Evaluation of the perilesional normal-appearing white matter (NAWM) microenvironment in relapsing-remitting multiple sclerosis with quantitative T1rho magnetic resonance imaging

Introduction

Multiple sclerosis (MS) is a chronic immune-mediated disease of the central nervous system (CNS), characterized by demyelination, neuroinflammation, and neurodegeneration (1-4). Key pathological features include focal lesions with loss of myelin, axonal damage, blood-brain barrier breakdown, glial activation and macrophage infiltration (5,6). However, growing evidence suggests that pathological changes extend beyond these focal abnormalities into the normal-appearing white matter (NAWM), where there are variable degrees of diffuse tissue damage (7-9) even in the absence of visible lesions on conventional structural magnetic resonance imaging (MRI). These changes within the NAWM have shown to contribute to worsening progression of disability (10).

Previous studies have demonstrated diffusion tensor imaging (DTI) to be a highly sensitive technique for detecting microstructural tissue abnormalities in the brain (11-15). DTI has been used to investigate characteristics of water diffusion and white matter integrity in MS lesions and the NAWM. Fractional anisotropy (FA) measures the directionality of water molecular diffusion, has been reported sensitive to white matter integrity and fibre track disruption (14,15). In contrast, mean diffusivity (MD) is influenced by increased water movement, and therefore can reflect oedema, inflammation, and axonal and myelin damage (11). In addition to FA and MD, axial diffusivity (AD), which measures diffusion parallel to axonal fibers, has been considered a marker of axonal integrity, linked to axonal damage or degeneration, and radial diffusivity (RD), reflecting diffusion perpendicular to fibers, may be an indicator of myelin integrity. In comparison to healthy controls, patients with MS have shown lower FA and higher MD in the NAWM (11,16,17), reflecting underlying microstructural changes in areas appearing otherwise unaffected on conventional MRI. In MS, RD is typically compared to controls increased due to myelin loss, while a decrease in AD may result from axonal loss, although an increase in AD has also been reported in some cases, which has been interpreted as a compensatory mechanism to maintain functionality in the presence of white matter damage (11).

Quantitative T1rho imaging is an emerging technique that evaluates the macromolecular content in tissues by measuring the characteristics of spin-lattice relaxation in the rotating frame (18). T1rho relaxation is sensitive to low-frequency interactions (19,20), and therefore interactions between protons and macromolecules, making it a valuable tool for detecting early pathological changes in neurological diseases such as MS. Compared to other macromolecular sensitive techniques such as magnetization transfer imaging, T1rho offers several practical and physiological advantages. The magnetization transfer ratio (MTR), commonly used to characterize the magnetization transfer, is highly sensitive to scanner settings and pulse sequence design (21). While T1rho is also influenced by sequence parameters, it is generally more standardized and reproducible (20). Gonyea et al. additionally reported that T1rho may offer improved lesion contrast compared to conventional T2-weighted imaging (22). Furthermore, T1rho has the potential to offer fast scan times (23).

In our T1rho method approach, we incorporated a 180° refocusing pulse between the two rotary-echo spin-lock pulses, which effectively reduces the impact of both B0 and B1 field inhomogeneities (20). We have demonstrated high repeatability of T1rho values within the brain across serial scans, ensuring that even small variations in T1rho values reflect physiological or pathological changes rather than technical inconsistencies (20). The technique may therefore be well-suited for the detection of subtle changes in MS and monitoring of changes through longitudinal imaging over time. To date, few studies have investigated the use of T1rho in MS, although early research has suggested prolonged T1rho values in MS lesions and the NAWM when compared to healthy controls (22,24).

The aim of this study was to further evaluate the role of T1rho MRI in assessing microstructural changes in intralesional and perilesional NAWM regions in patients with relapsing-remitting MS, with reference to DTI as the more established technique. Specifically, we aimed to investigate the spatial and distance-dependent variations of potential microstructural changes in the NAWM, by stratifying the NAWM into regional layers. This study aims to provide a more comprehensive understanding of how MS may affect the perilesional white matter at different proximities to macroscopic lesions. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1075/rc).

Methods

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by The Joint Chinese University of Hong Kong-New Territories East Cluster Clinical Research Ethics Committee (No. 2020.650). Written informed consent was obtained from all participants. We prospectively recruited 30 patients with relapsing-remitting MS and 30 age matched healthy controls for inclusion into the study. The inclusion criteria for MS patients were: (I) aged between 18 and 55 years with a diagnosis of MS based on the revised McDonald criteria (25); (II) relapsing-remitting disease course; and (III) ability to provide written informed consent to participate in the study. Exclusion criteria for patients included: (I) contraindications for MRI, such as metallic implants, claustrophobia, or pregnancy; (II) inability to remain still during MRI scanning due to symptoms such as tremor, spasticity, involuntary movements, spasms, cognitive impairment, coughing, or shortness of breath; (III) MS relapse within the past 30 days; (IV) systemic glucocorticoid or psychoactive therapy within the last 3 months; (V) history of drug or alcohol misuse; (VI) other neurological or major psychiatric disorders; and (VII) datasets compromised by artifact. For controls, the inclusion criteria were: (I) aged 18 years or older; and (II) voluntary participation; and the exclusion criteria were: (I) a diagnosis of MS or neuromyelitis optica spectrum disorder (NMOSD); (II) any known brain disease, including brain tumor, Alzheimer’s disease, other neurodegenerative diseases, epilepsy, Parkinson’s disease, or traumatic brain injury; and (III) contraindication to MRI or inability to tolerate the scan without movement due to symptoms (Figure 1).

Figure 1 Flowchart of the study workflow, including subject recruitment, MRI acquisition, lesion and perilesional region analysis, DTI and T1rho quantification. AD, axial diffusivity; DTI, diffusion tensor imaging; FA, fractional anisotropy; FLAIR, fluid-attenuated inversion recovery; MD, mean diffusivity; MRI, magnetic resonance imaging; MS, multiple sclerosis; RD, radial diffusivity; ROI, region of interest; T1WI, T1-weighted imaging; T2WI, T2-weighted imaging; TSL, time of spin-lock.

MRI protocol

MRI was performed using a Philips Ingenia Elition 3.0T X scanner (Philips Medical Systems, Best, The Netherlands). The imaging protocol for patients included three-dimensional (3D) T1-weighted fast field echo (FFE), two-dimensional (2D) axial T2-weighted turbo spin echo (TSE), 3D T2-weighted fluid-attenuated inversion recovery (FLAIR) with fat suppression (FS), and post-contrast 3D T1-weighted FFE with FS performed following the administration of 0.1 mL/kg gadoteric acid contrast agent (Dotarem, gadoteric acid 0.5 mmol/mL; Guerbet, Roissy CdG Cedex, France) (Table S1). 3D echo-planar imaging (EPI) DTI was acquired with a data acquisition matrix of 112×112; field of view (FOV) of 224 mm × 224 mm; data repetition time (TR)/echo time (TE), 9,060/90 ms; parallel imaging factor, 2.5 along the anterior-posterior and 1 along the feet-head direction; with 80 contiguous sagittal slices (resolution =2.0×2.0×2.0 mm³) covering the whole brain. A pair of diffusion gradients was applied along 32 non-collinear directions with a b-value of 1,000 s/mm². Additionally, one set of images with no diffusion weighting (b =0 s/mm²) was acquired.

Our 3D T1rho-prepared TSE sequence was acquired with a data acquisition matrix of 144×144; FOV of 258 mm × 258 mm; TR/TE, 2,500/25 ms, inversion recovery time, 1,650 ms, TSE factor, 182; sensitivity-encoding factor, 2 along the anterior-posterior and 1 along the feet-head direction, number of signal averages (NSA), 1; frequency of spin-lock (FSL), 300 Hz, time of spin-lock (TSL), 0, 10, 25, 45 and 75 ms; with 80 contiguous sagittal slices (3D isotropic resolution, 1.8×1.8×1.8 mm³) for whole brain coverage. B1 and B0 field inhomogeneity compensation for T1rho preparation was implemented in the pulse sequence (26). The same protocol was followed for controls, excluding the post-contrast 3D T1-weighted FFE FS sequence.

DTI processing

The FMRIB Software Library (FSL, Oxford, UK; http://www.fmrib.ox.ac.uk/fsl) DTI imaging processing toolbox was used for DTI data processing. Diffusion data for each subject was preprocessed using FSL’s eddy current correction tools to correct for distortions resulting from eddy currents and brain motion. The b0 image from each participant served as a reference for realigning the diffusion data. Following this, a brain mask was generated from the b0 image using automated skull stripping with the brain extraction tool (BET). The brain mask was then applied to perform diffusion tensor estimation using FMRIB’s diffusion toolbox. The diffusion toolbox calculated the FA, AD, and RD values for each brain voxel. FA and MD were derived from the eigenvalues (λ1, λ2, λ3) of the diffusion tensor automatically, with representing the average of the eigenvalues. AD, which reflects the diffusivity of water along the direction of axonal fibers (λ1), and RD, calculated as (λ2 + λ3)/2, reflecting diffusivity perpendicular to the fibers, were also determined using the eigenvalues (27).

T1rho quantification

T1rho quantification was conducted in MATLAB R2021a (MathWorks, USA). At each pixel of the image, the intensity follows a mono-exponential decay model:

Ik=I0exp(−TSLk/T1rho)[1]

where I_k denotes the image intensity of the kth T1rho-weighted image acquired at with TSL_k, and I₀ represents the intensity at TSL =0. T1rho-weighted images were smoothed using a 5×5 window. Following smoothing, the data was fitted to Eq. [1] using non-linear least square fitting (20) and the mean and standard deviation (SD) of T1rho values for pixels were quantified (Figure 2).

Figure 2 Schematic diagram of the T1rho pulse sequence and T1rho quantification. 3D, three-dimensional; Mag., magnetization; SPAIR, spectral attenuated inversion recovery; TSE, turbo spin echo; TSL, time of spin-lock.

Lesion selection and perilesional region analysis

Abnormal supratentorial T2/FLAIR white matter hyper-intensities greater than 3 mm by conventional neuroradiologic criteria (25) were defined as MS lesions. Only non-enhancing lesions, showing no enhancement on comparison of the pre- and post-contrast T1-weighted images, were chosen for analysis in this study. Lesions were manually defined on 3D DTI and T1rho images with reference to the T2-weighted images on ITK-SNAP (version 3.8.0, USA) program. During segmentation, the researcher (L.W.) avoided large vessels, cerebrospinal fluid (CSF), ventricles, and other nearby MS lesions. All regions of interest (ROIs) were reviewed and verified by a neuroradiologist with more than 10 years of experience (T.Y.S.). To assess inter-observer variation, a group of 54 lesions was delineated by a second researcher with 10 years research experience (Q.H.A.). The Dice similarity coefficients for spatial overlap between the two observers were 0.81 for both the T1rho and DTI ROI masks, indicating consistency and reproducibility of the ROIs. The intra-class correlation coefficient (ICC) for T1rho measurements was 0.94, indicating excellent inter-rater reproducibility. ICC values for FA, MD, RD, and AD were also good to excellent, being 0.88, 0.90, 0.91, and 0.91, respectively.

Five perilesional layer masks were created by expanding each of the original lesion masks beyond and perpendicular to the lesion margin each by 2 mm respectively (layer 1: ≥0 and <2 mm from the lesion; layer 2: ≥2 and <4 mm from the lesion; layer 3: ≥4 and <6 mm from the lesion; layer 4: ≥6 and <8 mm from the lesion; layer 5: ≥8 and <10 mm from the lesion), using iterative dilation in MATLAB R2021a (MathWorks, USA) (Figure 3). A 2 cm diameter ROI was manually drawn on the corresponding white matter of controls to obtain matched comparative normal white matter masks.

Figure 3 Representative examples of lesional and perilesional NAWM layer masks. (A) Axial T2-weighted, (B) T1rho, and (C) raw DTI images demonstrating an MS lesion; and (D) T1rho, and (E) DTI images demonstrating the lesion and the five perilesional NAWM layer masks from iterative dilation with perilesional layer 1: ≥0 and <2 mm from the lesion, perilesional layer 2: ≥2 and <4 mm from the lesion, perilesional layer 3: ≥4 and <6 mm from the lesion, perilesional layer 4: ≥6 and <8 mm from the lesion, and perilesional layer 5: ≥8 and <10 mm from the lesion. DTI, diffusion tensor imaging; MS, multiple sclerosis; NAWM, normal-appearing white matter.

Statistical analysis

T1rho values and DTI parameters are reported as means ± SDs. The Shapiro-Wilk test was used to assess normality. T1rho values and DTI metrics (FA, MD, RD, and AD) within MS lesions and the five perilesional layers were compared to the normal white matter, and the intralesional and fiver perilesional layers were compared between each other using the Wilcoxon signed-rank test and paired t-test. Bonferroni correction was applied to adjust for multiple comparisons across intralesional and five perilesional layers, to control for the increased risk of Type I error associated with multiple statistical testing.

A P value <0.05 was considered to indicate a statistically significant difference. Pearson’s and Spearman’s correlation coefficient were calculated to assess the correlation between T1rho and DTI metrics, as well as the relationship between T1rho values and EDSS scores and disease duration. Analyses were performed using MedCalc (version 20.100; MedCalc Software) and Microsoft Excel, version 16.63 (Microsoft, Redmond, WA, USA).

Results

The demographics of the study participants are summarized in Table 1 and Table S2. A total of 217 non-contrast-enhancing lesions were included for analysis in this study. A significant decrease in T1rho values was observed from the lesional to the perilesional regions, with values progressively decreasing from the intralesional regions (111.82±27.58 ms) to the perilesional layer 5 (78.06±4.76 ms) (P value <0.01). T1rho values in the lesional and all perilesional NAWM layers in MS patients were higher than that of the corresponding normal white matter of controls (P value <0.01) (Table 2).

FA values increased significantly from the intralesional regions to the perilesional layer 2 (P value <0.01). Both MD and RD decreased from the intralesional regions to the perilesional layer 3 (P value <0.01). AD showed consistent decreases from the intralesional regions to the perilesional layer 3, and from perilesional 4 to 5 (P value <0.01). All DTI metrics in the intralesional and perilesional regions demonstrated significant differences when compared to control normal white matter (P value <0.01) (Table 2, Figure 4).

Figure 4 Comparisons among MS lesions, perilesional layers and the corresponding normal white matter of controls. (A) T1rho value; (B) FA; (C) MD; (D) RD; (E) AD. Horizontal bars through the boxes indicate the mean values of data points. Error bars indicate the 95th percentile of all measurements. *, statistical significance. AD, axial diffusivity; FA, fractional anisotropy; MD, mean diffusivity; MS, multiple sclerosis; RD, radial diffusivity.

Pearson’s and Spearman’s correlation analyses demonstrated moderate correlations between T1rho values and DTI metrics within MS lesions. Specifically, a significant negative correlation was observed between T1rho values and FA (r=−0.32, P value <0.01), and positive correlations were found between T1rho values and MD (r=0.73, P value <0.01), RD (r=0.68, P value <0.01), and AD (r=0.71, P value <0.01). Significant correlations were also found between T1rho values and MD, AD, and RD in the perilesional areas, with a pattern of progressively lower correlation coefficients (r, 0.23 to 0.73, P value <0.01) between T1rho values and the DTI metrics as the distance from the lesion increased. T1rho showed mostly low and insignificant correlations with FA in the perilesional layers (Table 3).

Discussion

This study highlights the potential of T1rho imaging in investigating intralesional and perilesional changes in MS T1rho offers different biophysical contrast mechanisms beyond conventional MRI techniques, however, its application in MS has remained previously relatively underexplored. In this study, we stratify the perilesional white matter into five distinct layers, to spatially characterize the microstructural changes occurring in the perilesional NAWM. Previous studies have demonstrated a disruption of the blood-brain barrier in MS, which facilitates the infiltration of T cells and macrophages, and increased expression of inflammatory proteins (1). These abnormal cellular and protein expressions and their associated low-frequency chemical exchanges with the free water pool can be quantified using T1rho imaging, which has been found to be sensitive to macromolecules (22). In this study, we observed that T1rho values in all perilesional NAWM layers in MS patients were consistently higher than that of the corresponding normal white matter of controls. Whilst conventional MRI sequences adequately demonstrate MS lesions, they are not sensitive to subtle or diffuse microstructural abnormalities in the NAWM, and this study demonstrates the ability of T1rho imaging in detecting these microstructural changes not apparent on conventional imaging. A significant decrease in T1rho values from the intralesional regions to the perilesional layer 5 may indicate a progressive reduction in the severity of pathological changes with increasing distance from lesions. Although the absolute differences in T1rho values between layers were small, their consistency and stepwise decline aligns with the expected pathology of tissue damage in MS. As the distance from the lesion increases, there may be reductions in oedema and inflammatory cell infiltration. Additionally, the progressive loss of myelin and axonal integrity is likely to be more pronounced closer to the lesion, while the perilesional white matter further from the lesion may show less damage. The reduction in T1rho values with distance could indicate this spatial gradient of microstructural changes and the potential of T1rho imaging to assess the spatial extent and severity of tissue damage in MS.

T1rho and DTI imaging may capture different aspects of tissue properties. DTI primarily reflects water molecular diffusion (11), which can be influenced by inflammation and the integrity of white matter, whereas T1rho quantifies interactions between water protons and macromolecules (18), making it particularly sensitive to macromolecular changes that may not be fully captured by DTI alone. As with previous studies, DTI demonstrated significant changes in the NAWM (11,14,28-30). Both DTI and T1rho captured differences transitioning from the lesion to the proximal NAWM, and demonstrated differences between the NAWM and the normal white matter of controls. However, T1rho was particularly effective in showing the gradual changes in successive perilesional layers extending outwards from the lesion. T1rho may offer advantages over DTI in assessing and characterising the extent of these subtle perilesional changes as evidenced by the spatial gradient patterns in the NAWM evolving outside of the lesion boundaries, which were not consistently shown in the DTI metrics. This suggests that T1rho may be more sensitive to detecting progressive alterations in macromolecular composition and tissue integrity occurring beyond the immediate lesion area. In this study, we observed correlations between T1rho and all DTI metrics at the lesion site. In the perilesional NAWM, T1rho showed significant correlations with MD, AD, and RD across the perilesional layers, but mostly low and insignificant correlations with FA were observed. A possible explanation may be that FA is more sensitive to the structural integrity and directional coherence of white matter fibers, which may only be significantly disrupted within the lesion itself. MD, AD, and RD are more closely associated with demyelination and axonal loss, which may be more directly associated with the microenvironmental macromolecular content changes detected in T1rho.

This study has some limitations. Firstly, the cross-sectional nature of this study limits our ability to assess for the potential evolving nature of the microstructural changes detected. As this was an exploratory study aimed at evaluating the feasibility of T1rho mapping in capturing microstructural changes in the perilesional NAWM in relapsing-remitting multiple sclerosis (RRMS), we focused on detailed spatial profiling within the cross-sectional cohort. Secondly, standardized thresholds for T1rho remain largely undefined, which may limit its interpretation as well as direct comparisons across different patient populations.

In this study, most patients received disease modifying therapies (DMTs), which are the cornerstone of long-term management in RRMS and shown to reduce relapse rates, disability progression, and new lesion formation (31). It is unclear whether the use of DMTs may have affected the severity of microstructural changes observed in the NAWM. In this study, we found no significant correlation between T1rho values in lesions and NAWM layers with Expanded Disability Status Scale (EDSS) and disease duration, which may reflect medication use, or simply the multifactorial nature of clinical disability in RRMS. To note, although the sample size was relatively small in this study, the sample size is comparable or larger than other studies using T1rho MRI in MS to date.

Conclusions

In summary, this study evaluates the microstructural changes occurring in MS, particularly in the perilesional NAWM areas. Our findings demonstrate that T1rho is effective in detecting microstructural changes in the NAWM, and may provide important information about the spatial extent and severity of NAWM changes. T1rho imaging may have a potential role to complement DTI in MS assessment. Future studies incorporating T1rho alongside other advanced MRI techniques would be helpful.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1075/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1075/dss

Funding: This study was supported by the Research Grants Council Early Careers Scheme (No. 24106022).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1075/coif). Y.X.J.W. serves as the Editor-In-Chief of Quantitative Imaging in Medicine and Surgery. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was approved by The Joint Chinese University of Hong Kong-New Territories East Cluster Clinical Research Ethics Committee (No. 2020.650). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Written informed consent was obtained from all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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