Top

European Radiology Experimental

Published in:

Open Access 01-12-2018 | Original article

Comparison of DTI analysis methods for clinical research: influence of pre-processing and tract selection methods

Authors: Volker Ressel, Hubertus J. A. van Hedel, Ianina Scheer, Ruth O’Gorman Tuura

Published in: European Radiology Experimental | Issue 1/2018

Abstract

Background

The primary aim was to compare fractional anisotropy (FA) values derived with different diffusion tensor imaging (DTI) analysis approaches (atlas-based, streamline tractography, and combined). A secondary aim was to compare FA values and number of tracts (NT) with the clinical motor outcome quantified by the functional independence measure for children (WeeFIM).

Methods

Thirty-nine DTI datasets of children with acquired brain injury were analysed. Regions of interest for the ipsilesional corticospinal tract were defined and mean FA and NT were calculated. We evaluated FA values with Spearman correlation, the Friedman and Wilcoxon tests, and Bland-Altman analysis. DTI values were compared to WeeFIM values by non-parametric partial correlation and accuracy was assessed by receiver operating characteristics analysis.

Results

The FA values from all approaches correlated significantly with each other (p < 0.001). However, the FA values from streamline tractography were significantly higher (mean ± standard deviation (SD), 0.52 ± 0.08) than those from the atlas-based (0.42 ± 0.11) or the combined approach (0.41 ± 0.11) (p < 0.001 for both). FA and NT values correlated significantly with WeeFIM values (atlas-based FA, partial correlation coefficient (ρ) = 0.545, p = 0.001; streamline FA, ρ = 0.505, p = 0.002; NT, ρ = 0.434, p = 0.008; combined FA, ρ = 0.611, p < 0.001). FA of the atlas-based approach (sensitivity 90%, specificity 67%, area under the curve 0.82) and the combined approach (87%, 67%, 0.82), provided the highest predictive accuracy for outcome compared to FA (70%, 67%, 0.67) and NT (50%, 100%, 0.79, respectively) of the streamline approach.

Conclusion

FA values from streamline tractography were higher than those from the atlas-based and combined approach. The atlas-based and combined approach offer the best predictive accuracy for motor outcome, although both atlas-based and streamline tractography approaches provide significant predictors of clinical outcome.

Basser PJ, Mattiello J, LeBihan D (1994) Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 103:247–254CrossRef

Le Bihan D, Mangin JF, Poupon C et al (2001) Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 13:534–546CrossRef

Lerner A, Mogensen MA, Kim PE, Shiroishi MS, Hwang DH, Law M (2014) Clinical applications of diffusion tensor imaging. World Neurosurg 82:96–109CrossRef

Ressel V, O'Gorman Tuura RL, Scherer I, van Hedel HJA (2017) Diffusion tensor imaging predicts motor outcome in children with acquired brain injury. Brain Imaging Behav 11:1373–1384CrossRef

Smith SM, Jenkinson M, Johansen-Berg H et al (2006) Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31:1487–1505CrossRef

Soares JM, Marques P, Alves V, Sousa N (2013) A hitchhiker’s guide to diffusion tensor imaging. Front Neurosci 7:31CrossRef

Jones DK, Cercignani M (2010) Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed 23:803–820CrossRef

Borich MR, Wadden KP, Boyd LA (2012) Establishing the reproducibility of two approaches to quantify white matter tract integrity in stroke. Neuroimage 59:2393–2400CrossRef

Van Hecke W, Emsell L (2016) Strategies and challenges in DTI analysis. In: Van Hecke W, Emsell L, Sunaert S (eds) Diffusion tensor imaging: a practical handbook. Springer, New York, pp 153–173CrossRef

10.

Smith SM, Jenkinson M, Woolrich MW et al (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(S1):208–219CrossRef

11.

Leemans A, Jeurissen B, Sijbers J, Jones DK (2009) ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. In: Proceedings of the 17th scientific meeting. International Society for Magnetic Resonance in Medicine, Honolulu, p 3537

12.

Jenkinson M, Bannister P, Brady JM, Smith SM (2002) Improved optimisation for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17:825–841CrossRef

13.

Nagae LM, Hoon AH Jr, Stashinko E et al (2007) Diffusion tensor imaging in children with periventricular leukomalacia: variability of injuries to white matter tracts. AJNR Am J Neuroradiol 28:1213–1222CrossRef

14.

Msall ME, DiGaudio K, Rogers BT et al (1994) The functional independence measure for children (WeeFIM). Conceptual basis and pilot use in children with developmental disabilities. Clin Pediatr (Phila) 33:421–430CrossRef

15.

Dawson-Saunders B, Trapp RG (1994) Basic clinical biostatistics, 2nd edn. McGraw-Hill, New York

16.

Crivello F, Schormann T, Tzourio-Mazoyer N, Roland PE, Zilles K, Mazoyer BM (2002) Comparison of spatial normalization procedures and their impact on functional maps. Hum Brain Mapp 16:228–250CrossRef

17.

Brett M, Leff AP, Rorden C, Ashburner J (2001) Spatial normalization of brain images with focal lesions using cost function masking. Neuroimage 14:486–500CrossRef

18.

Crinion J, Ashburner J, Leff A, Brett M, Price C, Friston K (2007) Spatial normalization of lesioned brains: performance evaluation and impact on fMRI analyses. Neuroimage 37:866–875CrossRef

19.

Mukherjee P, Chung SW, Berman JI, Hess CP, Henry RG (2008) Diffusion tensor MR imaging and fiber tractography: technical considerations. AJNR Am J Neuroradiol 29:843–852CrossRef

20.

Lilja Y, Gustafsson O, Ljungberg M, Nilsson D, Starck G (2016) Impact of region-of-interest method on quantitative analysis of DTI data in the optic tracts. BMC Med Imaging 16:42CrossRef

21.

Froeling M, Pullens P, Leemans A (2016) DTI analysis methods: region of interest analysis. In: Van Hecke W, Emsell L, Sunaert S (eds) Diffusion tensor imaging: a practical handbook. Springer, New York, pp 175–182CrossRef

22.

Snook L, Plewes C, Beaulieu C (2007) Voxel based versus region of interest analysis in diffusion tensor imaging of neurodevelopment. Neuroimage 34:243–252CrossRef

23.

Jones DK, Knösche TR, Turner R (2013) White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage 73:239–254CrossRef

24.

Qin W, Yu CS, Zhang F et al (2009) Effects of echo time on diffusion quantification of brain white matter at 1.5 T and 3.0 T. Magn Reson Med 61:755–760CrossRef

25.

Hulkower MB, Poliak DB, Rosenbaum SB, Zimmerman ME, Lipton ML (2013) A decade of DTI in traumatic brain injury: 10 years and 100 articles later. AJNR Am J Neuroradiol 34:2064–2074CrossRef

Title: Comparison of DTI analysis methods for clinical research: influence of pre-processing and tract selection methods
Authors: Volker Ressel
Hubertus J. A. van Hedel
Ianina Scheer
Ruth O’Gorman Tuura
Publication date: 01-12-2018
Publisher: Springer International Publishing
Published in: European Radiology Experimental / Issue 1/2018
Electronic ISSN: 2509-9280
DOI: https://doi.org/10.1186/s41747-018-0066-1

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Comparison of DTI analysis methods for clinical research: influence of pre-processing and tract selection methods

Abstract

Background

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 1/2018

Use of the Hourglass peripheral embolisation device: early experiences

The role of standard non-ECG gated chest CT in cardiac assessment: design and rationale of the Cardiac Pathologies in standard chest CT (CaPaCT) study

MRI assessment of altered dynamic changes in liver haemodynamics following a meal challenge in compensated cirrhosis

The impact of sarcopenia and myosteatosis on postoperative outcomes in patients with inflammatory bowel disease

Automated assessment of paraspinal muscle fat composition based on the segmentation of chemical shift encoding-based water/fat-separated images

Radiomics of liver MRI predict metastases in mice