Minimisation of Signal Intensity Differences in Distortion Correction Approaches of Brain Magnetic Resonance Diffusion Tensor Imaging.
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, whic...
| Publicado en: | European Radiology Vol. 28; no. 10; pp. 4314 - 4324 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131578466&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131578466 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Oct2018 vid: 28 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 131578466 131578466 NLM29651768 10.1007/s00330-018-5382-6 NLM29651768 131578466 ppf: 4314 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Minimisation of Signal Intensity Differences in Distortion Correction Approaches of Brain Magnetic Resonance Diffusion Tensor Imaging. aug: au: Lee, Dong-Hoon Lee, Do-Wan Henry, David Park, Hae-Jin Han, Bong-Soo Woo, Dong-Cheol affil: Faculty of Health Sciences and Brain & Mind Centre, The University of Sydney, Sydney, New South Wales, Australia sug: subj: Magnetic Resonance Imaging Methods Brain Brain Pathology Telencephalon Algorithms Telencephalon Pathology ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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