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Scalable and flexible management of medical image big data

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Abstract

Digital imaging plays a critical role for image guided diagnosis and clinical trials, and the amount of image data is fast growing. There are two major requirements for image data management: scalability for massive scales and support of comprehensive queries. Traditional Picture Archiving and Communication Systems (PACS for short) are based on relational data management systems and suffer from limited scalability and query support. Therefore, new systems that support fast, scalable and comprehensive queries on image data are highly demanded. In this paper, we introduce two alternative approaches: DCMRL/XMLStore (RL/XML for short)—a parallel, hybrid relational and XML data management approach, and DCMDocStore (DOC for short)—a NoSQL document store approach. DCMRL/XMLStore manages DICOM images as binary large objects and metadata as relational tables and XML documents based on IBM DB2, which is parallelized through data partitioning. DCMDocStore manages DICOM metadata as JSON objects, and DICOM images as encoded attachments in MongoDB running on multiple nodes. We have delivered two open source systems DCMRL/XMLStore and DCMDocStore. Both systems support scalable data management and comprehensive queries. We also evaluated them with nearly one million DICOM images from National Biomedical Imaging Archive. The results show that, DCMDocStore demonstrates high data loading speed, high scalability and fault tolerance. DCMRL/XMLStore provides efficient queries, but comes with slower data loading. Traditional PACS systems have inherent limitations on flexible queries and scalability for massive amount of images.

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References

  1. N. E. M. Association. DICOM Supplement 145: Whole Slide Microscopic Image IOD and SOP Classes (2018). ftp://medical.nema.org/medical/dicom/final/sup145_ft.pdf

  2. Kahn Jr., C.E., Langlotz, C.P., Channin, D.S., Rubin, D.L.: Informatics in radiology: an information model of the DICOM standard. Radiographics 31(1), 295–304 (2011)

    Article  Google Scholar 

  3. Kim, K.J., Kim, B., Lee, H., Choi, H., Jeon, J.-J., Ahn, J.-H., Lee, K.H.: Predicting the fidelity of JPEG2000 compressed CT images using DICOM header information. Med. Phys. 38(12), 6449–6457 (2011)

    Article  Google Scholar 

  4. Källman, H.-E., Halsius, E., Folkesson, M., Larsson, Y., Stenström, M., Båth, M.: Automated detection of changes in patient exposure in digital projection radiography using exposure index from DICOM header metadata. Acta Oncol. 50(6), 960–965 (2011)

    Article  Google Scholar 

  5. Jahnen, A., Kohler, S., Hermen, J., Tack, D., Back, C.: Automatic computed tomography patient dose calculation using DICOM header metadata. Radiat. Protect. Dosim. 147(1–2), 317–320 (2011)

    Article  Google Scholar 

  6. Dave, J.K., Gingold, E.L.: Extraction of CT dose information from DICOM metadata: automated matlab-based approach. Am. J. Roentgenol. 200(1), 142–145 (2013)

    Article  Google Scholar 

  7. Källman, H.-E., Halsius, E., Olsson, M., Stenström, M.: DICOM Metadata repository for technical information in digital medical images. Acta Oncol. 48(2), 285–288 (2009)

    Article  Google Scholar 

  8. Rampado, O., Garelli, E., Zatteri, R., Escoffier, U., De Lucchi, R., Ropolo, R.: Patient dose evaluation by means of DICOM images for a direct radiography system. Radiol. Med. 113(8), 1219–1228 (2008)

    Article  Google Scholar 

  9. N. E. M. Association: Digital Imaging and Communications in Medicine (DICOM) Part 3: Information Object Definitions (2018). ftp://medical.nema.org/medical/dicom/2013/output/pdf/part03.pdf

  10. Open Source Clinical Image and Object Management (2018). http://www.dcm4che.org/

  11. Huang, H.K.: PACS and Imaging Informatics: Basic Principles and Applications, 2nd edn. Wiley-Blackwell, Hoboken (2010)

    Google Scholar 

  12. National Biomedical Imaging Archive (2018). https://imaging.nci.nih.gov/ncia/login.jsf

  13. Aji, A., Wang, F., Vo, H., Lee, R., Liu, Q., Zhang, X., Saltz, J.: Hadoop-GIS: a high performance spatial data warehousing system over mapreduce. Proc VLDB Endow 6(11), 1009–1020 (2013)

    Article  Google Scholar 

  14. Pavlo, A., Paulson, E., Rasin, A., Abadi, D.J., DeWitt, D.J., Madden, S., Stonebraker, M.: A comparison of approaches to large-scale data analysis. In: SIGMOD, pp. 165–178 (2009)

  15. Dell, Siemens Partner On Image Sharing, Archiving (2018). http://www.informationweek.com/healthcare/clinical-information-systems/dell-siemens-partner-on-image-sharing-archiving-/d/d-id/1102877?

  16. Ambra Health (2018). https://ambrahealth.com/

  17. Rascovsky, S.J., Delgado, J.A., Sanz, A., Calvo, V.D., Castrillón, G.: Informatics in radiology: use of CouchDB for document-based storage of DICOM objects. Radiographics 32(3), 913–927 (2012)

    Article  Google Scholar 

  18. Apache CoachDB (2018). http://couchdb.apache.org/

  19. Savaris, A., Harder, T., von Wangenheim, A.: Evaluating a row-store data model for full-content DICOM management. In: 2014 IEEE 27th International Symposium on Computer-Based Medical Systems (CBMS), pp. 193–198 (2014)

  20. Apache Cassandra (2018). http://cassandra.apache.org/

  21. JavaScript Object Notation (JSON) (2018). http://json.org/

  22. Yu, C., Yao, Z.: XML-based DICOM data format. J. Digit. Imaging 23(2), 192–202 (2010)

    Article  MathSciNet  Google Scholar 

  23. W3C. XML Path Language (XPath) (2018). http://www.w3.org/TR/xpath-30/

  24. W3C. XQuery 1.0: An XML Query Language (2018). http://www.w3.org/TR/xquery/

  25. Oracle XML DB

  26. Beyer, K., Cochrane, R.J., Josifovski, V., Kleewein, J., Lapis, G., Lohman, G., Lyle, B., Özcan, F., Pirahesh, H., Seemann, N., Truong, T., Linden, B.V., Vickery, B., Zhang, C.: System RX: one part relational, one part XML. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, Baltimore, Maryland (2005)

  27. DeWitt, D.J., Gray, J.: Parallel database systems: the future of high performance database processing. Commun. ACM 25(6), 85–98 (1992)

    Article  Google Scholar 

  28. Database Partitioning, Table Partitioning, and MDC for DB2 9 (2018). http://www.redbooks.ibm.com/redbooks/pdfs/sg247467.pdf

  29. DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P., Vogels, W.: Dynamo: amazon’s highly available key-value store. In: Proceedings of Twenty-First ACM SIGOPS Symposium on Operating Systems Principles, Stevenson, Washington, USA (2007)

  30. MongoDB (2018). http://www.mongodb.com

  31. Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Chandra, T., Fikes, A., Gruber, R.E.: Bigtable: a distributed storage system for structured data. ACM Trans. Comput. Syst. 26(2), 1–26 (2008)

    Article  Google Scholar 

  32. Garcia-Molina, H., Ullman, J.D., Widom, J.: Database Systems: The Complete Book. Prentice Hall Press, Upper Saddle River (2008)

    Google Scholar 

  33. Gilbert, S., Lynch, N.: Brewer’s conjecture and the feasibility of consistent, available, partition-tolerant web services. SIGACT News 33(2), 51–59 (2002)

    Article  Google Scholar 

  34. DICOM PS3.18 2013—Web Services (2018). http://medical.nema.org/dicom/2013/output/pdf/part18.pdf

  35. DCMRL/XMLStore (2018). https://github.com/tengdj/DCMRLXMLStore

  36. DCMDOCStore (2018). https://github.com/tengdj/DCMDOCStore

  37. The Cancer Imaging Archive (TCIA) (2018). http://www.cancerimagingarchive.net/

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Acknowledgements

Funding was provided by National Science Foundation (Grant nos. ACI 1443054 and IIS 1350885) and National Institutes of Health (Grant no. K25CA181503).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA

    Dejun Teng

  2. Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USA

    Jun Kong

  3. Department of Biomedical Informatics and Department of Computer Science, Stony Brook University, Stony Brook, NY, USA

    Fusheng Wang

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  1. Dejun Teng
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Correspondence to Dejun Teng.

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Teng, D., Kong, J. & Wang, F. Scalable and flexible management of medical image big data. Distrib Parallel Databases 37, 235–250 (2019). https://doi.org/10.1007/s10619-018-7230-8

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