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Automated Registration and Color Labeling of Serial 3D Double Inversion Recovery MR Imaging for Detection of Lesion Progression in Multiple Sclerosis

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Abstract

Automated co-registration and subtraction techniques have been shown to be useful in the assessment of longitudinal changes in multiple sclerosis (MS) lesion burden, but the majority depend on T2-fluid-attenuated inversion recovery sequences. We aimed to investigate the use of a novel automated temporal color complement imaging (CCI) map overlapped on 3D double inversion recovery (DIR), and to assess its diagnostic performance for detecting disease progression in patients with multiple sclerosis (MS) as compared to standard review of serial 3D DIR images. We developed a fully automated system that co-registers and compares baseline to follow-up 3D DIR images and outputs a pseudo-color RGB map in which red pixels indicate increased intensity values in the follow-up image (i.e., progression; new/enlarging lesion), blue-green pixels represent decreased intensity values (i.e., disappearing/shrinking lesion), and gray-scale pixels reflect unchanged intensity values. Three neuroradiologists blinded to clinical information independently reviewed each patient using standard DIR images alone and using CCI maps based on DIR images at two separate exams. Seventy-six follow-up examinations from 60 consecutive MS patients who underwent standard 3 T MR brain MS protocol that included 3D DIR were included. Median cohort age was 38.5 years, with 46 women, 59 relapsing–remitting type MS, and median follow-up interval of 250 days (interquartile range: 196–394 days). Lesion progression was detected in 67.1% of cases using CCI review versus 22.4% using standard review, with a total of 182 new or enlarged lesions using CCI review versus 28 using standard review. There was a statistically significant difference between the two methods in the rate of all progressive lesions (P < 0.001, McNemar’s test) as well as cortical progressive lesions (P < 0.001). Automated CCI maps using co-registered serial 3D DIR, compared to standard review of 3D DIR alone, increased detection rate of MS lesion progression in patients undergoing clinical brain MRI exam.

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Availability of Data and Material

The data that supports the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Dendrou CA, Fugger L, Friese MA. Immunopathology of multiple sclerosis. Nature Reviews Immunology 2015;15(9):545-558. https://doi.org/10.1038/nri3871

    Article  CAS  PubMed  Google Scholar 

  2. Bakshi R, Ariyaratana S, Benedict RH, Jacobs L. Fluid-attenuated inversion recovery magnetic resonance imaging detects cortical and juxtacortical multiple sclerosis lesions. Arch Neurol 2001;58(5):742-748. https://doi.org/10.1001/archneur.58.5.742

    Article  CAS  PubMed  Google Scholar 

  3. Filippi M, Paty DW, Kappos L, Barkhof F, Compston DA, Thompson AJ, Zhao GJ, Wiles CM, McDonald WI, Miller DH. Correlations between changes in disability and T2-weighted brain MRI activity in multiple sclerosis: a follow-up study. Neurology 1995;45(2):255-260. https://doi.org/10.1212/wnl.45.2.255

    Article  CAS  PubMed  Google Scholar 

  4. Erbayat Altay E, Fisher E, Jones SE, Hara-Cleaver C, Lee JC, Rudick RA. Reliability of classifying multiple sclerosis disease activity using magnetic resonance imaging in a multiple sclerosis clinic. JAMA Neurol 2013;70(3):338-344. https://doi.org/10.1001/2013.jamaneurol.211

    Article  PubMed  Google Scholar 

  5. Barkhof F, Simon JH, Fazekas F, Rovaris M, Kappos L, de Stefano N, Polman CH, Petkau J, Radue EW, Sormani MP, Li DK, O'Connor P, Montalban X, Miller DH, Filippi M. MRI monitoring of immunomodulation in relapse-onset multiple sclerosis trials. Nat Rev Neurol 2011;8(1):13-21. https://doi.org/10.1038/nrneurol.2011.190

    Article  PubMed  Google Scholar 

  6. Traboulsee A, Simon JH, Stone L, Fisher E, Jones DE, Malhotra A, Newsome SD, Oh J, Reich DS, Richert N, Rammohan K, Khan O, Radue EW, Ford C, Halper J, Li D. Revised Recommendations of the Consortium of MS Centers Task Force for a Standardized MRI Protocol and Clinical Guidelines for the Diagnosis and Follow-Up of Multiple Sclerosis. AJNR Am J Neuroradiol 2016;37(3):394-401. https://doi.org/10.3174/ajnr.A4539

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wattjes MP, Rovira À, Miller D, Yousry TA, Sormani MP, de Stefano MP, Tintoré M, Auger C, Tur C, Filippi M, Rocca MA, Fazekas F, Kappos L, Polman C, Frederik B, Xavier M. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis--establishing disease prognosis and monitoring patients. Nat Rev Neurol 2015;11(10):597-606. https://doi.org/10.1038/nrneurol.2015.157

    Article  CAS  PubMed  Google Scholar 

  8. Molyneux PD, Miller DH, Filippi M, Yousry TA, Radü EW, Adèr HJ, Barkhof F. Visual analysis of serial T2-weighted MRI in multiple sclerosis: intra- and interobserver reproducibility. Neuroradiology 1999;41(12):882-888. https://doi.org/10.1007/s002340050860

    Article  CAS  PubMed  Google Scholar 

  9. Tan IL, van Schijndel RA, Fazekas F, Filippi M, Freitag P, Miller DH, Yousry TA, Pouwels PJ, Adèr HJ, Barkhof F. Image registration and subtraction to detect active T(2) lesions in MS: an interobserver study. J Neurol 2002;249(6):767-773. https://doi.org/10.1007/s00415-002-0712-6

    Article  PubMed  Google Scholar 

  10. Danelakis A, Theoharis T, Verganelakis DA. Survey of automated multiple sclerosis lesion segmentation techniques on magnetic resonance imaging. Comput Med Imaging Graph 2018;70:83-100. https://doi.org/10.1016/j.compmedimag.2018.10.002

    Article  PubMed  Google Scholar 

  11. Galletto Pregliasco A, Collin A, Guéguen A, Metten MA, Aboab J, Deschamps R, Gout O, Duron L, Sadik JC, Savatovsky J, Lecler A. Improved Detection of New MS Lesions during Follow-Up Using an Automated MR Coregistration-Fusion Method. AJNR Am J Neuroradiol 2018;39(7):1226-1232. https://doi.org/10.3174/ajnr.A5690

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Jain S, Sima DM, Ribbens A, Cambron M, Maertens A, Van Hecke W, De Mey J, Barkhof F, Steenwijk MD, Daams M, Maes F, Van Huffel S, Vrenken H, Smeets D. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images. Neuroimage Clin 2015;8:367-375. https://doi.org/10.1016/j.nicl.2015.05.003

    Article  PubMed  PubMed Central  Google Scholar 

  13. Bilello M, Arkuszewski M, Nucifora P, Nasrallah I, Melhem ER, Cirillo L, Krejza J. Multiple sclerosis: identification of temporal changes in brain lesions with computer-assisted detection software. Neuroradiol J 2013;26(2):143-150. https://doi.org/10.1177/197140091302600202

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Calabrese M, Agosta F, Rinaldi F, Mattisi I, Grossi P, Favaretto A, Atzori M, Bernardi V, Barachino L, Rinaldi L, Perini P, Gallo P, Filippi M. Cortical lesions and atrophy associated with cognitive impairment in relapsing-remitting multiple sclerosis. Arch Neurol 2009;66(9):1144-1150. https://doi.org/10.1001/archneurol.2009.174

    Article  PubMed  Google Scholar 

  15. Mike A, Glanz BI, Hildenbrand P, Meier D, Bolden K, Liguori M, Dell'Oglio E, Healy BC, Bakshi R, Guttmann CR. Identification and clinical impact of multiple sclerosis cortical lesions as assessed by routine 3T MR imaging. AJNR Am J Neuroradiol 2011;32(3):515-521. https://doi.org/10.3174/ajnr.A2340

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Seewann A, Kooi EJ, Roosendaal SD, Pouwels PJ, Wattjes MP, van der Valk P, Barkhof F, Polman CH, Geurts JJ. Postmortem verification of MS cortical lesion detection with 3D DIR. Neurology 2012;78(5):302-308. https://doi.org/10.1212/WNL.0b013e31824528a0

    Article  CAS  PubMed  Google Scholar 

  17. Wattjes MP, Lutterbey GG, Gieseke J, Träber F, Klotz L, Schmidt S, Schild HH. Double inversion recovery brain imaging at 3T: diagnostic value in the detection of multiple sclerosis lesions. AJNR Am J Neuroradiol 2007;28(1):54-59.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Park CC, Thongkham DW, Sadigh G, Saindane AM, Chu R, Bakshi R, Allen JW, Hu R. Detection of Cortical and Deep Gray Matter Lesions in Multiple Sclerosis Using DIR and FLAIR at 3T. Journal of Neuroimaging 2021;31(2):408-414. https://doi.org/10.1111/jon.12822

    Article  PubMed  Google Scholar 

  19. Geurts JJG, Pouwels PJW, Uitdehaag BMJ, Polman CH, Barkhof F, Castelijns JA. Intracortical Lesions in Multiple Sclerosis: Improved Detection with 3D Double Inversion-Recovery MR Imaging. Radiology 2005;236(1):254-260. https://doi.org/10.1148/radiol.2361040450

    Article  PubMed  Google Scholar 

  20. Carpenter WA, Stiles RG, Sheppard SK. Color map of contrast enhancement on MR images: use of desktop computers. AJR Am J Roentgenol 1994;162(1):223-226. https://doi.org/10.2214/ajr.162.1.8273670

    Article  CAS  PubMed  Google Scholar 

  21. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977;33(1):159-174.

    Article  CAS  PubMed  Google Scholar 

  22. Battaglini M, Rossi F, Grove RA, Stromillo ML, Whitcher B, Matthews PM, De Stefano N. Automated identification of brain new lesions in multiple sclerosis using subtraction images. J Magn Reson Imaging 2014;39(6):1543-1549. https://doi.org/10.1002/jmri.24293

    Article  PubMed  Google Scholar 

  23. Zopfs D, Laukamp KR, Paquet S, Lennartz S, Pinto Dos Santos D, Kabbasch C, Bunck A, Schlamann M, Borggrefe J. Follow-up MRI in multiple sclerosis patients: automated co-registration and lesion color-coding improves diagnostic accuracy and reduces reading time. Eur Radiol 2019;29(12):7047–7054. https://doi.org/10.1007/s00330-019-06273-x

    Article  PubMed  Google Scholar 

  24. Van Heerden J, Rawlinson D, Zhang AM, Chakravorty R, Tacey MA, Desmond PM, Gaillard F. Improving multiple sclerosis plaque detection using a semiautomated assistive approach. AJNR Am J Neuroradiol 2015;36(8):1465-1471. https://doi.org/10.3174/ajnr.A4375

    Article  PubMed  PubMed Central  Google Scholar 

  25. Bink A, Schmitt M, Gaa J, Mugler JP, 3rd, Lanfermann H, Zanella FE. Detection of lesions in multiple sclerosis by 2D FLAIR and single-slab 3D FLAIR sequences at 3.0 T: initial results. Eur Radiol 2006;16(5):1104–1110. https://doi.org/10.1007/s00330-005-0107-z

  26. De Graaf WL, Zwanenburg JJ, Visser F, Wattjes MP, Pouwels PJ, Geurts JJ, Polman CH, Barkhof F, Luijten PR, Castelijns JA. Lesion detection at seven Tesla in multiple sclerosis using magnetisation prepared 3D-FLAIR and 3D-DIR. Eur Radiol 2012;22(1):221-231. https://doi.org/10.1007/s00330-011-2242-z

    Article  PubMed  Google Scholar 

  27. Saindane AM. Is Gadolinium-based Contrast Material Needed for MRI Follow-up of Multiple Sclerosis? Radiology 2019;291(2):436-437. https://doi.org/10.1148/radiol.2019190319

    Article  PubMed  Google Scholar 

  28. Moraal B, Wattjes MP, Geurts JJ, Knol DL, Van Schijndel RA, Pouwels PJ, Vrenken H, Barkhof F. Improved detection of active multiple sclerosis lesions: 3D subtraction imaging. Radiology 2010;255(1):154-163. https://doi.org/10.1148/radiol.09090814

    Article  PubMed  Google Scholar 

  29. Tan IL, Van Schijndel RA, Pouwels PJ, Adèr HJ, Barkhof F. Serial isotropic three-dimensional fast FLAIR imaging: using image registration and subtraction to reveal active multiple sclerosis lesions. AJR Am J Roentgenol 2002;179(3):777-782. https://doi.org/10.2214/ajr.179.3.1790777

    Article  PubMed  Google Scholar 

  30. Wang W, Van Heerden J, Tacey MA, Gaillard F. Neuroradiologists Compared with Non-Neuroradiologists in the Detection of New Multiple Sclerosis Plaques. American Journal of Neuroradiology 2017;38(7):1323-1327. doi: https://doi.org/10.3174/ajnr.A5185

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Funding

Emory University Department of Radiology Seed Grant.

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Authors and Affiliations

  1. Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road NE, Suite BG20, Atlanta, GA, 30322, USA

    Charlie C. Park, Marijn E. Brummer, Gelareh Sadigh, Amit M. Saindane, Mark E. Mullins, Jason W. Allen & Ranliang Hu

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  1. Charlie C. Park
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  2. Marijn E. Brummer
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  3. Gelareh Sadigh
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Contributions

“All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by all authors. The first draft of the manuscript was written by CCP, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ranliang Hu.

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Ethics Approval

Ethical approval to conduct this study was obtained from the Emory University Institutional Review Board (approval ID IRB 00103126). This is a non-interventional study with retrospective analysis of radiology images, which were obtained from PACS without direct contribution from the studied population. Therefore, the data obtained for evaluation did not require individual consent per the IRB approval.

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Dr. Sadigh receives research support from the National Multiple Sclerosis Society.

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Park, C.C., Brummer, M.E., Sadigh, G. et al. Automated Registration and Color Labeling of Serial 3D Double Inversion Recovery MR Imaging for Detection of Lesion Progression in Multiple Sclerosis. J Digit Imaging 36, 450–457 (2023). https://doi.org/10.1007/s10278-022-00737-1

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