Yearb Med Inform 2016; 25(S 01): S23-S31
DOI: 10.15265/IYS-2016-s004
Original Article
Georg Thieme Verlag KG Stuttgart

Imaging Informatics: 25 Years of Progress

J. P. Agrawal
1   Department of Radiology, Mayo Clinic, Rochester, MN, USA
,
B. J. Erickson
1   Department of Radiology, Mayo Clinic, Rochester, MN, USA
,
C. E. Kahn Jr.
1   Department of Radiology, Mayo Clinic, Rochester, MN, USA
› Institutsangaben
Weitere Informationen

Correspondence to:

Charles E. Kahn, Jr.
Department of Radiology
3400 Spruce Street, 1 Silverstein
Philadelphia, PA 19104
USA

Publikationsverlauf

30. Juni 2016

Publikationsdatum:
06. März 2018 (online)

 

Summary

The science and applications of informatics in medical imaging have advanced dramatically in the past 25 years. This article provides a selective overview of key developments in medical imaging informatics. Advances in standards and technologies for compression and transmission of digital images have enabled Picture Archiving and Communications Systems (PACS) and teleradiology. Research in speech recognition, structured reporting, ontologies, and natural language processing has improved the ability to generate and analyze the reports of imaging procedures. Informatics has provided tools to address workflow and ergonomic issues engendered by the growing volume of medical image information. Research in computer-aided detection and diagnosis of abnormalities in medical images has opened new avenues to improve patient care. The growing number of medical-imaging examinations and their large volumes of information create a natural platform for “big data“ analytics, particularly when joined with high-dimensional genomic data. Radiogenomics investigates relationships between a disease’s genetic and gene-expression characteristics and its imaging phenotype; this emerging field promises to help us better understand disease biology, prognosis, and treatment options. The next 25 years offer remarkable opportunities for informatics and medical imaging together to lead to further advances in both disciplines and to improve health.


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  • References

  • 1 Bui AAT, Taira RK. Medical imaging informatics. New York: Springer; 2010
  • 2 Wheeler PS, Simborg DW, Gitlin JN. The Johns Hopkins radiology reporting system. Radiology 1976; 119: 315-9.
  • 3 Bell DS, Greenes RA, Doubilet P. Form-based clinical input from a structured vocabulary: Initial application in ultrasound reporting. In: Frisse ME. editor. Proceedings of the 16th Annual Symposium on Computer Applications in Medical Care. New York: McGraw-Hill; 1992. p. 789-90
  • 4 Sistrom CL. Conceptual approach for the design of radiology reporting interfaces: the talking template. J Digit Imaging 2005; 18: 176-87.
  • 5 Sistrom CL, Langlotz CP. A framework for improving radiology reporting. J Am Coll Radiol 2005; 2: 159-67.
  • 6 Dunnick NR, Langlotz CP. The radiology report of the future: a summary of the 2007 Intersociety Conference. J Am Coll Radiol 2008; 5: 626-9.
  • 7 Langlotz CP. Structured radiology reporting: are we there yet?. Radiology 2009; 253: 23-5.
  • 8 Marcovici PA, Taylor GA. Structured radiology reports are more complete and more effective than unstructured reports. AJR Am J Roentgenol 2014; 203: 1265-71.
  • 9 Channin DS, Mongkolwat P, Kleper V, Sepukar K, Rubin DL. The caBIG Annotation and Image Markup project. J Digit Imaging 2010; 23: 217-25.
  • 10 Douglas PS, Hendel RC, Cummings JE, Dent JM, Hodgson JM, Hoffmann U. et al American College of Cardiology Foundation (ACCF).. ACCF/ACR/ AHA/ASE/ASNC/HRS/NASCI/RSNA/SAIP/ SCAI/SCCT/SCMR 2008 Health Policy Statement on Structured Reporting in Cardiovascular Imaging. J Am Coll Cardiol 2009; 53: 76-90.
  • 11 de Baca ME, Madden JF, Kennedy M. Electronic pathology reporting: digitizing the College of American Pathologists cancer checklists. Arch Pathol Lab Med 2010; 134: 663-4.
  • 12 Rubin DL. Creating and curating a terminology for radiology: ontology modeling and analysis. J Digit Imaging 2008; 21: 355-62.
  • 13 Budovec JJ, Lam CA, Kahn Jr. CE. Radiology Gamuts Ontology: differential diagnosis for the Semantic Web. RadioGraphics 2014; 34: 254-64.
  • 14 Smith B, Arabandi S, Brochhausen M, Calhoun M, Ciccarese P, Doyle S. et al. Biomedical imaging ontologies: A survey and proposal for future work. J Pathol Inform 2015; 6: 37.
  • 15 Friedman C, Alderson PO, Austin JH, Cimino JJ, Johnson SB. A general natural-language text processor for clinical radiology. J Am Med Inform Assoc 1994; 1: 161-74.
  • 16 Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan BG. A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform 2001; 34: 301-10.
  • 17 Lakhani P, Kim W, Langlotz CP. Automated extraction of critical test values and communications from unstructured radiology reports: an analysis of 9.3 million reports from 1990 to 2011. Radiology 2012; 265: 809-18.
  • 18 Sistrom CL, Dreyer KJ, Dang PP, Weilburg JB, Boland GW, Rosenthal DI. et al. Recommendations for additional imaging in radiology reports: multifactorial analysis of 5.9 million examinations. Radiology 2009; 253: 453-61.
  • 19 Dang PA, Kalra MK, Blake MA, Schultz TJ, Stout M, Lemay PR. et al. Natural language processing using online analytic processing for assessing recommendations in radiology reports. J Am Coll Radiol 2008; 5: 197-204.
  • 20 Pham AD, Neveol A, Lavergne T, Yasunaga D, Clément O, Meyer G. et al. Natural language processing of radiology reports for the detection of thromboembolic diseases and clinically relevant incidental findings. BMC Bioinform 2014; 15: 266.
  • 21 Sohn S, Ye Z, Liu H, Chute CG, Kullo IJ. Identifying abdominal aortic aneurysm cases and controls using natural language processing of radiology reports. AMIA Jt Summits Transl Sci Proc 2013; 2013: 249-53.
  • 22 Baxter B, Hitchner L, Maguire Jr. G. Characteristics of a Protocol for Exchanging Digital Image Information. 1st Intl Conf and Workshop on Picture Archiving and Communication Systems: SPIE Proceedings 1982 p. 273-7
  • 23 Haney MJ, Johnston RL, O’Brien JWD. On standards for the storage of images and data. SPIE Medical Imaging 1982; p. 294-7
  • 24 Schneider RH. The role of standards in the development of systems for communicating and archiving medical images. SPIE Medical Imaging 1982; p. 270-1
  • 25 Wendler T, Meyer-Ebrecht D. Proposed standard for variable format picture processing and a codec approach to match diverse imaging devices. SPIE Medical Imaging 1982; p. 298-307
  • 26 Horii SC. Introduction to “Minutes: NEMA Ad hoc Technical Committee and American College of Radiology’s Subcommittee on Computer Standards”. J Digit Imaging 2005; 18: 5-22.
  • 27 Horii SC, Hill DG, Blume HR, Best DE, Thompson B, Fuscoe C. et al. An update on American College of Radiology-National Electrical Manufacturers Association standards activity. J Digit Imaging 1990; 3: 146-51.
  • 28 Bidgood Jr. WD, Horii SC. Introduction to the ACR-NEMA DICOM standard. RadioGraphics 1992; 12: 345-55.
  • 29 Kahn Jr CE, Carrino JA, Flynn MJ, Peck DJ, Horii SC. DICOM and radiology: past, present, and future. J Am Coll Radiol 2007; 4: 652-7.
  • 30 Henderson M, Behlen FM, Parisot C, Siegel EL, Channin DS. Integrating the Healthcare Enterprise: a primer - part 4. The role of existing standards in IHE. RadioGraphics 2001; 21: 1597-603.
  • 31 Channin DS. M:I-2 and IHE: Integrating the Healthcare Enterprise, year 2. RadioGraphics 2000; 20: 1261-2.
  • 32 Channin DS, Parisot C, Wanchoo V, Leontiev A, Siegel EL. Integrating the Healthcare Enterprise: a primer - part 3. What does IHE do for ME? RadioGraphics 2001; 21: 1351-8.
  • 33 IHE.. Engaging HIT Stakeholders in a Proven Process. http://ihe.net/IHE_Process. Accessed October 26, 2015
  • 34 Bittner K, Spence I. Use Case Modeling. Boston, MA: Addison-Wesley; 2002
  • 35 Huffman DA. A Method for the Construction of Minimum-Redundancy Codes. Proceedings of the IRE 1952; 40: 1098-101.
  • 36 Abramson N. Information Theory and Coding. New York, NY: McGraw-Hill; 1963
  • 37 Kagadis GC, Langer SG. Informatics in Medical Imaging. Boca Raton, FL: Taylor & Francis Group, LLC; 2012
  • 38 Ahmed N, Natarajan T, Rao KR. Discrete cosine transform. IEEE Transactions on Computers 1974; C-23: 90-3.
  • 39 Wallace GK. The JPEG still picture compression standard. IEEE Transactions on Consumer Electronics 1992; 38: xviii-xxxiv.
  • 40 Gillespy 3rd T, Rowberg AH. Displaying radiologic images on personal computers: image storage and compression - Part 2. J Digit Imaging 1994; 7: 1-12.
  • 41 Hearaly BC, Viprakasit D, Johnston WK. The Future of Teleradiology in Medicine Is Here Today. In: Kumar S, Krupinski EA. editors. Teleradiology. Berlin, Heidelberg: Springer; 2008. p. 11-20
  • 42 Fincke EM, Padalka G, Lee D, van Holsbeeck M, Sargsyan AE, Hamilton DR. et al. Evaluation of shoulder integrity in space: first report of musculoskeletal US on the International Space Station. Radiology 2005; 234: 319-22.
  • 43 Sargsyan AE, Hamilton DR, Jones JA, Melton S, Whitson PA, Kirkpatrick AW. et al. FAST at MACH 20: clinical ultrasound aboard the International Space Station. J Trauma 2005; 58: 35-9.
  • 44 Jones JA, Sargsyan AE, Barr YR, Melton S, Hamilton DR, Dulchavsky SA. et al. Diagnostic ultrasound at MACH 20: retroperitoneal and pelvic imaging in space. Ultrasound Med Biol 2009; 35: 1059-67.
  • 45 ESR white paper on teleradiology: an update from the teleradiology subgroup. Insights into imaging 2014; 5: 1-8.
  • 46 Ranschaert ER, Boland GW, Duerinckx AJ, Barneveld Binkhuysen FH. Comparison of European (ESR) and American (ACR) white papers on teleradiology: patient primacy is paramount. J Am Coll Radiol 2015; 12: 174-82.
  • 47 Silva 3rd E, Breslau J, Barr RM, Liebscher LA, Bohl M, Hoffman T. et al. ACR white paper on teleradiology practice: a report from the Task Force on Teleradiology Practice. J Am Coll Radiol 2013; 10: 575-85.
  • 48 Siegel EL, Kolodner RM. Filmless Radiology. New York: Springer; 2001
  • 49 Dwyer SJ, Stewart BK. Clinical uses of grayscale workstations. In: Hendee WR, Trueblood JH. editors. 1993 AAPM Summer School on Digital Radiology. Madison, WI: Medical Physics Publishing; 1993. p. 241-64
  • 50 Stewart BK, Aberle DR, Boechat MI. et al. Clinical utilization of grayscale workstations. IEEE Eng Med Biol 1993; 12: 86-100.
  • 51 Krupinski EA, Flynn MJ, Hirschorn DS. Displays. IT Reference Guide for the Practicing Radiologist. [serial online]. 2013 Available at: www.acr.org/~/media/ACR/Documents/PDF/Advocacy/IT%20Reference%20Guide/IT%20 Ref%20Guide%20Displays.pdf. Accessed October 29, 2015
  • 52 Agarwal TK. Sanjeev Vendor neutral archive in PACS. Indian J Radiol Imaging 2012; 22: 242-5.
  • 53 Kagadis GC, Nagy P, Langer S, Flynn M, Stark-schall G. Anniversary paper: roles of medical physicists and health care applications of informatics. Med Phys 2008; 35: 119-27.
  • 54 Erickson BJ, Persons KR, Hangiandreou NJ, James EM, Hanna CJ, Gehring DG. Requirements for an enterprise digital image archive. J Digit Imaging 2001; 14: 72-82.
  • 55 Andriole KP, Morin RL, Arenson RL, Carrino JA, Erickson BJ, Horii SC. et al. Addressing the coming radiology crisis--the Society for Computer Applications in Radiology transforming the radiological interpretation process (TRIP) initiative. J Digit Imaging 2004; 17: 235-43.
  • 56 Andriole KP, Morin RL. Transforming medical imaging: the first SCAR TRIP conference. A position paper from the SCAR TRIP subcommittee of the SCAR research and development committee. J Digit Imaging 2006; 19: 6-16.
  • 57 Harisinghani MG, Blake MA, Saksena M, Hahn PF, Gervais D, Zalis M. et al. Importance and effects of altered workplace ergonomics in modern radiology suites. RadioGraphics 2004; 24: 615-27.
  • 58 Lodwick GS, Turner Jr. AH, Lusted LB, Templeton AW. Computer-aided analysis of radiographic images. J Chronic Dis 1966; 19: 485-96.
  • 59 Nishikawa RM, Haldemann RC, Papaioannou J. et al. Initial experience with a prototype clinical intelligent mammography workstation for computer-aided diagnosis. Medical Imaging 1995: Image Processing. San Diego, CA: SPIE; 1995. p. 65-71
  • 60 Wang S, Summers RM. Machine learning and radiology. Med Image Anal 2012; 16: 933-51.
  • 61 Chan HP, Doi K, Vyborny CJ, Schmidt RA, Metz CE, Lam KL. et al. Improvement in radiologists’ detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis. Invest Radiol 1990; 25: 1102-10.
  • 62 Ashizawa K, MacMahon H, Ishida T, Nakamura K, Vyborny CJ, Katsuragawa S. et al. Effect of an artificial neural network on radiologists’ performance in the differential diagnosis of interstitial lung disease using chest radiographs. Am J Roentgenol 1999; 172: 1311-5.
  • 63 Udupa JK. Three-dimensional visualization and analysis methodologies: A current perspective. RadioGraphics 1999; 19: 783-806.
  • 64 Calhoun PS, Kuszyk BS, Heath DG, Carley JC, Fishman EK. Three-dimensional volume rendering of spiral CT data: theory and method. RadioGraphics 1999; 19: 745-64.
  • 65 Heath DG, Soyer PA, Kuszyk BS. et al. Three-dimensional spiral CT during arterial portography: comparison of three rendering techniques. Radio-Graphics 1995; 15: 1001-11.
  • 66 Gouraud H. Continuous shading of curved surfaces. IEEE Transactions on Computers 1971; C-20: 623-9.
  • 67 Phong BT. Illumination for computer generated pictures. Commun ACM 1975; 18: 311-7.
  • 68 Robb RA, Greenleaf JF, Ritman EL, Johnson SA, Sjostrand JD, Herman GT. al. Three-dimensional visualization of the intact thorax and contents: a technique for cross-sectional reconstruction from multiplanar x-ray views. Comput Biomed Res 1974; 7: 395-419.
  • 69 Herman GT, Liu HK. Display of three-dimensional information in computed tomography. J Comput Assist Tomogr 1977; 1: 155-60.
  • 70 Herman GT, Liu HK. Three-dimensional display of human organs from computed tomograms. Comput Graph Image Proc 1979; 9: 1-21.
  • 71 Fuchs H, Kedem ZM, Uselton SP. Optimal surface reconstruction from planar contours. Commun ACM 1977; 20: 693-702.
  • 72 Rubin GD, Dake MD, Napel SA, McDonnell CH, Jeffrey Jr. RB. Three-dimensional spiral CT angiography of the abdomen: initial clinical experience. Radiology 1993; 186: 147-52.
  • 73 Castillo M. Diagnosis of disease of the common carotid artery bifurcation: CT angiography vs catheter angiography. AJR Am J Roentgenol 1993; 161: 395-8.
  • 74 Marks MP, Napel S, Jordan JE, Enzmann DR. Diagnosis of carotid artery disease: preliminary experience with maximum-intensity-projection spiral CT angiography. Am J Roentgenol 1993; 160: 1267-71.
  • 75 Napel S, Marks MP, Rubin GD, Dake MD, McDonnell CH, Song SM. et al. CT angiography with spiral CT and maximum intensity projection. Radiology 1992; 185: 607-10.
  • 76 Brink JA, Lim JT, Wang G, Heiken JP, Deyoe LA, Vannier MW. Technical optimization of spiral CT for depiction of renal artery stenosis: in vitro analysis. Radiology 1995; 194: 157-63.
  • 77 Drebin RA, Carpenter L, Hanrahan P. Volume rendering. SIGGRAPH Comput Graph 1988; 22: 65-74.
  • 78 Ney DR, Fishman EK, Magid D, Drebin RA. Volumetric rendering of computed tomography data: principles and techniques. IEEE Comput Graph Appl 1990; 10: 24-32.
  • 79 Fishman EK, Magid D, Ney DR, Chaney EL, Pizer SM, Rosenman JG. et al. Three-dimensional imaging. Radiology 1991; 181: 321-37.
  • 80 Rubin GD, Beaulieu CF, Argiro V, Ringl H, Norbash AM, Feller JF. et al. Perspective volume rendering of CT and MR images: applications for endoscopic imaging. Radiology 1996; 199: 321-30.
  • 81 Erickson BJ, Meenan C, Langer S. Standards for business analytics and departmental workflow. J Digit Imaging 2013; 26: 53-7.
  • 82 Nagy PG, Warnock MJ, Daly M, Toland C, Meenan CD, Mezrich RS. Informatics in radiology: automated Web-based graphical dashboard for radiology operational business intelligence. RadioGraphics 2009; 29: 1897-906.
  • 83 Huser V, Rasmussen LV, Oberg R, Starren JB. Implementation of workflow engine technology to deliver basic clinical decision support functionality. BMC Med Res Methodol 2011; 11: 43.
  • 84 Mans R, van der Aalst W, Russell N. Implementation of a healthcare process in four different work-flow systems. Eindhoven, Netherlands: Technische Universiteit Eindhoven; 2009
  • 85 Erickson BJ, Langer SG, Blezek DJ, Ryan WJ, French TL. DEWEY: the DICOM-enabled workflow engine system. J Digit Imaging 2014; 27: 309-13.
  • 86 Kansagra AP, Yu JP, Chatterjee AR, Lenchik L, Chow DS, Prater AB. et al. Big Data and the future of radiology informatics. Acad Radiol 2016; 23: 30-42.
  • 87 Pentecost MJ. Big data. J Am Coll Radiol 2015; 12: 129.
  • 88 Boubela RN, Kalcher K, Huf W, Nasel C, Moser E. Big Data approaches for the analysis of large-scale fMRI data using Apache Spark and GPU processing: a demonstration on resting-state fMRI data from the Human Connectome Project. 2015 9. 492.
  • 89 Liebeskind DS, Albers GW, Crawford K, Derdeyn CP, George MS, Palesch YY. et al. Imaging in StrokeNet: realizing the potential of Big Data. Stroke 2015; 46: 2000-6.
  • 90 Margolies LR, Pandey G, Horowitz ER, Mendel-son DS. BBreast imaging in the era of Big Data: structured reporting and data mining. AJR Am J Roentgenol 2016; 206: 259-64.
  • 91 Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. Washington (DC): National Academies Press; 2011
  • 92 Hsu W, Markey MK, Wang MD. Biomedical imaging informatics in the era of precision medicine: progress, challenges, and opportunities. J Am Med Inform Assoc 2013; 20: 1010-3.
  • 93 Mazurowski MA. Radiogenomics: what it is and why it is important. J Am Coll Radiol 2015; 12: 862-6.
  • 94 Rosenstein BS, West CM, Bentzen SM. et al. Radiogenomics: radiobiology enters the era of big data and team science. Int J Radiat Oncol Biol Phys 2014; 89: 709-13.
  • 95 Gevaert O, Mitchell LA, Achrol AS, Xu J, Echegaray S, Steinberg GK. et al. Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology 2014; 273: 168-74.
  • 96 Karlo CA, Di Paolo PL, Chaim J, Hakimi AA, Ostrovnaya I, Russo P. et al. Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. Radiology 2014; 270: 464-71.
  • 97 Grimm LJ, Zhang J, Mazurowski MA. Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms. J Magn Reson Imaging 2015; 42: 902-7.
  • 98 Kerns SL, West CM, Andreassen CN, Barnett GC, Bentzen SM, Burnet NG. et al. Radiogenomics: the search for genetic predictors of radiotherapy response. Future Oncol 2014; 10: 2391-406.

Correspondence to:

Charles E. Kahn, Jr.
Department of Radiology
3400 Spruce Street, 1 Silverstein
Philadelphia, PA 19104
USA

  • References

  • 1 Bui AAT, Taira RK. Medical imaging informatics. New York: Springer; 2010
  • 2 Wheeler PS, Simborg DW, Gitlin JN. The Johns Hopkins radiology reporting system. Radiology 1976; 119: 315-9.
  • 3 Bell DS, Greenes RA, Doubilet P. Form-based clinical input from a structured vocabulary: Initial application in ultrasound reporting. In: Frisse ME. editor. Proceedings of the 16th Annual Symposium on Computer Applications in Medical Care. New York: McGraw-Hill; 1992. p. 789-90
  • 4 Sistrom CL. Conceptual approach for the design of radiology reporting interfaces: the talking template. J Digit Imaging 2005; 18: 176-87.
  • 5 Sistrom CL, Langlotz CP. A framework for improving radiology reporting. J Am Coll Radiol 2005; 2: 159-67.
  • 6 Dunnick NR, Langlotz CP. The radiology report of the future: a summary of the 2007 Intersociety Conference. J Am Coll Radiol 2008; 5: 626-9.
  • 7 Langlotz CP. Structured radiology reporting: are we there yet?. Radiology 2009; 253: 23-5.
  • 8 Marcovici PA, Taylor GA. Structured radiology reports are more complete and more effective than unstructured reports. AJR Am J Roentgenol 2014; 203: 1265-71.
  • 9 Channin DS, Mongkolwat P, Kleper V, Sepukar K, Rubin DL. The caBIG Annotation and Image Markup project. J Digit Imaging 2010; 23: 217-25.
  • 10 Douglas PS, Hendel RC, Cummings JE, Dent JM, Hodgson JM, Hoffmann U. et al American College of Cardiology Foundation (ACCF).. ACCF/ACR/ AHA/ASE/ASNC/HRS/NASCI/RSNA/SAIP/ SCAI/SCCT/SCMR 2008 Health Policy Statement on Structured Reporting in Cardiovascular Imaging. J Am Coll Cardiol 2009; 53: 76-90.
  • 11 de Baca ME, Madden JF, Kennedy M. Electronic pathology reporting: digitizing the College of American Pathologists cancer checklists. Arch Pathol Lab Med 2010; 134: 663-4.
  • 12 Rubin DL. Creating and curating a terminology for radiology: ontology modeling and analysis. J Digit Imaging 2008; 21: 355-62.
  • 13 Budovec JJ, Lam CA, Kahn Jr. CE. Radiology Gamuts Ontology: differential diagnosis for the Semantic Web. RadioGraphics 2014; 34: 254-64.
  • 14 Smith B, Arabandi S, Brochhausen M, Calhoun M, Ciccarese P, Doyle S. et al. Biomedical imaging ontologies: A survey and proposal for future work. J Pathol Inform 2015; 6: 37.
  • 15 Friedman C, Alderson PO, Austin JH, Cimino JJ, Johnson SB. A general natural-language text processor for clinical radiology. J Am Med Inform Assoc 1994; 1: 161-74.
  • 16 Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan BG. A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform 2001; 34: 301-10.
  • 17 Lakhani P, Kim W, Langlotz CP. Automated extraction of critical test values and communications from unstructured radiology reports: an analysis of 9.3 million reports from 1990 to 2011. Radiology 2012; 265: 809-18.
  • 18 Sistrom CL, Dreyer KJ, Dang PP, Weilburg JB, Boland GW, Rosenthal DI. et al. Recommendations for additional imaging in radiology reports: multifactorial analysis of 5.9 million examinations. Radiology 2009; 253: 453-61.
  • 19 Dang PA, Kalra MK, Blake MA, Schultz TJ, Stout M, Lemay PR. et al. Natural language processing using online analytic processing for assessing recommendations in radiology reports. J Am Coll Radiol 2008; 5: 197-204.
  • 20 Pham AD, Neveol A, Lavergne T, Yasunaga D, Clément O, Meyer G. et al. Natural language processing of radiology reports for the detection of thromboembolic diseases and clinically relevant incidental findings. BMC Bioinform 2014; 15: 266.
  • 21 Sohn S, Ye Z, Liu H, Chute CG, Kullo IJ. Identifying abdominal aortic aneurysm cases and controls using natural language processing of radiology reports. AMIA Jt Summits Transl Sci Proc 2013; 2013: 249-53.
  • 22 Baxter B, Hitchner L, Maguire Jr. G. Characteristics of a Protocol for Exchanging Digital Image Information. 1st Intl Conf and Workshop on Picture Archiving and Communication Systems: SPIE Proceedings 1982 p. 273-7
  • 23 Haney MJ, Johnston RL, O’Brien JWD. On standards for the storage of images and data. SPIE Medical Imaging 1982; p. 294-7
  • 24 Schneider RH. The role of standards in the development of systems for communicating and archiving medical images. SPIE Medical Imaging 1982; p. 270-1
  • 25 Wendler T, Meyer-Ebrecht D. Proposed standard for variable format picture processing and a codec approach to match diverse imaging devices. SPIE Medical Imaging 1982; p. 298-307
  • 26 Horii SC. Introduction to “Minutes: NEMA Ad hoc Technical Committee and American College of Radiology’s Subcommittee on Computer Standards”. J Digit Imaging 2005; 18: 5-22.
  • 27 Horii SC, Hill DG, Blume HR, Best DE, Thompson B, Fuscoe C. et al. An update on American College of Radiology-National Electrical Manufacturers Association standards activity. J Digit Imaging 1990; 3: 146-51.
  • 28 Bidgood Jr. WD, Horii SC. Introduction to the ACR-NEMA DICOM standard. RadioGraphics 1992; 12: 345-55.
  • 29 Kahn Jr CE, Carrino JA, Flynn MJ, Peck DJ, Horii SC. DICOM and radiology: past, present, and future. J Am Coll Radiol 2007; 4: 652-7.
  • 30 Henderson M, Behlen FM, Parisot C, Siegel EL, Channin DS. Integrating the Healthcare Enterprise: a primer - part 4. The role of existing standards in IHE. RadioGraphics 2001; 21: 1597-603.
  • 31 Channin DS. M:I-2 and IHE: Integrating the Healthcare Enterprise, year 2. RadioGraphics 2000; 20: 1261-2.
  • 32 Channin DS, Parisot C, Wanchoo V, Leontiev A, Siegel EL. Integrating the Healthcare Enterprise: a primer - part 3. What does IHE do for ME? RadioGraphics 2001; 21: 1351-8.
  • 33 IHE.. Engaging HIT Stakeholders in a Proven Process. http://ihe.net/IHE_Process. Accessed October 26, 2015
  • 34 Bittner K, Spence I. Use Case Modeling. Boston, MA: Addison-Wesley; 2002
  • 35 Huffman DA. A Method for the Construction of Minimum-Redundancy Codes. Proceedings of the IRE 1952; 40: 1098-101.
  • 36 Abramson N. Information Theory and Coding. New York, NY: McGraw-Hill; 1963
  • 37 Kagadis GC, Langer SG. Informatics in Medical Imaging. Boca Raton, FL: Taylor & Francis Group, LLC; 2012
  • 38 Ahmed N, Natarajan T, Rao KR. Discrete cosine transform. IEEE Transactions on Computers 1974; C-23: 90-3.
  • 39 Wallace GK. The JPEG still picture compression standard. IEEE Transactions on Consumer Electronics 1992; 38: xviii-xxxiv.
  • 40 Gillespy 3rd T, Rowberg AH. Displaying radiologic images on personal computers: image storage and compression - Part 2. J Digit Imaging 1994; 7: 1-12.
  • 41 Hearaly BC, Viprakasit D, Johnston WK. The Future of Teleradiology in Medicine Is Here Today. In: Kumar S, Krupinski EA. editors. Teleradiology. Berlin, Heidelberg: Springer; 2008. p. 11-20
  • 42 Fincke EM, Padalka G, Lee D, van Holsbeeck M, Sargsyan AE, Hamilton DR. et al. Evaluation of shoulder integrity in space: first report of musculoskeletal US on the International Space Station. Radiology 2005; 234: 319-22.
  • 43 Sargsyan AE, Hamilton DR, Jones JA, Melton S, Whitson PA, Kirkpatrick AW. et al. FAST at MACH 20: clinical ultrasound aboard the International Space Station. J Trauma 2005; 58: 35-9.
  • 44 Jones JA, Sargsyan AE, Barr YR, Melton S, Hamilton DR, Dulchavsky SA. et al. Diagnostic ultrasound at MACH 20: retroperitoneal and pelvic imaging in space. Ultrasound Med Biol 2009; 35: 1059-67.
  • 45 ESR white paper on teleradiology: an update from the teleradiology subgroup. Insights into imaging 2014; 5: 1-8.
  • 46 Ranschaert ER, Boland GW, Duerinckx AJ, Barneveld Binkhuysen FH. Comparison of European (ESR) and American (ACR) white papers on teleradiology: patient primacy is paramount. J Am Coll Radiol 2015; 12: 174-82.
  • 47 Silva 3rd E, Breslau J, Barr RM, Liebscher LA, Bohl M, Hoffman T. et al. ACR white paper on teleradiology practice: a report from the Task Force on Teleradiology Practice. J Am Coll Radiol 2013; 10: 575-85.
  • 48 Siegel EL, Kolodner RM. Filmless Radiology. New York: Springer; 2001
  • 49 Dwyer SJ, Stewart BK. Clinical uses of grayscale workstations. In: Hendee WR, Trueblood JH. editors. 1993 AAPM Summer School on Digital Radiology. Madison, WI: Medical Physics Publishing; 1993. p. 241-64
  • 50 Stewart BK, Aberle DR, Boechat MI. et al. Clinical utilization of grayscale workstations. IEEE Eng Med Biol 1993; 12: 86-100.
  • 51 Krupinski EA, Flynn MJ, Hirschorn DS. Displays. IT Reference Guide for the Practicing Radiologist. [serial online]. 2013 Available at: www.acr.org/~/media/ACR/Documents/PDF/Advocacy/IT%20Reference%20Guide/IT%20 Ref%20Guide%20Displays.pdf. Accessed October 29, 2015
  • 52 Agarwal TK. Sanjeev Vendor neutral archive in PACS. Indian J Radiol Imaging 2012; 22: 242-5.
  • 53 Kagadis GC, Nagy P, Langer S, Flynn M, Stark-schall G. Anniversary paper: roles of medical physicists and health care applications of informatics. Med Phys 2008; 35: 119-27.
  • 54 Erickson BJ, Persons KR, Hangiandreou NJ, James EM, Hanna CJ, Gehring DG. Requirements for an enterprise digital image archive. J Digit Imaging 2001; 14: 72-82.
  • 55 Andriole KP, Morin RL, Arenson RL, Carrino JA, Erickson BJ, Horii SC. et al. Addressing the coming radiology crisis--the Society for Computer Applications in Radiology transforming the radiological interpretation process (TRIP) initiative. J Digit Imaging 2004; 17: 235-43.
  • 56 Andriole KP, Morin RL. Transforming medical imaging: the first SCAR TRIP conference. A position paper from the SCAR TRIP subcommittee of the SCAR research and development committee. J Digit Imaging 2006; 19: 6-16.
  • 57 Harisinghani MG, Blake MA, Saksena M, Hahn PF, Gervais D, Zalis M. et al. Importance and effects of altered workplace ergonomics in modern radiology suites. RadioGraphics 2004; 24: 615-27.
  • 58 Lodwick GS, Turner Jr. AH, Lusted LB, Templeton AW. Computer-aided analysis of radiographic images. J Chronic Dis 1966; 19: 485-96.
  • 59 Nishikawa RM, Haldemann RC, Papaioannou J. et al. Initial experience with a prototype clinical intelligent mammography workstation for computer-aided diagnosis. Medical Imaging 1995: Image Processing. San Diego, CA: SPIE; 1995. p. 65-71
  • 60 Wang S, Summers RM. Machine learning and radiology. Med Image Anal 2012; 16: 933-51.
  • 61 Chan HP, Doi K, Vyborny CJ, Schmidt RA, Metz CE, Lam KL. et al. Improvement in radiologists’ detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis. Invest Radiol 1990; 25: 1102-10.
  • 62 Ashizawa K, MacMahon H, Ishida T, Nakamura K, Vyborny CJ, Katsuragawa S. et al. Effect of an artificial neural network on radiologists’ performance in the differential diagnosis of interstitial lung disease using chest radiographs. Am J Roentgenol 1999; 172: 1311-5.
  • 63 Udupa JK. Three-dimensional visualization and analysis methodologies: A current perspective. RadioGraphics 1999; 19: 783-806.
  • 64 Calhoun PS, Kuszyk BS, Heath DG, Carley JC, Fishman EK. Three-dimensional volume rendering of spiral CT data: theory and method. RadioGraphics 1999; 19: 745-64.
  • 65 Heath DG, Soyer PA, Kuszyk BS. et al. Three-dimensional spiral CT during arterial portography: comparison of three rendering techniques. Radio-Graphics 1995; 15: 1001-11.
  • 66 Gouraud H. Continuous shading of curved surfaces. IEEE Transactions on Computers 1971; C-20: 623-9.
  • 67 Phong BT. Illumination for computer generated pictures. Commun ACM 1975; 18: 311-7.
  • 68 Robb RA, Greenleaf JF, Ritman EL, Johnson SA, Sjostrand JD, Herman GT. al. Three-dimensional visualization of the intact thorax and contents: a technique for cross-sectional reconstruction from multiplanar x-ray views. Comput Biomed Res 1974; 7: 395-419.
  • 69 Herman GT, Liu HK. Display of three-dimensional information in computed tomography. J Comput Assist Tomogr 1977; 1: 155-60.
  • 70 Herman GT, Liu HK. Three-dimensional display of human organs from computed tomograms. Comput Graph Image Proc 1979; 9: 1-21.
  • 71 Fuchs H, Kedem ZM, Uselton SP. Optimal surface reconstruction from planar contours. Commun ACM 1977; 20: 693-702.
  • 72 Rubin GD, Dake MD, Napel SA, McDonnell CH, Jeffrey Jr. RB. Three-dimensional spiral CT angiography of the abdomen: initial clinical experience. Radiology 1993; 186: 147-52.
  • 73 Castillo M. Diagnosis of disease of the common carotid artery bifurcation: CT angiography vs catheter angiography. AJR Am J Roentgenol 1993; 161: 395-8.
  • 74 Marks MP, Napel S, Jordan JE, Enzmann DR. Diagnosis of carotid artery disease: preliminary experience with maximum-intensity-projection spiral CT angiography. Am J Roentgenol 1993; 160: 1267-71.
  • 75 Napel S, Marks MP, Rubin GD, Dake MD, McDonnell CH, Song SM. et al. CT angiography with spiral CT and maximum intensity projection. Radiology 1992; 185: 607-10.
  • 76 Brink JA, Lim JT, Wang G, Heiken JP, Deyoe LA, Vannier MW. Technical optimization of spiral CT for depiction of renal artery stenosis: in vitro analysis. Radiology 1995; 194: 157-63.
  • 77 Drebin RA, Carpenter L, Hanrahan P. Volume rendering. SIGGRAPH Comput Graph 1988; 22: 65-74.
  • 78 Ney DR, Fishman EK, Magid D, Drebin RA. Volumetric rendering of computed tomography data: principles and techniques. IEEE Comput Graph Appl 1990; 10: 24-32.
  • 79 Fishman EK, Magid D, Ney DR, Chaney EL, Pizer SM, Rosenman JG. et al. Three-dimensional imaging. Radiology 1991; 181: 321-37.
  • 80 Rubin GD, Beaulieu CF, Argiro V, Ringl H, Norbash AM, Feller JF. et al. Perspective volume rendering of CT and MR images: applications for endoscopic imaging. Radiology 1996; 199: 321-30.
  • 81 Erickson BJ, Meenan C, Langer S. Standards for business analytics and departmental workflow. J Digit Imaging 2013; 26: 53-7.
  • 82 Nagy PG, Warnock MJ, Daly M, Toland C, Meenan CD, Mezrich RS. Informatics in radiology: automated Web-based graphical dashboard for radiology operational business intelligence. RadioGraphics 2009; 29: 1897-906.
  • 83 Huser V, Rasmussen LV, Oberg R, Starren JB. Implementation of workflow engine technology to deliver basic clinical decision support functionality. BMC Med Res Methodol 2011; 11: 43.
  • 84 Mans R, van der Aalst W, Russell N. Implementation of a healthcare process in four different work-flow systems. Eindhoven, Netherlands: Technische Universiteit Eindhoven; 2009
  • 85 Erickson BJ, Langer SG, Blezek DJ, Ryan WJ, French TL. DEWEY: the DICOM-enabled workflow engine system. J Digit Imaging 2014; 27: 309-13.
  • 86 Kansagra AP, Yu JP, Chatterjee AR, Lenchik L, Chow DS, Prater AB. et al. Big Data and the future of radiology informatics. Acad Radiol 2016; 23: 30-42.
  • 87 Pentecost MJ. Big data. J Am Coll Radiol 2015; 12: 129.
  • 88 Boubela RN, Kalcher K, Huf W, Nasel C, Moser E. Big Data approaches for the analysis of large-scale fMRI data using Apache Spark and GPU processing: a demonstration on resting-state fMRI data from the Human Connectome Project. 2015 9. 492.
  • 89 Liebeskind DS, Albers GW, Crawford K, Derdeyn CP, George MS, Palesch YY. et al. Imaging in StrokeNet: realizing the potential of Big Data. Stroke 2015; 46: 2000-6.
  • 90 Margolies LR, Pandey G, Horowitz ER, Mendel-son DS. BBreast imaging in the era of Big Data: structured reporting and data mining. AJR Am J Roentgenol 2016; 206: 259-64.
  • 91 Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. Washington (DC): National Academies Press; 2011
  • 92 Hsu W, Markey MK, Wang MD. Biomedical imaging informatics in the era of precision medicine: progress, challenges, and opportunities. J Am Med Inform Assoc 2013; 20: 1010-3.
  • 93 Mazurowski MA. Radiogenomics: what it is and why it is important. J Am Coll Radiol 2015; 12: 862-6.
  • 94 Rosenstein BS, West CM, Bentzen SM. et al. Radiogenomics: radiobiology enters the era of big data and team science. Int J Radiat Oncol Biol Phys 2014; 89: 709-13.
  • 95 Gevaert O, Mitchell LA, Achrol AS, Xu J, Echegaray S, Steinberg GK. et al. Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology 2014; 273: 168-74.
  • 96 Karlo CA, Di Paolo PL, Chaim J, Hakimi AA, Ostrovnaya I, Russo P. et al. Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. Radiology 2014; 270: 464-71.
  • 97 Grimm LJ, Zhang J, Mazurowski MA. Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms. J Magn Reson Imaging 2015; 42: 902-7.
  • 98 Kerns SL, West CM, Andreassen CN, Barnett GC, Bentzen SM, Burnet NG. et al. Radiogenomics: the search for genetic predictors of radiotherapy response. Future Oncol 2014; 10: 2391-406.