Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

T1- Thresholds in Black Holes Increase Clinical-Radiological Correlation in Multiple Sclerosis Patients

  • Christian Thaler ,

    c.thaler@uke.de

    Affiliation Department of Diagnostic and Interventional Neuroradiology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany

  • Tobias Faizy,

    Affiliation Department of Diagnostic and Interventional Neuroradiology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany

  • Jan Sedlacik,

    Affiliation Department of Diagnostic and Interventional Neuroradiology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany

  • Brigitte Holst,

    Affiliation Department of Diagnostic and Interventional Neuroradiology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany

  • Jan-Patrick Stellmann,

    Affiliations Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany, Institute for Neuroimmunology and Clinical MS Research, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany

  • Kim Lea Young,

    Affiliations Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany, Institute for Neuroimmunology and Clinical MS Research, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany

  • Christoph Heesen,

    Affiliations Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany, Institute for Neuroimmunology and Clinical MS Research, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany

  • Jens Fiehler,

    Affiliation Department of Diagnostic and Interventional Neuroradiology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany

  • Susanne Siemonsen

    Affiliations Department of Diagnostic and Interventional Neuroradiology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany, Institute for Neuroimmunology and Clinical MS Research, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany

T1- Thresholds in Black Holes Increase Clinical-Radiological Correlation in Multiple Sclerosis Patients

  • Christian Thaler, 
  • Tobias Faizy, 
  • Jan Sedlacik, 
  • Brigitte Holst, 
  • Jan-Patrick Stellmann, 
  • Kim Lea Young, 
  • Christoph Heesen, 
  • Jens Fiehler, 
  • Susanne Siemonsen
PLOS
x

Abstract

Background

Magnetic Resonance Imaging (MRI) is an established tool in diagnosing and evaluating disease activity in Multiple Sclerosis (MS). While clinical-radiological correlations are limited in general, hypointense T1 lesions (also known as Black Holes (BH)) have shown some promising results. The definition of BHs is very heterogeneous and depends on subjective visual evaluation.

Objective

We aimed to improve clinical-radiological correlations by defining BHs using T1 relaxation time (T1-RT) thresholds to achieve best possible correlation between BH lesion volume and clinical disability.

Method

40 patients with mainly relapsing-remitting MS underwent MRI including 3-dimensional fluid attenuated inversion recovery (FLAIR), magnetization-prepared rapid gradient echo (MPRAGE) before and after Gadolinium (GD) injection and double inversion-contrast magnetization-prepared rapid gradient echo (MP2RAGE) sequences. BHs (BHvis) were marked by two raters on native T1-weighted (T1w)-MPRAGE, contrast-enhancing lesions (CE lesions) on T1w-MPRAGE after GD and FLAIR lesions (total-FLAIR lesions) were detected separately. BHvis and total-FLAIR lesion maps were registered to MP2RAGE images, and the mean T1-RT were calculated for all lesion ROIs. Mean T1 values of the cortex (CTX) were calculated for each patient. Subsequently, Spearman rank correlations between clinical scores (Expanded Disability Status Scale and Multiple Sclerosis Functional Composite) and lesion volume were determined for different T1-RT thresholds.

Results

Significant differences in T1-RT were obtained between all different lesion types with highest T1 values in visually marked BHs (BHvis: 1453.3±213.4 ms, total-FLAIR lesions: 1394.33±187.38 ms, CTX: 1305.6±35.8 ms; p<0.05). Significant correlations between BHvis/total-FLAIR lesion volume and clinical disability were obtained for a wide range of T1-RT thresholds. The highest correlation for BHvis and total-FLAIR lesion masks were found at T1-RT>1500 ms (Expanded Disability Status Scale vs. lesion volume: rBHvis = 0.442 and rtotal-FLAIR = 0.497, p<0.05; Multiple Sclerosis Functional Composite vs. lesion volume: rBHvis = -0.53 and rtotal-FLAIR = -0.627, p<0.05).

Conclusion

Clinical-radiological correlations in MS patients are increased by application of T1-RT thresholds. With the short acquisition time of the MP2RAGE sequences, quantitative T1 maps could be easily established in clinical studies.

Introduction

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system leading to demyelination, axonal loss and the formation of astrocytic scars [1]. Magnetic resonance imaging (MRI) is a valuable tool for diagnosis and monitoring disease activity in MS.[2,3] White matter lesions can be detected in T2-weighted (T2w) images as areas of high signal intensity. Hyperintensities on T2w images correspond to a wide spectrum of histopathological changes, ranging from edema and mild demyelination to glial scars or liquid necrosis. [4,5,6]. In comparison, persistent non-enhancing T1-hypointense lesions (commonly known as black holes (BH)) are discussed as a more specific marker for demyelination, axonal loss and tissue damage[7,8] and are thought to provide a more accurate classification of the microstructural damage in MS patients. However, histopathological studies demonstrated that even BHs themselves present pathological diversity.[9] To discriminate the different stages of cell damage it was observed that the degree of hypointensity in a BH seems to reflect the extent of axonal loss and therefore might distinguish between demyelinated and partially remyelinated lesions.[10,11] Nevertheless, the definition of BHs is very heterogeneous and in general depends on visual evaluation.

It is still uncertain whether BHs correlate with the patients' clinical status, since some research groups could not find any significant correlation but others did.[1218] A number of studies suggested that lesion intensity or T1 relaxation time (T1-RT) might be useful to increase the correlation between BH volume and clinical measures.[16,18] In a study by Tam et al. (2011), a T1 lesion load was computed at different signal intensity thresholds and the resulting lesion volumes at a given intensity level were correlated with the Expanded Disability Status Scale (EDSS).[16] The strongest correlations were found by limiting the BH regions to a small subset of the voxels with lowest signal intensity. However, scan-to-scan variations affect the quantitative signal intensity, which reduces the reliability of the measurements. To minimize scan-to-scan variations, quantitative T1-RT mapping has been found to increase accuracy and reproducibility.[19] With the recently introduced double inversion-contrast magnetization-prepared rapid gradient echo (MP2RAGE) sequence it is now possible to generate T1-RT maps in clinically acceptable scan times on the fly.[20] We hypothesized that higher correlation coefficients for BH lesion volume and clinical disability can be achieved by applying T1-RT thresholds. To our knowledge, this is the first study investigating the range of correlation coefficients for BH volumes and EDSS as well as the MSFC at multiple T1-RT thresholds.

Materials and Methods

Forty patients (26 females, 14 males; 36.9 ± 10.6 years) with mainly relapsing-remitting multiple sclerosis (RRMS) were enrolled in this study. One patient was diagnosed with primary-progressive (PPMS) and two with secondary-progressive multiple sclerosis (SPMS). Disease duration ranged from 1 month up to 22 years. Clinical status was assessed by using the EDSS by experienced neurologists according to published guidelines.[21] The Multiple Sclerosis Functional Composite (MSFC) was additionally assessed at 30 patients and included the Symbol Digit Modalities Test (SDMT), the 9-Hole Peg Test (9-HPT) and the Timed 25-Foot Walk (T25-FW). Within the MSFC the Paced Auditory Serial Additon Test (PASAT) was substituded by the SDMT based on higher patient acceptance and at least equal sensitivity.[22] Scores for each subtest were converted to z-scores using a published dataset.[23] Subsequently, the overall MSFC score was calculated with following formula: MSFC Score = {Z9-HPT+ZT25-FW+ZSDMT}/3. The patient characteristics are summarized in Table 1. The study was approved by the local Ethical Committee Hamburg (Ethik-Kommission der Ärztekammer Hamburg) following the guidelines of the Declaration of Helsinki and patients provided written informed consent.

thumbnail
Table 1. Subject population.

Age (years), EDSS, disease duration (years), gender and MS-type statistics for the 40 patients in this study.

https://doi.org/10.1371/journal.pone.0144693.t001

MRI data acquisition

All MR scans were performed on a 3 Tesla MR scanner (Skyra, Siemens Medical Systems, Erlangen, Germany). The MR protocol included a sagittal 3-dimensional fluid attenuated inversion recovery (FLAIR) (echo-time (TE) = 390 ms, repetition time (TR) = 4700 ms, inversion time (TI) = 1800 ms, 192 slices, field of view (FOV) = 256 mm, voxel size = 1.0 x 1.0 x 1.0 mm), a T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) sequence before and after Gadolinium injection (TE = 2.43 ms, TR = 1900 ms, TI = 900 ms, 192 slices, FOV = 256 mm, voxel size = 1.0 x 1.0 x 1.0 mm, flip-angle = 9°) and MP2RAGE (TE = 2.98 ms, TR = 5000 ms, TI = 700 ms, 176 slices, FOV = 256 mm, voxel size = 1.0 x 1.0 x 1.0 mm, flip angle = 4°). Gadolinium was applied to detect enhancing lesions, which were excluded in the volumetric calculations of BHsvis. The MP2RAGE sequence acquisition time was 11 minutes and 17 seconds.

Image analysis

BHs were defined as non-enhancing lesions that appear hypointense on T1w-images with signal intensity below cortex and are concordant with hyperintense lesions on a T2w-image. BH-lesion detection was performed by two independent raters who were blinded to the patients' clinical status. For each patient, T1 hypointense lesions were outlined on T1w images according to the definition above by both raters using the software Analyze 11.0 (AnalyzeDirect, Inc. KS, USA). The resulting BH lesion masks of both raters were binarized and patient-wise multiplied to obtain a consensus mask. Subsequently, T1w-MPRAGE images were linearly registered to the MP2RAGE images and the transformation applied to the BH consensus masks. We will refer to these manually segmented BH as visually marked BHs (BHvis). In a second step, contrast enhancing (CE-lesions) were marked applying the same algorithm as described for BH lesion outlining.

Additionally, a second lesion segmentation was performed, by marking hyperintense lesions in FLAIR images. As every BHvis appears hyperintense in FLAIR images, the BHsvis are element of the FLAIR lesions. FLAIR lesion masks (total-FLAIR) were created by using an open source lesion segmentation software (LST: Lesion Segmentation Tool).[24] To choose the optimal initial threshold κ, lesion segmentation was run with different thresholds. The final threshold was set at κ = 0.3. The semi-automatically segmented total-FLAIR lesion masks were visually controlled and corrected if necessary. Afterwards the lesion masks were registered to the MP2RAGE images. In addition, a pure-FLAIR lesion mask was calculated by subtracting the BHvis and CE lesion mask from the corresponding total-FLAIR mask leaving only those lesions, that were visible in FLAIR but did not correspond to a BHvis or CE lesion. For each patient, total-FLAIR, pure-FLAIR, CE and BHvis lesion number, volume and mean T1-RT were calculated. In addition, voxel-wise T1-RT values were obtained for each lesion group. Furthermore, mean T1-RT values of the cortex were calculated for each patient by manually placing region of interests (ROIs) in the frontal, parietal, temporal and occipital cortex. BHvis and total-FLAIR lesion volumes were also computed at eleven different T1-RT thresholds including all lesions (BHthr, total-FLAIRthr) with a mean T1-RT above the given threshold (Fig 1). T1-RT thresholds were ranging from 700 ms to 1700 ms.

thumbnail
Fig 1. Example of ROI volumes overlaid on T1w images before and after setting a T1-RT threshold.

A: Axial slice of the cerebrum with multiple T1 hypointense lesions. B: Manually placed ROIs without applied threshold: BHvis (red), CE lesion (green). C: Remaining ROIs after applying the obtained optimal threshold of T1-RT >1500 ms with highest correlation to EDDS; CE lesions were not affected by thresholds.

https://doi.org/10.1371/journal.pone.0144693.g001

Statistical analysis

Statistical analysis was performed by using Statistics in R 3.0.0 and IBM SPSS 21.0. Interrater reliability for BHvis lesions masks was quantified using the Dice overlap coefficient. T1-RTs in the different lesion groups were compared using unpaired two-samples t-test. Spearman rank correlations between EDSS and voxel-/lesion volume were determined for different T1-RT thresholds. Voxel and lesion volumes were computed for BHvis lesion masks as well as for total-FLAIR and pure-FLAIR lesion masks.

Results

Interrater Reliability

Because of the diverse definitions of BHs and the heterogeneous presentation of T1w hypointense lesions, a Dice overlap coefficient was generated to quantify interrater reliability. When comparing the BHvis masks of both raters from all patients, an overall Dice coefficient of 0.8 was calculated.

Quantitative T1 values

For every patient, BHvis, pure-FLAIR and total-FLAIR lesion numbers, volumes and mean T1-RT were computed and are displayed in Table 2. There were significant differences in T1-RT between all lesion groups (p<0.05) with highest T1-RT values in BHsvis, which was also the lesion group with the largest standard deviation. The mean BHvis number exceeded the mean total-FLAIR lesion number, which is caused by the procedure that only voxels with intensity lower than cortex were marked as BHsvis in T1w images, so that in some cases one large FLAIR lesion appeared as multiple smaller BHsvis. CE lesions were detected in 15 patients. The T1-RT measured in the cortex was very uniform with the lowest standard deviation (1305.6 ± 35.8 ms). The mean T1-RT in BHsvis was significantly lower than T1-RT values measured in the cortex (T1-RTBHvis = 1453.3 ± 213.4 ms vs. T1-RTCortex = 1305.6 ± 35.8 ms; p<0.05). Fig 2 presents T1-RT in the different lesion types.

thumbnail
Table 2. Lesion mask characteristics.

Mean number, volume and T1-RT for different lesion classifications and cortex.

https://doi.org/10.1371/journal.pone.0144693.t002

thumbnail
Fig 2. Box Plots showing T1-RT distribution in different lesion types.

T1-RT = T1 relaxation time; total-FLAIR lesions = lesions from the semi-automatically segmented FLAIR lesions masks; pure-FLAIR lesions = lesions from the semi-automatically segmented FLAIR lesions masks that did not correspond to a BHvis or CE lesion; BHvis = manually segmented black holes without applied threshold, BHsthr1500 = manually segmented black holes with T1-RT higher than 1500 ms.

https://doi.org/10.1371/journal.pone.0144693.g002

Spearman rank correlations

Lesion volume and Expanded Disability Status Scale.

Spearman rank correlation coefficients for BHthr volume and EDSS as well as total-FLAIRthr lesion volume and EDSS were calculated for eleven different T1-RT thresholds ranging from 700 ms to 1700 ms. A T1-RT threshold of 700 ms included all BHsvis and total-FLAIR lesions and similar correlation coefficients were obtained (rBHthr = 0.408, rtotal-FLAIRthr = 0.389). For BHsthr, correlation showed constant values until it decreases to rBHthr = 0.342 at a T1-RT threshold of 1300 ms. Maximum correlation of rBHthr = 0.442 was obtained by including only lesions with mean T1-RT >1500 ms. A similar trend was observed for ROIs of the total-FLAIR lesion masks. As T1-RT thresholds increase, only hypointense lesions in MP2RAGE images remain. Starting at a minimum of rtotal-FLAIRthr = 0.389 at a T1-RT of 700 ms, the correlations increased to a peak of rtotal-FLAIRthr = 0.497 at a T1-RT threshold of 1500 ms. Regarding BHsthr, no significant correlations were found for T1-RT >1700 ms or at higher thresholds. For total-FLAIRthr lesions, no significant correlations were found for T1-RT >1600 ms or higher. At the optimal T1-RT threshold of 1500 ms 21% of the initial total-FLAIRthr lesion volume and 54% of the initial BHvis volume remain. Table 3 and Fig 3 present the computed correlation coefficients. In comparison, volumes determined from the application of voxel-wise thresholds did not increase correlation coefficients.

thumbnail
Table 3. Spearman rank correlations coefficients for lesion volume and EDSS at different T1-RT thresholds.

T1-TR thresholds in ms, BHthr volume = mean cumulative BHthr volume per patient in mm3, total-FLAIRthr lesion volume = mean cumulative total-FLAIRthr lesion volume per patient in mm3, Spearman r = Spearman rank coefficients for lesion volume and EDSS.

https://doi.org/10.1371/journal.pone.0144693.t003

thumbnail
Fig 3. Spearman rank correlation coefficients for lesion volume and EDSS at different T1-RT thresholds.

▲ = BHsthr volume vs. EDSS, ◙ = total-FLAIRthr lesion volume vs. EDSS (black symbols indicate a significant correlation). T1-RT = T1 relaxation time.

https://doi.org/10.1371/journal.pone.0144693.g003

Lesion volume and Multiple Sclerosis Functional Composite.

In 30 patients Spearman rank correlation coefficients for lesion volumes and MSFC were obtained for twelve different T1-RT thresholds ranging from 700 to 1800 ms. At a T1-RT threshold of 700 ms a correlation coefficient of -0.313 was obtained for total-FLAIR lesion volume and MSFC. As thresholds raise also correlation coefficients for total-FLAIR lesion volume and MSFC show increasing values until maximum correlation is achieved at T1-RT>1500 ms (rtotal-FLAIRthr = -0.627). For BHthr no significant correlations were found for T1-RT threshold between 700 and 1200 ms. At 1300 ms a significant correlation coefficient of -0.373 is obtained. Correlations coeffecients increase to a peak of rBHthr = -0.53 at a T1-RT threshold of 1500 ms. A detailed overview is presented in Table 4 and Fig 4.

thumbnail
Table 4. Spearman rank correlations coefficients for lesion volume and MSFC at different T1-RT thresholds.

T1-TR thresholds in ms, BHthr volume = mean cumulative BHthr volume per patient in mm3, total-FLAIRthr lesion volume = mean cumulative total-FLAIRthr lesion volume per patient in mm3, Spearman r = Spearman rank coefficients for lesion volume and EDSS.

https://doi.org/10.1371/journal.pone.0144693.t004

thumbnail
Fig 4. Spearman rank correlation coefficients for lesion volume and MSFC at different T1-RT thresholds.

▲ = BHsthr volume vs. MSFC, ◙ = total-FLAIRthr lesion volume vs. MSFC (black symbols indicate a significant correlation). T1-RT = T1 relaxation time.

https://doi.org/10.1371/journal.pone.0144693.g004

The highest correlation was found at T1-RT >1500 ms for both lesion masks. Altogether, by using the total-FLAIR lesion masks as basis for successive thresholding, slightly higher correlation coefficients are presented between lesion volumes and EDSS as well as MSFC. Regarding BHsthr, two patients of our study group had to be excluded at the optimal threshold of 1500 ms because they did not show lesions with T1-RT values below signal intensity. By using the total-FLAIRthr lesion masks 15 patients were excluded at given threshold.

Correlation coefficients were also obtained for voxel-wise instead of lesion-wise analyses. Since all patients displayed lesions containing voxels above the applied thresholds, no patient was excluded for voxel-wise analysis. The resulting correlation coefficients showed no meaningful difference in comparison to correlation coefficients obtained from the corresponding lesion-wise analysis.

We also found a significant association for EDSS and lesion number but with decreased correlation coefficients. For BHsthr the highest correlation was found at T1-RT >1500 ms with a Spearman correlation coefficient of 0.352 (p = 0.02) and for total-FLAIRthr lesions the highest correlation was found at T1-RT >1400 ms with a Spearman correlation coefficient of 0.476 (p = 0.003).

Correlation analyses were als perfomed for number of black holes and disease duration (Spearman rank coefficient of 0.422; p = 0.003) as well as for cumulative black hole volume and disease duration (Spearman rank coefficient of 0.433; p = 0.003). After applying T1 RT thresholds only minor changes in Spearman rank coefficients were observed for lesion volume and disease duration

There was no significant correlation between EDSS or MSFC and disease duration in our study cohort.

Discussion

The aim of this study was to assess the influence of T1-RT variations on the correlation between BH lesion volume and clinical disability. Our results suggest, that T1-RT has a significant influence on clinical-radiological correlations and may improve the differentiation of T1w hypointense lesions. T1-RT helps to provide further information on pathological changes in T1 hypointense lesions, indicating the degree of tissue damage within the observed region, such as demyelination, axonal loss and gliosis.[7,9,2527] It has already been observed in previous studies, that T1-RT differs significantly in normal appearing white matter in patients with RRMS and SPMS compared with healthy controls.[28,29] However, only few studies exist focusing on T1-RT in BHs and its impact on clinical-radiological correlation. Just recently Simioni et al. (2014) reported a relation between increased T1-RT in T1 hypointense lesions and cognitive performance in MS patients but no correlation with other clinical deficits was performed.[18]

In our patient group, the strongest correlation between lesion volume and clinical scores, measured by EDSS and MSFC, was observed at a T1-RT threshold of 1500 ms for lesions remaining in the BHvis masks as well as for lesions remaining in the total-FLAIR lesion masks. Regarding BHvis lesion volume and MSFC, significant correlations were only achieved after applying thresholds. Similar results were presented by Tam et al. (2011), showing that the strongest correlation was achieved by only including the darkest voxels in T1 lesion masks.[16] Unlike Tam et al. (2011) we didn't measure relative intensities from T1-weighted images but quantitative T1 values. Thus, scan-to-scan variations were minimized and a higher reliability was achieved. Our results show that the best clinical correlation was achieved by only including lesions with T1-RT clearly higher than cortex, indicating high severity of tissue damage.[9]

We found similar correlation coefficients by using the manually processed BHvis masks and the semi-automatically segmented total-FLAIR lesion masks. It is not surprising that correlation coefficients derived from thresholding both of these masks have their peak at T1-RT = 1500 ms, as the BHvis mask is element of the total-FLAIR lesion mask, so that T1 hypointense lesions with the darkest voxels remaining in both masks can be expected to largely overlap. However, higher correlation coefficients were obtained by using the total-FLAIR lesion masks as a basis for thresholding. A reason for this observation might be, that the manual BH lesion segmentation suffers from observer variability, which might lead to decreased objectivity and sensitivity corresponding to voxels below the applied threshold that might have been missed as BH-voxels. Also there is no uniform definition of BHs as some authors define them as lesions on T1-weighted images with lower signal intensity compared with surrounding normal-appearing white matter[30,31] while others define them as lesions on T1-weighted images with signal intensity below cortex[32,33]. These definitions are therefore not only dependent on the rater but also on sequence parameters. In our study BHs were defined as the latter and mean T1-RT in BHsvis differed significantly from T1-RT measured in total-FLAIR lesions and cortex (T1-RTBHvis = 1453.3 ± 213.4 ms vs. T1-RTCortex = 1305.6 ± 35.8 ms and T1-RTtotal-FLAIR = 1394.33 ± 187.38, p < 0.05). As darker voxels seem to have a stronger impact on clinical disability, we suggest defining BHs by T1-RT to increase clinical-radiological correlations and help setting a more objective definition. (Semi-) automatic segmentation might be a useful tool to increase objectivity and establish a more specific definition of BHs.

For semi-automatic lesion segmentation we used the Lesion Segmentation Toolbox, which is implemented in SPM8. LST is a well established segmentation software and widely used in former MS clinical trials.[24,34,35] Moreover, good agreement was found when comparing LST to manual segmentation.[24]

A possible limitation of our study is, that we did not classify lesions by their location, as cortical lesions have been found to contribute to higher levels of physical and cognitive disability.[16,36] Nevertheless, every detected lesion regardless of its location was affected by T1-RT thresholding. Further investigation of the localization of lesions and corresponding T1-RT will be useful for a better understanding of these two important radiologic parameters.

Secondly, we also included patients with CE lesions, which indicate active inflammation and might therefore impair the patients' clinical status. However, controlling for CE lesions did not impact on study results. Therefore we believe correlates are robust in patients with and without CE lesions.

We used the EDSS to measure clinical disability, which is well known for its intrinsic limitation. Beside its bias towards mobility and high inter-observer variability especially in the lower range, subtle clinical progression is often missed by its non-linear rating.[37] However, correlation coefficients were also obtained by using the MSFC, which extends measures of functional status and correlates with EDSS scores in MS patients.[38]

In conclusion, our findings suggest that assessing T1 lesions in patients with Multiple sclerosis by its T1-RTs increases clinical-radiological correlations. With the fast acquisition time of the MP2RAGE sequences, generating quantitative T1 maps can be established in clinical studies. Further emphasis should be laid on longitudinal observation to assess the prognostic value of these measurements.

Supporting Information

Author Contributions

Conceived and designed the experiments: CT JS BH SS. Performed the experiments: CT JS SS TF JPS KLY CH. Analyzed the data: CT SS. Contributed reagents/materials/analysis tools: JS JPS KLY CH JF SS. Wrote the paper: CT JS JPS CH JF SS.

References

  1. 1. Evangelou N, Esiri MM, Smith S, Palace J, Matthews PM. Quantitative pathological evidence for axonal loss in normal appearing white matter in multiple sclerosis. Ann Neurol. 2000; 47(3):391–5. pmid:10716264
  2. 2. Polman CH Reingold SC, Edan G, Filippi M, Hartung HP, Kappos L, Lublin FD, Metz LM, McFarland HF, O'Connor PW, Sandberg-Wollheim M, Thompson AJ, Weinshenker BG, Wolinsky JS. Diagnostic criteria for multiple sclerosis: 2005 revisions to the ‘‘McDonald Criteria”. Ann Neurol 2005; 58(6):840–846 pmid:16283615
  3. 3. Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, Fujihara K, Havrdova E, Hutchinson M, Kappos L, Lublin FD, Montalban X, O'Connor P, Sandberg-Wollheim M, Thompson AJ, Waubant E, Weinshenker B, Wolinsky JS. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald Criteria. Ann Neurol 2011; 69:292–302. pmid:21387374
  4. 4. MacKay A, Laule C, Vavasour I, Bjarnason T, Kolind S, Madler B. Insights into brain microstructure from the T2 distribution. Magn Reson Imaging. 2006; 24(4):515–25. pmid:16677958
  5. 5. Laule C, Leung E, Lis DK, Traboulsee AL, Paty DW, MacKay AL, et al. Myelin water imaging in multiple sclerosis: quantitative correlations with histopathology. Mult Scler. 2006; 12(6):747–53. pmid:17263002
  6. 6. De Groot CJ, Bergers E, Kamphorst W, Ravid R, Polman CH, Barkhof F, et al. Post-mortem MRI-guided sampling of multiple sclerosis brain lesions: increased yield of active demyelinating and (p)reactive lesions. Brain. 2001; 124(8):1635–45.
  7. 7. van Walderveen MAA, Kamphorst W and Scheltens P. Histopathologic correlate of hypointense lesions on T1-weighted spin-echo MRI in multiple sclerosis. Neurology 1998; 50:1282–1288. pmid:9595975
  8. 8. Bitsch A, Kuhlmann T, Stadelmann C, Lassmann H, Lucchinetti C, Brück W. A longitudinal MRI study of histopathologically defined hypointense multiple sclerosis lesions. Ann Neurol 2001; 49(6):793–796. pmid:11409432
  9. 9. Jonkman LE, Soriano AL, Amor S, Barkhof F, van der Valk P, Vrenken H, Geurts JJ. Can MS lesion stages be distinguished with MRI? A postmortem MRI and histopathology study. J Neurol. 2015; 262(4):1074–80. pmid:25761376
  10. 10. Barkhof F, Bruck W, De Groot CJ, Bergers E, Hulshof S, Geurts J, Polman CH, van der Valk P. Remyelinated lesions in multiple sclerosis: magnetic resonance image appearance. Arch Neurol 2003; 60:1073–1081. pmid:12925362
  11. 11. Riva M, Ikonomidou VN, Ostuni JJ, van Gelderen P, Auh S, Ohayon JM, Tovar-Moll F, Richert ND, Duyn JH, Bagnato F. Tissue-specific imaging is a robust methodology to differantiate in vivo T1 black holes with advanced multiple sclerosis-induced damage. AJNR Am J Neuroradiol 2009; 30:1394–1401. pmid:19406765
  12. 12. Giugni E, Pozzilli C, Bastianello S, Gasperini C, Paolillo A, Koudriavtseva T, et al. MRI measures and their relations with clinical disability in relapsing– remitting and secondary progressive multiple sclerosis. Mult Scler 1997; 3:221–5. pmid:9372503
  13. 13. O'Riordan JI, Gawne Cain M, Coles A, Wang L, Compston DA, Tofts P, et al. T1 hypointense lesion load in secondary progressive multiple sclerosis: a comparison of pre versus post contrast loads and of manual versus semi automated threshold techniques for lesion segmentation. Mult Scler 1998; 4:408–12. pmid:9839300
  14. 14. Simon JH, Lull J, Jacobs LD, Rudick RA, Cookfair DL, Herndon RM. A longitudinal study of T1 hypointense lesions in relapsing MS: MSCRG trial of interferon beta- 1a. Multiple Sclerosis Collaborative Research Group. Neurology 2000; 55:185–92. pmid:10908888
  15. 15. Truyen L, van Waesberghe JH, van Walderveen MA, van Oosten BW, Polman CH, Hommes OR, et al. Accumulation of hypointense lesions (‘‘black holes”) on T1 spin-echo MRI correlates with disease progression in multiple sclerosis. Neurology 1996; 47:1469–76. pmid:8960729
  16. 16. Tam RC, Traboulsee A, Riddehough A, Sheikhzadeh F, Li DKB. The impact of intensity variations in T1-hypointense lesions on clinical correlations in multiple sclerosis Mult Scler August 2011; 17:949–957. pmid:21502309
  17. 17. Castriota-Scanderbeg A, Fasano F, Filippi M, Caltagirone C. T1 relaxation maps allow differentiation between pathologic tissue subsets in relapsing- remitting and secondary progressive multiple sclerosis. Mult Scler 2004; 10(5):556–561. pmid:15471373
  18. 18. Simioni S, Amaru F, Bonnier G,Kober T, Rotzinger D, Du Pasquier R, et al. MP2RAGE provides new clinically-compatible correlates of mild cognitive deficits in relapsing-remitting multiple sclerosis. J Neurol 2014; 261:1606–1613. pmid:24912471
  19. 19. Venkatesan R, Lin W, Haacke EM. Accurate determination of spin-density and T1 in the presence of RF-field inhomogeneities and flip-angle miscalibration. Magn Reson Med 1998; 40(4):592–602. pmid:9771576
  20. 20. Kober T, Granziera C, Ribes D, Browaeys P, Schluep M, Meuli R, et al. MP2RAGE multiple sclerosis magnetic resonance imaging at 3 T. Invest Radiol 2012; 47(6):346–352. pmid:22543966
  21. 21. Kappos L, Lechner-Scott J, Lienert C. Neurostatus—Neurostatus training and documentation DVD for a standardized neurological examination and assessment of Kurtzke’s functional systems and expanded disability status scale for MS patients. 2002, 2007, Basel, Switzerland. Available: http://neurostatus.net; 2015 [accessed 01.06.2015].
  22. 22. Parmenter BA, Weinstock-Guttman B, Garg N, Munschauer F, Benedict RH. Screening for cognitive impairment in multiple sclerosis using the Symbol digit Modalities Test. Mult Scler. 2007; 13(1):52–7. pmid:17294611
  23. 23. Fischer JS, Rudick RA, Cutter GR, Reingold SC. The multiple sclerosis functional composite measure (MSFC): An integrated approach to MS clinical outcome assessment. National MS Society Clinical Outcomes Assessment Task Force. Mult Scler 1999; 5:244–250. pmid:10467383
  24. 24. Schmidt P, Gaser C, Arsic M, Buck D, Förschler A, Berthele A, et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. Neuroimage 2012; 59(4):3774–83. pmid:22119648
  25. 25. Larsson HB, Frederiksen J, Petersen J, Nordenbo A, Zeeberg I, Henriksen O, et al. Assessment of demyelination, edema, and gliosis by in vivo determination of T1 and T2 in the brain of patients with acute attack of multiple sclerosis. Magn Reson Med 1989; 11(3):337–348. pmid:2779421
  26. 26. Loevner LA, Grossman RI, McGowan JC, Ramer KN, Cohen JA. Characterization of multiple sclerosis plaques with T1-weighted MR and quantitative magnetization transfer. AJNR Am J Neuroradiol. 1995; 16:1473–1479. pmid:7484636
  27. 27. Schmierer K, Scaravilli F, Altmann DR, Barker GJ, Miller DH. Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Ann Neurol 2004; 56(3):407–415. pmid:15349868
  28. 28. Griffin CM, Dehmeshki J, Chard DT, Parker GJ, Barker GJ, Thompson AJ, et al. T1 histograms of normal-appearing brain tissue are abnormal in early relapsing-remitting multiple sclerosis. Mult Scler 2002; 8(3):211–216. pmid:12120692
  29. 29. Manfredonia F, Ciccarelli O, Khaleeli Z, Tozer DJ, Sastre-Garriga J, Miller DH, et al. Normal-appearing brain T1 relaxation time predicts disability in early primary progressive multiple sclerosis. Arch Neurol. 2007; 64(3):411–5. pmid:17353385
  30. 30. Mitjana R, Tintoré M, Rocca MA, Auger C, Barkhof F, Filippi M, et al. Diagnostic value of brain chronic black holes on T1-weighted MR images in clinically isolated syndromes. Mult Scler. 2014; 20(11):1471–7. pmid:24576831
  31. 31. Sahraian M., Radue E., Haller S., Kappos L. Black holes in multiple sclerosis: definition, evolution, and clinical correlations. Acta Neurol Scand 2010; 122:1–8. pmid:20003089
  32. 32. Pichler A, Enzinger C, Fuchs S, Plecko-Startinig B, Gruber-Sedlmayr U, Linortner P, et al. Differences and similarities in the evolution of morphologic brain abnormalities between paediatric and adult-onset multiple sclerosis. Mult Scler. 2013; 19(2):167–72. pmid:22717380
  33. 33. Enzinger C, Ropele S, Smith S, Strasser-Fuchs S, Poltrum B, Schmidt H, et al. Accelerated evolution of brain atrophy and "black holes" in MS patients with APOE-epsilon 4. Ann Neurol. 2004; 55(4):563–9. pmid:15048896
  34. 34. Jain S, Sima DM, Ribbens A, Cambron M, Maertens A, Van Hecke W, et al. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images. Neuroimage Clin. 2015; 8:367–75. pmid:26106562
  35. 35. Caligiuri ME, Perrotta P, Augimeri A, Rocca F, Quattrone A, Cherubini A. Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review. Neuroinformatics. 2015; 13(3):261–76. pmid:25649877
  36. 36. Roosendaal SD, Moraal B, Pouwels PJ, Vrenken H, Castelijns JA, Barkhof F, et al. Accumulation of cortical lesions in MS: relation with cognitive impairment. Mult Scler. 2009; 15(6):708–714. pmid:19435749
  37. 37. Noseworthy JH, Vandervoort MK, Wong CJ, Ebers GC. Interrater variability with the Expanded Disability Status Scale (EDSS) and Functional Systems (FS) in a multiple sclerosis clinical trial. The Canadian Cooperation MS Study Group. Neurology 1990; 40(6):971–5. pmid:2189084
  38. 38. Miller D M, Rudick R A, Cutter G, Baier M, Fischer J S. Clinical significance of the multiple sclerosis functional composite: relationship to patient-reported quality of life. Arch Neurol 2000; 57(9):1319–24. pmid:10987899