Log in

Role of New Imaging Capabilities with MRI and CT in the Evaluation of Bronchiectasis

  • Bronchiectasis (A. Schmid, Section Editor)
  • Published:
Current Pulmonology Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Bronchiectasis is the permanent abnormal dilation of the airways and can be associated with various pathogeneses and outcomes. Proper diagnosis and evaluation of bronchiectasis employs x-ray CT, though emerging research has also shown MRI to be sensitive to the disease. The goal of this article is to review recent research in CT and MRI of bronchiectasis and associated diseases.

Recent Findings

Techniques in quantitative CT continue to improve and may improve consistency in bronchiectasis evaluation and reduce reader scoring load. While CT is the “gold standard” for evaluation of bronchiectasis, new techniques in magnetic resonance imaging have dramatically increased in sensitivity to bronchiectasis though without patient exposure to ionizing radiation. MRI may therefore be an attractive alternative to CT in some cases, yet further clinical trials are necessary.

Summary

Although bronchiectasis is an irreversible disease, new understanding of airway structure-function relationships may improve clinical management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. •• Bueno J, Flors L. The role of imaging in the diagnosis of bronchiectasis: the key is in the distribution. Radiología. 2018;60(1):39–48. https://doi.org/10.1016/j.rxeng.2017.06.005This paper describes the current practices of bronchiectasis diagnosis, imaging findings, and their classification.

    Article  CAS  PubMed  Google Scholar 

  2. Gallucci M, di Palmo E, Bertelli L, Camela F, Ricci G, Pession A. A pediatric disease to keep in mind: diagnostic tools and management of bronchiectasis in pediatric age. Ital J Pediatr. 2017;43(1):117. https://doi.org/10.1186/s13052-017-0434-0.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Chan ED, Iseman MD. Bronchiectasis. In: Murray & Nadel’s textbook of respiratory medicine. Philadelphia: Saunders Elsevier; 2016. p. 2064.

  4. Weycker D, Hansen GL, Seifer FD. Prevalence and incidence of noncystic fibrosis bronchiectasis among US adults in 2013. Chron Respir Dis. 2017;14(4):377–84. https://doi.org/10.1177/1479972317709649.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Contarini M, Finch S, Chalmers JD. Bronchiectasis: a case-based approach to investigation and management. Eur Respir Rev. 2018;27(149). https://doi.org/10.1183/16000617.0016-2018.

    Article  PubMed  Google Scholar 

  6. • Singh A, Bhalla AS, Jana M. Bronchiectasis revisited: imaging-based pattern approach to diagnosis. Curr Probl Diagn Radiol. 2019;48(1):53–60. https://doi.org/10.1067/j.cpradiol.2017.12.001This article discusses image-based algorithmic approaches towards the etiological diagnosis of bronchiectasis.

    Article  PubMed  Google Scholar 

  7. Verbanck S, et al. The quantitative link of lung clearance index to bronchial segments affected by bronchiectasis. Thorax. 2018;73(1):82–4. https://doi.org/10.1136/thoraxjnl-2017-210496.

    Article  PubMed  Google Scholar 

  8. Biederer J, Mirsadraee S, Beer M, Molinari F, Hintze C, Bauman G, et al. MRI of the lung (3/3)-current applications and future perspectives. Insights Imaging. 2012;3(4):373–86. https://doi.org/10.1007/s13244-011-0142-z.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Perera PL, Screaton NJ. Radiological features of bronchiectasis. 2011:44–67. https://doi.org/10.1183/1025448x.10003510.

    Chapter  Google Scholar 

  10. Murphy KP, Maher MM, O'Connor OJ. Imaging of cystic fibrosis and pediatric bronchiectasis. AJR Am J Roentgenol. 2016;206(3):448–54. https://doi.org/10.2214/AJR.15.14437.

    Article  PubMed  Google Scholar 

  11. •• Hill AT, et al. British Thoracic Society Guideline for bronchiectasis in adults. Thorax. 2019;74(Suppl 1):1–69. https://doi.org/10.1136/thoraxjnl-2018-212463British Thoracic Society provided a detail guideline and good practice points in the diagnosis, treatment, and management of bronchiectasis.

    Article  PubMed  Google Scholar 

  12. Kim JS, Müller NL, Park CS, Grenier P, Herold CJ. Cylindrical bronchiectasis: diagnostic findings in thin-section CT. Am J Roentgenol. 1997;168(3):751–4.

    Article  CAS  Google Scholar 

  13. Webb WR, Muller NL, Naidich DP. Standardized terms for high-resolution computed tomography of the lung: a proposed glossary. J Thorac Imaging. 1993;8(3):167–75.

    Article  CAS  PubMed  Google Scholar 

  14. Reid LM. Reduction in bronchial subdivision in bronchiectasis. Thorax. 1950;5(3):233–47.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Milliron B, et al. Bronchiectasis: mechanisms and imaging clues of associated common and uncommon diseases. Radiographics. 2015;35(4):1011–30. https://doi.org/10.1148/rg.2015140214.

    Article  PubMed  Google Scholar 

  16. Reiff DB, Wells AU, Carr DH, Cole PJ, Hansell DM. CT findings in bronchiectasis: limited value in distinguishing between idiopathic and specific types. AJR Am J Roentgenol. 1995;165(2):261–7. https://doi.org/10.2214/ajr.165.2.7618537.

    Article  CAS  PubMed  Google Scholar 

  17. Agarwal R, Gupta D, Aggarwal AN, Saxena AK, Chakrabarti A, **dal SK. Clinical significance of hyperattenuating mucoid impaction in allergic bronchopulmonary aspergillosis: an analysis of 155 patients. Chest. 2007;132(4):1183–90. https://doi.org/10.1378/chest.07-0808.

    Article  PubMed  Google Scholar 

  18. Aksamit TR, O'Donnell AE, Barker A, Olivier KN, Winthrop KL, Daniels MLA, et al. Adult patients with bronchiectasis: a first look at the US bronchiectasis research registry. Chest. 2017;151(5):982–92. https://doi.org/10.1016/j.chest.2016.10.055.

    Article  PubMed  Google Scholar 

  19. Cantin L, Bankier AA, Eisenberg RL. Bronchiectasis. AJR Am J Roentgenol. 2009;193(3):W158–71. https://doi.org/10.2214/AJR.09.3053.

    Article  PubMed  Google Scholar 

  20. Kwak N, Lee CH, Lee HJ, Kang YA, Lee JH, Han SK, et al. Non-tuberculous mycobacterial lung disease: diagnosis based on computed tomography of the chest. Eur Radiol. 2016;26(12):4449–56. https://doi.org/10.1007/s00330-016-4286-6.

    Article  PubMed  Google Scholar 

  21. • Rademacher J, Welte T. Bronchiectasis--diagnosis and treatment. Dtsch Arztebl Int. 2011;108(48):809–15. https://doi.org/10.3238/arztebl.2011.0809This is a review article about the diagnostic evaluation and treatment in patients with non-cystic fibrosis bronchictasis.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Dodd JD, Souza CA, Muller NL. Conventional high-resolution CT versus helical high-resolution MDCT in the detection of bronchiectasis. AJR Am J Roentgenol. 2006;187(2):414–20. https://doi.org/10.2214/AJR.05.0723.

    Article  PubMed  Google Scholar 

  23. Hill LE, Ritchie G, Wightman AJ, Hill AT, Murchison JT. Comparison between conventional interrupted high-resolution CT and volume multidetector CT acquisition in the assessment of bronchiectasis. Br J Radiol. 2010;83(985):67–70. https://doi.org/10.1259/bjr/96908158.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bhalla M, Turcios N, Aponte V, Jenkins M, Leitman BS, McCauley D, et al. Cystic fibrosis: scoring system with thin-section CT. Radiology. 1991;179(3):783–8. https://doi.org/10.1148/radiology.179.3.2027992.

    Article  CAS  PubMed  Google Scholar 

  25. Brody AS, Klein JS, Molina PL, Quan J, Bean JA, Wilmott RW. High-resolution computed tomography in young patients with cystic fibrosis: distribution of abnormalities and correlation with pulmonary function tests. J Pediatr. 2004;145(1):32–8. https://doi.org/10.1016/j.jpeds.2004.02.038.

    Article  PubMed  Google Scholar 

  26. Nathanson I, et al. Ultrafast computerized tomography of the chest in cystic fibrosis: a new scoring system. Pediatr Pulmonol. 1991;11(1):81–6. https://doi.org/10.1002/ppul.1950110112.

    Article  CAS  PubMed  Google Scholar 

  27. Naidich DP, et al. Computed tomography of bronchiectasis. J Comput Assist Tomogr. 1982;6(3):437–44.

    Article  CAS  PubMed  Google Scholar 

  28. Diaz AA, Young TP, Maselli DJ, Martinez CH, Gill R, Nardelli P, et al. Quantitative CT measures of bronchiectasis in smokers. Chest. 2017;151(6):1255–62. https://doi.org/10.1016/j.chest.2016.11.024.

    Article  PubMed  Google Scholar 

  29. Parr DG, Guest PG, Reynolds JH, Dowson LJ, Stockley RA. Prevalence and impact of bronchiectasis in alpha1-antitrypsin deficiency. Am J Respir Crit Care Med. 2007;176(12):1215–21. https://doi.org/10.1164/rccm.200703-489OC.

    Article  PubMed  Google Scholar 

  30. de Brito MC, et al. Radiologist agreement on the quantification of bronchiectasis by high-resolution computed tomography. Radiol Bras. 2017;50(1):26–31. https://doi.org/10.1590/0100-3984.2015.0146.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Alzeer AH. HRCT score in bronchiectasis: correlation with pulmonary function tests and pulmonary artery pressure. Ann Thorac Med. 2008;3(3):82–6. https://doi.org/10.4103/1817-1737.39675.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Chalmers JD, Goeminne P, Aliberti S, McDonnell M, Lonni S, Davidson J, et al. The bronchiectasis severity index. An international derivation and validation study. Am J Respir Crit Care Med. 2014;189(5):576–85. https://doi.org/10.1164/rccm.201309-1575OC.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Martinez-Garcia MA, et al. Multidimensional approach to non-cystic fibrosis bronchiectasis: the FACED score. Eur Respir J. 2014;43(5):1357–67. https://doi.org/10.1183/09031936.00026313.

    Article  PubMed  Google Scholar 

  34. Rosales-Mayor E, Polverino E, Raguer L, Alcaraz V, Gabarrus A, Ranzani O, et al. Comparison of two prognostic scores (BSI and FACED) in a Spanish cohort of adult patients with bronchiectasis and improvement of the FACED predictive capacity for exacerbations. PLoS One. 2017;12(4):e0175171. https://doi.org/10.1371/journal.pone.0175171.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Costa JC, et al. The Bronchiectasis Severity Index and FACED score for assessment of the severity of bronchiectasis. Pulmonology. 2018. https://doi.org/10.1016/j.rppnen.2017.08.009.

    Article  Google Scholar 

  36. Webb WR, Muller NL, Naidich DP. High-Resolution CT of the Lung. 4th ed. Lippincott Williams and Wilkins; 2009. p. 603.

  37. • Pu J, et al. CT based computerized identification and analysis of human airways: a review. Med Phys. 2012;39(5):2603–16. https://doi.org/10.1118/1.4703901This review article is based on computerized identification and analysis of human airways.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Quan K, et al. Tapering analysis of airways with bronchiectasis. Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105742G (2018). https://doi.org/10.1117/12.2292306.

  39. Aykac D, Hoffman EA, McLennan G, Reinhardt JM. Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images. IEEE Trans Med Imaging. 2003;22(8):940–50. https://doi.org/10.1109/TMI.2003.815905.

    Article  PubMed  Google Scholar 

  40. Estepar RS, et al. Accurate airway wall estimation using phase congruency. Med Image Comput Comput Assist Interv. 2006;9(Pt 2):125–34.

    PubMed  Google Scholar 

  41. Fabijanska A. Two-pass region growing algorithm for segmenting airway tree from MDCT chest scans. Comput Med Imaging Graph. 2009;33(7):537–46. https://doi.org/10.1016/j.compmedimag.2009.04.012.

    Article  PubMed  Google Scholar 

  42. Gu S, Fuhrman C, Meng X, Siegfried JM, Gur D, Leader JK, et al. Computerized identification of airway wall in CT examinations using a 3D active surface evolution approach. Med Image Anal. 2013;17(3):283–96. https://doi.org/10.1016/j.media.2012.11.003.

    Article  PubMed  Google Scholar 

  43. Mori K, et al. Recognition of bronchus in three-dimensional X-ray CT images with applications to virtualized bronchoscopy system, in Proceedings of 13th International Conference on Pattern Recognition. 1996. Vienna, Austria. p. 528–532.

  44. Kiraly AP, Higgins WE, McLennan G, Hoffman EA, Reinhardt JM. Three-dimensional human airway segmentation methods for clinical virtual bronchoscopy. Acad Radiol. 2002;9(10):1153–68. https://doi.org/10.1016/S1076-6332(03)80517-2.

    Article  PubMed  Google Scholar 

  45. Lo P, Sporring J, Ashraf H, Pedersen JJ, de Bruijne M. Vessel-guided airway tree segmentation: a voxel classification approach. Med Image Anal. 2010;14(4):527–38. https://doi.org/10.1016/j.media.2010.03.004.

    Article  PubMed  Google Scholar 

  46. Nakano Y, Muro S, Sakai H, Hirai T, Chin K, Tsukino M, et al. Computed tomographic measurements of airway dimensions and emphysema in smokers. Correlation with lung function. Am J Respir Crit Care Med. 2000;162(3 Pt 1):1102–8. https://doi.org/10.1164/ajrccm.162.3.9907120.

    Article  CAS  PubMed  Google Scholar 

  47. Ochs RA, et al. Automated classification of lung bronchovascular anatomy in CT using AdaBoost. Med Image Anal. 2007;11(3):315–24. https://doi.org/10.1016/j.media.2007.03.004.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Ortner M, et al. 3D vector flow guided segmentation of airway wall in MSCT. In: Proceedings of the 6th International Conference on Advances in Visual Computing 2010. Springer: Las Vegas, NV. p. 302–311.

    Chapter  Google Scholar 

  49. Saba OI, Hoffman EA, Reinhardt JM. Maximizing quantitative accuracy of lung airway lumen and wall measures obtained from X-ray CT imaging. J Appl Physiol (1985). 2003;95(3):1063–75. https://doi.org/10.1152/japplphysiol.00962.2002.

    Article  Google Scholar 

  50. Sonka M, Park W, Hoffman EA. Rule-based detection of intrathoracic airway trees. IEEE Trans Med Imaging. 1996;15(3):314–26. https://doi.org/10.1109/42.500140.

    Article  CAS  PubMed  Google Scholar 

  51. DeBoer EM, et al. Automated CT scan scores of bronchiectasis and air trap** in cystic fibrosis. Chest. 2014;145(3):593–603. https://doi.org/10.1378/chest.13-0588.

    Article  PubMed  Google Scholar 

  52. • Yu N, et al. Computerized identification of bronchiectasis using a 3D quantitative CT protocol. J Med Imaging Health Inform. 2016;6(5):1303–8. https://doi.org/10.1166/jmihi.2016.1917This study proposes a novel three-dimensional automatic method to identify bronchiectasis.

    Article  Google Scholar 

  53. Xu Z, Bagci U, Foster B, Mansoor A, Udupa JK, Mollura DJ. A hybrid method for airway segmentation and automated measurement of bronchial wall thickness on CT. Med Image Anal. 2015;24(1):1–17. https://doi.org/10.1016/j.media.2015.05.003.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Perez-Rovira A, et al. Automatic airway-artery analysis on lung CT to quantify airway wall thickening and bronchiectasis. Med Phys. 2016;43(10). https://doi.org/10.1118/1.4963214.

    Article  PubMed  Google Scholar 

  55. Odry BL, et al. An evaluation of automated broncho-arterial ratios for reliable assessment of bronchiectasis. Proceedings of SPIE - The International Society for Optical Engineering 6915, 2008. https://doi.org/10.1117/12.772579.

  56. Meng Q, et al. Accurate airway segmentation based on intensity structure analysis and graph-cut. Proceedings of the SPIE 2016. 97842G. https://doi.org/10.1117/12.2216670.

  57. Charbonnier JP, Rikxoort EMV, Setio AAA, Schaefer-Prokop CM, Ginneken BV, Ciompi F. Improving airway segmentation in computed tomography using leak detection with convolutional networks. Med Image Anal. 2017;36:52–60. https://doi.org/10.1016/j.media.2016.11.001.

    Article  PubMed  Google Scholar 

  58. Weinheimer, O., et al., Fully automated lobe-based airway taper index calculation in a low dose MDCT CF study over 4 time-points. Medical Imaging: Image Processing. 2017. https://doi.org/10.1117/12.2254387.

  59. Juarez AGU, Tiddens H, de Bruijne M. Automatic airway segmentation in chest CT using convolutional neural networks. Image Analysis for Moving Organ, Breast, and Thoracic Images. 2018. p. 238–250. https://doi.org/10.1007/978-3-030-00946-5_24.

    Chapter  Google Scholar 

  60. Naseri Z, Sherafat S, Abrishami Moghaddam H, Modaresi M, Pak N, Zamani F. Semi-automatic methods for airway and adjacent vessel measurement in bronchiectasis patterns in lung HRCT images of cystic fibrosis patients. J Digit Imaging. 2018;31(5):727–37. https://doi.org/10.1007/s10278-018-0076-9.

    Article  PubMed  PubMed Central  Google Scholar 

  61. •• Roach DJ, et al. Ultrashort echo-time magnetic resonance imaging is a sensitive method for the evaluation of early cystic fibrosis lung disease. Ann Am Thorac Soc. 2016;13(11):1923–31. https://doi.org/10.1513/AnnalsATS.201603-203OCThis paper compares ultrashort echo time MR image (UTE MRI) with conventional computed tomography (CT) and showed significant correlation in their imaging scores with CF patients.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Kruger SJ, Fain SB, Johnson KM, Cadman RV, Nagle SK. Oxygen-enhanced 3D radial ultrashort echo time magnetic resonance imaging in the healthy human lung. NMR Biomed. 2014;27(12):1535–41. https://doi.org/10.1002/nbm.3158.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Ma W, Sheikh K, Svenningsen S, Pike D, Guo F, Etemad-Rezai R, et al. Ultra-short echo-time pulmonary MRI: evaluation and reproducibility in COPD subjects with and without bronchiectasis. J Magn Reson Imaging. 2015;41(5):1465–74. https://doi.org/10.1002/jmri.24680.

    Article  PubMed  Google Scholar 

  64. Ohno Y, et al. Pulmonary high-resolution ultrashort TE MR imaging: comparison with thin-section standard- and low-dose computed tomography for the assessment of pulmonary parenchyma diseases. J Magn Reson Imaging. 2016;43(2):512–32. https://doi.org/10.1002/jmri.25008.

    Article  PubMed  Google Scholar 

  65. Altes TA, Eichinger M, Puderbach M. Magnetic resonance imaging of the lung in cystic fibrosis. Proc Am Thorac Soc. 2007;4(4):321–7. https://doi.org/10.1513/pats.200611-181HT.

    Article  PubMed  Google Scholar 

  66. •• Svenningsen S, et al. Noncystic fibrosis bronchiectasis: regional abnormalities and response to airway clearance therapy using pulmonary functional magnetic resonance imaging. Acad Radiol. 2017;24(1):4–12. https://doi.org/10.1016/j.acra.2016.08.021This paper showed the capability of hyperpolarized gas MRI in finding the structure-function abnormalities in patients with non-cystic fibrosis bronchiectasis which CT could not detect and it also responded to airway clearance therapy (ACT).

    Article  PubMed  Google Scholar 

  67. Eichinger M, et al. Contrast-enhanced 3D MRI of lung perfusion in children with cystic fibrosis--initial results. Eur Radiol. 2006;16(10):2147–52. https://doi.org/10.1007/s00330-006-0257-7.

    Article  PubMed  Google Scholar 

  68. Fiel SB, et al. Magnetic resonance imaging in young adults with cystic fibrosis. Chest. 1987;91:181–4.

    Article  CAS  PubMed  Google Scholar 

  69. Fink C, et al. Partially parallel three-dimensional magnetic resonance imaging for the assessment of lung perfusion--initial results. Investig Radiol. 2003;38(8):482–8. https://doi.org/10.1097/01.rli.0000067490.97837.82.

    Article  Google Scholar 

  70. Heidemann RM, Griswold MA, Kiefer B, Nittka M, Wang J, Jellus V, et al. Resolution enhancement in lung 1H imaging using parallel imaging methods. Magn Reson Med. 2003;49(2):391–4. https://doi.org/10.1002/mrm.10349.

    Article  CAS  PubMed  Google Scholar 

  71. Puderbach M, Eichinger M, Gahr J, Ley S, Tuengerthal S, Schmähl A, et al. Proton MRI appearance of cystic fibrosis: comparison to CT. Eur Radiol. 2007;17(3):716–24. https://doi.org/10.1007/s00330-006-0373-4.

    Article  PubMed  Google Scholar 

  72. Mayer D, et al. Hybrid segmentation and virtual bronchoscopy based on CT images. Acad Radiol. 2004;11(5):551–65. https://doi.org/10.1016/j.acra.2004.01.012.

    Article  PubMed  Google Scholar 

  73. Sodhi KS, Gupta P, Shrivastav A, Saxena AK, Mathew JL, Singh M, et al. Evaluation of 3 T lung magnetic resonance imaging in children with allergic bronchopulmonary aspergillosis: pilot study. Eur J Radiol. 2019;111:88–92. https://doi.org/10.1016/j.ejrad.2018.12.021.

    Article  PubMed  Google Scholar 

  74. Eichinger M, Optazaite DE, Kopp-Schneider A, Hintze C, Biederer J, Niemann A, et al. Morphologic and functional scoring of cystic fibrosis lung disease using MRI. Eur J Radiol. 2012;81(6):1321–9. https://doi.org/10.1016/j.ejrad.2011.02.045.

    Article  PubMed  Google Scholar 

  75. Helbich TH, et al. Cystic fibrosis: CT assessment of lung involvement in children and adults. Radiology. 1999;213(2):537–44. https://doi.org/10.1148/radiology.213.2.r99nv04537.

    Article  CAS  PubMed  Google Scholar 

  76. Sileo C, Corvol H, Boelle PY, Blondiaux E, Clement A, Ducou le Pointe H. HRCT and MRI of the lung in children with cystic fibrosis: comparison of different scoring systems. J Cyst Fibros. 2014;13(2):198–204. https://doi.org/10.1016/j.jcf.2013.09.003.

    Article  PubMed  Google Scholar 

  77. Ohno Y, Koyama H, Yoshikawa T, Matsumoto K, Takahashi M, van Cauteren M, et al. T2* measurements of 3-T MRI with ultrashort TEs: capabilities of pulmonary function assessment and clinical stage classification in smokers. AJR Am J Roentgenol. 2011;197(2):W279–85. https://doi.org/10.2214/AJR.10.5350.

    Article  PubMed  Google Scholar 

  78. Altes TA, Johnson M, Fidler M, Botfield M, Tustison NJ, Leiva-Salinas C, et al. Use of hyperpolarized helium-3 MRI to assess response to ivacaftor treatment in patients with cystic fibrosis. J Cyst Fibros. 2017;16(2):267–74. https://doi.org/10.1016/j.jcf.2016.12.004.

    Article  PubMed  Google Scholar 

  79. Maglione M, Montella S, Mollica C, Carnovale V, Iacotucci P, de Gregorio F, et al. Lung structure and function similarities between primary ciliary dyskinesia and mild cystic fibrosis: a pilot study. Ital J Pediatr. 2017;43(1):34. https://doi.org/10.1186/s13052-017-0351-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Nyilas S, et al. Novel magnetic resonance technique for functional imaging of cystic fibrosis lung disease. Eur Respir J. 2017;50(6). https://doi.org/10.1183/13993003.01464-2017.

    Article  PubMed  Google Scholar 

  81. Wielputz MO, et al. Multicentre standardisation of chest MRI as radiation-free outcome measure of lung disease in young children with cystic fibrosis. J Cyst Fibros. 2018;17(4):518–27. https://doi.org/10.1016/j.jcf.2018.05.003.

    Article  PubMed  Google Scholar 

  82. Leutz-Schmidt P, Stahl M, Sommerburg O, Eichinger M, Puderbach MU, Schenk JP, et al. Non-contrast enhanced magnetic resonance imaging detects mosaic signal intensity in early cystic fibrosis lung disease. Eur J Radiol. 2018;101:178–83. https://doi.org/10.1016/j.ejrad.2018.02.023.

    Article  PubMed  Google Scholar 

  83. Pennati F, et al. Assessment of pulmonary structure-function relationships in young children and adolescents with cystic fibrosis by multivolume proton-MRI and CT. J Magn Reson Imaging. 2018;48(2):531–42. https://doi.org/10.1002/jmri.25978.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Stahl M, et al. Comparison of lung clearance index and magnetic resonance imaging for assessment of lung disease in children with cystic fibrosis. Am J Respir Crit Care Med. 2017;195(3):349–59. https://doi.org/10.1164/rccm.201604-0893OC.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Robert P. Thomen.

Ethics declarations

Conflict of Interest

Ummul Afia Shammi and Robert P Thomen declare no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Bronchiectasis

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shammi, U.A., Thomen, R.P. Role of New Imaging Capabilities with MRI and CT in the Evaluation of Bronchiectasis. Curr Pulmonol Rep 8, 166–176 (2019). https://doi.org/10.1007/s13665-019-00240-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13665-019-00240-z

Keywords

Navigation