Brain and Mind Centre

Author Of 2 Presentations

Imaging Poster Presentation

P0589 - Implications of registration of white matter structures for MS connectome analysis (ID 1945)

Speakers
Presentation Number
P0589
Presentation Topic
Imaging

Abstract

Background

To assess the connectome disruption in patients with multiple sclerosis (MS), methods have been developed that utilise connectomes derived from healthy controls (HC) which require the accurate registration of white matter structures from patients with MS onto HCs whole brain streamline tractograms.

MRI T1 based image registration may be inaccurately mapped to HC derived tractograms due to the lack of information within the relatively homogenous T1 signal of white matter.

Fibre orientation distributions (FODs), created by the constrained spherical deconvolution (CSD) model of white matter diffusion data, have the potential to improve white matter registration as it holds information of all fibre directions for each voxel.

Objectives

Here we compare the accuracy and utility of FOD-based, T1-based and FA-based registration of white matter tracts in MS.

Methods

10 MS patients and 10 age matched healthy controls underwent 3T MRI scanning including 1mm 3D T1 and single shell diffusion weighted imaging. FOD maps were created using CSD algorithm designed for single shell data. Tensor and FA maps were created.

Non-linear registration was undertaken directly from each patient to each control (ie. 100 warps) with ANTs using T1 and FA images, and through an MNI template intermediary.

Registration was performed directly from each patient to controls using FOD maps using MRtrix3 FOD-based registration, as well as through a custom population FOD template.

Each registration method was assessed in transforming three white matter tracts, corticospinal tracts (CST), anterior thalamic radiations (ATR) and optic radiations (OR), from patients to controls. The resultant transformed segmentations from each registration method was compared to the control segmentation by calculating its Dice coefficient.

Further to this each method was assessed using a tract segmentation that had the cortical ribbon and juxtacortical tract removed.

Statistically significant differences were assessed by non-parametric Kruskal-Wallis test with Dunn’s post hoc testing.

Results

For combination of all tracts, the highest Dice coefficients were with direct FA (median = 0.727, IQR 0.06215) and direct T1 (median = 0.72185, IQR 0.056525) with no significant difference found.

For combination of all cropped tracts, the highest Dice coefficients were with FOD population template (median = 0.7673, IQR 0.0468), direct FOD (median = 0.76565, IQR 0.050175) and direct FA registration (median = 0.7626, IQR 0.060025) with no significant difference found.

When utilising an intermediary template, both T1 and FA based methods performed worse, whereas the FOD population template performed similarly.

Conclusions

FA and T1 based registration outperformed FOD based, despite more white matter information. This was driven by poorer juxtacortical registration in the FOD based method. This is important in the analysis of MS due to the high prevalence of juxtacortical lesional pathology.

Collapse
Imaging Poster Presentation

P0622 - Quanitifying the T1 hypointensity of MS lesions (ID 1063)

Speakers
Presentation Number
P0622
Presentation Topic
Imaging

Abstract

Background

Within multiple sclerosis (MS) lesions, T1 weighted hypo-intensity correlates with pathological markers of irreversible damage, including axonal degeneration. Studies have found that T1 “black holes” have a higher correlation with clinical disability than do T2 weighted lesions, and that measurable T1 signal drop out occurs within slowly enlarging lesions or chronic active lesions, as opposed to inactive lesions, suggestive of ongoing axonal degeneration.

To perform this quantitative analysis two pre-processing steps are necessary. The first is correction of low frequency intensity nonuniformity present in image data, also known as bias fields. The second is normalisation of the signal between subjects and timepoints. Both of these steps need to maintain representative contrast between pathological and normal appearing white matter (NAWM) to provide accurate results. Validation of these methods within the MS context is needed to support their use as a biomarker in MS.

Objectives

To assess the accuracy N34 bias correction on maintaining accurate MS lesion contrast

To assess the accuracy of Freesurfer normalization tool6 in maintaining accurate relative signal intensity

Methods

21 relapsing MS and 26 progressive MS subjects had MRI brain scans with 1mm3 3D T1 fast spoiled gradient echo (FSPGR), and 0.6 x 0.46mm2 FLAIR sequences. FLAIR images were resampled and linearly registered to the T1 using FLIRT. MS lesions were segmented from the FLAIR using JIM software.

To calculate an accurate measure of local tissue contrast between MS lesions and surrounding NAWM, the lesion segmentations were dilated and masked for white matter segmentation. The lesion mask was then subtracted from the segmentation to create an edge of NAWM surrounding the lesion. The measure of raw local tissue contrast was taken as the T1 value of

lesion core / lesion edge

N3 bias correction was done with the Freesurfer command mri_nu_correct.mni. Normalization of the T1 sequences was undertaken with Freesurfers mri_normalize which imposes a hard ceiling effect on the white matter peak found, creating an homogenous NAWM intensity of 110 and CSF intensity of ~35.

Correlations of each MS lesion between the bias field corrected and local tissue contrast was explored. Correlations between the bias field corrected and normalized values was explored.

Results

A total of 2401 lesions were analysed. N3 bias corrected T1 images correlated with local tissue contrast of the raw T1 images with an R2 = 0.7556. Normalized T1 images correlated with N3 bias corrected images with an R2 = 0.9415. Average MS lesion normalized T1 intensity was similar between relapsing MS and progressive MS cohorts (82.927 vs 82.793, p = 0.42).

Conclusions

In this analysis T1 intensity was able to be corrected for bias fields and normalized to a set range with reasonable accuracy in maintaining lesion contrast. This supports the use of these methods as a biomarker in quantifying the T1 signal within MS lesions.

Collapse

Presenter Of 2 Presentations

Imaging Poster Presentation

P0589 - Implications of registration of white matter structures for MS connectome analysis (ID 1945)

Speakers
Presentation Number
P0589
Presentation Topic
Imaging

Abstract

Background

To assess the connectome disruption in patients with multiple sclerosis (MS), methods have been developed that utilise connectomes derived from healthy controls (HC) which require the accurate registration of white matter structures from patients with MS onto HCs whole brain streamline tractograms.

MRI T1 based image registration may be inaccurately mapped to HC derived tractograms due to the lack of information within the relatively homogenous T1 signal of white matter.

Fibre orientation distributions (FODs), created by the constrained spherical deconvolution (CSD) model of white matter diffusion data, have the potential to improve white matter registration as it holds information of all fibre directions for each voxel.

Objectives

Here we compare the accuracy and utility of FOD-based, T1-based and FA-based registration of white matter tracts in MS.

Methods

10 MS patients and 10 age matched healthy controls underwent 3T MRI scanning including 1mm 3D T1 and single shell diffusion weighted imaging. FOD maps were created using CSD algorithm designed for single shell data. Tensor and FA maps were created.

Non-linear registration was undertaken directly from each patient to each control (ie. 100 warps) with ANTs using T1 and FA images, and through an MNI template intermediary.

Registration was performed directly from each patient to controls using FOD maps using MRtrix3 FOD-based registration, as well as through a custom population FOD template.

Each registration method was assessed in transforming three white matter tracts, corticospinal tracts (CST), anterior thalamic radiations (ATR) and optic radiations (OR), from patients to controls. The resultant transformed segmentations from each registration method was compared to the control segmentation by calculating its Dice coefficient.

Further to this each method was assessed using a tract segmentation that had the cortical ribbon and juxtacortical tract removed.

Statistically significant differences were assessed by non-parametric Kruskal-Wallis test with Dunn’s post hoc testing.

Results

For combination of all tracts, the highest Dice coefficients were with direct FA (median = 0.727, IQR 0.06215) and direct T1 (median = 0.72185, IQR 0.056525) with no significant difference found.

For combination of all cropped tracts, the highest Dice coefficients were with FOD population template (median = 0.7673, IQR 0.0468), direct FOD (median = 0.76565, IQR 0.050175) and direct FA registration (median = 0.7626, IQR 0.060025) with no significant difference found.

When utilising an intermediary template, both T1 and FA based methods performed worse, whereas the FOD population template performed similarly.

Conclusions

FA and T1 based registration outperformed FOD based, despite more white matter information. This was driven by poorer juxtacortical registration in the FOD based method. This is important in the analysis of MS due to the high prevalence of juxtacortical lesional pathology.

Collapse
Imaging Poster Presentation

P0622 - Quanitifying the T1 hypointensity of MS lesions (ID 1063)

Speakers
Presentation Number
P0622
Presentation Topic
Imaging

Abstract

Background

Within multiple sclerosis (MS) lesions, T1 weighted hypo-intensity correlates with pathological markers of irreversible damage, including axonal degeneration. Studies have found that T1 “black holes” have a higher correlation with clinical disability than do T2 weighted lesions, and that measurable T1 signal drop out occurs within slowly enlarging lesions or chronic active lesions, as opposed to inactive lesions, suggestive of ongoing axonal degeneration.

To perform this quantitative analysis two pre-processing steps are necessary. The first is correction of low frequency intensity nonuniformity present in image data, also known as bias fields. The second is normalisation of the signal between subjects and timepoints. Both of these steps need to maintain representative contrast between pathological and normal appearing white matter (NAWM) to provide accurate results. Validation of these methods within the MS context is needed to support their use as a biomarker in MS.

Objectives

To assess the accuracy N34 bias correction on maintaining accurate MS lesion contrast

To assess the accuracy of Freesurfer normalization tool6 in maintaining accurate relative signal intensity

Methods

21 relapsing MS and 26 progressive MS subjects had MRI brain scans with 1mm3 3D T1 fast spoiled gradient echo (FSPGR), and 0.6 x 0.46mm2 FLAIR sequences. FLAIR images were resampled and linearly registered to the T1 using FLIRT. MS lesions were segmented from the FLAIR using JIM software.

To calculate an accurate measure of local tissue contrast between MS lesions and surrounding NAWM, the lesion segmentations were dilated and masked for white matter segmentation. The lesion mask was then subtracted from the segmentation to create an edge of NAWM surrounding the lesion. The measure of raw local tissue contrast was taken as the T1 value of

lesion core / lesion edge

N3 bias correction was done with the Freesurfer command mri_nu_correct.mni. Normalization of the T1 sequences was undertaken with Freesurfers mri_normalize which imposes a hard ceiling effect on the white matter peak found, creating an homogenous NAWM intensity of 110 and CSF intensity of ~35.

Correlations of each MS lesion between the bias field corrected and local tissue contrast was explored. Correlations between the bias field corrected and normalized values was explored.

Results

A total of 2401 lesions were analysed. N3 bias corrected T1 images correlated with local tissue contrast of the raw T1 images with an R2 = 0.7556. Normalized T1 images correlated with N3 bias corrected images with an R2 = 0.9415. Average MS lesion normalized T1 intensity was similar between relapsing MS and progressive MS cohorts (82.927 vs 82.793, p = 0.42).

Conclusions

In this analysis T1 intensity was able to be corrected for bias fields and normalized to a set range with reasonable accuracy in maintaining lesion contrast. This supports the use of these methods as a biomarker in quantifying the T1 signal within MS lesions.

Collapse