Background

In multiple sclerosis (MS), perilesional chronic inflammation appears on in vivo 3T susceptibility-based magnetic resonance imaging (MRI) as non-gadolinium-enhancing paramagnetic rim lesions (PRL). A higher PRL burden has been recently associated with a more aggressive disease course. The visual detection of PRL by experts is time-consuming and can be subjective.

Objectives

To develop a multimodal convolutional neural network (CNN) capable of automatically detecting PRL on 3D-T2*w-EPI unwrapped phase and 3D-T2w-FLAIR images.

Methods

124 MS cases (87 relapsing remitting MS, 16 primary progressive MS and 21 secondary progressive MS) underwent 3T MRI (MAGNETOM Prisma and MAGNETOM Skyra, Siemens Healthcare, Erlangen, Germany). Two neurologists visually inspected FLAIR magnitude and EPI phase images and annotated 462 PRL. 4857 lesions detected by an automatic segmentation (La Rosa et al. 2019) without overlap with PRL were considered non-PRL. The prototype RimNet was built upon two single CNNs, each fed with 3D patches centered on candidate lesions in phase and FLAIR images, respectively. A two-step feature-map fusion, initially after the first convolutional block and then before the fully connected layers, enhances the extraction of low and high-level multimodal features. For comparison, two unimodal CNNs were trained with phase and FLAIR images. The areas under the ROC curve (AUC) were used for evaluation (DeLong et al. 1988). The operating point was set at a lesion-wise specificity of 0.95. The patient-wise assessment was conducted by using a clinically relevant threshold of four rim+ lesions per patient (Absinta et al. 2019).

Results

RimNet (AUC=0.943) outperformed the phase and FLAIR image unimodal networks (AUC=0.913 and 0.855, respectively, P’s <0.0001). At the operating point, RimNet showed higher lesion-wise sensitivity (70.6%) than the unimodal phase network (62.1%), but lower than the experts (77.7%). At the patient level, RimNet performed with sensitivity of 86.8% and specificity of 90.7%. Individual expert ratings yielded averaged sensitivity and specificity values of 76.3% and 99.4%, respectively.

Conclusions

The excellent performance of RimNet supports its further development as an assessment tool to automatically detect PRL in MS. Interestingly, the unimodal FLAIR network performed reasonably well despite the absence of a paramagnetic rim, suggesting that morphometric features such as volume or shape might be a distinguishable feature of PRL.

Background

Postmortem MRI provides precious insights into the relation of MRI metrics to pathoanatomical features of multiple sclerosis (MS) and can help to understand the basis of damage and repair.

Objectives

To investigate the respective features of MS lesions in the cortex and in the white matter using multiparametric postmortem MR imaging at 3T and identify discriminant characteristics of white matter lesion subgroups.

Methods

We scanned three fixed brains of secondary-progressive MS patients (mean disease duration 15.3 years) on a standard clinical 3T-MRI scanner with following sequences: Magnetization Transfer Saturation (MTsat), T1-relaxometry (T1-rt), Myelin Water Fraction (MWF) and Diffusion Tensor - Fractional Anisotropy (DTI-FA). We compared these metrics between (i) cortical lesions (CL, n=118) and normal-appearing grey matter (NAGM, n=186) and (ii) white matter lesions (WML, n=140) and normal-appearing white matter (NAWM, n=53) using a Mann-Whitney U test. Then, we analyzed the differences between different subgroups of WML (periventricular lesions -PVL-, n=38, WM part of leukocortical lesions -WMLCL-, n=36, subcortical lesions -SCL-, n=66, and areas of “dirty white matter” -DWM-, n=15) by performing a Kruskal-Wallis test and a Mann-Whitney U tests for direct comparison. Bonferroni correction for multiple-testing was applied.

Results

CL exhibited lower MTsat (p<0.001), higher T1-rt (p<0.001) and MWF (p<0.01) than normal appearing cortical tissue. WML showed lower MTsat (p<0.001), higher T1-rt (p<0.001), and lower MWF (p<0.001) than normal appearing white matter. DTI-FA did not differ between CL/WML and NAWM/NAGM. MTsat values were lower in the PVL (p<0.001) and higher in the DWM (p<0.001) in comparison to all other lesion subgroups. T1-rt were higher in PVL (p<0.001) compared to the other lesion subgroups. MWF values were higher in DWM and SCL (p<0.01), not statistically different between PVL and WMLCL. DTI-FA values were lower in WMLCL in comparison to all other subgroups (p<0.01) and did not differ between the other categories.

Conclusions

Postmortem MRI metrics in WML/CL as well as in different subgroups of WML, are compatible with myelin damage and tissue destruction. Interestingly, MWF was higher in CL than in NAGM, which might correspond to a predominance of “myelin blistering” pathology in the cortex. Ongoing work aims to directly correlate our findings with detailed histopathological characterization including electron microscopy of myelin damage.

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.