Optimizing Analysis, Visualization, and Navigation of Large Image Data Sets: One 5000-Section CT Scan Can Ruin Your Whole Day

Published Online:https://doi.org/10.1148/radiol.11091276

An understanding of radiologists’ visual and interpretive behaviors, explored in conjunction with the capabilities of innovative advanced technologies, may provide useful new paradigms for interpreting results from medical imaging examinations.

The technology revolution in image acquisition, instrumentation, and methods has resulted in vast data sets that far outstrip the human observers’ ability to view, digest, and interpret modern medical images by using traditional methods. This may require a paradigm shift in the radiologic interpretation process. As human observers, radiologists must search for, detect, and interpret targets. Potential interventions should be based on an understanding of human perceptual and attentional abilities and limitations. New technologies and tools already in use in other fields can be adapted to the health care environment to improve medical image analysis, visualization, and navigation through large data sets. This historical psychophysical and technical review touches on a broad range of disciplines but focuses mainly on the analysis, visualization, and navigation of image data performed during the interpretive process. Advanced postprocessing, including three-dimensional image display, multimodality image fusion, quantitative measures, and incorporation of innovative human-machine interfaces, will likely be the future. Successful new paradigms will integrate image and nonimage data, incorporate workflow considerations, and be informed by evidence-based practices. This overview is meant to heighten the awareness of the complexities and limitations of how radiologists interact with images, particularly the large image sets generated today. Also addressed is how human-machine interface and informatics technologies could combine to transform the interpretation process in the future to achieve safer and better quality care for patients and a more efficient and effective work environment for radiologists.

© RSNA, 2011

Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11091276/-/DC1

References

  • 1 Kundel HL, Nodine CF, Conant EF, Weinstein SP. Holistic component of image perception in mammogram interpretation: gaze-tracking study. Radiology 2007;242(2):396–402. LinkGoogle Scholar
  • 2 Carmody DP, Nodine CF, Kundel HL. Finding lung nodules with and without comparative visual scanning. Percept Psychophys 1981;29(6):594–598. Crossref, MedlineGoogle Scholar
  • 3 Kundel HL, Nodine CF, Krupinski EA, Mello-Thoms C. Using gaze-tracking data and mixture distribution analysis to support a holistic model for the detection of cancers on mammograms. Acad Radiol 2008;15(7):881–886. Crossref, MedlineGoogle Scholar
  • 4 Kundel HL, La Follette PS. Visual search patterns and experience with radiological images. Radiology 1972;103(3):523–528. LinkGoogle Scholar
  • 5 Morin RL. Cross-sectional imaging: a technology crisis upon us. J Am Coll Radiol 2006;3(3):218–219. Crossref, MedlineGoogle Scholar
  • 6 Andriole KP, Morin RL, Arenson RL, 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(4):235–243. Crossref, MedlineGoogle Scholar
  • 7 Jacobson FL, Berlanstein BP, Andriole KP. Paradigms of perception in clinical practice. J Am Coll Radiol 2006;3(6):441–445. Crossref, MedlineGoogle Scholar
  • 8 Andriole KP, Barish MA, Khorasani R. Advanced image processing in the clinical arena: issues to consider. J Am Coll Radiol 2006;3(4):296–298. Crossref, MedlineGoogle Scholar
  • 9 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(1):6–16. Crossref, MedlineGoogle Scholar
  • 10 American College of Radiology. Practice guideline for digital radiography. Reston, Va: American College of Radiology, 2009. Google Scholar
  • 11 American College of Radiology. Practice guideline for determinants of image quality in digital mammography. Reston, Va: American College of Radiology, 2007. Google Scholar
  • 12 Williams MB, Yaffe MJ, Maidment AD, Martin MC, Seibert JA, Pisano ED. Image quality in digital mammography: image acquisition. J Am Coll Radiol 2006;3(8):589–608. Crossref, MedlineGoogle Scholar
  • 13 Siegel E, Krupinski EA, Samei E, et al.. Digital mammography image quality: image display. J Am Coll Radiol 2006;3(8):615–627. Crossref, MedlineGoogle Scholar
  • 14 Krupinski EA, Williams MB, Andriole KP, et al.. Digital radiography image quality: image processing and display. J Am Coll Radiol 2007;4(6):389–400. Crossref, MedlineGoogle Scholar
  • 15 Novelline RA. How to study the chest. In: Squire’s fundamentals of radiology. Cambridge, Mass: Harvard University Press, 2004; 80–91. Google Scholar
  • 16 Chakraborty DP. ROC curves predicted by a model of visual search. Phys Med Biol 2006;51(14):3463–3482. Crossref, MedlineGoogle Scholar
  • 17 Krupinski EA, Berger WG, Dallas WJ, Roehrig H. Searching for nodules: what features attract attention and influence detection? Acad Radiol 2003;10(8):861–868. Crossref, MedlineGoogle Scholar
  • 18 Palmer J. Attention in visual search: distinguishing four causes of a set size effect. Curr Dir Psychol Sci 1995;4(4):118–123. CrossrefGoogle Scholar
  • 19 Eckstein MP, Thomas JP, Palmer J, Shimozaki SS. A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays. Percept Psychophys 2000;62(3):425–451. Crossref, MedlineGoogle Scholar
  • 20 Judy PF, Swensson RG, Szulc M. Lesion detection and signal-to-noise ratio in CT images. Med Phys 1981;8(1):13–23. Crossref, MedlineGoogle Scholar
  • 21 Swensson RG, Judy PF. Detection of noisy visual targets: models for the effects of spatial uncertainty and signal-to-noise ratio. Percept Psychophys 1981;29(6):521–534. Crossref, MedlineGoogle Scholar
  • 22 Wolfe JM, Reynolds JH. Visual search. In: Basbaum AIKaneko AShepherd GMWestheimer G, eds. The senses: a comprehensive reference. Vol 2, Vision II. San Diego, Calif: Academic Press, 2008; 275–280. CrossrefGoogle Scholar
  • 23 Wolfe JM, Horowitz TS. What attributes guide the deployment of visual attention and how do they do it? Nat Rev Neurosci 2004;5(6):495–501. Crossref, MedlineGoogle Scholar
  • 24 Berbaum KS, Franken EA, Dorfman DD, et al.. Satisfaction of search in diagnostic radiology. Invest Radiol 1990;25(2):133–140. Crossref, MedlineGoogle Scholar
  • 25 Berbaum KS, Franken EA, Dorfman DD, et al.. Time course of satisfaction of search. Invest Radiol 1991;26(7):640–648. Crossref, MedlineGoogle Scholar
  • 26 Nodine CF, Krupinski EA, Kundel HL, Toto L, Herman GT. Satisfaction of search (SOS). Invest Radiol 1992;27(7):571–573. Crossref, MedlineGoogle Scholar
  • 27 Gur D, Rockette HE, Armfield DR, et al.. Prevalence effect in a laboratory environment. Radiology 2003;228(1):10–14. LinkGoogle Scholar
  • 28 Gur D, Rockette HE, Warfel T, Lacomis JM, Fuhrman CR. From the laboratory to the clinic: the “prevalence effect.”. Acad Radiol 2003;10(11):1324–1326. Crossref, MedlineGoogle Scholar
  • 29 Wolfe JM, Horowitz TS, Kenner NM. Cognitive psychology: rare items often missed in visual searches. Nature 2005;435(7041):439–440. Crossref, MedlineGoogle Scholar
  • 30 Freedman M, Osicka T. Perceptual effect of CAD in reading chest radiographs. In: Samei EKrupinski E, eds. The handbook of medical image perception and techniques. Cambridge, England: Cambridge University Press, 2010; 290–303. Google Scholar
  • 31 Doi K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 2007;31(4-5):198–211. Crossref, MedlineGoogle Scholar
  • 32 Freedman M. State-of-the-art screening for lung cancer (part 1): the chest radiograph. Thorac Surg Clin 2004;14(1):43–52. Crossref, MedlineGoogle Scholar
  • 33 Berbaum KS, Caldwell RT, Schartz KM, Thompson BH, Franken EA. Does computer-aided diagnosis for lung tumors change satisfaction of search in chest radiography? Acad Radiol 2007;14(9):1069–1076. Crossref, MedlineGoogle Scholar
  • 34 Horsch K, Giger ML, Metz CE. Prevalence scaling: applications to an intelligent workstation for the diagnosis of breast cancer. Acad Radiol 2008;15(11):1446–1457. Crossref, MedlineGoogle Scholar
  • 35 Kobayashi T, Xu XW, MacMahon H, Metz CE, Doi K. Effect of a computer-aided diagnosis scheme on radiologists’ performance in detection of lung nodules on radiographs. Radiology 1996;199(3):843–848. LinkGoogle Scholar
  • 36 Freedman MT, Osicka T, Lo SC, et al.. Methods for identifying changes in radiologists’ behavioral operating point of sensitivity-specificity trade-offs within an ROC study of the use of computer aided detection of lung cancer. In: Proceedings of SPIE: medical imaging 2001—image processing and performance. Vol 4324. Bellingham, Wash: International Society for Optical Engineering, 2001; 1311–1319. Google Scholar
  • 37 Meyer CR, Boes JL, Kim B, et al.. Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations. Med Image Anal 1997;1(3):195–206. Crossref, MedlineGoogle Scholar
  • 38 Maintz JBA, Viergever MA. A survey of medical image registration. Med Image Anal 1998;2(1):1–36. Crossref, MedlineGoogle Scholar
  • 39 American College of Radiology. ACR standard for communication: diagnostic radiology—impact of reporting errors Reston, Va: American College of Radiology, 1995. Google Scholar
  • 40 American College of Radiology. ACR practice guideline for communication of diagnostic imaging findings Reston, Va: American College of Radiology, 2005. Google Scholar
  • 41 American College of Radiology. Breast Imaging Reporting and Data System (BI-RADS) atlas. http://www.acr.org/SecondaryMainMenuCategories/ACRStore/FeaturedCategories/QualityandSafety/birads_atlas.aspx. Accessed June 10, 2010. Google Scholar
  • 42 Bell D, Greenes R, Doubilet P. Form-based clinical input from a structured vocabulary: initial application in ultrasound reporting. Proc Annu Symp Comput Appl Med Care 1992789–790. MedlineGoogle Scholar
  • 43 Bell D, Greenes R. Evaluation of UltraSTAR: performance of a collaborative structured data entry system. Proc Annu Symp Comput Appl Med Care 1994216–222. MedlineGoogle Scholar
  • 44 Douglas PS, Hendel RC, Cummings JE, et al.. 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(1):76–90. Crossref, MedlineGoogle Scholar
  • 45 RadLex. A lexicon for uniform indexing and retrieval of radiology information resources. Radiological Society of North America. http://www.rsna.org/Radlex/. Accessed June 10, 2010. Google Scholar
  • 46 Sistrom CL, Langlotz CP. A framework for improving radiology reporting. J Am Coll Radiol 2005;2(2):159–167. Crossref, MedlineGoogle Scholar
  • 47 Dunnick NR, Langlotz CP. The radiology report of the future: a summary of the 2007 Intersociety Conference. J Am Coll Radiol 2008;5(5):626–629. Crossref, MedlineGoogle Scholar
  • 48 Reiner BI, Knight N, Siegel EL. Radiology reporting, past, present, and future: the radiologist’s perspective. J Am Coll Radiol 2007;4(5):313–319. Crossref, MedlineGoogle Scholar
  • 49 Hanna D, Griswold P, Leape LL, Bates DW. Communicating critical test results: safe practice recommendations. Jt Comm J Qual Patient Saf 2005;31(2):68–80. Crossref, MedlineGoogle Scholar
  • 50 Getty DJ. Stereoscopic and biplane imaging. In: Samei EFlynn MJ, eds. 2003 syllabus: categorical course in diagnostic radiology physics—advances in digital mammography. Oak Brook, Ill: Radiological Society of North America, 2003; 199–209. Google Scholar
  • 51 Getty DJ, Green PJ. Clinical medical applications for stereoscopic 3D displays. J Soc Inf Disp 2007;15(6):377–384. CrossrefGoogle Scholar
  • 52 Getty DJ, D’Orsi CJ, Pickett RM. Stereoscopic digital mammography: improved accuracy of lesion detection in breast cancer screening. In: Krupinski EA, ed. Proceedings of the International Workshop on Digital Mammography, 2008; 74–79. CrossrefGoogle Scholar
  • 53 3D Forums Stereoscopic Discussion Forums. Active versus passive 3D glasses. http://www.3d-forums.com/active-vs-passive-3d-glasses-t81.html. Accessed June 29, 2010. Google Scholar
  • 54 Planar Stereo Mirror. http://www.planar3d.com/. Beaverton, Ore. Accessed June 29, 2010. Google Scholar
  • 55 NVIDIA Corporation 3D Vision. http://www.nvidia.com/object/product_geforce_3D_VisionKit_us.html. Santa Clara, Calif. Accessed June 29, 2010. Google Scholar
  • 56 3D Forums Stereoscopic Discussion Forums. Autostereoscopic displays. http://www.3d-forums.com/autostereoscopic-displays-t1.html. Accessed June 29, 2010. Google Scholar
  • 57 Visbox. VisCube-SX Multiscreen Immersive 3D Display. http://www.visbox.com/viscube-SX.html. Champaign, Ill. Accessed June 10, 2010. Google Scholar
  • 58 Zhang S, Demiralp C, Keefe DF, et al.. An immersive virtual environment for DT-MRI volume visualization applications: a case study. In: IEEE Visualization, San Diego, Calif: IEEE Computer Society, 2001; 437–584. Google Scholar
  • 59 Rubin GD, Napel S, Leung AN. Volumetric analysis of volumetric data: achieving a paradigm shift. Radiology 1996;200(2):312–317. LinkGoogle Scholar
  • 60 Dalrymple NC, Prasad SR, Freckleton MW, Chintapalli KN. Informatics in radiology (infoRAD): introduction to the language of three-dimensional imaging with multidetector CT. RadioGraphics 2005;25(5):1409–1428. LinkGoogle Scholar
  • 61 Rankin SC. Spiral CT: vascular applications. Eur J Radiol 1998;28(1):18–29. Crossref, MedlineGoogle Scholar
  • 62 Rubin GD. 3-D imaging with MDCT. Eur J Radiol 2003;45(suppl 1):S37–S41. Crossref, MedlineGoogle Scholar
  • 63 Rubin GD. Multislice imaging for three-dimensional examinations. In: Silverman PM, ed. Multislice helical tomography: a practical approach to clinical protocols. Philadelphia, Pa: Lippincott Williams * Wilkins, 2002; 317–324. Google Scholar
  • 64 Raman R, Napel S, Beaulieu CF, Bain ES, Jeffrey RB, Rubin GD. Automated generation of curved planar reformations from volume data: method and evaluation. Radiology 2002;223(1):275–280. LinkGoogle Scholar
  • 65 Napel S, Rubin GD, Jeffrey RB. STS-MIP: a new reconstruction technique for CT of the chest. J Comput Assist Tomogr 1993;17(5):832–838. Crossref, MedlineGoogle Scholar
  • 66 Ravenel JG, McAdams HP. Multiplanar and three-dimensional imaging of the thorax. Radiol Clin North Am 2003;41(3):475–489. Crossref, MedlineGoogle Scholar
  • 67 Kuszyk BS, Heath DG, Bliss DF, Fishman EK. Skeletal 3-D CT: advantages of volume rendering over surface rendering. Skeletal Radiol 1996;25(3):207–214. Crossref, MedlineGoogle Scholar
  • 68 van Dam IE, van Sörnsen de Koste JR, Hanna GG, Muirhead R, Slotman BJ, Senan S. Improving target delineation on 4-dimensional CT scans in stage I NSCLC using a deformable registration tool. Radiother Oncol 2010;96(1):67–72. Crossref, MedlineGoogle Scholar
  • 69 Barfett JJ, Fierstra J, Willems PW, Mikulis DJ, Krings T. Intravascular functional maps of common neurovascular lesions derived from volumetric 4D CT data. Invest Radiol 2010;45(7):370–377. Crossref, MedlineGoogle Scholar
  • 70 Badea CT, Johnston SM, Subashi E, Qi Y, Hedlund LW, Johnson GA. Lung perfusion imaging in small animals using 4D micro-CT at heartbeat temporal resolution. Med Phys 2010;37(1):54–62. Crossref, MedlineGoogle Scholar
  • 71 Li G, Citrin D, Camphausen K, et al.. Advances in 4D medical imaging and 4D radiation therapy. Technol Cancer Res Treat 2008;7(1):67–81. Crossref, MedlineGoogle Scholar
  • 72 Brunetti J, Caggiano A, Rosenbluth B, Vialotti C. Technical aspects of positron emission tomography/computed tomography fusion planning. Semin Nucl Med 2008;38(2):129–136. Crossref, MedlineGoogle Scholar
  • 73 Schillaci O, Filippi L, Manni C, Santoni R. Single-photon emission computed tomography/computed tomography in brain tumors. Semin Nucl Med 2007;37(1):34–47. Crossref, MedlineGoogle Scholar
  • 74 Schillaci O, Filippi L, Danieli R, Simonetti G. Single-photon emission computed tomography/computed tomography in abdominal diseases. Semin Nucl Med 2007;37(1):48–61. Crossref, MedlineGoogle Scholar
  • 75 Bjelkhagen HI, Mirlis E. Color holography to produce highly realistic three-dimensional images. Appl Opt 2008;47(4):A123–A133. Crossref, MedlineGoogle Scholar
  • 76 Langehanenberg P, Ivanova L, Bernhardt I, et al.. Automated three-dimensional tracking of living cells by digital holographic microscopy. J Biomed Opt 2009;14(1):014018. Crossref, MedlineGoogle Scholar
  • 77 Vannan MA, Cao QL, Pandian NG, Sugeng L, Schwartz SL, Dalton MN. Volumetric multiplexed transmission holography of the heart with echocardiographic data. J Am Soc Echocardiogr 1995;8(5 pt 1):567–575. Crossref, MedlineGoogle Scholar
  • 78 Pinto FJ, Veiga F, Lopes MG, de Pádua F. Dynamic three-dimensional echocardiography: a new era in ultrasound technology. Rev Port Cardiol 1997;16(10):787–795; 745–746. MedlineGoogle Scholar
  • 79 Galeotti JM, Siegel M, Stetten G. Real-time tomographic holography for augmented reality. Opt Lett 2010;35(14):2352–2354. Crossref, MedlineGoogle Scholar
  • 80 Del Socorro Hernández-Montes M, Furlong C, Rosowski JJ, et al.. Optoelectronic holographic otoscope for measurement of nano-displacements in tympanic membranes. J Biomed Opt 2009;14(3):034023. Crossref, MedlineGoogle Scholar
  • 81 Robertson DD, Sutherland CJ, Chan BW, Hodge JC, Scott WW, Fishman EK. Depiction of pelvic fractures using 3D volumetric holography: comparison of plain X-ray and CT. J Comput Assist Tomogr 1995;19(6):967–974. Crossref, MedlineGoogle Scholar
  • 82 Biwasaka H, Saigusa K, Aoki Y. The applicability of holography in forensic identification: a fusion of the traditional optical technique and digital technique. J Forensic Sci 2005;50(2):393–399. Crossref, MedlineGoogle Scholar
  • 83 Prevedello LM, Andriole KP, Gill R, Oliva IB, Khorasani R. Post-processing tools for radiologists: understanding the principles, obstacles and utility of embedding advanced processing into the routine workflow in thoracic radiology [abstr]. In: Radiological Society of North America scientific assembly and annual meeting program. Oak Brook, Ill: Radiological Society of North America, 2007; 840. Google Scholar
  • 84 Prevedello LM, Andriole KP, Khorasani R. Advanced processing tools in radiology: what do we know, what should we know, and how can we utilize them in an evidence-based approach? [abstr]. In: Radiological Society of North America scientific assembly and annual meeting program. Oak Brook, Ill: Radiological Society of North America, 2008; 911. Google Scholar
  • 85 Lidén M, Andersson T, Geijer H.. Alternative user interface devices for improved navigation of CT datasets. J Digit Imaging doi:10.1007/s10278-009-9252-2. Published online December 1, 2009. MedlineGoogle Scholar
  • 86 Goyal N, Jain N, Rachapalli V. Ergonomics in radiology. Clin Radiol 2009;64(2):119–126. Crossref, MedlineGoogle Scholar
  • 87 Prabhu SP, Gandhi S, Goddard PR. Ergonomics of digital imaging. Br J Radiol 2005;78(931):582–586. Crossref, MedlineGoogle Scholar
  • 88 Weiss DL, Siddiqui KM, Scopelliti J. Radiologist assessment of PACS user interface devices. J Am Coll Radiol 2006;3(4):265–273. Crossref, MedlineGoogle Scholar
  • 89 Greene JR. Design and development of a new facility for teaching and research in clinical anatomy. Anat Sci Educ 2009;2(1):34–40. Crossref, MedlineGoogle Scholar
  • 90 Wachs JP, Stern HI, Edan Y, Gillam M, Handler J, Feied C, Smith M. A hand gesture sterile tool for browsing MRI images in the OR. J Am Med Inform Assoc 2008 Feb 28. [Epub ahead of print]. Google Scholar
  • 91 Wachs JP, Stern HI, Edan Y, et al.. A gesture-based tool for sterile browsing of radiology images. J Am Med Inform Assoc 2008;15(3):321–323. [Published correction appears in J Am Med Inform Assoc 2009;16(3):284.]. Crossref, MedlineGoogle Scholar
  • 92 Fernàndez-Bayó J, Barbero O, Rubies C, Sentís M, Donoso L. Distributing medical images with internet technologies: a DICOM web server and a DICOM java viewer. RadioGraphics 2000;20(2):581–590. LinkGoogle Scholar
  • 93 Arenson RL, Andriole KP, Avrin DE, Gould RG. Computers in imaging and health care: now and in the future. J Digit Imaging 2000;13(4):145–156. Crossref, MedlineGoogle Scholar
  • 94 Zhang J, Sun J, Stahl JN. PACS and Web-based image distribution and display. Comput Med Imaging Graph 2003;27(2-3):197–206. Crossref, MedlineGoogle Scholar
  • 95 Tellis WM, Andriole KP. Integrating multiple clinical information systems using the Java message service framework to enable the delivery of urgent exam results at the point of care. J Digit Imaging 2005;18(4):316–325. Crossref, MedlineGoogle Scholar
  • 96 Bandon D, Lovis C, Geissbühler A, Vallée JP. Enterprise-wide PACS: beyond radiology, an architecture to manage all medical images. Acad Radiol 2005;12(8):1000–1009. Crossref, MedlineGoogle Scholar
  • 97 Andriole KP, Prevedello LM, Dufault A, et al.. Augmenting the impact of technology adoption with financial incentive to improve radiology report signature times. J Am Coll Radiol 2010;7(3):198–204. Crossref, MedlineGoogle Scholar
  • 98 Flanders AE, Wiggins RH, Gozum ME. Handheld computers in radiology. RadioGraphics 2003;23(4):1035–1047. LinkGoogle Scholar
  • 99 Raman B, Raman R, Raman L, Beaulieu CF. Radiology on handheld devices: image display, manipulation, and PACS integration issues. RadioGraphics 2004;24(1):299–310. LinkGoogle Scholar
  • 100 Nakata N, Kandatsu S, Suzuki N, Fukuda K. Informatics in Radiology (infoRAD): mobile wireless DICOM server system and PDA with high-resolution display—feasibility of group work for radiologists. RadioGraphics 2005;25(1):273–283. LinkGoogle Scholar
  • 101 Andriole KP, Khorasani R. Cloud computing: what is it and could it be useful? J Am Coll Radiol 2010;7(4):252–254. Crossref, MedlineGoogle Scholar
  • 102 Lin A, Harris M, Zalis M. Initial observations of electronic medical record usage during CT and MRI interpretation: frequency of use and impact on workflow. AJR Am J Roentgenol 2010;195(1):188–193. Crossref, MedlineGoogle Scholar

Article History

Received August 12, 2009; revision requested September 29; revision received August 16, 2010; accepted October 4; final version accepted October 27.
Published online: May 2011
Published in print: May 2011