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High-resolution diffusion kurtosis imaging at 3T enabled by advanced post-processing.

Mohammadi S, Tabelow K, Ruthotto L, Feiweier T, Polzehl J, Weiskopf N - Front Neurosci (2015)

Bottom Line: Currently DKI is acquired only at low spatial resolution (2-3 mm isotropic), because of the lower signal-to-noise ratio (SNR) and higher artifact level associated with the technically more demanding DKI.Furthermore, we applied a powerful edge preserving denoising method, denoted as multi-shell orientation-position-adaptive smoothing (msPOAS).We showed that the combination of REMATCH and msPOAS increased the contrast between gray and white matter in mean kurtosis (MK) maps by about 35% and at the same time preserves the original distribution of MK values, whereas standard Gaussian smoothing strongly biases the distribution.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London London, UK ; Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf Hamburg, Germany.

ABSTRACT
Diffusion Kurtosis Imaging (DKI) is more sensitive to microstructural differences and can be related to more specific micro-scale metrics (e.g., intra-axonal volume fraction) than diffusion tensor imaging (DTI), offering exceptional potential for clinical diagnosis and research into the white and gray matter. Currently DKI is acquired only at low spatial resolution (2-3 mm isotropic), because of the lower signal-to-noise ratio (SNR) and higher artifact level associated with the technically more demanding DKI. Higher spatial resolution of about 1 mm is required for the characterization of fine white matter pathways or cortical microstructure. We used restricted-field-of-view (rFoV) imaging in combination with advanced post-processing methods to enable unprecedented high-quality, high-resolution DKI (1.2 mm isotropic) on a clinical 3T scanner. Post-processing was advanced by developing a novel method for Retrospective Eddy current and Motion ArtifacT Correction in High-resolution, multi-shell diffusion data (REMATCH). Furthermore, we applied a powerful edge preserving denoising method, denoted as multi-shell orientation-position-adaptive smoothing (msPOAS). We demonstrated the feasibility of high-quality, high-resolution DKI and its potential for delineating highly myelinated fiber pathways in the motor cortex. REMATCH performs robustly even at the low SNR level of high-resolution DKI, where standard EC and motion correction failed (i.e., produced incorrectly aligned images) and thus biased the diffusion model fit. We showed that the combination of REMATCH and msPOAS increased the contrast between gray and white matter in mean kurtosis (MK) maps by about 35% and at the same time preserves the original distribution of MK values, whereas standard Gaussian smoothing strongly biases the distribution.

No MeSH data available.


Quantitative assessment of the effect of noise on the WM/GM MK contrast. (A) To this end, the difference between the mean MK value within WM and GM was calculated for MK maps obtained from: all (original SNR100), 66% (original SNR44), and 50% (original SNR25) of the data. The same contrast was calculated after applying msPOAS on each subset: all (msPOAS SNR100), 66% (msPOAS SNR44), and 50% (msPOAS SNR25) of the data. (B) Furthermore, the relative difference with respect to ΔMK from the original SNR100 dataset was calculated. If less data and no denoising were employed to estimate the MK, the contrast between WM and GM was reduced. If msPOAS was used the contrast stayed approximately the same.
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Figure 11: Quantitative assessment of the effect of noise on the WM/GM MK contrast. (A) To this end, the difference between the mean MK value within WM and GM was calculated for MK maps obtained from: all (original SNR100), 66% (original SNR44), and 50% (original SNR25) of the data. The same contrast was calculated after applying msPOAS on each subset: all (msPOAS SNR100), 66% (msPOAS SNR44), and 50% (msPOAS SNR25) of the data. (B) Furthermore, the relative difference with respect to ΔMK from the original SNR100 dataset was calculated. If less data and no denoising were employed to estimate the MK, the contrast between WM and GM was reduced. If msPOAS was used the contrast stayed approximately the same.

Mentions: With decreasing image SNR the MK maps became noisier and the delineation of tissue-boundaries became more challenging (arrows in Figure 10); msPOAS helped to recover some of these anatomical structures. Furthermore, with decreasing SNR the WM-GM contrast in MK maps decreased (by up to 15%), whereas if msPOAS was used the contrast increased (by about 10%, Figure 11). Furthermore, if msPOAS was used the GM-WM contrast remained almost the same independent of the decreased SNR level.


High-resolution diffusion kurtosis imaging at 3T enabled by advanced post-processing.

Mohammadi S, Tabelow K, Ruthotto L, Feiweier T, Polzehl J, Weiskopf N - Front Neurosci (2015)

Quantitative assessment of the effect of noise on the WM/GM MK contrast. (A) To this end, the difference between the mean MK value within WM and GM was calculated for MK maps obtained from: all (original SNR100), 66% (original SNR44), and 50% (original SNR25) of the data. The same contrast was calculated after applying msPOAS on each subset: all (msPOAS SNR100), 66% (msPOAS SNR44), and 50% (msPOAS SNR25) of the data. (B) Furthermore, the relative difference with respect to ΔMK from the original SNR100 dataset was calculated. If less data and no denoising were employed to estimate the MK, the contrast between WM and GM was reduced. If msPOAS was used the contrast stayed approximately the same.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4285740&req=5

Figure 11: Quantitative assessment of the effect of noise on the WM/GM MK contrast. (A) To this end, the difference between the mean MK value within WM and GM was calculated for MK maps obtained from: all (original SNR100), 66% (original SNR44), and 50% (original SNR25) of the data. The same contrast was calculated after applying msPOAS on each subset: all (msPOAS SNR100), 66% (msPOAS SNR44), and 50% (msPOAS SNR25) of the data. (B) Furthermore, the relative difference with respect to ΔMK from the original SNR100 dataset was calculated. If less data and no denoising were employed to estimate the MK, the contrast between WM and GM was reduced. If msPOAS was used the contrast stayed approximately the same.
Mentions: With decreasing image SNR the MK maps became noisier and the delineation of tissue-boundaries became more challenging (arrows in Figure 10); msPOAS helped to recover some of these anatomical structures. Furthermore, with decreasing SNR the WM-GM contrast in MK maps decreased (by up to 15%), whereas if msPOAS was used the contrast increased (by about 10%, Figure 11). Furthermore, if msPOAS was used the GM-WM contrast remained almost the same independent of the decreased SNR level.

Bottom Line: Currently DKI is acquired only at low spatial resolution (2-3 mm isotropic), because of the lower signal-to-noise ratio (SNR) and higher artifact level associated with the technically more demanding DKI.Furthermore, we applied a powerful edge preserving denoising method, denoted as multi-shell orientation-position-adaptive smoothing (msPOAS).We showed that the combination of REMATCH and msPOAS increased the contrast between gray and white matter in mean kurtosis (MK) maps by about 35% and at the same time preserves the original distribution of MK values, whereas standard Gaussian smoothing strongly biases the distribution.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London London, UK ; Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf Hamburg, Germany.

ABSTRACT
Diffusion Kurtosis Imaging (DKI) is more sensitive to microstructural differences and can be related to more specific micro-scale metrics (e.g., intra-axonal volume fraction) than diffusion tensor imaging (DTI), offering exceptional potential for clinical diagnosis and research into the white and gray matter. Currently DKI is acquired only at low spatial resolution (2-3 mm isotropic), because of the lower signal-to-noise ratio (SNR) and higher artifact level associated with the technically more demanding DKI. Higher spatial resolution of about 1 mm is required for the characterization of fine white matter pathways or cortical microstructure. We used restricted-field-of-view (rFoV) imaging in combination with advanced post-processing methods to enable unprecedented high-quality, high-resolution DKI (1.2 mm isotropic) on a clinical 3T scanner. Post-processing was advanced by developing a novel method for Retrospective Eddy current and Motion ArtifacT Correction in High-resolution, multi-shell diffusion data (REMATCH). Furthermore, we applied a powerful edge preserving denoising method, denoted as multi-shell orientation-position-adaptive smoothing (msPOAS). We demonstrated the feasibility of high-quality, high-resolution DKI and its potential for delineating highly myelinated fiber pathways in the motor cortex. REMATCH performs robustly even at the low SNR level of high-resolution DKI, where standard EC and motion correction failed (i.e., produced incorrectly aligned images) and thus biased the diffusion model fit. We showed that the combination of REMATCH and msPOAS increased the contrast between gray and white matter in mean kurtosis (MK) maps by about 35% and at the same time preserves the original distribution of MK values, whereas standard Gaussian smoothing strongly biases the distribution.

No MeSH data available.