Minimisation of Signal Intensity Differences in Distortion Correction Approaches of Brain Magnetic Resonance Diffusion Tensor Imaging
To evaluate the effects of signal intensity differences between the b0 image and diffusion tensor imaging (DTI) in the image registration process.
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.
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.
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.
• 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.
KeywordsDiffusion tensor imaging Diffusion tractography Corpus callosum Cortico-spinal tract Diagnostic imaging
Dice similarity coefficient
Diffusion tensor imaging
Echo planar imaging
Fibre assignment with the continuous tracking
Normalised mutual information
Normalised cross correlation
Region of interest
Simple image intensity compensation
Superior longitudinal fasciculus
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
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.
Written informed consent was obtained from all subjects (patients) in this study.
Institutional Review Board approval was obtained.
• performed at one institution
- 4.Wu WT, Rigolo L, O'Donnell LJ, Norton I, Shriver S, Golby AJ (2012) Visual pathway study using in vivo diffusion tensor imaging tractography to complement classic anatomy. Neurosurgery 70Google Scholar
- 7.Lee DH, Lee DW, Han BS (2015) Simple image intensity compensation (SIMIC) method prior to application of distortion correction algorithms in brain diffusion tensor magnetic resonance imaging: Validation test for two cost functions of distortion correction algorithms. International Journal of Imaging Systems and Technology 25:328–333CrossRefGoogle Scholar
- 22.Damon BM, Froeling M, Buck AK et al (2017) Skeletal muscle diffusion tensor-MRI fiber tracking: rationale, data acquisition and analysis methods, applications and future directions. NMR Biomed 30Google Scholar
- 23.Schlaffke L, Rehmann R, Froeling M et al (2017) Diffusion tensor imaging of the human calf: Variation of inter- and intramuscle-specific diffusion parameters. Journal of Magnetic Resonance Imaging. https://doi.org/10.1002/jmri.25650