University of Basel
Department of Biomedical Engineering

Author Of 1 Presentation

Imaging Oral Presentation

PS11.04 - Quantitative susceptibility mapping classifies white matter lesions with different myelin and axonal content and quantifies diffuse pathology in MS

Abstract

Background

Quantitative susceptibility mapping (QSM) identifies iron accumulation and myelin loss in smoldering white matter lesions (WMLs). Yet, QSM may be also used to provide a broader understanding of focal and diffuse MS pathology.

Objectives

To study QSM features across WMLs, to assess myelin and axonal loss in WMLs with different QSM features and to quantify QSM pathology in normal-appearing white and cortical grey matter (NAWM, NAGM).

Methods

Ninety-one MS patients (62 RRMS, 29 PMS) and 72 healthy controls (HC) underwent QSM, myelin water imaging (MWI) and multishell diffusion at 3T MRI. In WMLs, cortical lesions (CLs), NAWM and NAGM, we extracted mean QSM, myelin water fraction (MWF) and neurite density index (NDI). WMLs were classified into 5 groups according to their appearance on 3D-EPI QSM: (i) isointense; (ii) with hyperintense rim, Rim+ (iii); with hypointense rim relative to the lesion core, hypo Rim; (iv) hyperintense; (v) hypointense. Mann-Whitney and Kruskal-Wallis test with Dunn’s correction for multiple comparison were used to compare (a) lesion types and (b) specific lesions vs all other WMLs. Voxel-wise comparisons of NAWM QSM were performed using Threshold-Free Cluster Enhancement (TFCE) clustering. Cortical analysis of QSM NAGM and GM-HC was performed using FreeSurfer and compared using a General Linear model (GLM).

Results

Of 1136 WMLs in QSM maps, we detected: (i) 314 (27.6%), (ii) 183 (16.1%), (iii) 16 (1.41%), (iv) 577 (50.8%) and (v) 46 (4.05%) WML. All WML exhibited lower NDI than NAWM and WM-HC (P<0.0001). Isointense lesions exhibited higher NDI (P=0.0115) and MWF (P<0.0001) than other WMLs. Rim + and hyperintense lesions exhibited lower MWF than NAWM and WM-HC (P<0.0001). Rim + lesions showed lower MWF and NDI than other WML types (P<0.001). Hypo Rim+ lesions and hypointense lesions exhibited higher MWF than other WMLs (P=0.0006, P<0.05). Hyperintense lesions exhibited lower MWF than other WMLs types (P<0.01) except Rim+ lesions. TFCE and vertex-wise cortical surface analysis showed areas throughout the NA tissue, where QSM is either lower or higher compared to healthy tissue in HC and in PMS compared to RMS (P<0.01).

Conclusions

QSM is sensitive to diffuse and focal pathology with various myelin and axonal characteristics. We hypothesize that isointense WMLs show high repair activity, hypointense WMLs are remyelinated lesions and hyperintense WMLs are chronic inactive lesions. MRI-histopathology work is ongoing to confirm these findings.

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Author Of 6 Presentations

Machine Learning/Network Science Late Breaking Abstracts

LB1213 - Attention-based deep learning identifies a new microstructural diffusion MRI contrast sensitive to focal pathology and related to patient disability (ID 2074)

Speakers
Presentation Number
LB1213
Presentation Topic
Machine Learning/Network Science

Abstract

Background

Microstructural biophysical models reconstructed from advanced diffusion MRI (dMRI) data provide quantitative measures (qMs), which inform about the brain tissue microenvironment, based on different assumptions.

Objectives

To compare the sensitivity of available qMs to focal pathology in multiple sclerosis (MS), and to explore which qMs– or combinations of qMs – are best correlated with patients disability.

Methods

dMRI (1.8 mm isotropic resolution, 149 directions, b-values were 0, 700, 1000, 2000, 3000 s/mm2) was acquired from 67 relapsing-remitting and 33 progressive MS patients (median EDSS: 2.5). The qMs for the isotropic and intra-axonal compartments were derived from the following available models: Ball and Stick, NODDI, SMT-NODDI, MCMDI, NODDIDA, DIAMOND, Microstructure Bayesian approach (MB) and microstructure fingerprinting. In total, 13 qMs were included and subject-wise normalized within brain tissue (nqMs).

To identify the nqMs sensitive to focal pathology, an attention-based convolutional neural network (aCNN) was built to (a) classify randomly sampled WM lesion and perilesional WM patches and (b) generate attention weights (AWs) representing the relative importance of the qMs in the classification. Twenty patients were randomly selected in the test dataset (709 lesion patches and 746 perilesional WM patches), and the rest were in the cross-validation (CV) dataset (2925 lesion patches and 3176 perilesional WM patches). The performance metric was the area under the receiver operating characteristic curve (AUC). Because of the correlation between the nqMs, which may influence the relative AWs, we performed 10-fold CV and selected the nqMS that most contributed to the classification.

To assess which nqMS – or combination of nqMS was best correlated with EDSS, we used Spearman’s correlation coefficient (ρ) with two-sided 20000 permutation tests and followed by Bonferroni correction.

Results

The test AUC was 0.911 indicating the aCNN learned the right AWs to differentiate lesions and perilesional WM. The most discriminating nqMs included isotropic and intra-axonal compartments from MB, the neural density index (NDI) from the NODDI and the intra-axonal compartment from MCMDI.

The sum of isotropic and intra-axonal compartments of the MB (sMB) showed the strongest correlation with EDSS (ρ=-0.40,corr. p<0.0001) followed by the sum of sMB and NDI (ρ=-0.30,corr. p<0.05), and the sum of sMB and intra-axonal compartment from MCMDI (ρ=-0.32,corr. p<0.05). None of the selected nqMs as a single measure and their other combinations correlated with EDSS.

Conclusions

By performing aCNN-aided selection of the openly available WM quantitative measures, we have identified the measures most sensitive to MS focal pathology; furthermore, we have derived a new contrast that – by combining the measures of isotropic and intracellular diffusion – strongly correlated with patients’ disability.

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Imaging Poster Presentation

P0534 - Advanced magnetic resonance imaging for myelin and axonal density in MS: correlation with clinical disability and serum neurofilament levels (ID 1781)

Abstract

Background

Myelin water imaging (MWI) and neurite orientation dispersion and density imaging (NODDI) provide sensitive surrogate markers of myelin and axonal content in lesions and normal-appearing tissue. However, to date, there is scarce information about the relationship of these measures with (i) disability; and (ii) the axonal damage specific biomarker serum neurofilament light chain (sNfL).

Objectives

To explore the correlation of MWI and NODDI measures in MS lesions and in normal-appearing (NA) brain tissue with disability and sNfL.

Methods

Ninety-one MS patients (62 relapsing-remitting MS-RRMS and 29 progressive MS-PMS) underwent MWI and NODDI. Mean myelin water fraction (MWF) and neurite density index (NDI) were extracted in white matter lesions (WMLs), cortical lesions (CLs), NA white matter (NAWM) and cortical NA gray matter (CNAGM). For sNfL, a logarithmic transformation was applied to comply with normality assumption. Correlation studies between MRI measures, sNfL and EDSS were performed using linear models, with age and gender as covariates. The models were performed for the whole sample and for patients with clinical deficits only (EDSS >1).

Results

MWF and NDI did not correlate with EDSS when the entire cohort was considered (P>0.05). However, for those patients with clinical deficits (EDSS> 1), NDI in WMLs was associated with EDSS (NDI: P<0.01, beta=-10.00; N=74). We also found that MWF and NDI in WMLs were related to sNfL (MWF: P<0.01, beta=0.13; NDI: P<0.01, beta=-3.60). Again, this correlation was stronger in patients with EDSS>1 (MWF: P<0.01, beta=0.13; NDI: P <0.01, beta=-3.60).

Conclusions

Imaging surrogate markers of myelin and axon pathology in WML – and not in CLs and NA tissues - are correlated with disability and sNfL. Interestingly, associations between those imaging markers and disability/sNFL were more evident in patients with clinical deficits as compared to those without neurological deficits.

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Imaging Poster Presentation

P0538 - Applying advanced diffusion MRI in MS: a comparison of 20 diffusion MRI models to identify microstructural features of focal damage (ID 1338)

Speakers
Presentation Number
P0538
Presentation Topic
Imaging

Abstract

Background

Advanced diffusion-weighted MRI (DW-MRI) sequences, in combination with biophysical models, provide unprecedented information on the microstructural properties of both healthy and pathological brain tissue.

Nevertheless, it is nowadays challenging to identify the most accurate biophysical model to describe focal microstructural pathology in multiple sclerosis (MS) patients, due to the lack of appropriate comparative studies.

Objectives

To investigate the specificity and sensitivity of 124 independent features derived from 20 diffusion microstructural models to differentiate specific features of tissue alterations in white matter (WM) lesions compared to the surrounding normal-appearing WM (NAWM).

Methods

The study included 102 MS patients: RRMS: 66%, SPMS: 18%, PPMS: 16%, mean age 46±14; female 64%, disease duration 12.16±18.18 years, median Expanded Disability Status Scale (EDSS): 2.5.

DW-MRI data were acquired with 1.8mm isotropic resolution isotropic and with the b-values [0, 700, 1000, 2000, 3000] s/mm2.

Lesion masks were generated with a deep learning network algorithm and manually corrected if required. Voxels of NAWM tissue were randomly chosen outside the lesion masks.

The following microstructural models were applied: DTI, Non-parametric DTI, DKI, Ball and Stick, Ball and Sticks, Ball and Rockets, NODDI-Watson, AMICO-NODDI, NODDI-Bingham, SMT-NODDI, NODDIDA, SMT, MCMDI, CHARMED, IVIM, sIVIM, Microstructure Fingerprinting, Microstructure Bayesian, DIAMOND, and DIAMOND isotropic-restricted.

The classification was performed using logistic regression on 300’000 voxels, equally divided in lesion and NAWM voxels. Features were scored according to the Area Under the Curve (AUC), sensitivity, and specificity.

Results

The intra-axonal signal fraction of the Microstructure Bayesian approach scored maximum with AUC=0.87, for threshold=0.5 sensitivity=0.79, sensitivity=0.83. AUC = 0.86 were attributed to the intra-axonal signal fraction of Ball and rockets, NODDI-Watson, AMICO-NODDI, NODDI-Bingham, SMT-NODDI and the extra-axonal perpendicular signal fraction of the Microstructure Bayesian approach. Low AUC scores (<0.75) were achieved by DTI and parameters not related to signal fractions, e.g. orientation dispersion.

Conclusions

Among available microstructural models, the Microstructure Bayesian appeared to best differentiate voxels with microstructural damage in WM lesions compared to NAWM. Very similar, albeit slightly lower accuracy, was achieved by NODDI-based models. In general, models with estimates intra-axonal signal fraction tend to perform better in this type of classification, showing that intra-axonal component may be the dominant factor in distinguishing the two types of tissue. Further analysis will explore the advantage of including combinations of independent features.

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Imaging Poster Presentation

P0545 - Automatic MS lesions segmentation using LeMan-PV as a clinical decision-support tool: a longitudinal analysis (ID 1590)

Abstract

Background

LeMan-PV is a prototype that performs cross-sectional and longitudinal detection of Multiple Sclerosis (MS) lesions, which has been validated on conventional (cMRI) and advanced magnetic resonance imaging at 3T (Fartaria et al. 2019). Since this software provides a report that is available shortly after image acquisition, it may be ideal as clinical decision-support tool.

Objectives

To assess LeMan-PV as clinical decision-support tool in a monocentric real-world cMRI dataset from the Swiss Multiple Sclerosis Cohort.

Methods

262 MS patients underwent cMRI at Basel University Hospital in a mean of 3.5 follow-up sessions, with an average of 399 days between two consecutive sessions. cMRI sequences were acquired at 1.5T and 3T in 725 and 195 sessions, respectively. Cross-sectional and longitudinal MS lesions segmentation (i.e. identification of new and enlarging lesions - NLs, ELs) was performed using the LeMAN-PV prototype software. An expert neuroradiologist performed a radiological reading of the number of NLs and ELs in the most recent acquisition by comparing it to the previous one (ground truth, GT), considering only lesions with a diameter larger than 3 mm. The minimum volume thresholds to identify an NL and an EL were chosen by minimizing the patient-wise error between the automated count and the expert ground truth. Two scenarios were evaluated by first assuming disease activity if one or more EL were present, and second by considering activity if NL were present in the new acquisition.

Results

The volume thresholds chosen were 11 and 12 mm3 for ELs and NLs, respectively. For those, LeMan-PV detected 11% more of both ELs and NLs than the neuroradiologist. In the patient-wise evaluation of cases with both sessions acquired at 1.5T (70%), LeMan-PV showed sensitivities of 93% and 78% and specificities of 62% and 43% when evaluating ELs and NLs. For the 3T pairs of sessions (8%), values were 68% and 72% for ELs and 73% and 68% for NLs. Finally, for cases with a first acquisition at 1.5T and a second at 3T (22%), values were 76% and 73% for ELs and 71% and 65% for NLs.

Conclusions

The count of new and enlarging MS lesions using LeMan-PV were close to the one performed by an expert neuroradiologist; the software performed better when assessing disease activity via detection of enlarging lesions rather than by identifying new lesions. More 3T data is being currently collected at 3T to provide a size-matched inter-scanner comparison.

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Imaging Poster Presentation

P0647 - Studying intralesional axonal damage in MS white matter lesions with diffusion MRI biophysical models (ID 694)

Abstract

Background

Advanced diffusion-weighted MRI (DW-MRI) sequences, in combination with biophysical models, provide new information on the microstructural properties of the tissue.

Objectives

To investigate the differences in intra-axonal signal fraction (IASF) between perilesional normal-appearing white matter (pl-NAWM), white matter lesions (WML) without (rim-) and with paramagnetic rim (rim+) comparing eight biophysical diffusion models.

Methods

The study included 102 MS patients: RRMS: 66%, SPMS: 18%, PPMS: 16%, mean age 46±14; female 64%, disease duration 12.16±18.18 yrs, median EDSS: 2.5.

DW-MRI data were acquired with 1.8mm isotropic resolution and b-values [0, 700, 1000, 2000, 3000] s/mm2.

Lesion masks were generated with a deep-learning-based method and manually corrected if required; pl-NAWM was defined as a region of 3-voxels around each WML; 225 paramagnetic rim lesions were manually identified based on 3D EPI and 2330 were labelled as rim-.

The following microstructural models were applied: Ball and Stick, Ball and Rockets, AMICO-NODDI, SMT-NODDI, MCMDI, NODDIDA, CHARMED, Microstructure Bayesian approach.

Delta (WML - pl-NAWM) was calculated for each WML, and one-side Mann Whitney U was used to compare the delta between models, followed by Bonferroni to correct for multiple testing.

Mean difference and Cohen's d was used to assess differences between lesions with extensive axonal damage (rim+) and other WML (rim-).

Results

All models applied in this study reported low IASF in rim+ WML, medium IASF in rim- WML and relatively high IASF in pl-NAWM. However, a broad spectrum of IASF values was identified from the different models: relatively simple models such as Ball and Stick and CHARMED, showed low delta IASF within lesions, while MCMDI models reported the highest significant difference compared to other models (p<0.0001). The comparison between WML and pl-NAWM mean IASF across models showed that MCDMI exhibited the highest difference (mean 0.13, Cohen’s d 1.34). AMICO-NODDI and SMT-NODDI showed close results (mean difference 0.12/0.12 and Cohen’s d 1.46/1.51).

The models best discriminating IASF between rim+ and rim- lesions were MCMDI and NODIDDA (mean 0.08/0.07, Cohen’s d -0.69/-0.70).

Conclusions

We compared eight WM diffusion models for assessment of intralesional axonal damage in MS patients. The comparison between WML and pl-NAWM showed that robustness of the method, identified with SMT-based and NODDI-based models, it is crucial. For the comparison between lesions with a high level of damage (rim +) and other WML, the diffusivity estimation appeared to play an important role. The method which appeared both robust and able to estimate the diffusivity of the tissue was MCMDI, which performed best in both cases.

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Pathogenesis – the Blood-Brain Barrier Poster Presentation

P0945 - Brain choroid plexus volume in Multiple Sclerosis versus Neuromyelitis Optica Spectrum Disease (ID 1476)

Abstract

Background

Neuromyelitis optica spectrum disease (NMOSD) and multiple sclerosis (MS) have a different pathophysiology. Accumulating evidence suggests that the choroid plexus plays a pivotal role in the pathogenesis of MS. However, MRI data comparing the choroid plexus volume between MS and NMOSD are scarce.

Objectives

To compare the choroid plexus volume in MS vs. NMOSD in vivo using high-resolution 3D MRI data. Migraine patients and healthy individuals served as control groups.

Methods

We included 95 MS patients [45% secondary progressive (SP); mean age 51.0±11.5 years; disease duration 20.8±10.4 years, 62% female; median Expanded Disability Status Scale (EDSS) 4.0], 43 NMOSD patients [28/43 anti-aquaporin 4 antibody positive; 11/43 anti-myelin oligodendrocyte glycoprotein antibody positive; 87% female; mean age 50.0±13.8 years; disease duration 6.8±7.3 years, median EDSS 3.0], 38 migraine patients [mean age 39±13 years, 79% female; 15/38 migraine with aura] and 65 healthy individuals [HCs, mean age 41±17 years, 48% female]. The choroid plexus of the lateral ventricles and T2-weighted (T2w) white matter lesions (WMLs) were segmented fully automated on T1-weighted (T1w) magnetization-prepared rapid gradient echo (MPRAGE) images and fluid attenuated inversion recovery sequences (FLAIR, voxel size of both sequences 1x1x1 mm3), respectively, using a supervised deep learning algorithm (multi-dimensional gated recurrent units). Total intracranial volume (TIV) and lateral ventricle volumes were assessed fully automated using Freesurfer. All outputs were reviewed and manually corrected (if necessary) using 3D-Slicer by trained raters who were blinded to the clinical information. Group differences were analyzed using multivariable generalized linear models (GLMs) adjusted for age, gender, TIV and lateral ventricle volume. Cohens’ d was used to calculate the standardized difference between the respective groups. Given p-values are adjusted for multiple comparisons (Bonferroni).

Results

Mean choroid plexus was larger in MS compared to NMOSD (1907±455 vs. 1467±408 µl; p<0.001, d=0.86), HCs (1663±424 µl; p=0.007, d=1.17) and migraine (1527±366 µl; p=0.02, d=0.72). There was no statistical difference in the choroid plexus volume between NMOSD, migraine and HCs. The choroid plexus was marginally larger in RRMS than SPMS (1959±482 vs. 1875±476 µl; p=0.28; d=0.17) and in untreated MS patients compared to MS patients on disease modifying therapy (2111±382 vs. 1876±459 µl; p=0.36). However, these differences did not reach statistical significance after correction for multiple comparisons. There was no association between the choroid plexus volume and total T2w WML volume in MS.

Conclusions

Patients with MS have larger choroid plexus than HCs, migraine and NMOSD patients. Further studies are warranted to investigate the respective roles of the choroid plexus in the pathogenesis of MS and NMOSD.

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Presenter Of 2 Presentations

Imaging Poster Presentation

P0538 - Applying advanced diffusion MRI in MS: a comparison of 20 diffusion MRI models to identify microstructural features of focal damage (ID 1338)

Speakers
Presentation Number
P0538
Presentation Topic
Imaging

Abstract

Background

Advanced diffusion-weighted MRI (DW-MRI) sequences, in combination with biophysical models, provide unprecedented information on the microstructural properties of both healthy and pathological brain tissue.

Nevertheless, it is nowadays challenging to identify the most accurate biophysical model to describe focal microstructural pathology in multiple sclerosis (MS) patients, due to the lack of appropriate comparative studies.

Objectives

To investigate the specificity and sensitivity of 124 independent features derived from 20 diffusion microstructural models to differentiate specific features of tissue alterations in white matter (WM) lesions compared to the surrounding normal-appearing WM (NAWM).

Methods

The study included 102 MS patients: RRMS: 66%, SPMS: 18%, PPMS: 16%, mean age 46±14; female 64%, disease duration 12.16±18.18 years, median Expanded Disability Status Scale (EDSS): 2.5.

DW-MRI data were acquired with 1.8mm isotropic resolution isotropic and with the b-values [0, 700, 1000, 2000, 3000] s/mm2.

Lesion masks were generated with a deep learning network algorithm and manually corrected if required. Voxels of NAWM tissue were randomly chosen outside the lesion masks.

The following microstructural models were applied: DTI, Non-parametric DTI, DKI, Ball and Stick, Ball and Sticks, Ball and Rockets, NODDI-Watson, AMICO-NODDI, NODDI-Bingham, SMT-NODDI, NODDIDA, SMT, MCMDI, CHARMED, IVIM, sIVIM, Microstructure Fingerprinting, Microstructure Bayesian, DIAMOND, and DIAMOND isotropic-restricted.

The classification was performed using logistic regression on 300’000 voxels, equally divided in lesion and NAWM voxels. Features were scored according to the Area Under the Curve (AUC), sensitivity, and specificity.

Results

The intra-axonal signal fraction of the Microstructure Bayesian approach scored maximum with AUC=0.87, for threshold=0.5 sensitivity=0.79, sensitivity=0.83. AUC = 0.86 were attributed to the intra-axonal signal fraction of Ball and rockets, NODDI-Watson, AMICO-NODDI, NODDI-Bingham, SMT-NODDI and the extra-axonal perpendicular signal fraction of the Microstructure Bayesian approach. Low AUC scores (<0.75) were achieved by DTI and parameters not related to signal fractions, e.g. orientation dispersion.

Conclusions

Among available microstructural models, the Microstructure Bayesian appeared to best differentiate voxels with microstructural damage in WM lesions compared to NAWM. Very similar, albeit slightly lower accuracy, was achieved by NODDI-based models. In general, models with estimates intra-axonal signal fraction tend to perform better in this type of classification, showing that intra-axonal component may be the dominant factor in distinguishing the two types of tissue. Further analysis will explore the advantage of including combinations of independent features.

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Imaging Poster Presentation

P0647 - Studying intralesional axonal damage in MS white matter lesions with diffusion MRI biophysical models (ID 694)

Abstract

Background

Advanced diffusion-weighted MRI (DW-MRI) sequences, in combination with biophysical models, provide new information on the microstructural properties of the tissue.

Objectives

To investigate the differences in intra-axonal signal fraction (IASF) between perilesional normal-appearing white matter (pl-NAWM), white matter lesions (WML) without (rim-) and with paramagnetic rim (rim+) comparing eight biophysical diffusion models.

Methods

The study included 102 MS patients: RRMS: 66%, SPMS: 18%, PPMS: 16%, mean age 46±14; female 64%, disease duration 12.16±18.18 yrs, median EDSS: 2.5.

DW-MRI data were acquired with 1.8mm isotropic resolution and b-values [0, 700, 1000, 2000, 3000] s/mm2.

Lesion masks were generated with a deep-learning-based method and manually corrected if required; pl-NAWM was defined as a region of 3-voxels around each WML; 225 paramagnetic rim lesions were manually identified based on 3D EPI and 2330 were labelled as rim-.

The following microstructural models were applied: Ball and Stick, Ball and Rockets, AMICO-NODDI, SMT-NODDI, MCMDI, NODDIDA, CHARMED, Microstructure Bayesian approach.

Delta (WML - pl-NAWM) was calculated for each WML, and one-side Mann Whitney U was used to compare the delta between models, followed by Bonferroni to correct for multiple testing.

Mean difference and Cohen's d was used to assess differences between lesions with extensive axonal damage (rim+) and other WML (rim-).

Results

All models applied in this study reported low IASF in rim+ WML, medium IASF in rim- WML and relatively high IASF in pl-NAWM. However, a broad spectrum of IASF values was identified from the different models: relatively simple models such as Ball and Stick and CHARMED, showed low delta IASF within lesions, while MCMDI models reported the highest significant difference compared to other models (p<0.0001). The comparison between WML and pl-NAWM mean IASF across models showed that MCDMI exhibited the highest difference (mean 0.13, Cohen’s d 1.34). AMICO-NODDI and SMT-NODDI showed close results (mean difference 0.12/0.12 and Cohen’s d 1.46/1.51).

The models best discriminating IASF between rim+ and rim- lesions were MCMDI and NODIDDA (mean 0.08/0.07, Cohen’s d -0.69/-0.70).

Conclusions

We compared eight WM diffusion models for assessment of intralesional axonal damage in MS patients. The comparison between WML and pl-NAWM showed that robustness of the method, identified with SMT-based and NODDI-based models, it is crucial. For the comparison between lesions with a high level of damage (rim +) and other WML, the diffusivity estimation appeared to play an important role. The method which appeared both robust and able to estimate the diffusivity of the tissue was MCMDI, which performed best in both cases.

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