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Measurement of the permeability, perfusion, and histogram characteristics in relapsing-remitting multiple sclerosis using dynamic contrast-enhanced MRI with extended Tofts linear model
Correspondence Address: Source of Support: None, Conflict of Interest: None DOI: 10.4103/0028-3886.232324
Keywords: Dynamic contrast-enhanced magnetic resonance imaging, extended Tofts linear model, histogram, multiple sclerosis
Relapsing-remitting multiple sclerosis (RRMS) is a chronic, degenerative disease of the central nervous system. It is usually characterized by recurrent episodes of immune-mediated demyelination, glial scar formation, and axonal loss leading to variable clinical outcomes.[1] Many studies have shown the abnormalities of blood–brain barrier (BBB) in MS.[2],[3],[4],[5],[6],[7],[8],[9] Pathological changes of different lesions in MS is not the same.[4],[8],[10] Furthermore, with the effect of multiple space characteristics of lesions on normal-appearing white matter (NAWM) regions, the abnormalities of permeability and perfusion related to the pathology changes were also observed in NAWM regions.[3],[5],[6],[7],[10] Dynamic contrast-enhanced T1-weighted magnetic resonance imaging (DCE-MRI) is a promising noninvasive imaging method that enables the quantification of the abnormality of subtle microvascular environment and is increasingly used to estimate permeability in situ ations with blood brain barrier (BBB) leakage.[11],[12] Although this technique was originally developed to estimate the BBB permeability in MS and brain tumors,[2],[3],[13] only a few studies have focused both on the permeability and perfusion abnormality in MS.[3],[7] Ingrisch et al.,[7] used a two-compartment Tofts model to analyze the permeability and perfusion in MS. Their study demonstrated the feasibility of three-dimensional (3D) T1-weighted DCE-MRI for the quantitative assessment of permeability and perfusion in MS. However, different problems in the evaluation of kinetic parameters can occur due to different models.[14],[15] In a previous study,[7] the authors have assumed that the signal nonlinearities did not play an important role, which may cause an underestimation of the arterial concentration. The extended Tofts model, also known as the generalized Tofts and Kermode (TK)[16] or extended TK (ETK) model, is the extended version of Tofts model considering the effect of the intravascular space.[17] It is based on the assumption of a fast plasma flow through the capillary compartment.[18] Cramer et al.,[3] reported that the extended Tofts model is dependent on the true permeability, blood volume, and measurement conditions, which gives more unpredictable results as perfusion and blood volume increase. However, a review by Khalifa et al.,[19] concluded that the ETK model is considered the best-established model for analyzing T1-weighted DCE-MR images. Even so, the extended Tofts model has been widely used in the brain,[19],[20],[21],[22],[23] but there are a fewer studies of their application in MS.[3] A study by Seber et al.,[24] showed that the nonlinear character of the model can lead to the ill-conditioning of the estimation. The extended Tofts linear model is not so sensitive to these problems and is suitable for the analysis of microvascular tissue.[25] The histogram describes the statistical information contained in the image in a simple manner; it shows the number of pixels in the whole image having the same intensity.[26] During the past decade, the number of studies using histogram approaches to improve the heterogeneity of tumor has increased drastically.[26] Hayes et al.,[27] used this methodology to investigate the distributions of the permeability parameters of breast cancer in DCE-MRI. Peng et al.,[28] calculated the histogram and permeability parameters for evaluation of changes in brain tumor heterogeneity, which respond to radiotherapy. These studies demonstrated that the histogram parameters were significantly sensitive to small changes or treatment effects. In this study, for the first time, we applied the heterogeneity of histogram to estimate the potential for MS lesion characterization. The aim of the present study was to investigate the application value of using DCE-MRI with extended Tofts linear model for RRMS and its correlation with expanded disability status scale (EDSS) scores and disease duration. To this end, we compared the permeability, perfusion, and histogram parameters among different lesions and NAWM regions in RRMS patients, and determined whether the disruption of the microvascular stages of the lesions is related to alterations of lesions, that in turn were related to the clinical manifestations of the disease. We hypothesized that (1) the permeability, perfusion, and histogram parameters of lesions and NAWM regions were different from each other, (2) the alterations of these parameters can reflect the different microvascular stages of the lesions, and (3) the disruption of the microvascular stages may be related to alterations in the contribution of lesions to the clinical manifestations of the disease.
Patients In total, 30 RRMS patients (10 male and 20 female patients; mean age 43.6 ± 15.3 years; age range 19–78 years; median expanded disability status scale (EDSS) 1.0, range 0.0–5.0; median disease duration 3.4 years, range 0.25–16.5 years) were recruited from our hospital. All patients were assessed clinically by an experienced neurologist who was unaware of the MRI results. All patients had no corticosteroid or immunosuppressant treatment for at least 3 months before the scan. All RRMS patients meet the McDonald 2010 criteria [29] and had no other neurological diseases or psychiatric problems. Twenty-five out of the 30 patients were in the clinical remission stage, and 5 patients were in the relapse stage when the MRI examination was performed. This study was approved by the local Ethics Committee of our hospital. All the included patients gave written informed consent prior to participation in the study. Data acquisitions MRI was performed on a 3.0 Tesla MR General Electric (GE) Imaging System (Waukesha, Wisconsin, USA) using an eight-channel phased-array head coil. The imaging protocol included: precontrast T1-weighted image (WI): Repetition time (TR)/echo time (TE) = 1750/24 ms, scan matrix size 320 × 224; T2 WI: TR/TE = 3900/98 ms, matrix 288 × 224; fluid-attenuated inversion recovery (FLAIR): TR/TE = 8000/120 ms, matrix 256 × 160, slice thickness/gap = 5.0/0.0 mm, field of view (FOV) 24 cm × 24 cm. For DCE-MRI, a three-dimensional (3D) fast spoiled gradient recalled echo (SPGR) was used to achieve sufficient spatial assessement and resolution for the subsequent analysis. A series of flip angles of 3°, 6°, 9°, 12°, 15° were performed to obtain T1 mapping. Dynamic acquisition: After three phases of non-enhanced acquisition, a bolus of contrast (gadodiamide injection, GE Healthcare Ireland) was injected with a flow rate of 2.0 ml/s followed by a 20 ml flush of saline with the same flow intravenously. The contrast was injected by an automatic double-bolus injection (Medrad, Pittsburgh, PA, Spectris Solaris MR injector system) with a dose of 0.2 ml/kg. The parameters were 256 × 160 matrix size, TR/TE 6.664/2.8 ms, field of view (FOV) 24 cm × 24 cm, slice thickness 5.0 mm, spatial resolution 1.5 mm × 0.9 mm × 5.0 mm, bandwidth 31.25 kHz, flip angle 12°. In total, 360 volumes (slices number 12, 40 phases, 6 seconds per phase) were acquired in the total measurement time of 4 minutes. Data post-processing T1-weighted contrast-enhanced sequences were acquired following the DCE-MRI sequence. The T2 lesion load of the patients was determined as the number of hyperintense lesions on the FLAIR images (lesions >5.0 mm), and the number of contrast-enhancing (CE) lesions was assessed on conventional T1-weighted contrast-enhanced sequences. DCE-MRI data post-processing was performed using the Omni Kinetics software (GE Healthcare, China). A map of the baseline signal was calculated by averaging overall time points before the arrival of contrast in the vascular compartment. The region of interest (ROI) was defined in the superior sagittal sinus to measure the concentration-time curves. An extended Tofts linear model was used to simplify the definition of ROI in lesions and NAWM. ROIs were fixed in size (20–30 mm 2) and placed to CE lesions, nonenhancing (NE) lesions, and normal appearing white matter (NAWM) regions to acquire the MR imaging biomarkers, including the volume transfer constant (K trans), rate constant of backflux (Kep), volume of the extravascular extracellular space per unit volume of tissue (Ve), fractional plasma volume (Vp) and perfusion parameters, cerebral blood flow (CBF), and cerebral blood volume (CBV). The concentration-time curves and artificial color mappings and histogram were made, and the correlation among imaging biomarkers, EDSS scores, and disease duration were also analyzed. ROIs were hand traced by two experienced radiologists; discussions resolved any differences of opinion. NAWM regions included NAWMregions near the lesions and NAWM regions far from the lesions (distance ≥5.0 mm).[30] We defined NAWM regions far from the lesions as NAWM regions on a symmetrical brain avoiding arterial or venous structures, to exclude the false-positive results due to physiological differences. Statistical analysis Statistical analysis of the data was carried out by using statistical package for the social science (SPSS) version 21.0 software (SPSS, Inc., Chicago, IL). The one-sample Kolmogorov–Smirnov test showed that the permeability, perfusion, and histogram parameters of the lesions and NAWM regions did not conform to the normal distribution. Therefore, we used median (P25 ~ P75) to describe these parameters. The Kruskal–Wallis Hrank sum testinSAS 9.2 software was used to compare the differences of these parameters. We compared the K trans, Ve, Vp, CBF, and CBV between the groups by using the Nememyi test. We compared the skewness and kurtosis of MS lesions using nonparametric tests. The concentration-time curves and artificial color mappings were performed. The correlation among imaging biomarkers, EDSS scores, and disease duration were analyzed using Spearman correlative analyses based on the SPSS 21.0 software.
Characteristics of lesions on conventional magnetic resonance imaging In all the 30 patients, CE and NE lesions were detected on postcontrast T1-weighted images and precontrast FLAIR images. Frontal lobe, temporal lobe, and parietal lobe were the sites of predilection for the MS lesions, particularly the periventricular areas [Figure 1].
The permeability, perfusion, and histogram parameters Permeability parameters The results of permeability parameters of MS lesions and NAWM regions are shown in [Figure 2] and [Table 1]. K trans values of CE lesions were significantly higher (P < 0.05) than that of NE (red and yellow) lesions and the NAWM regions both near and far from NE lesions (green and blue) [Figure 2]a. However, there were no significant differences in NE lesions compared with NAWM regions. This observation could be explained since the vessel expansion of CE lesions, the Kep map, shows no significant differences among CE lesions, NE lesions, and NAWM regions [Figure 2]b. This could be partially explained by the Kep values as being primarily determined by the tissue concentration-time-curve (CTC) shape rather than the absolute concentration values according to the Tofts modeling, so it was expected that their derived Kep values should be close.[11],[17],[31] [Figure 2]c and [Figure 2]d shows that Ve and Vp values of CE lesions were significantly higher (P < 0.05) than that of NE lesions and NAWM regions. Obvious compensatory dilation of blood vessels and collateral circulation was observed around the lesion.
Perfusion parameters [Figure 2]e and f shows that CBF and CBV of CE lesions were significantly higher (P < 0.05) than that of NE lesions and the NAWM regions both near and far from NE lesions. In contrast, there were no significant differences in NE lesions compared with NAWM regions. The CBF and CBV of the NAWM regions close to the NE lesions had no significant differences with the NAWM regions far from the lesions. Compensatory dilation of blood vessels around the CE lesion was the same as in permeability maps. Histogram parameters [Figure 3] shows the histogram of MS lesions. For NE lesions, the skewness of Ve value was significantly higher (P < 0.05) than that of CE lesions. This result showed that the skewness of Ve value in NE lesions increased to the right side of the histogram. However, the skewness of Ve value in CE lesions was more close to zero, indicating that the Ve histogram in CE lesions was more close to normal distribution. Furthermore, the skewness of K trans, Vp, CBF, and CBV in NE lesions had no significant differences compare with CE lesions.
Kurtosis reflects the peak values of the distribution. In our study, the kurtosis of K trans and Ve values in CE lesions showed a flatter curve and a decreased peak height. However, the kurtosis of Vp, CBF, and CBV in NE lesions had no significant differences when compared with the CE lesions [Table 2].
Correlation among imaging biomarkers, EDSS scores, and disease duration We found no significant correlation among the K trans, Kep, Ve, Vp, CBF, and CBV values with the EDSS scores and disease duration in MS lesions (P > 0.05).
In our study, we demonstrated the application value of using DCE-MRI with extended Tofts linear model for quantification of abnormality of cerebral perfusion and permeability in RRMS and its correlation with EDSS scores and disease duration. The MR imaging biomarkers K trans, Ve, Vp, CBF, and CBV of CE lesions were significantly higher than that of NE lesions. Moreover, an important finding of this study is that the Ve histogram in CE lesions was more close to normal distribution compared with NE lesions; this can be helpful in distinguishing CE lesions from NE lesions in MS. The kurtosis of K trans and Ve values in CE lesions showed a more flat curve and a decreased peak height, showing that the peak of CE lesions was lower than that of normal distribution. However, there was no significant correlation among the K trans, Kep, Ve, Vp, CBF, and CBV with EDSS scores and disease duration in MS lesions. Using the extended Tofts linear model, we detected a significant difference of K trans, Ve, Vp, CBF, and CBV in CE lesions compared with NE lesions and the NAWM regions, which is consistent with the previous findings in MS lesions.[7],[10] A possible explanation for this situation could be the existence of vasodilation that occurs during inflammation in CE lesions. The typical plaques of MS are known as “Dawson's fingers,” and their main pathological processes include an inflammatory reaction, axonal loss and glial cell regeneration.[10],[32] Studies have showed that the pathological changes of different lesions in MS are different.[4],[8],[10] The center of CE lesions reveals mainly destruction of the neuraxis, the proliferation of glial cells, demyelination, and ischemic changes.[4],[10] The compensatory dilation of blood vessels and collateral circulation forms around the lesions. However, the pathology of NE lesions consists of two kinds of changes: one is the occurrence of vascular occlusion caused by the proliferation of glial cells and collagen, and the other is a part of the inevitable vascular expansion after a major injury, resulting in regeneration of inflammatory activity.[30] In this study, we found that the CBF and CBV of NE lesions were slightly higher than that of the NAWM regions, which is consistent with the findings of the previous studies.[8],[9] This may be related in part to the vascular channel dilatation, caused by the reconstruction phase after the inflammatory activity. Multiple lesions of MS also created an effect on the NAWM regions, resulting in axonal swelling and an increased numbers of enlarged microglia being observed in the NAWM regions also. The cell density in voxels increased due to the infiltration of myelin proteins and macrophages in the NAWM regions. This can lead to the effect of a decrease of EES and the permeability of the microcirculation. In addition, the abnormalities of T1 value were found in the NAWM regions.[5] Bester et al.,[6] reported that the perfusion characteristics of the NAWM regions were related to the degree of acute inflammation in MS lesions. Moll et al.,[30] reported that the abnormalities in the NAWM regions depend on the proximity to lesions; thus, axonal swelling can be found in the NAWM regions close to the lesions but not in the NAWM regions far from the lesions. In this study, we also found no significant difference of permeability and perfusion parameters between the NAWM regions close to the lesions produced by MS and the NAWM regions far from the lesions. This may be because the majority of lesions in our study were not in the active stage. Some proliferation of the glial cells and a slight dilatation of vascular structures, however, did appear in the NAWM regions close to lesions. The correlation analysis shows that the permeability and perfusion parameters have no significant correlation with the status of clinical disability. This could be related to the fact that microvascular abnormality may not have immediately led to changes in the disability; it may also have been due to the fact that the reconstruction activity in the region after the inflammatory activity may have limited the clinical manifestations. In addition, the small sample size and cross-sectional design may have been responsible for this situation.[33] To the best of our knowledge, this study was the first to use DCE-MRI with extended Tofts linear model for quantification of abnormality of cerebral perfusion and permeability in RRMS and to assess its correlation with the EDSS scores and the disease duration. More importantly, no study so far has attempted to correlate the histogram parameters with the pathology of MS lesions. Previous studies have reported that skewness and kurtosis are useful parameters and are significantly sensitive to small changes or treatment effects.[26],[27],[28] Our study demonstrated that the skewness of CE lesions were more close to zero, indicating that CE lesions were more close to normal distribution. In addition, the K trans and Ve values in CE lesions showed a flatter curve and a decreased peak height than that of the NE lesions. Hence, we believe that the above histogram parameters can be used to distinguish CE lesions from NE lesions. There are some limitations in this study. First, this study has a cross-sectional design; longitudinal studies are necessary to determine whether or not permeability and perfusion parameters undergo dynamic changes after therapy. Second, our results should be interpreted with caution because of the small sample size, which may not be an accurate reflection of the microvascular changes prevalent in MS. Although previous studies [7] have investigated the feasibility of quantification of the permeability and perfusion changes by performing studies in even lesser number of patients, a larger sample size is needed in future studies. Third, we used the EDSS scoring system as a measure of disability, which may be insensitive to small amounts of change.[33],[34] Although the EDSS has been the most widely used measure of disability in MS, some advanced and more sensitive measurements should be used in further studies conducted in this area.[35]
Our study demonstrated that DCE-MRI with extended Tofts linear model can quantitatively measure the permeability and perfusion characteristic in MS lesions and in NAWM regions. The K trans, Ve, Vp, CBF, and CBV values of CE lesions were significantly higher than that of NE lesions, which is in accordance with previous studies. However, we found no significant difference among these parameters in the NE lesions and in NAWM regions. In addition, we observed that the histogram parameters of CE lesions and NE lesions in MS were different from each other, which indicated that the histogram can be helpful to distinguish the pathology of MS lesions. Interestingly, we did not find any correlation among the K trans, Kep, Ve, Vp, CBF, and CBV with the EDSS scores and the disease duration in MS lesions. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2]
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