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European Radiology

, Volume 28, Issue 10, pp 4314–4323 | Cite as

Minimisation of Signal Intensity Differences in Distortion Correction Approaches of Brain Magnetic Resonance Diffusion Tensor Imaging

  • Dong-Hoon Lee
  • Do-Wan Lee
  • David Henry
  • Hae-Jin Park
  • Bong-Soo Han
  • Dong-Cheol Woo
Neuro

Abstract

Objectives

To evaluate the effects of signal intensity differences between the b0 image and diffusion tensor imaging (DTI) in the image registration process.

Methods

To correct signal intensity differences between the b0 image and DTI data, a simple image intensity compensation (SIMIC) method, which is a b0 image re-calculation process from DTI data, was applied before the image registration. The re-calculated b0 image (b0ext) from each diffusion direction was registered to the b0 image acquired through the MR scanning (b0nd) with two types of cost functions and their transformation matrices were acquired. These transformation matrices were then used to register the DTI data. For quantifications, the dice similarity coefficient (DSC) values, diffusion scalar matrix, and quantified fibre numbers and lengths were calculated.

Results

The combined SIMIC method with two cost functions showed the highest DSC value (0.802 ± 0.007). Regarding diffusion scalar values and numbers and lengths of fibres from the corpus callosum, superior longitudinal fasciculus, and cortico-spinal tract, only using normalised cross correlation (NCC) showed a specific tendency toward lower values in the brain regions.

Conclusion

Image-based distortion correction with SIMIC for DTI data would help in image analysis by accounting for signal intensity differences as one additional option for DTI analysis.

Key points

We evaluated the effects of signal intensity differences at DTI registration.

The non-diffusion-weighted image re-calculation process from DTI data was applied.

SIMIC can minimise the signal intensity differences at DTI registration.

Keywords

Diffusion tensor imaging Diffusion tractography Corpus callosum Cortico-spinal tract Diagnostic imaging 

Abbreviations

CC

Corpus callosum

CST

Cortico-spinal tract

DSC

Dice similarity coefficient

DTI

Diffusion tensor imaging

EPI

Echo planar imaging

FA

Fractional anisotropy

FACT

Fibre assignment with the continuous tracking

NMI

Normalised mutual information

NCC

Normalised cross correlation

RA

Relative anisotropy

RD

Radial diffusivity

ROI

Region of interest

SIMIC

Simple image intensity compensation

SLF

Superior longitudinal fasciculus

SSIM

Structural similarity

VR

Volume ratio

Notes

Funding

This study was supported by grants of Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education [NRF (www.nrf.re.kr): NRF- 2017R1A6A3A03012461 and NRF-2015R1C1A1A02036526] and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute [KHIDI (www.khidi.or.kr): HI14C1090], funded by the Ministry of Health & Welfare, Republic of Korea. This study was also supported by the 2017 University of Sydney Postdoctoral Fellowship Scheme (192237).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Dr. Dong-Hoon Lee.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• experimental

• performed at one institution

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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Faculty of Health Sciences and Brain & Mind CentreThe University of SydneySydneyAustralia
  2. 2.Center for Bioimaging of New Drug Development, and MR Core Lab., Asan Institute for Life SciencesAsan Medical CenterSeoulRepublic of Korea
  3. 3.Department of Radiation OncologyAjou University School of MedicineSuwonRepublic of Korea
  4. 4.Department of Radiological Science, College of Health ScienceYonsei UniversityWonjuRepublic of Korea
  5. 5.Department of Convergence Medicine, Asan Medical CenterUniversity of Ulsan College of MedicineSeoulRepublic of Korea

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