Skip to main content
Top
Published in: European Radiology 6/2019

Open Access 01-06-2019 | Magnetic Resonance

Technical challenges of quantitative chest MRI data analysis in a large cohort pediatric study

Authors: Anh H. Nguyen, Adria Perez-Rovira, Piotr A. Wielopolski, Juan A. Hernandez Tamames, Liesbeth Duijts, Marleen de Bruijne, Andrea Aliverti, Francesca Pennati, Tetyana Ivanovska, Harm A. W. M. Tiddens, Pierluigi Ciet

Published in: European Radiology | Issue 6/2019

Login to get access

Abstract

Objectives

This study was conducted in order to evaluate the effect of geometric distortion (GD) on MRI lung volume quantification and evaluate available manual, semi-automated, and fully automated methods for lung segmentation.

Methods

A phantom was scanned with MRI and CT. GD was quantified as the difference in phantom’s volume between MRI and CT, with CT as gold standard. Dice scores were used to measure overlap in shapes. Furthermore, 11 subjects from a prospective population-based cohort study each underwent four chest MRI acquisitions. The resulting 44 MRI scans with 2D and 3D Gradwarp were used to test five segmentation methods. Intraclass correlation coefficient, Bland–Altman plots, Wilcoxon, Mann–Whitney U, and paired t tests were used for statistics.

Results

Using phantoms, volume differences between CT and MRI varied according to MRI positions and 2D and 3D Gradwarp correction. With the phantom located at the isocenter, MRI overestimated the volume relative to CT by 5.56 ± 1.16 to 6.99 ± 0.22% with body and torso coils, respectively. Higher Dice scores and smaller intraobject differences were found for 3D Gradwarp MR images. In subjects, semi-automated and fully automated segmentation tools showed high agreement with manual segmentations (ICC = 0.971–0.993 for end-inspiratory scans; ICC = 0.992–0.995 for end-expiratory scans). Manual segmentation time per scan was approximately 3–4 h and 2–3 min for fully automated methods.

Conclusions

Volume overestimation of MRI due to GD can be quantified. Semi-automated and fully automated segmentation methods allow accurate, reproducible, and fast lung volume quantification. Chest MRI can be a valid radiation-free imaging modality for lung segmentation and volume quantification in large cohort studies.

Key Points

• Geometric distortion varies according to MRI setting and patient positioning.
• Automated segmentation methods allow fast and accurate lung volume quantification.
• MRI is a valid radiation-free alternative to CT for quantitative data analysis.
Appendix
Available only for authorised users
Literature
1.
go back to reference Barreto MM, Rafful PP, Rodrigues RS et al (2013) Correlation between computed tomographic and magnetic resonance imaging findings of parenchymal lung diseases. Eur J Radiol 82:e492–e501CrossRefPubMed Barreto MM, Rafful PP, Rodrigues RS et al (2013) Correlation between computed tomographic and magnetic resonance imaging findings of parenchymal lung diseases. Eur J Radiol 82:e492–e501CrossRefPubMed
2.
go back to reference Kuo W, Ciet P, Tiddens HA, Zhang W, Guillerman RP, van Straten M (2014) Monitoring cystic fibrosis lung disease by computed tomography. Radiation risk in perspective. Am J Respir Crit Care Med 189:1328–1336 Kuo W, Ciet P, Tiddens HA, Zhang W, Guillerman RP, van Straten M (2014) Monitoring cystic fibrosis lung disease by computed tomography. Radiation risk in perspective. Am J Respir Crit Care Med 189:1328–1336
3.
go back to reference Tiddens HA, Stick SM, Davis S (2014) Multi-modality monitoring of cystic fibrosis lung disease: the role of chest computed tomography. Paediatr Respir Rev 15:92–97PubMed Tiddens HA, Stick SM, Davis S (2014) Multi-modality monitoring of cystic fibrosis lung disease: the role of chest computed tomography. Paediatr Respir Rev 15:92–97PubMed
4.
go back to reference van Rikxoort EM, van Ginneken B (2013) Automated segmentation of pulmonary structures in thoracic computed tomography scans: a review. Phys Med Biol 58:R187–R220CrossRefPubMed van Rikxoort EM, van Ginneken B (2013) Automated segmentation of pulmonary structures in thoracic computed tomography scans: a review. Phys Med Biol 58:R187–R220CrossRefPubMed
5.
go back to reference Mansoor A, Bagci U, Foster B et al (2015) Segmentation and image analysis of abnormal lungs at CT: current approaches, challenges, and future trends. Radiographics 35:1056–1076CrossRefPubMed Mansoor A, Bagci U, Foster B et al (2015) Segmentation and image analysis of abnormal lungs at CT: current approaches, challenges, and future trends. Radiographics 35:1056–1076CrossRefPubMed
6.
go back to reference Tiddens HA, Rosenow T (2014) What did we learn from two decades of chest computed tomography in cystic fibrosis? Pediatr Radiol 44:1490–1495CrossRefPubMed Tiddens HA, Rosenow T (2014) What did we learn from two decades of chest computed tomography in cystic fibrosis? Pediatr Radiol 44:1490–1495CrossRefPubMed
7.
go back to reference Walker A, Liney G, Metcalfe P, Holloway L (2014) MRI distortion: considerations for MRI based radiotherapy treatment planning. Australas Phys Eng Sci Med 37:103–113CrossRefPubMed Walker A, Liney G, Metcalfe P, Holloway L (2014) MRI distortion: considerations for MRI based radiotherapy treatment planning. Australas Phys Eng Sci Med 37:103–113CrossRefPubMed
8.
go back to reference Langlois S, Desvignes M, Constans JM, Revenu M (1999) MRI geometric distortion: a simple approach to correcting the effects of non-linear gradient fields. J Magn Reson Imaging 9:821–831CrossRefPubMed Langlois S, Desvignes M, Constans JM, Revenu M (1999) MRI geometric distortion: a simple approach to correcting the effects of non-linear gradient fields. J Magn Reson Imaging 9:821–831CrossRefPubMed
9.
go back to reference Karger CP, Höss A, Bendl R, Canda V, Schad L (2006) Accuracy of device-specific 2D and 3D image distortion correction algorithms for magnetic resonance imaging of the head provided by a manufacturer. Phys Med Biol 51:N253–N261 Karger CP, Höss A, Bendl R, Canda V, Schad L (2006) Accuracy of device-specific 2D and 3D image distortion correction algorithms for magnetic resonance imaging of the head provided by a manufacturer. Phys Med Biol 51:N253–N261
10.
go back to reference Torfeh T, Hammoud R, McGarry M, Al-Hammadi N, Perkins G (2015) Development and validation of a novel large field of view phantom and a software module for the quality assurance of geometric distortion in magnetic resonance imaging. Magn Reson Imaging 33:939–949 Torfeh T, Hammoud R, McGarry M, Al-Hammadi N, Perkins G (2015) Development and validation of a novel large field of view phantom and a software module for the quality assurance of geometric distortion in magnetic resonance imaging. Magn Reson Imaging 33:939–949
11.
go back to reference Stanescu T, Jans HS, Wachowicz K, Fallone BG (2010) Investigation of a 3D system distortion correction method for MR images. J Appl Clin Med Phys 11:2961CrossRefPubMed Stanescu T, Jans HS, Wachowicz K, Fallone BG (2010) Investigation of a 3D system distortion correction method for MR images. J Appl Clin Med Phys 11:2961CrossRefPubMed
12.
go back to reference Donato F Jr, Costa DN, Yuan Q, Rofsky NM, Lenkinski RE, Pedrosa I (2014) Geometric distortion in diffusion-weighted MR imaging of the prostate-contributing factors and strategies for improvement. Acad Radiol 21:817–823 Donato F Jr, Costa DN, Yuan Q, Rofsky NM, Lenkinski RE, Pedrosa I (2014) Geometric distortion in diffusion-weighted MR imaging of the prostate-contributing factors and strategies for improvement. Acad Radiol 21:817–823
13.
go back to reference Petersch B, Bogner J, Fransson A, Lorang T, Pötter R (2004) Effects of geometric distortion in 0.2T MRI on radiotherapy treatment planning of prostate cancer. Radiother Oncol 71:55–64 Petersch B, Bogner J, Fransson A, Lorang T, Pötter R (2004) Effects of geometric distortion in 0.2T MRI on radiotherapy treatment planning of prostate cancer. Radiother Oncol 71:55–64
14.
go back to reference Sumanaweera T, Glover G, Song S, Adler J, Napel S (1994) Quantifying MRI geometric distortion in tissue. Magn Reson Med 31:40–47 Sumanaweera T, Glover G, Song S, Adler J, Napel S (1994) Quantifying MRI geometric distortion in tissue. Magn Reson Med 31:40–47
15.
go back to reference Deeley MA, Chen A, Datteri R et al (2011) Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study. Phys Med Biol 56:4557–4577CrossRefPubMedPubMedCentral Deeley MA, Chen A, Datteri R et al (2011) Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study. Phys Med Biol 56:4557–4577CrossRefPubMedPubMedCentral
16.
go back to reference Hong C, Lee DH, Han BS (2014) Characteristics of geometric distortion correction with increasing field-of-view in open-configuration MRI. Magn Reson Imaging 32:786–790CrossRefPubMed Hong C, Lee DH, Han BS (2014) Characteristics of geometric distortion correction with increasing field-of-view in open-configuration MRI. Magn Reson Imaging 32:786–790CrossRefPubMed
17.
go back to reference Baldwin LN, Wachowicz K, Thomas SD, Rivest R, Fallone BG (2007) Characterization, prediction, and correction of geometric distortion in 3 T MR images. Med Phys 34:388–399 Baldwin LN, Wachowicz K, Thomas SD, Rivest R, Fallone BG (2007) Characterization, prediction, and correction of geometric distortion in 3 T MR images. Med Phys 34:388–399
18.
go back to reference Kohlmann P, Strehlow J, Jobst B et al (2015) Automatic lung segmentation method for MRI-based lung perfusion studies of patients with chronic obstructive pulmonary disease. Int J Comput Assist Radiol Surg 10:403–417CrossRefPubMed Kohlmann P, Strehlow J, Jobst B et al (2015) Automatic lung segmentation method for MRI-based lung perfusion studies of patients with chronic obstructive pulmonary disease. Int J Comput Assist Radiol Surg 10:403–417CrossRefPubMed
19.
go back to reference Lui JK, LaPrad AS, Parameswaran H, Sun Y, Albert MS, Lutchen KR (2013) Semiautomatic segmentation of ventilated airspaces in healthy and asthmatic subjects using hyperpolarized 3He MRI. Comput Math Methods Med 2013:624683 Lui JK, LaPrad AS, Parameswaran H, Sun Y, Albert MS, Lutchen KR (2013) Semiautomatic segmentation of ventilated airspaces in healthy and asthmatic subjects using hyperpolarized 3He MRI. Comput Math Methods Med 2013:624683
20.
go back to reference Zhuo J, Gullapalli RP (2006) AAPM/RSNA physics tutorial for residents: MR artifacts, safety, and quality control. Radiographics 26:275–297CrossRefPubMed Zhuo J, Gullapalli RP (2006) AAPM/RSNA physics tutorial for residents: MR artifacts, safety, and quality control. Radiographics 26:275–297CrossRefPubMed
21.
go back to reference Kruithof CJ, Kooijman MN, van Duijn CM et al (2014) The generation R study: biobank update 2015. Eur J Epidemiol 29:911–927CrossRefPubMed Kruithof CJ, Kooijman MN, van Duijn CM et al (2014) The generation R study: biobank update 2015. Eur J Epidemiol 29:911–927CrossRefPubMed
22.
go back to reference Kooijman MN, Kruithof CJ, van Duijn CM et al (2016) The generation R study: design and cohort update 2017. Eur J Epidemiol 31:1243–1264CrossRefPubMed Kooijman MN, Kruithof CJ, van Duijn CM et al (2016) The generation R study: design and cohort update 2017. Eur J Epidemiol 31:1243–1264CrossRefPubMed
23.
go back to reference Yushkevich PA, Piven J, Hazlett HC et al (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:1116–1128CrossRefPubMed Yushkevich PA, Piven J, Hazlett HC et al (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:1116–1128CrossRefPubMed
24.
go back to reference Fedorov A, Beichel R, Kalpathy-Cramer J et al (2012) 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30:1323–1341CrossRefPubMedPubMedCentral Fedorov A, Beichel R, Kalpathy-Cramer J et al (2012) 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30:1323–1341CrossRefPubMedPubMedCentral
25.
go back to reference Criminisi A, Sharp T, Blake A (2008) GeoS: geodesic image segmentation. Computer vision - ECCV 2008, Pt I, proceedings. Springer, Berlin, pp 99–112 Criminisi A, Sharp T, Blake A (2008) GeoS: geodesic image segmentation. Computer vision - ECCV 2008, Pt I, proceedings. Springer, Berlin, pp 99–112
26.
go back to reference Ivanovska T, Hegenscheid K, Laqua R et al (2012) A fast and accurate automatic lung segmentation and volumetry method for MR data used in epidemiological studies. Comput Med Imaging Graph 36:281–293CrossRefPubMed Ivanovska T, Hegenscheid K, Laqua R et al (2012) A fast and accurate automatic lung segmentation and volumetry method for MR data used in epidemiological studies. Comput Med Imaging Graph 36:281–293CrossRefPubMed
27.
go back to reference Pennati F, Quirk JD, Yablonskiy DA, Castro M, Aliverti A, Woods JC (2014) Assessment of regional lung function with multivolume (1)H MR imaging in health and obstructive lung disease: comparison with (3)He MR imaging. Radiology 273:580–590 Pennati F, Quirk JD, Yablonskiy DA, Castro M, Aliverti A, Woods JC (2014) Assessment of regional lung function with multivolume (1)H MR imaging in health and obstructive lung disease: comparison with (3)He MR imaging. Radiology 273:580–590
28.
go back to reference Zou KH, Warfield SK, Bharatha A et al (2004) Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol 11:178–189CrossRefPubMedPubMedCentral Zou KH, Warfield SK, Bharatha A et al (2004) Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol 11:178–189CrossRefPubMedPubMedCentral
29.
go back to reference Ivanovska T, Ciet P, Perez-Rovira A et al (2017) Fully automated lung volume assessment from MRI in a population-based child cohort study. Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP. https://doi.org/10.5220/0006075300530058 Ivanovska T, Ciet P, Perez-Rovira A et al (2017) Fully automated lung volume assessment from MRI in a population-based child cohort study. Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP. https://​doi.​org/​10.​5220/​0006075300530058​
Metadata
Title
Technical challenges of quantitative chest MRI data analysis in a large cohort pediatric study
Authors
Anh H. Nguyen
Adria Perez-Rovira
Piotr A. Wielopolski
Juan A. Hernandez Tamames
Liesbeth Duijts
Marleen de Bruijne
Andrea Aliverti
Francesca Pennati
Tetyana Ivanovska
Harm A. W. M. Tiddens
Pierluigi Ciet
Publication date
01-06-2019
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 6/2019
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-018-5863-7

Other articles of this Issue 6/2019

European Radiology 6/2019 Go to the issue