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Reduced perfusion in white matter lesions in multiple sclerosis
European Journal of Radiology, Volume 84, Issue 12, December
- 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.
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.
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.
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.
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.
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.
Multiple sclerosis (MS) is a frequent cause of non-traumatic disability in young adults in the western world . 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 . 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  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 , , , , , , , , , , , , and  or other MRI perfusion methods , , and . Some studies focused on analyzing absolute perfusion parameters  and  while other investigated relative perfusion, e.g., in particular lesions related to contralateral NAWM  or in different WM regions related to hippocampus . Studies comparing perfusion in MS patients with healthy controls reported lower CBF and unchanged CBV , , and  or both lower CBF and lower CBV  in different WM regions in patients with relapsing remitting MS compared to healthy controls. A study that analyzed perfusion measures in particular WML  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
Sixty-nine MS patients, diagnosed according to the revised McDonald Criteria  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.
|A. Demographics and clinical information|
|Age, years, mean ± SD (range)||35.3 ± 7.2 (21–49)|
|MS subtype (patients n =)|
|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 =)|
|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 =)|
|lesions in corpus callosum||47|
|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 , 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 . 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  and , 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  as well as studies of dementia  and  and brain changes in coronary heart disease .
- (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.
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.
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.
|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|
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  as well as between patients with relapsing remitting MS and primary progressive MS  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.
|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|
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
|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|
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.
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.  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  and . Perivascular inflammation commonly found in MS leads to hyalinization and subsequent alterations in the blood vessel structure  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  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  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.
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.
PhD project nr 39,569, entitled “MRI biomarkers of disease activity and progression in multiple sclerosis”.
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.
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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.
© 2015 Published by Elsevier B.V.