Multiple Sclerosis Resource Centre

Welcome to the Multiple Sclerosis Resource Centre. This website is intended for international healthcare professionals with an interest in Multiple Sclerosis. By clicking the link below you are declaring and confirming that you are a healthcare professional

You are here

Reduced perfusion in white matter lesions in multiple sclerosis

European Journal of Radiology, Volume 84, Issue 12, December

Highlights

  • White matter lesions as a whole showed reduced perfusion compared to normal appearing white matter in patient with multiple sclerosis.
  • Analysis with semi-automated and fully automated method for detection of white matter lesions gave similar results.
  • Automatically generated binary masks of brain tissue are a promissing tool for perfusion analysis.

ABSTRACT

Objective

To investigate dynamic susceptibility contrast (DSC) perfusion weighted imaging (PWI) in white matter lesions (WML) in patients with multiple sclerosis (MS), using automatically generated binary masks of brain tissue.

Background

WML in MS have in some studies demonstrated perfusion abnormalities compared to normal appearing white matter (NAWM), however perfusion changes in WML in MS have in general not been well documented.

Methods

DSC PWI was performed at 1.5 Tesla in 69 newly diagnosed MS patients. Parametric perfusion maps representing cerebral blood volume (CBV), cerebral blood flow (CBF) and mean transit time (MTT) were obtained. Binary masks of WML, white matter (WM) and grey matter (GM) were automatically generated and co-registered to the perfusion maps. The WML mask was manually edited and modified to correct for errors in the automatic lesion detection. Perfusion parameters were derived both from WML and NAWM using the manually modified WML mask, and using the original non-modified WML mask (with and without GM exclusion mask). Differences in perfusion measures between WML and NAWM were analyzed.

Results

CBF was significantly lower (p < 0.001) and MTT significantly higher (p < 0.001) in WML compared to NAWM. CBV did not show significant difference between WML and NAWM. The non-modified WML mask gave similar results as manually modified WML mask if the GM exclusion mask was used in the analysis.

Conclusions

DSC PWI revealed lower CBF and higher MTT, consistent with reduced perfusion, in WML compared to NAWM in patients with early MS. Automatically generated binary masks are a promising tool in perfusion analysis of WML.

Abbreviations: AIF - arterial input function, CBF - cerebral blood flow, CBV - cerebral blood volume, DSC - dynamic susceptibility contrast, MRI - magnetic resonance imaging, GM - grey matter, MS - multiple sclerosis, MTT - mean transit time, NAWM - normal appearing white matter, PWI - perfusion weighted imaging, ROI - region of interest, WM - white matter, WML - white matter lesions.

Keywords: Dynamic susceptibility contrast, Perfusion weighted imaging, Magnetic resonance imaging, Multiple sclerosis, Binary mask.

1. Introduction

Multiple sclerosis (MS) is a frequent cause of non-traumatic disability in young adults in the western world [1]. In recent years, magnetic resonance imaging (MRI) has become a central tool in diagnosis and follow-up of MS patients due to its high sensitivity and specificity for detecting white matter lesions (WML) in brain tissue [2]. However, the MRI methods currently used in clinical practice are subjective and limited mostly to a qualitative evaluation of structural images. Assessment of the hemodynamic properties of the lesions may provide additional radiological information beyond what is obtainable from structural scans. Such information can be derived from dynamic susceptibility contrast (DSC) perfusion weighted imaging (PWI), also known as DSC MRI. This is a well-established MRI perfusion method [3] that provides several hemodynamic parameters, potentially interesting for characterizing brain tissue in MS.

There are only a few published MR perfusion studies in MS. Most studies have been performed in the last years, using DSC PWI [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], and [16] or other MRI perfusion methods [17], [18], and [19]. Some studies focused on analyzing absolute perfusion parameters [4] and [6] while other investigated relative perfusion, e.g., in particular lesions related to contralateral NAWM [5] or in different WM regions related to hippocampus [11]. Studies comparing perfusion in MS patients with healthy controls reported lower CBF and unchanged CBV [4], [5], and [11] or both lower CBF and lower CBV [6] in different WM regions in patients with relapsing remitting MS compared to healthy controls. A study that analyzed perfusion measures in particular WML [5] revealed that most non-enhancing lesions demonstrated lower CBF compared to contralateral NAWM. Most previous studies were performed on small samples, usually comprising fewer than 45 subjects. The studies usually required manual segmentation of brain regions or WML. The relatively small number of perfusion studies in MS, the limited number of study participants and lack of consistent findings may partly be due to differences in perfusion imaging methodology as well as complex, time-consuming, non-standardized and operator-dependent post processing procedures.

Recent developments in image analysis methods and in MRI technology open new possibilities for faster, unbiased and more reliable analysis of perfusion data. The purpose of this study was to analyze the perfusion changes in WML related to NAWM in patients with MS, using automatically generated binary masks of WML, white matter (WM) and grey matter (GM). This approach provides an unbiased evaluation of perfusion measures, facilitates standardized analysis of larger data material, seems to be easily reproducible and, as far as we know, has not been used for perfusion analysis of WML in MS before.

2. Material and methods

2.1. Subjects

Sixty-nine MS patients, diagnosed according to the revised McDonald Criteria [2] were included in this prospective study. All patients were diagnosed with MS in the period from January 2009 to October 2012 and were recruited from an ongoing clinico-radiological project in our institution. The patients were identified consecutively from the hospital registry in 2011 and 2012 and/or reported by the treating clinicians. The MRI was acquired within a week of the neurological examination. All patients were clinically stable for at least six weeks before the neurological examination. The inclusion criteria were: age 18–50 years, no more than three years since MS diagnosis, at least six weeks since the last relapse or steroid treatment, no prior neurological, neurovascular or psychiatric disease, no head injury or substance abuse. Pregnant or breastfeeding patients and patients with a previous adverse reaction to gadolinium injection were excluded. We also had to exclude eight patients from the original group of 77 patients due to technical reasons, as the images were not suitable for image analysis (image acquisition incomplete or of inadequate quality). The characteristics of the patient cohort (demographics, clinical information and radiological information) are presented in Table 1. Written informed consent was obtained from all study participants.

Table 1 Characteristics of the study participants, n = 69

A. Demographics and clinical information
Age, years, mean ± SD (range) 35.3 ± 7.2 (21–49)
Female:male ratio 2:1
MS subtype (patients n =)
 RR MS 66
 PP MS 2
 SP MS 1
EDSS score, mean ± SD 2.0 ± 0.95
Disease duration, months, mean ± SD (range) 30 ± 29.2 (3–158)
Time from diagnosis to MRI, months, mean ± SD (range) 14.2 ± 9.6 (1–34)
Disease modifying treatmenta (patients n =)
 no treatment 15
 first line treatment 45
 second line treatment 9
B. Radiological characteristics
Patients by number of WML (patients n =)
 fewer than 10 WML 18
 between 10 and 20 WML 21
 more than 20 lesions WML 30
Patients by distribution of WML (patients n =)
 supratentorial lesions 65
 infratentorial lesions 46
 lesions in corpus callosum 47
All WML
 number of lesion voxelsb, mean (range) 538 (24–1543)
 lesion loadb, ml, mean (range) 5.72 (0.50–32.38)
Contrast enhancing WML
 total number of enhancing lesions 7
 number of enhancing lesions per patient 1
 number of lesion voxelsb, mean (range) 59 (4–169)
 volume of enhancing lesionsb, ml, mean (range) 1.25 (0.08–3.55)

a Disease modifying treatment: first line: interferon, glatiramer acetate, teriflunomide, dimethylfumarate; second line: natalizumab, fingolimod, alemtuzumab.

b Number of WML voxels and lesion load based on manually modified WML masks co-registered to perfusion maps.

EDSS: expanded disability status scale; MRI: magnetic resonance imaging; MS: multiple sclerosis; PP: primary progressive; RR: relapsing remittig; SD: standard deviation; SP: secondary progressive, WML: white matter lesions.

2.2. Image acquisition

All MRI scans were acquired on the same 1.5 Tesla scanner (Avanto, Siemens Medical, Erlangen, Germany) equipped with a 12-channel head coil. The imaging protocol included the following sequences in all subjects:

  • 1 DSC perfusion sequence (19 axial slices; FOV: 230 × 230 mm; slice thickness: 5 mm; voxel size: 1.8 × 1.8 × 5 mm; TR: 1400 ms; TE: 30 ms; flip angle: 90°). I.v. contrast agent (Dotarem, Laboratoire Guerbet, Paris, France) was administered at a dose 0.2 ml/kg and injection rate 5 ml/sec.
  • 2 Pre-contrast sagittal 3D T1 MPRAGE (FOV: 240 × 240 mm; slice thickness: 1.2 mm; voxel size: 1.3 × 1.3 × 1.2 mm; TR: 2400 ms; TE: 3.61 ms; TI: 1000 ms; flip angle: 8°.
  • 3 Pre-contrast sagittal 3D FLAIR (FOV: 260 × 260 mm; slice thickness: 1 mm; voxel size: 1 × 1 × 1 mm; TR: 6000 ms; TE: 333 ms; TI: 2200 ms.
  • 4 Post-contrast sagittal 3D T1 MPRAGE, with parameters identical to those of pre-contrast 3D T1, acquired approximately 7 min after contrast agent injection following the PWI acquisition.

2.3. Image analysis

The image analysis included following steps: (a) perfusion analysis—generation of parametric perfusion maps, (b) tissue segmentation—generation of binary masks of GM, WM and WML, (c) co-registration of the binary masks and structural T1 and FLAIR series to the perfusion maps, (d) manual editing and modification of the automatically generated WML mask by neuroradiologist, and (e) region-of-interest (ROI) analysis—extraction of perfusion parameters from tissues of interest. Schematic flow chart for the image processing procedure is shown in Fig. 1.

  • (a) Perfusion analysis was applied to the DSC PWI data, resulting in parametric perfusion maps of cerebral blood volume (CBV), cerebral blood flow (CBF) and mean transit time (MTT). The AIF was determined automatically in each subject [20], and a population based average AIF was then constructed from the individual AIFs, as this has been shown to provide more stable inter-patient perfusion estimates [21]. Due to partial volume effects in the measured AIFs combined with non-linear dose-response conditions in DSC PWI, the obtained CBF and CBV values do not generally reflect absolute perfusion and blood volume values [3] and [22], and are consequently reported in arbitrary units. In seven subjects contrast enhancing lesions were observed (totally seven lesions, i.e., one lesion per subject). In these subjects an additional perfusion analysis was performed. Leakage correction was used in this analysis in order to correct for contrast agent extravasation and to obtain more accurate perfusion values. Perfusion analysis was performed using the nordicICE software package (www.nordicneurolab.com).
  • (b) Tissue segmentation was performed to derive binary masks representing WM, GM and WML. The WM and GM masks were created from the volumetric T1 series using the Matlab-based Statistical Parametric Mapping toolbox (SPM8; http://www.fil.ion.ucl.ac.uk/spm). WML segmentation was performed using the CASCADE software (ki.se/en/nvs/cascade, Karolinska Institute, Stockholm, Sweden). CASCADE segments WML according to a reproducible statistical definition and is a fully automated method that can use any combination of volumetric MRI series. In the present study T1 and FLAIR series were used as input sequences. The method has previously been used in MS studies [23] as well as studies of dementia [24] and [25] and brain changes in coronary heart disease [26].
  • (c) Co-registration of the structural series and binary masks to the perfusion maps was performed in SPM8. Since the overall spatial resolution in the analysis was limited by the resolution of the PWI, the structural series and the binary masks were down-sampled to the PWI space resulting in structural FLAIR and T1 series as well as binary masks of GM, WM and WML in register with the PWI-data, and then re-sampled to the PWI space. Fig. 2 shows perfusion maps and corresponding co-registered binary masks and structural images in a sample patient.
  • (d) Manual editing and modification of the automatically generated WML mask was performed in order to correct for errors in the automatic WML detection and segmentation. In all subjects the automatically generated WML mask was manually edited and visually inspected by one neuroradiologist (PS) with approximately ten years of experience in neuroradiology. Editing was performed by adding the automatically generated WML segments as overlays on anatomical FLAIR and T1 series, allowing the neuroradiologist to add and remove pixels from the WML mask (see Fig. 3). Manual editing and modification of the WML mask was performed in NordicICE.
  • (e) ROI analysis was finally performed to extract perfusion parameters from the corresponding WML and NAWM compartments. Both the original (i.e., automatically generated and non-modified) WML mask and the manually modified WML mask were used as basis for the ROI analysis. When using the original non-modified WML mask, ROI analysis for WML was performed both with this mask alone as well as using the GM mask as exclusion mask to remove possible contribution from falsely classified GM regions as WML. In ROI analysis for NAWM the WM mask was used as inclusion mask and the WML masks were used as exclusion masks, as shown schematically in Fig. 1. The ROI area was limited by the spatial resolution of PWI. The lesion threshold was 1.8 × 1.8 mm. In the seven subjects with contrast enhancing lesions an additional mask containing only the enhancing lesion was manually created by removing all remaining non-enhancing lesions from the WML mask, and additional ROI analysis was performed for the enhancing lesions in comparison to NAWM and to the non-enhancing lesions.
gr1

Fig. 1 Schematic flow chart for image processing procedure.CBF: cerebral blood flow; CBV: cerebral blood volume; GM: grey matter; MTT: mean transit time; NAWM: normal appearing white matter; WM: white matter; WML: white matter lesions.Binary masks of WML, WM and GMwere co-registered to the perfusion maps; WML mask was manually modified and perfusion parameters were calculated for the whole compartments of WML and NAWM.

gr2

Fig. 2 Co-registered binary masks and structural series to perfusion maps in a sample patient.

gr3

Fig. 3 Manual editing and modification of WML mask in a sample patient.

2.4. Statistical analysis

Differences in perfusion measures between WML and NAWM were tested using paired samples t-test. Significance level was set to p < 0.05 for all comparisons and p-values from t-tests are presented below. Statistical analysis was performed in SPSS Statistics v. 21 (IBM, Chicago IL, USA). Additional statistical analysis was performed with exclusion of subjects with enhancing lesions and subjects with progressive type of MS. Also additional statistical analyses were performed in subjects with contrast enhancing lesions. Since some of the lesions were very small (i.e., under 10 pixels) the median values of the perfusion parameters were used for analysis in individual enhancing lesions.

3. Results

Perfusion measures were derived as mean values from the WML and NAWM as a whole. When manually modified WML mask was used in the ROI analysis, CBF was found to be significantly lower (p < 0.001) and MTT significantly higher (p < 0.001) in WML compared to NAWM, while CBV did not show significant difference (p = 0.627) between WML and NAWM (Fig. 4 and Table 2). Using the original non-modified WML mask combined with GM mask as exclusion mask the results remained similar—with CBF being significantly lower (p < 0.001), MTT significantly higher (p < 0.001) and CBV without significant difference (p = 0.088) in WML compared to NAWM. Using the original non-modified WML mask alone (i.e., without GM mask as exclusion mask), CBF did not show significant difference (p = 0.248) while MTT was significantly higher (p < 0.001) and CBV also significantly higher (p = 0.001) in WML compared to NAWM.

gr4

Fig. 4 Perfusion measures in WML and NAWM (obtained using manually modified WML mask).

Table 2 Perfusion measures (mean) in WML and NAWM obtained with different use of binary masks, with significance levels for difference, n = 69.

WML NAWM Difference between WML and NAWM P-value of differencea
a) Manually modified WML mask
CBV 7.82 ± 2.39 7.98 ± 2.31 −0.16 ± 2.81 0.627
CBF 101.97 ± 28.99 130.32 ± 41.91 −28.35 ± 38.61 <0.001
MTT 4.34 ± 0.86 3.45 ± 0.73 0.90 ± 0.44 <0.001
 
Non-modified WML mask
b) GM mask used as exclusion mask
CBV 7.45 ± 2.47 7.97 ± 2.31 −0.52 ± 2.51 0.088
CBF 100.93 ± 34.51 130.20 ± 41.88 −29.27 ± 31.86 <0.001
MTT 4.29 ± 0.79 3.45 ± 0.73 0.84 ± 0.56 <0.001
 
c) No exclusion mask
CBV 9.26 ± 2.84 as above 1.29 ± 2.96 0.001
CBF 136.66 ± 47.64 as above 6.46 ± 45.99 0.248
MTT 3.96 ± 0.88 as above 0.51 ± 0.46 <0.001

a t-Test.

CBF: cerebral blood flow (arbitrary units); CBV: cerebral blood volume (arbitrary units); GM: grey matter; MTT: mean transit time (sec.); NAWM: normal appearing white matter; WML: white matter lesions.

Since it has previously been reported differences in perfusion patterns between enhancing and non-enhancing lesions in MS [5] as well as between patients with relapsing remitting MS and primary progressive MS [6] additional statistical analysis was performed with exclusion of subjects with enhancing lesions (n= 7) and subjects with progressive type of MS (n = 3). The results of the study remained similar after exclusion of these subjects form the analysis (Table 3). We also performed a separate statistical analysis for the contrast enhancing lesions. We identified no significant difference in perfusion parameters in these lesions compared to NAWM or compared to non-enhancing lesions in the same patients, see Table 4.

Table 3 Perfusion measures as in Table 2, with exclusion of subjects with contrast enhancing lesions and subjects with progressive type of MS, n = 59.

WML NAWM Difference between WML and NAWM P-value of differencea
a) Manually modified WML mask
CBV 7.63 ± 2.28 7.70 ± 2.23 −0.06 ± 2.73 0.860
CBF 99.71 ± 29.15 123.56 ± 30.78 −23.85 ± 30.30 <0.001
MTT 4.35 ± 0.86 3.46 ± 0.72 0.89 ± 0.44 <0.001
 
Non-modified WML mask
b) GM mask used as exclusion mask
CBV 7.22 ± 2.29 7.69 ± 2.23 −0.46 ± 2.42 0.147
CBF 97.99 ± 32.79 123.45 ± 30.76 −25.46 ± 26.78 <0.001
MTT 4.28 ± 0.75 3.46 ± 0.72 0.82 ± 0.59 <0.001
 
c) No exclusion mask
CBV 9.09 ± 2.56 as above 1.33 ± 2.84 0.001
CBF 134.32 ± 47.41 as above 10.75 ± 41.29 0.050
MTT 3.94 ± 0.87 as above 0.48 ± 0.45 <0.001

a t-Test.

CBF: cerebral blood flow (arbitrary units); CBV: cerebral blood volume (arbitrary units); GM: grey matter; MTT: mean transit time (sec.); NAWM: normal appearing white matter; WML: white matter lesions

Table 4 Perfusion measures (with leakage correction) in contrast enhancing WML, n = 7.

Compared to NAWM Enhancing WML NAWM Difference between WML and NAWM P-value of differencea
CBV 2.58 ± 1.19 2.95 ± 1.28 −0.37 ± 0.65 0.182
CBF 30.60 ± 8.81 39.15 ± 7.52 −8.54 ± 10.24 0.069
MTT 4.85 ± 1.25 4.30 ± 1.27 -0.55 ± 0.77 0.104
Compared to non-enhancing WML Enhancing WML Non-enhancing WML Diff. between enhancing and non-enhancing WML P-value of differencea
CBV as above 2.89 ± 1.95 −0.32 ± 0.96 0.414
CBF as above 30.95 ± 8.69 −0.34 ± 7.23 0.904
MTT as above 5.14 ± 1.52 −0.28 ± 0.71 0.332

a t-Test.

CBF: cerebral blood flow (arbitrary units); CBV: cerebral blood volume (arbitrary units); MTT: mean transit time (sec.); NAWM: normal appearing white matter; WML: white matter lesions.

4. Discussion

The main finding in this study was that CBF was found to be significantly lower and MTT significantly higher in WML compared to NAWM in patients recently diagnosed with MS, consistent with reduced WML perfusion. The results were obtained in a relatively large patient cohort using automated analysis tools, thereby minimizing user bias in the analysis results. In the current study, perfusion in WML was estimated in comparison to global WM perfusion (excluding WML). Previous PWI studies in MS mainly focused on perfusion analysis in manually defined cerebral regions and did not study perfusion parameters in the WML compartment as a whole. Analyzing perfusion properties in the whole WML compartment is a simplified approach, and is theoretically possible to do with fully automated methods that are available today, e.g. an automated method was used by Bester et al. [16] for perfusion analysis in NAWM in a longitudinal cohort.

Our results indicate a general hemodynamic impairment of the visually affected WM and can be explained by histopathological changes found in WML in MS. The mechanism of these histopathological changes is heterogeneous, comprising hypoxia-like metabolic injury, at least in a subset of lesions [27] and [28]. Perivascular inflammation commonly found in MS leads to hyalinization and subsequent alterations in the blood vessel structure [29] probably resulting in increased stiffness of the vessel wall over time. These pathological changes are likely to influence hemodynamic parameters in WML. We can only speculate about the histopathological changes underlying the perfusion findings, since we have no pathological correlation in our study.

Despite the differences in methodology between studies, the main results of our study are at least partly in accordance with previous perfusion studies in MS. In the previously mentioned study by Ge et al. from 2005 [5] perfusion measures in manually segmented WML were analyzed in comparison to manually defined regions in the contralateral NAWM in 17 MS subjects (totally 75 lesions). The authors reported lower CBF and higher MTT (in line with our findings) but lower CBV in the majority of non-enhancing lesions. Our studies differ in methodology. We analyzed perfusion parameters in all lesions in a subject in comparison to the whole NAWM, while in the cited study the perfusion parameters were analyzed lesion-by-lesion and compared to manually defined regions in contralateral white matter. The differences in results between our studies can at least in part be explained by different methodology. The median disease duration in both studies was comparable (1.8 years in our study vs. 2.7 in the cited study). In our study the patients were scanned after being clinically stable for 6 weeks. Thus a lower CBF and higher MTT could be typical of perfusion in WML in clinically stable MS.

There was no significant difference in perfusion parameters in contrast enhancing lesions compared to the non-enhancing lesions or compared to NAWM. In the previously mentioned study [5] the authors reported higher CBV and higher CBF in contrast enhancing lesions (n = 17) compared to contralateral white matter. Our results did not support these findings. However, the total number of enhancing lesions in our material is very low (n = 7) so no definite conclusions about perfusion parameters in contrast enhancing lesions can be drawn from our study.

Manual editing and modification of the automatically generated WML mask was performed in our study in order to quality-check the fully automated method. Analysis with the manually modified WML mask served as a “gold standard” because the automatically detected and segmented lesions were visually inspected and modified (if needed) on this mask, ensuring the lesion segmentation to be as correct as possible. Analysis with automatically generated non-modified WML mask was compared with this “gold standard” and was found to be reliable if a GM exclusion mask was included in the analysis. Not including GM exclusion mask in the analysis gave different results compared to the “gold standard”, most probably because of GM regions falsely qualified as WML by the software. Thus, currently available automated WML segmentation methods, used with appropriate correction, appear to be robust for analysis of perfusion metrics in WML, enabling analysis in large patient cohorts with minimal user bias. This is strength of our study. Another strength of the study is our relatively large patient group (69 subjects), representing a homogeneous cohort. Most of other perfusion studies of this type were performed on 45 or fewer subjects.

Our study has some limitations. Only relatively newly diagnosed patients were included in the study, thus we have limited means to conclude about perfusion changes in advanced disease. Further, it was not possible to establish a control group for perfusion analysis of WML in patients with other pathological conditions. Establishing a well-matched control group can be generally difficult because WML are not often seen in this age group and may have different etiologies. It is therefore unclear whether the revealed perfusion changes in WML are typical for early MS or whether it is a general feature of WML. Further studies of WML in MS patients at different disease stages could possibly explain this. There were few contrast enhancing lesions (n = 7) in our material so the power to conclude about perfusion changes in these lesions is limited.

In conclusion, this study showed reduced perfusion in WML compared to NAWM in newly diagnosed MS patients. The automated method is a promising tool in perfusion analysis of WML in MS. Further investigations are needed to study the correlation between perfusion parameters and clinical features as well as pathological findings.

Conflicts of interest

Piotr Sowa received honoraria for lectures from Novartis, Genzyme and Biogen Idec.

Atle Bjørnerud is a consultant for NordicNeuroLab AS, Bergen, Norway.

Gro O. Nygaard received unrestricted research grants from Novartis, and from the Odd Fellow’s Foundation for Multiple Sclerosis Research.

Elisabeth G. Celius received funding for travel and speaker’s fees from Almirall, Biogen Idec, Genzyme, Merck Serono, Novartis, Sanofi-Aventis and Teva, and received research support from Biogen Idec and Novartis.

Hanne F. Harbo received an unrestricted research grant from Novartis, and support for travelling and speaking honoraria from Biogen Idec, Novartis, Sanofi-Aventis and Teva.

Soheil Damangir, Gabriela Spulber, Paulina Due-Tønnessen and Mona K. Beyer do not report any conflicts of interest.

Ethical standards

We declare that our study was approved by the local ethics committee and has therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

Funding

PhD project nr 39,569, entitled “MRI biomarkers of disease activity and progression in multiple sclerosis”.

Acknowledgments

This study received funding from South-Eastern Norway Regional Health Authority (Helse Sør-Øst). MRI scans and clinical tests were performed within a previous study financed by the same institution (Gro O. Nygaard MD, project nr 2011059, entitled “Genetic risk factors and MRI and clinical outcome in MS”). Piotr Sowa received a grant for MRI acquisitions from the Odd Fellow's Foundation for Multiple Sclerosis Research in Norway.

The authors thank neuGRID (www.neugrid4you.eu), European infrastructure for neuroimaging research, for providing software and computational resources for WML segmentation.

References

  • [1] M.J. Tullman. Overview of the epidemiology, diagnosis, and disease progression associated with multiple sclerosis. Am. J. Manag. Care. 2013;19:S15-20
  • [2] C.H. Polman, S.C. Reingold, B. Banwell, M. Clanet, J.A. Cohen, M. Filippi, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann. Neurol.. 2011;69:292-302 Crossref
  • [3] A. Bjornerud, K.E. Emblem. A fully automated method for quantitative cerebral hemodynamic analysis using DSC-MRI. J. Cereb. Blood Flow Metab.. 2010;30:1066-1078 Crossref
  • [4] M. Law, A.M. Saindane, Y. Ge, J.S. Babb, G. Johnson, L.J. Mannon, et al. Microvascular abnormality in relapsing-remitting multiple sclerosis: perfusion MR imaging findings in normal-appearing white matter. Radiology. 2004;231:645-652 Crossref
  • [5] Y. Ge, M. Law, G. Johnson, J. Herbert, J.S. Babb, L.J. Mannon, et al. Dynamic susceptibility contrast perfusion MR imaging of multiple sclerosis lesions: characterizing hemodynamic impairment and inflammatory activity. AJNR Am. J. Neuroradiol.. 2005;26:1539-1547
  • [6] S. Adhya, G. Johnson, J. Herbert, H. Jaggi, J.S. Babb, R.I. Grossman, et al. Pattern of hemodynamic impairment in multiple sclerosis: dynamic susceptibility contrast perfusion MR imaging at 3.0 T. Neuroimage. 2006;33:1029-1035 Crossref
  • [7] R. Zivadinov, N. Bergsland, M. Stosic, J. Sharma, F. Nussenbaum, J. Durfee, et al. Use of perfusion- and diffusion-weighted imaging in differential diagnosis of acute and chronic ischemic stroke and multiple sclerosis. Neurol. Res.. 2008;30:816-826 Crossref
  • [8] A.W. Varga, G. Johnson, J.S. Babb, J. Herbert, R.I. Grossman, M. Inglese. White matter hemodynamic abnormalities precede sub-cortical gray matter changes in multiple sclerosis. J. Neurol. Sci.. 2009;282:28-33 Crossref
  • [9] R.I. Aviv, P.L. Francis, R. Tenenbein, P. O'Connor, L. Zhang, A. Eilaghi, et al. Decreased frontal lobe gray matter perfusion in cognitively impaired patients with secondary-progressive multiple sclerosis detected by the bookend technique. AJNR Am. J. Neuroradiol.. 2012;33:1779-1785 Crossref
  • [10] M. Ingrisch, S. Sourbron, D. Morhard, B. Ertl-Wagner, T. Kumpfel, R. Hohlfeld, et al. Quantification of perfusion and permeability in multiple sclerosis: dynamic contrast-enhanced MRI in 3D at 3T. Invest. Radiol.. 2012;47:252-258 Crossref
  • [11] E.Z. Papadaki, V.C. Mastorodemos, E.Z. Amanakis, K.C. Tsekouras, A.E. Papadakis, N.D. Tsavalas, et al. White matter and deep gray matter hemodynamic changes in multiple sclerosis patients with clinically isolated syndrome. Magn. Reson. Med.. 2012;68:1932-1942 Crossref
  • [12] P.L. Francis, R. Jakubovic, P. O'Connor, L. Zhang, A. Eilaghi, L. Lee, et al. Robust perfusion deficits in cognitively impaired patients with secondary-progressive multiple sclerosis. AJNR Am. J. Neuroradiol.. 2013;34:62-67 Crossref
  • [13] D. Peruzzo, M. Castellaro, M. Calabrese, E. Veronese, F. Rinaldi, V. Bernardi, et al. Heterogeneity of cortical lesions in multiple sclerosis: an MRI perfusion study. J. Cereb. Blood Flow Metab.. 2013;33:457-463 Crossref
  • [14] E.Z. Papadaki, P.G. Simos, V.C. Mastorodemos, T. Panou, T.G. Maris, A.H. Karantanas, et al. Regional MRI perfusion measures predict motor/executive function in patients with clinically isolated syndrome. Behav. Neurol.. 2014;2014:252419
  • [15] E.Z. Papadaki, P.G. Simos, T. Panou, V.C. Mastorodemos, T.G. Maris, A.H. Karantanas, et al. Hemodynamic evidence linking cognitive deficits in clinically isolated syndrome to regional brain inflammation. Eur. J. Neurol.. 2014;21:499-505 Crossref
  • [16] M. Bester, N.D. Forkert, J.P. Stellmann, L. Aly, A. Drabik, K.L. Young, et al. Increased perfusion in normal appearing white matter in high inflammatory multiple sclerosis patients. PLoS One. 2015;10:e0119356 Crossref
  • [17] C. Steen, M. D'Haeseleer, J.M. Hoogduin, Y. Fierens, M. Cambron, J.P. Mostert, et al. Cerebral white matter blood flow and energy metabolism in multiple sclerosis. Mult. Scler.. 2013;19:1282-1289 Crossref
  • [18] S.P. Cramer, H.B. Larsson. Accurate determination of blood-brain barrier permeability using dynamic contrast-enhanced T1-weighted MRI: a simulation and in vivo study on healthy subjects and multiple sclerosis patients. J. Cereb. Blood Flow Metab.. 2014;34:1655-1665 Crossref
  • [19] L. Debernard, T.R. Melzer, S. Van Stockum, C. Graham, C.A. Wheeler-Kingshott, J.C. Dalrymple-Alford, et al. Reduced grey matter perfusion without volume loss in early relapsing-remitting multiple sclerosis. J. Neurol. Neurosurg. Psychiatry. 2014;85:544-551
  • [20] K. Mouridsen, S. Christensen, L. Gyldensted, L. Ostergaard. Automatic selection of arterial input function using cluster analysis. Magn. Reson. Med.. 2006;55:524-531 Crossref
  • [21] K. Mouridsen, K.E. Emblem, A. Bjørnerud, D. Jennings, G. Sorensen. optimizes reproducibility Subject-specific AIF Of perfusion parameters in longitudinal DSC-MRI in comparison to session and population level AIF. 19th Annual Scientific Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine Montreal, Quebec, Canada Curran Associates, Inc. (2012). 2011;:1
  • [22] O. van, M.J. sch, E.J. Vonken, C.J. Bakker, M.A. Viergever. Correcting partial volume artifacts of the arterial input function in quantitative cerebral perfusion MRI. Magn. Reson. Med.. 2001;45:477-485
  • [23] G.O. Nygaard, K.B. Walhovd, P. Sowa, J.L. Chepkoech, A. Bjornerud, P. Due-Tonnessen, et al. Cortical thickness and surface area relate to specific symptoms in early relapsing-remitting multiple sclerosis. Mult. Scler.. 2015;21:402-414 Crossref
  • [24] M. Vuorinen, G. Spulber, S. Damangir, E. Niskanen, T. Ngandu, H. Soininen, et al. Midlife CAIDE dementia risk score and dementia-related brain changes up to 30 years later on magnetic resonance imaging. J. Alzheimers Dis.. 2015;44:93-101
  • [25] S. Damangir, A. Manzouri, K. Oppedal, S. Carlsson, M.J. Firbank, H. Sonnesyn, et al. Multispectral MRI segmentation of age related white matter changes using a cascade of support vector machines. J. Neurol. Sci.. 2012;322:211-216 Crossref
  • [26] M. Vuorinen, S. Damangir, E. Niskanen, J. Miralbell, M. Rusanen, G. Spulber, et al. Coronary heart disease and cortical thickness, gray matter and white matter lesion volumes on MRI. PLoS One. 2014;9:e109250 Crossref
  • [27] H. Lassmann. Hypoxia-like tissue injury as a component of multiple sclerosis lesions. J. Neurol. Sci.. 2003;206:187-191 Crossref
  • [28] C. Lucchinetti, W. Bruck, J. Parisi, B. Scheithauer, M. Rodriguez, H. Lassmann. Heterogeneity of multiple sclerosis lesions: implications for the pathogenesis of demyelination. Ann. Neurol.. 2000;47:707-717 Crossref
  • [29] H. Lassmann, W. Bruck, C.F. Lucchinetti. The immunopathology of multiple sclerosis: an overview. Brain Pathol.. 2007;17:210-218 Crossref

Footnotes

a Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway

b Institute of Clinical Medicine, University of Oslo, Oslo, Norway

c Intervention Center, Oslo University Hospital, Oslo, Norway

d Department of Physics, University of Oslo, Oslo, Norway

e Department of Neurology, Oslo University Hospital, Oslo, Norway

f Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden

g Department of Life Sciences and Health, Oslo and Akershus University College of Applied Sciences, Oslo, Norway

Corresponding author at: Department of Radiology and Nuclear Medicine, Oslo University, Hospital, Kirkeveien 166, P.O.Box 4956 Nydalen, N−0424 Oslo, Norway. Fax: +47 23 07 26 10.


Search this site

Stay up-to-date with our monthly e-alert

If you want to regularly receive information on what is happening in MS research sign up to our e-alert.

Subscribe »

About the Editors

  • Prof Timothy Vartanian

    Timothy Vartanian, Professor at the Brain and Mind Research Institute and the Department of Neurology, Weill Cornell Medical College, Cornell...
  • Dr Claire S. Riley

    Claire S. Riley, MD is an assistant attending neurologist and assistant professor of neurology in the Neurological Institute, Columbia University,...
  • Dr Rebecca Farber

    Rebecca Farber, MD is an attending neurologist and assistant professor of neurology at the Neurological Institute, Columbia University, in New...

This online Resource Centre has been made possible by a donation from EMD Serono, Inc., a business of Merck KGaA, Darmstadt, Germany.

Note that EMD Serono, Inc., has no editorial control or influence over the content of this Resource Centre. The Resource Centre and all content therein are subject to an independent editorial review.

The Grant for Multiple Sclerosis Innovation
supports promising translational research projects by academic researchers to improve understanding of multiple sclerosis (MS) for the ultimate benefit of patients.  For full information and application details, please click here

Journal Editor's choice

Recommended by Prof. Brenda Banwell

Causes of death among persons with multiple sclerosis

Gary R. Cutter, Jeffrey Zimmerman, Amber R. Salter, et al.

Multiple Sclerosis and Related Disorders, September 2015, Vol 4 Issue 5