attribute was chosen as the main exposure related parameter to instance our approach in the mammographic multisite study spanning data. Both institutions have Fuji CR systems in which a higher sensitivity value (S Value) is associated with a lower detector exposure to radiation [10-12]. Along with the Sensitivity DICOM attribute, complementary attributes such as Modality, Study Date, Study Description, Sensitivity and Acquisition Device Processing were also collected. Descriptive statistical tests were used to characterize the average, median and standard deviation of Sensitivity values during the study period. The Kruskal-Wallis statistical test was used to characterize exposure variation patterns both for intra-site and inter-site assessments. In this test the following hypotheses were considered: H0 - There are no differences between the Sensitivity median values over the study period and under the same mammographic projection performed in each health institution; H1 - There are differences between the Sensitivity median values over the study period and under the same mammographic projection performed in each health institution. Results The indexing process occurs over a 593 GBytes (Institution A) and 1362 GBytes (Institution B) information volume, and took about 93 hours at Institution A and 212 hours at Institution B. As a result of the indexing process we collected, data relating to images, from Computed Radiography studies and belonging to patients (Table 1). After indexing we performed the query and retrieval process where we assembled 8087 images, from 2047 mammographic studies belonging to 1757 patients. The statistical analysis (Table 2) highlights the following issues: High standard deviation for the Sensitivity values (much influenced by Minimum and Maximum Sensitivity values identified in the sample); Very low Sensitivity values reflecting a hyper-exposed radiation detector; Very high Sensitivity values reflecting a hypo-exposed radiation detector. Page 3 of 7

4 The results suggest a higher radiation exposure at Institution A, namely considering the 25 and 75 percentiles values analysis. From the analysis of the mammographic projections performed with Sensitivity values above and below the median exposure values (Table 3) we found that: At Institution A a sharp radiation exposure increase (decreasing Sensitivity values) was identified during 2009 and 2010 (high number of images with S value lower than the median) and a decrease during 2011; At Institution B the Sensitivity values analysis exhibited a reduction of the detector radiation exposure over time. The Kruskal-Wallis statistical test allows us to reject H0 in all the analysis and it demonstrates the Sensitivity values inhomogeneity at Institution A and Institution B. At Table 4 we can see: A discrepancy between Sensitivity median values obtained in different years in the same mammographic projection; The Sensitivity median values variation is statistically significant (p <0.001); Disparity between SD values belonging to the same type of projection performed over time. There are differences between the Sensitivity median values over the study period, under the same mammographic projection and performed in each institution. Conclusion The use of DICOM metadata mining tools such as the Dicoogle system in a hospital environment can result in gathering important data for the professional practice improvement. The developed exposure assessment methodology shows that the efficient mining of DICOM metadata, stored over disperse PACS archives of radiology departments may definitely contribute to quality control initiatives, namely initiatives related with radiation protection or protocols optimization. Personal information Milton Rodrigues dos Santos is Adjunct Professor at the School of Health Sciences, University of Aveiro. He received his X-ray Technologist Bachelor in 1992 and his Page 4 of 7

5 Graduation in 2011 from the School of Health Technology of Coimbra. He worked in several health institutions and was a radiology privet facility manager from 2003 to In 2004 conclude his postgraduate studies in Clinical Education Supervision and received his Msc in Information Management from the University of Aveiro in Presently is a PhD student at the Health Sciences Department of University of Aveiro. His current research interests include the application of information and communications technologies in DICOM data mining, radiology continuous quality improvement and the secondary use of radiology data. Augusto Silva is Assistant Professor at the Dep. of Electronics, Telecommunications and Informatics of the University of Aveiro. Lecturer at the School of Health of University of Aveiro. Current scientific interests include Medical Image Processing and Medical Imaging Informatics with focus on PACS and generic image repositories. He is the author and co-author of several papers and book chapters on these and has supervised and is supervising several PhD students involved with medical imaging informatics. Nelson Pacheco da Rocha is Full Professor of the University of Aveiro. He received his BSc degree in Electronics and Telecommunications Engineering in 1983 and his PhD in Electronics Engineering in 1992, from the University of Aveiro. He was the Head of the Computer and Telecommunications Centre ( ), the Head of the Health Sciences School ( ) and Pro-Rector of the University of Aveiro ( ). Since 2001, he is the Head of the Health Sciences Department of the University of Aveiro. His current research interests include the application of information and communications technologies to healthcare and social services, the secondary use of electronic health records, and the interconnection of human functionality and ambient assisted living services. He has been involved in various European and national funded research projects, has supervised. References Page 5 of 7

6 1. George, J., et al., Patient Dose Optimization in Plain Radiography Based on Standard Exposure Factors. British Journal of Radiology, (922): p Ng, C.K.C. and Z. Sun, Development of an Online Automatic Computed Radiography Dose Data Mining Program: A Preliminary Study. Computer Methods and Programs in Biomedicine, (1): p Tamm, E.P., et al., Quality Initiatives: CT Radiation Dose Reduction: How to Implement Change without Sacrificing Diagnostic Quality. Radiographics, (7): p Santos, M., et al., Clinical Data Mining in Small Hospital PACS: Contributions for Radiology Department Improvement, in Information Systems and Technologies for Enhancing Health and Social Care, 2013, IGI Global. p Hu, M., et al., Informatics in Radiology: Efficiency Metrics for Imaging Device Productivity. Radiographics, (2): p Ondategui-Parra, S., et al., Use of Productivity and Financial Indicators for Monitoring Performance in Academic Radiology Departments: U.S. Nationwide Survey. Radiology, (1): p Ondategui-Parra, S., S.M. Erturk, and P.R. Ros, Survey of the Use of Quality Indicators in Academic Radiology Departments. American Journal of Roentgenology, (5): p. W451-W Costa, C., et al., Dicoogle - an Open Source Peer-to-Peer PACS. Journal of Digital Imaging, 2010: p Costa, C., et al., Indexing and Retrieving DICOM Data in Disperse and Unstructured Archives. International Journal of Computer Assisted Radiology and Surgery, (1): p Fauber, T.L., Radiographic Imaging & Exposure. Fourth Edition ed2013, St. Louis: Elsevier Mosby. 11. Seibert, J.A., Computed Radiography Technology, Page 6 of 7

7 12. Christi Carter, B.V., Digital Radiography and PACS, 2010: Mosby Elsevier. Page 7 of 7