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WebParc: a tool for analysis of the topography and volume of stroke from MRI

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Abstract

The quantitative assessment of the anatomic consequences of cerebral infarction is critical in the study of the etiology and therapeutic response in patients with stroke. We present here an overview of the operation of “WebParc,” a computational system that provides measures of stroke lesion volume and location with respect to canonical forebrain neural systems nomenclature. Using a web-based interface, clinical imaging data can be registered to a template brain that contains a comprehensive set of anatomic structures. Upon delineation of the lesion, we can express the size and localization of the lesion in terms of the regions that are intersected within the template. We demonstrate the application of the system using MRI-based diffusion-weighted imaging and document measures of the validity and reliability of its uses. Intra- and inter-rater reliability is demonstrated, and characterized relative to the various classes of anatomic regions that can be assessed. The WebParc system has been developed to meet criteria of both efficiency and intuitive operator use in the real time analysis of stroke anatomy, so as to be useful in support of clinical care and clinical research studies. This article is an overview of its base-line operation with quantitative anatomic characterization of lesion size and location in terms of stroke distribution within the separate gray and white matter compartments of the brain.

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References

  1. Sorensen AG, Wu O, Copen WA, Davis TL, Gonzalez RG, Koroshetz WJ, Reese TG, Rosen BR, Wedeen VJ, Weisskoff RM (1999) Human acute cerebral ischemia: detection of changes in water diffusion anisotropy by using MR imaging. Radiology 212:785–792

    Google Scholar 

  2. Sorensen AG, Copen WA, Ostergaard L, Buonanno FS, Gonzalez RG, Rordorf G, Rosen BR, Schwamm LH, Weisskoff RM, Koroshetz WJ (1999) Hyperacute stroke: simultaneous measurement of relative cerebral blood volume, relative cerebral blood flow, and mean tissue transit time. Radiology 210:519–527

    Google Scholar 

  3. Schwamm LH, Koroshetz WJ, Sorensen AG, Wang B, Copen WA, Budzik R, Rordorf G, Buonanno FS, Schaefer PW, Gonzalez RG (1998) Time course of lesion development in patients with acute stroke: serial diffusion- and hemodynamic-weighted magnetic resonance imaging. Stroke 29:2268–2276

    Google Scholar 

  4. Schaefer PW, Hunter GJ, He J, Hamberg LM, Sorensen AG, Schwamm LH, Koroshetz WJ, Gonzalez RG (2002) Predicting cerebral ischemic infarct volume with diffusion and perfusion MR imaging. AJNR Am J Neuroradiol 23:1785–1794

    Google Scholar 

  5. Rivers CS, Wardlaw JM, Armitage PA, Bastin ME, Carpenter TK, Cvoro V, Hand PJ, Dennis MS (2006) Do acute diffusion- and perfusion-weighted MRI lesions identify final infarct volume in ischemic stroke? Stroke 37:98–104

    Article  Google Scholar 

  6. Lovblad KO, Baird AE, Schlaug G, Benfield A, Siewert B, Voetsch B, Connor A, Burzynski C, Edelman RR, Warach S (1997) Ischemic lesion volumes in acute stroke by diffusion-weighted magnetic resonance imaging correlate with clinical outcome. Ann Neurol 42:164–170

    Article  Google Scholar 

  7. Sulter G, Steen C, De Keyser J (1999) Use of the Barthel index and modified Rankin scale in acute stroke trials. Stroke 30:1538–1541

    Google Scholar 

  8. Baird AE, Dashe J, Connor A, Burzynski C, Schlaug G, Warach S (2000) Comparison of retrospective and prospective measurements of the national institutes of health stroke scale. Cerebrovasc Dis 10:80–81

    Article  Google Scholar 

  9. Thijs VN, Lansberg MG, Beaulieu C, Marks MP, Moseley ME, Albers GW (2000) Is early ischemic lesion volume on diffusion-weighted imaging an independent predictor of stroke outcome? A multivariable analysis. Stroke 31:2597–2602

    Google Scholar 

  10. Hand PJ, Wardlaw JM, Rivers CS, Armitage PA, Bastin ME, Lindley RI, Dennis MS (2006) MR diffusion-weighted imaging and outcome prediction after ischemic stroke. Neurology 66:1159–1163

    Article  Google Scholar 

  11. Group NIoNDaSRTPSS (1995) Tissue plasminogen activator for acute ischemic stroke. N Engl J Med 333:1581–1587

    Article  Google Scholar 

  12. Higashida RT, Furlan AJ, Roberts H, Tomsick T, Connors B, Barr J, Dillon W, Warach S, Broderick J, Tilley B, Sacks D (2003) Trial design and reporting standards for intra-arterial cerebral thrombolysis for acute ischemic stroke. Stroke 34:e109–e137

    Article  Google Scholar 

  13. del Zoppo GJ, Higashida RT, Furlan AJ, Pessin MS, Rowley HA, Gent M (1998) PROACT: a phase II randomized trial of recombinant pro-urokinase by direct arterial delivery in acute middle cerebral artery stroke. PROACT investigators. Prolyse in acute cerebral thromboembolism. Stroke 29:4–11

    Google Scholar 

  14. Furlan A, Higashida R, Wechsler L, Gent M, Rowley H, Kase C, Pessin M, Ahuja A, Callahan F, Clark WM, Silver F, Rivera F (1999) Intra-arterial prourokinase for acute ischemic stroke. The PROACT II study: a randomized controlled trial. Prolyse in acute cerebral thromboembolism. JAMA 282:2003–2011

    Article  Google Scholar 

  15. Mani RL, Eisenberg RL, McDonald EJ Jr, Pollock JA, Mani JR (1978) Complications of catheter cerebral arteriography: analysis of 5,000 procedures. I. Criteria and incidence. AJR Am J Roentgenol 131:861–865

    Google Scholar 

  16. Skalpe IO (1988) Complications in cerebral angiography with iohexol (omnipaque) and meglumine metrizoate (isopaque cerebral). Neuroradiology 30:69–72

    Article  Google Scholar 

  17. Heiserman JE, Dean BL, Hodak JA, Flom RA, Bird CR, Drayer BP, Fram EK (1994) Neurologic complications of cerebral angiography. AJNR Am J Neuroradiol 15:1401–1407; discussion 1408–1411

    Google Scholar 

  18. Waugh JR, Sacharias N (1992) Arteriographic complications in the DSA era. Radiology 182:243–246

    Google Scholar 

  19. Dyker AG, Lees KR (1998) Duration of neuroprotective treatment for ischemic stroke. Stroke 29:535–542

    Google Scholar 

  20. McCulloch J, Dewar D (2001) A radical approach to stroke therapy. Proc Natl Acad Sci USA 98:10989–10991

    Article  Google Scholar 

  21. McCulloch J (1992) Excitatory amino acid antagonists and their potential for the treatment of ischaemic brain damage in man. Br J Clin Pharmacol 34:106–114

    Google Scholar 

  22. Jonas S, Aiyagari V, Vieira D, Figueroa M (2001) The failure of neuronal protective agents versus the success of thrombolysis in the treatment of ischemic stroke. The predictive value of animal models. Ann N Y Acad Sci 939:257–267

    Article  Google Scholar 

  23. Singhal AB, Lo EH, Dalkara T, Moskowitz MA (2005) Advances in stroke neuroprotection: hyperoxia and beyond. Neuroimaging Clin N Am 15:697–720, xii–xiii

    Article  Google Scholar 

  24. Rivers CS, Wardlaw JM, Armitage PA, Bastin ME, Carpenter TK, Cvoro V, Hand PJ, Dennis MS (2006) Persistent infarct hyperintensity on diffusion-weighted imaging late after stroke indicates heterogeneous, delayed, infarct evolution. Stroke 37:1418–1423

    Article  Google Scholar 

  25. Lansberg MG, O’Brien MW, Tong DC, Moseley ME, Albers GW (2001) Evolution of cerebral infarct volume assessed by diffusion-weighted magnetic resonance imaging. Arch Neurol 58:613–617

    Article  Google Scholar 

  26. Eastwood JD, Lev MH, Provenzale JM (2003) Perfusion CT with iodinated contrast material. AJR Am J Roentgenol 180:3–12

    Google Scholar 

  27. Baird AE, Benfield A, Schlaug G, Siewert B, Lovblad KO, Edelman RR, Warach S (1997) Enlargement of human cerebral ischemic lesion volumes measured by diffusion-weighted magnetic resonance imaging. Ann Neurol 41:581–589

    Article  Google Scholar 

  28. Barber P, Darby D, Desmond P, Yang Q, Gerraty R, Jolley D, Donnan G, Tress B, Davis S (1998) Prediction of stroke outcome with echoplanar perfusion- and diffusion-weighted MRI. Neurology 51:418–426

    Google Scholar 

  29. Neumann-Haefelin T, Wittsack HJ, Wenserski F, Siebler M, Seitz RJ, Modder U, Freund HJ (1999) Diffusion- and perfusion-weighted MRI. The DWI/PWI mismatch region in acute stroke. Stroke 30:1591–1597

    Google Scholar 

  30. Caviness VS, Makris N, Montinaro E, Sahin NT, Bates JF, Schwamm L, Caplan D, Kennedy DN (2002) Anatomy of stroke, part II: volumetric characteristics with implications for the local architecture of the cerebral perfusion system. Stroke 33:2557–2564

    Article  Google Scholar 

  31. Caviness VS, Makris N, Montinaro E, Sahin NT, Bates JF, Schwamm L, Caplan D, Kennedy DN (2002) Anatomy of stroke, part I: an MRI-based topographic and volumetric system of analysis. Stroke 33:2549–2556

    Article  Google Scholar 

  32. Bogousslavsky J, Van Melle G, Regli F (1988) The Lausanne Stroke Registry: analysis of 1,000 consecutive patients with first stroke. Stroke 19:1083–1092

    Google Scholar 

  33. Caplan L, Babikian V, Helgason C, Hier DB, DeWitt D, Patel D, Stein R (1985) Occlusive disease of the middle cerebral artery. Neurology 35:975–982

    Google Scholar 

  34. Catani M, ffytche DH (2005) The rises and falls of disconnection syndromes. Brain 128:2224–2239

    Article  Google Scholar 

  35. Geschwind N (1965) Disconnexion syndromes in animals and man. I. Brain 88:237–294

    Article  Google Scholar 

  36. Geschwind N (1965) Disconnexion syndromes in animals and man. II. Brain 88:585–644

    Article  Google Scholar 

  37. Mesulam MM (1998) From sensation to cognition. Brain 121(Pt 6):1013–1052

    Article  Google Scholar 

  38. Mesulam MM (1990) Large-scale neurocognitive networks and distributed processing for attention, language, and memory. Ann Neurol 28:597–613

    Article  Google Scholar 

  39. Menezes NM, Ay H, Wang Zhu M, Lopez CJ, Singhal AB, Karonen JO, Aronen HJ, Liu Y, Nuutinen J, Koroshetz WJ, Sorensen AG (2007) The real estate factor: quantifying the impact of infarct location on stroke severity. Stroke 38:194–197

    Article  Google Scholar 

  40. Bilello M, Lao Z, Krejza J, Hillis AE, Herskovits EH (2006) Statistical atlas of acute stroke from magnetic resonance diffusion-weighted-images of the brain. Neuroinformatics 4:235–242

    Article  Google Scholar 

  41. Letovsky SI, Whitehead SH, Paik CH, Miller GA, Gerber J, Herskovits EH, Fulton TK, Bryan RN (1998) A brain image database for structure/function analysis. AJNR Am J Neuroradiol 19:1869–1877

    Google Scholar 

  42. Ay H, Arsava EM, Koroshetz WJ, Sorensen AG (2008) Middle cerebral artery infarcts encompassing the insula are more prone to growth. Stroke 39:373–378

    Article  Google Scholar 

  43. Nowinski WL, Qian G, Kirgaval Nagaraja BP, Thirunavuukarasuu A, Hu Q, Ivanov N, Parimal AS, Runge VM, Beauchamp NJ (2006) Analysis of ischemic stroke MR images by means of brain atlases of anatomy and blood supply territories. Acad Radiol 13:1025–1034

    Article  Google Scholar 

  44. Nowinski W, Qian G, Bhanu Prakash K, Volkau I, Hu Q, Thirunavuukarasuu A, Ivanov N, Parimal A, Qiao Y, Ananthasubramaniam A, Huang S, Runge V, Beauchamp N (2006) Design and development of a computer aided diagnosis system for processing of acute ischemic stroke MR images. WSEAS Trans Biol Biomed 3:401–407

    Google Scholar 

  45. Nowinski W, Qian G, Bhanu Prakash K, Volkau I, Thirunavuukarasuu A, Hu Q, Ananthasubramaniam A, Liu J, Gupta V, Ng T, Leong W, Beauchamp N (2007) A CAD system for acute ischemic stroke image processing. Int J Comput Assist Radiol Surg 2:220–222

    Article  Google Scholar 

  46. Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, Woods R, Paus T, Simpson G, Pike B, Holmes C, Collins L, Thompson P, MacDonald D, Iacoboni M, Schormann T, Amunts K, Palomero-Gallagher N, Geyer S, Parsons L, Narr K, Kabani N, Le Goualher G, Boomsma D, Cannon T, Kawashima R, Mazoyer B (2001) A probabilistic atlas and reference system for the human brain: international consortium for brain mapping (ICBM). Philos Trans R Soc Lond B Biol Sci 356:1293–1322

    Article  Google Scholar 

  47. Collins DL, Holmes CJ, Peters TM, Evans AC (1995) Automatic 3D model-based neuroanatomical segmentation. Hum Brain Mapp 3:190–208

    Article  Google Scholar 

  48. Moore SM, Beecher DE, Hoffman SA (2003) DICOM shareware: a public implementation of the DICOM standard. Proc SPIE 2165:772

    Article  Google Scholar 

  49. Filipek PA, Richelme C, Kennedy DN, Caviness VS Jr (1994) The young adult human brain: an MRI-based morphometric analysis. Cereb Cortex 4:344–360

    Article  Google Scholar 

  50. Filipek PA, Kennedy DN, Caviness VS Jr, Rossnick SL, Spraggins TA, Starewicz PM (1989) Magnetic resonance imaging-based brain morphometry: development and application to normal subjects. Ann Neurol 25:61–67

    Article  Google Scholar 

  51. Caviness VS, Makris N, Meyer J, Kennedy D (1996) MRI-based parcellation of human neocortex: an anatomically specified method with estimate of reliability. J Cogn Neurosci 8:566–588

    Article  Google Scholar 

  52. Rademacher J, Galaburda AM, Kennedy DN, Filipek PA, Caviness V (1992) Human cerebral cortex: localization, parcellation, and morphometry with magnetic resonance imaging. J Cogn Neurosci 4:352–374

    Article  Google Scholar 

  53. Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9:179–194

    Article  Google Scholar 

  54. Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17:143–155

    Article  Google Scholar 

  55. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, Zhang Y, De Stefano N, Brady JM, Matthews PM (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(Suppl 1):S208–S219

    Article  Google Scholar 

  56. Worth AJ, Makris N, Caviness VS Jr, Kennedy DN (1997) Neuroanatomical segmentation in MRI: technological objectives. Int J Pattern Recognit Artif Intell 11:1161–1187

    Article  Google Scholar 

  57. Luby M, Bykowski JL, Schellinger PD, Merino JG, Warach S (2006) Intra- and interrater reliability of ischemic lesion volume measurements on diffusion-weighted, mean transit time and fluid-attenuated inversion recovery MRI. Stroke 37:2951–2956

    Article  Google Scholar 

  58. Hevia-Montiel N, Jimenez-Alaniz JR, Medina-Banuelos V, Yanez-Suarez O, Rosso C, Samson Y, Baillet S (2007) Robust nonparametric segmentation of infarct lesion from diffusion-weighted MR images. In: Conf Proc IEEE Eng Med Biol Soc, pp 2102–2105

  59. Li W, Tian J, Li E, Dai J (2004) Robust unsupervised segmentation of infarct lesion from diffusion tensor MR images using multiscale statistical classification and partial volume voxel reclassification. Neuroimage 23:1507–1518

    Article  Google Scholar 

  60. Hellier P, Barillot C, Corouge I, Gibaud B, Le Goualher G, Collins DL, Evans A, Malandain G, Ayache N, Christensen GE, Johnson HJ (2003) Retrospective evaluation of intersubject brain registration. IEEE Trans Med Imaging 22:1120–1130

    Article  Google Scholar 

  61. Makris N, Hodge SM, Haselgrove C, Kennedy DN, Dale A, Fischl B, Rosen BR, Harris G, Caviness VS Jr, Schmahmann JD (2003) Human cerebellum: surface-assisted cortical parcellation and volumetry with magnetic resonance imaging. J Cogn Neurosci 15:584–599

    Article  Google Scholar 

  62. Kuller LH, Arnold AM, Longstreth WT Jr, Manolio TA, O’Leary DH, Burke GL, Fried LP, Newman AB (2007) White matter grade and ventricular volume on brain MRI as markers of longevity in the cardiovascular health study. Neurobiol Aging 28:1307–1315

    Article  Google Scholar 

  63. Au R, Massaro JM, Wolf PA, Young ME, Beiser A, Seshadri S, D’Agostino RB, DeCarli C (2006) Association of white matter hyperintensity volume with decreased cognitive functioning: the Framingham Heart Study. Arch Neurol 63:246–250

    Article  Google Scholar 

  64. Fisher CM (1989) Binswanger’s encephalopathy: a review. J Neurol 236:65–79

    Article  Google Scholar 

  65. Ay H, Arsava EM, Rosand J, Furie KL, Singhal AB, Schaefer PW, Wu O, Gonzalez RG, Koroshetz WJ, Sorensen AG (2008) Severity of leukoaraiosis and susceptibility to infarct growth in acute stroke. Stroke 39:1409–1413

    Article  Google Scholar 

  66. Gladstone DJ, Black SE, Hakim AM (2002) Toward wisdom from failure: lessons from neuroprotective stroke trials and new therapeutic directions. Stroke 33:2123–2136

    Article  Google Scholar 

  67. Ginsberg MD (2003) Adventures in the pathophysiology of brain ischemia: penumbra, gene expression, neuroprotection: the 2002 Thomas Willis lecture. Stroke 34:214–223

    Article  Google Scholar 

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Acknowledgments

This study was supported in part by NIH grants PO1 NS27950, EB005149, and DA09467, by Human Brain Project Grant NS34189, and by grants from the Fairway Trust and the Giovanni Armenise Harvard Foundation for Advanced Scientific Research.

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  1. David N. Kennedy

    Present address: Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA, USA

Authors and Affiliations

  1. Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA, USA

    Christian Haselgrove

  2. Center for Morphometric Analysis, Massachusetts General Hospital, 149 13th Street, Charlestown, Boston, MA, USA

    David N. Kennedy, Christian Haselgrove, Nikos Makris, Donald M. Goldin & Verne S. Caviness

  3. Department of Neurology, Massachusetts General Hospital, Boston, MA, USA

    David N. Kennedy, Christian Haselgrove, Nikos Makris, David Caplan & Verne S. Caviness

  4. Harvard Medical School, Boston, MA, USA

    David N. Kennedy, Nikos Makris, Michael H. Lev, David Caplan & Verne S. Caviness

  5. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA

    David N. Kennedy, Nikos Makris, Michael H. Lev & Verne S. Caviness

  6. Department of Radiology, Massachusetts General Hospital, Boston, MA, USA

    Michael H. Lev

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  1. David N. Kennedy
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Kennedy, D.N., Haselgrove, C., Makris, N. et al. WebParc: a tool for analysis of the topography and volume of stroke from MRI. Med Biol Eng Comput 48, 215–228 (2010). https://doi.org/10.1007/s11517-009-0571-8

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