Pediatric Radiology

, Volume 44, Issue 5, pp 566–575 | Cite as

Systematic protocol for assessment of the validity of BOLD MRI in a rabbit model of inflammatory arthritis at 1.5 tesla

  • Michael W. Chan
  • George Nathanael
  • Antonella Kis
  • Afsaneh Amirabadi
  • Anguo Zhong
  • Tammy Rayner
  • Ruth Weiss
  • Garry Detzler
  • Roland Jong
  • Harpal Gahunia
  • Rahim Moineddin
  • Adrian Crawley
  • Andrea S. Doria
Original Article

Abstract

Background

Blood-oxygen-level-dependent (BOLD) MRI has the potential to identify regions of early hypoxic and vascular joint changes in inflammatory arthritis. There is no standard protocol for analysis of BOLD MRI measurements in musculoskeletal disorders.

Objective

To optimize the following BOLD MRI reading parameters: (1) statistical threshold values (low, r > 0.01 versus high, r > 0.2); (2) summary measures of BOLD contrast (percentage of activated voxels [PT%] versus percentage signal difference between on-and-off signal intensities [diff_on_off]); and (3) direction of BOLD response (positive, negative and positive + negative).

Materials and methods

Using BOLD MRI protocols at 1.5 T, arthritic (n = 21) and contralateral (n = 21) knees of 21 juvenile rabbits were imaged at baseline and on days 1, 14 and 28 after a unilateral intra-articular injection of carrageenan. Nine non-injected rabbits served as external control knees (n = 18). By comparing arthritic to contralateral knees, receiver operating characteristic curves were used to determine diagnostic accuracy.

Results

Using diff_on_off and positive + negative responses, a threshold of r > 0.01 was more accurate than r > 0.2 (P = 0.03 at day 28). Comparison of summary measures yielded no statistically significant difference (P > 0.05). Although positive + negative (AUC = 0.86 at day 28) and negative responses (AUC = 0.90 at day 28) for PT% were the most diagnostically accurate, positive + negative responses for diff_on_off (AUC = 0.78 at day 28) also had acceptable accuracy.

Conclusions

The most clinically relevant reading parameters included a lower threshold of r > 0.01 and a positive + negative BOLD response. We propose that diff_on_off is a more clinically relevant summary measure of BOLD MRI, while PT% can be used as an ancillary measure.

Keywords

Inflammatory arthritis Rabbits Reading parameters Blood-oxygen-level-dependent MRI 

Notes

Acknowledgments

This study was supported by a Seed Grant provided by the Radiological Society of North America Foundation to Dr. Andrea S. Doria. Dr. Michael W. Chan received the 2011 Comprehensive Research Experience for Medical Students (CREMS), Faculty of Medicine, University of Toronto Summer Studentship Award for the conduct of the data analysis of this study.

Conflicts of interest

None.

References

  1. 1.
    Ravelli A, Martini A (2007) Juvenile idiopathic arthritis. Lancet 369:767–778PubMedCrossRefGoogle Scholar
  2. 2.
    Damasio MB, Malattia C, Martini A et al (2010) Synovial and inflammatory diseases in childhood: role of new imaging modalities in the assessment of patients with juvenile idiopathic arthritis. Pediatr Radiol 40:985–998PubMedCrossRefGoogle Scholar
  3. 3.
    Edmonds SE, Blake DR, Morris CJ et al (1993) An imaginative approach to synovitis – the role of hypoxic reperfusion damage in arthritis. J Rheumatol Suppl 37:26–31PubMedGoogle Scholar
  4. 4.
    Stevens CR, Blake DR, Merry P et al (1991) A comparative study by morphometry of the microvasculature in normal and rheumatoid synovium. Arthritis Rheum 34:1508–1513PubMedCrossRefGoogle Scholar
  5. 5.
    Taylor PC, Sivakumar B (2005) Hypoxia and angiogenesis in rheumatoid arthritis. Curr Opin Rheumatol 17:293–298PubMedCrossRefGoogle Scholar
  6. 6.
    Lovell DJ, Ruperto N, Goodman S et al (2008) Adalimumab with or without methotrexate in juvenile rheumatoid arthritis. N Engl J Med 359:810–820PubMedCrossRefGoogle Scholar
  7. 7.
    Lovell DJ, Giannini EH, Reiff A et al (2000) Etanercept in children with polyarticular juvenile rheumatoid arthritis. Pediatric Rheumatology Collaborative Study Group. N Engl J Med 342:763–769PubMedCrossRefGoogle Scholar
  8. 8.
    Beukelman T, Patkar NM, Saag KG et al (2011) 2011 American College of Rheumatology recommendations for the treatment of juvenile idiopathic arthritis: initiation and safety monitoring of therapeutic agents for the treatment of arthritis and systemic features. Arthritis Care Res 63:465–482CrossRefGoogle Scholar
  9. 9.
    Padhani AR (2002) Dynamic contrast-enhanced MRI in clinical oncology: current status and future directions. J Magn Reson Imaging 16:407–422PubMedCrossRefGoogle Scholar
  10. 10.
    Doria AS, Dick P (2005) Region-of-interest-based analysis of clustered BOLD MRI data in experimental arthritis. Acad Radiol 12:841–852PubMedCrossRefGoogle Scholar
  11. 11.
    Doria AS, Wang C, Zhong A et al (2011) Reliability and convergent validity of different BOLD MRI frameworks for data acquisition in experimental arthritis. Acad Radiol 18:615–625PubMedCrossRefGoogle Scholar
  12. 12.
    Doria AS, Crawley A, Gahunia H et al (2012) Correlative BOLD MR imaging of stages of synovitis in a rabbit model of antigen-induced arthritis. Pediatr Radiol 42:63–75PubMedCrossRefGoogle Scholar
  13. 13.
    Carlier PG, Bertoldi D, Baligand C et al (2006) Muscle blood flow and oxygenation measured by NMR imaging and spectroscopy. NMR Biomed 19:954–967PubMedCrossRefGoogle Scholar
  14. 14.
    Howe F, Robinson S, Rijken PJFW et al (2002) Determination of tumor vascular morphology and function by susceptibility MRI and immunohistochemistry. Proc Intl Soc Mag Reson Med 10Google Scholar
  15. 15.
    Motohashi N, Nakamichi Y, Mori I et al (1990) Concentration and degradation of hyaluronic acid in knee synovial fluid from carrageenin-induced rabbit arthritis. Chem Pharm Bull 38:1953–1956PubMedCrossRefGoogle Scholar
  16. 16.
    Imrie RC (1976) Animal models of arthritis. Lab Anim Sci 26:345–351PubMedGoogle Scholar
  17. 17.
    Winter JD, Estrada M, Cheng HL (2011) Normal tissue quantitative T1 and T2* MRI relaxation time responses to hypercapnic and hyperoxic gases. Acad Radiol 18:1159–1167PubMedCrossRefGoogle Scholar
  18. 18.
    O’Connor JP, Naish JH, Jackson A et al (2009) Comparison of normal tissue R1 and R*2 modulation by oxygen and carbogen. Magn Reson Med 61:75–83PubMedCrossRefGoogle Scholar
  19. 19.
    Glover GH, Lai S (1998) Self-navigated spiral fMRI: interleaved versus single-shot. Magn Reson Med 39:361–368PubMedCrossRefGoogle Scholar
  20. 20.
    Chung SC, Sohn JH, Lee B et al (2007) A comparison of the mean signal change method and the voxel count method to evaluate the sensitivity of individual variability in visuospatial performance. Neurosci Lett 418:138–142PubMedCrossRefGoogle Scholar
  21. 21.
    Lim DW, Min BC, Kim HJ et al (2008) Cerebral lateralization index based on intensity of bold signal of FMRI. Int J Neurosci 118:1628–1642PubMedCrossRefGoogle Scholar
  22. 22.
    Koizumi F, Matsuno H, Wakaki K et al (1999) Synovitis in rheumatoid arthritis: scoring of characteristic histopathological features. Pathol Int 49:298–304PubMedCrossRefGoogle Scholar
  23. 23.
    Oehler S, Neureiter D, Meyer-Scholten C et al (2002) Subtyping of osteoarthritic synoviopathy. Clin Exp Rheumatol 20:633–640PubMedGoogle Scholar
  24. 24.
    Ceponis A, Waris E, Monkkonen J et al (2001) Effects of low-dose, noncytotoxic, intraarticular liposomal clodronate on development of erosions and proteoglycan loss in established antigen-induced arthritis in rabbits. Arthritis Rheum 44:1908–1916PubMedCrossRefGoogle Scholar
  25. 25.
    Dawson J, Engelhardt P, Kastelic T et al (1999) Effects of soluble interleukin-1 type II receptor on rabbit antigen-induced arthritis: clinical, biochemical and histological assessment. Rheumatology 38:401–406PubMedCrossRefGoogle Scholar
  26. 26.
    Doria AS, Noseworthy M, Oakden W et al (2006) Dynamic contrast-enhanced MRI quantification of synovium microcirculation in experimental arthritis. AJR Am J Roentgenol 186:1165–1171PubMedCrossRefGoogle Scholar
  27. 27.
    Altman D (1991) Practical statistics for medical research. Chapman & Hall, London, pp 132–145Google Scholar
  28. 28.
    DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845PubMedCrossRefGoogle Scholar
  29. 29.
    Riegelman R, Hirsch R (1996) Studying a study and testing a test: how to read the health science literature, 3rd edn. Little, Brown and Company, BostonGoogle Scholar
  30. 30.
    Park SH, Goo JM, Jo CH (2004) Receiver operating characteristic (ROC) curve: practical review for radiologists. Korean J Radiol 5:11PubMedCentralPubMedCrossRefGoogle Scholar
  31. 31.
    Rahim H, Ibrahim F, Taib M (2008) Model order selection criterion for monitoring haemoglobin status in dengue patients using ARX model. Information Technology and Applications in Biomedicine International Conference 2008. doi:10.1109/ITAB.2008.4570537
  32. 32.
    Bennett CM, Wolford GL, Miller MB (2009) The principled control of false positives in neuroimaging. Soc Cogn Affect Neurosci 4:417–422PubMedCentralPubMedCrossRefGoogle Scholar
  33. 33.
    Lieberman MD, Cunningham WA (2009) Type I and type II error concerns in fMRI research: re-balancing the scale. Soc Cogn Affect Neurosci 4:423–428PubMedCentralPubMedCrossRefGoogle Scholar
  34. 34.
    Ramsey NF, Kirkby BS, Van Gelderen P et al (1996) Functional mapping of human sensorimotor cortex with 3D BOLD fMRI correlates highly with H2(15)O PET rCBF. J Cereb Blood Flow Metab 16:755–764PubMedCrossRefGoogle Scholar
  35. 35.
    Chung SC, Tack GR, Lee B et al (2004) The effect of 30% oxygen on visuospatial performance and brain activation: an fMRI study. Brain Cogn 56:279–285PubMedCrossRefGoogle Scholar
  36. 36.
    Chung SC, Lee B, Tack GR et al (2006) The effect of oxygen administration on visuospatial cognitive performance: time course data analysis of fMRI. Int J Neurosci 116:177–189PubMedCrossRefGoogle Scholar
  37. 37.
    Lustig C, Buckner RL (2004) Preserved neural correlates of priming in old age and dementia. Neuron 42:865–875PubMedCrossRefGoogle Scholar
  38. 38.
    Mattay VS, Callicott JH, Bertolino A et al (2000) Effects of dextroamphetamine on cognitive performance and cortical activation. Neuroimage 12:268–275PubMedCrossRefGoogle Scholar
  39. 39.
    Sohn J, Kim Y, Lee K et al (2002) An fMRI study to identify the brain structure for predicting the level of individual’s intelligence. Neuroimage 16:1024Google Scholar
  40. 40.
    Taylor SF, Welsh RC, Wager TD et al (2004) A functional neuroimaging study of motivation and executive function. Neuroimage 21:1045–1054PubMedCrossRefGoogle Scholar
  41. 41.
    Cohen MS, DuBois RM (1999) Stability, repeatability, and the expression of signal magnitude in functional magnetic resonance imaging. J Magn Reson Imaging 10:33–40PubMedCrossRefGoogle Scholar
  42. 42.
    Baudelet C, Cron GO, Gallez B (2006) Determination of the maturity and functionality of tumor vasculature by MRI: correlation between BOLD-MRI and DCE-MRI using P792 in experimental fibrosarcoma tumors. Magn Reson Med 56:1041–1049PubMedCrossRefGoogle Scholar
  43. 43.
    Howe FA, Robinson SP, McIntyre DJ et al (2001) Issues in flow and oxygenation dependent contrast (FLOOD) imaging of tumours. NMR Biomed 14:497–506PubMedCrossRefGoogle Scholar
  44. 44.
    Lanzen JL, Braun RD, Ong AL et al (1998) Variability in blood flow and pO2 in tumors in response to carbogen breathing. Int J Radiat Oncol Biol Phys 42:855–859PubMedCrossRefGoogle Scholar
  45. 45.
    Neeman M, Dafni H, Bukhari O et al (2001) In vivo BOLD contrast MRI mapping of subcutaneous vascular function and maturation: validation by intravital microscopy. Magn Reson Med 45:887–898PubMedCrossRefGoogle Scholar
  46. 46.
    Thews O, Kelleher DK, Vaupel P (2002) Dynamics of tumor oxygenation and red blood cell flux in response to inspiratory hyperoxia combined with different levels of inspiratory hypercapnia. Radiother Oncol 62:77–85PubMedCrossRefGoogle Scholar
  47. 47.
    Poublanc J, Han JS, Mandell DM et al (2013) Vascular steal explains early paradoxical blood oxygen level-dependent cerebrovascular response in brain regions with delayed arterial transit times. Cerebrovasc Dis Extra 3:55–64PubMedCentralPubMedCrossRefGoogle Scholar
  48. 48.
    Diergarten T, Martirosian P, Kottke R et al (2005) Functional characterization of prostate cancer by integrated magnetic resonance imaging and oxygenation changes during carbogen breathing. Invest Radiol 40:102–109PubMedCrossRefGoogle Scholar
  49. 49.
    Taylor NJ, Baddeley H, Goodchild KA et al (2001) BOLD MRI of human tumor oxygenation during carbogen breathing. J Magn Reson Imaging 14:156–163PubMedCrossRefGoogle Scholar
  50. 50.
    Ben Bashat D, Artzi M, Ben Ami H et al (2012) Hemodynamic response imaging: a potential tool for the assessment of angiogenesis in brain tumors. PLoS One 7:e49416PubMedCentralPubMedCrossRefGoogle Scholar
  51. 51.
    Sonveaux P, Jordan BF, Gallez B et al (2009) Nitric oxide delivery to cancer: why and how? Eur J Cancer 45:1352–1369PubMedCrossRefGoogle Scholar
  52. 52.
    Di Salle F, Esposito F, Elefante A et al (2003) High field functional MRI. Eur J Radiol 48:138–145PubMedCrossRefGoogle Scholar
  53. 53.
    Duong TQ, Silva AC, Lee SP et al (2000) Functional MRI of calcium-dependent synaptic activity: cross correlation with CBF and BOLD measurements. Magn Reson Med 43:383–392PubMedCrossRefGoogle Scholar
  54. 54.
    Preston AR, Thomason ME, Ochsner KN et al (2004) Comparison of spiral-in/out and spiral-out BOLD fMRI at 1.5 and 3 T. Neuroimage 21:291–301PubMedCrossRefGoogle Scholar
  55. 55.
    Change C, Glover GH (2011) Variable density spiral-in/out fMRI. Magn Reson Med 65:1287–1296CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Michael W. Chan
    • 1
  • George Nathanael
    • 1
  • Antonella Kis
    • 1
  • Afsaneh Amirabadi
    • 1
  • Anguo Zhong
    • 1
  • Tammy Rayner
    • 1
  • Ruth Weiss
    • 1
  • Garry Detzler
    • 1
  • Roland Jong
    • 2
  • Harpal Gahunia
    • 1
  • Rahim Moineddin
    • 3
  • Adrian Crawley
    • 4
    • 5
  • Andrea S. Doria
    • 1
    • 4
  1. 1.Department of Diagnostic ImagingThe Hospital for Sick ChildrenTorontoCanada
  2. 2.Department of Pathology and Laboratory MedicineMount Sinai HospitalTorontoCanada
  3. 3.Department of Public HealthFamily and Community MedicineTorontoCanada
  4. 4.Department of Medical ImagingUniversity of TorontoTorontoCanada
  5. 5.Department of Medical ImagingToronto Western HospitalTorontoCanada

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