Quantitative MRI texture analysis in chronic active multiple sclerosis lesions

ObjectiveRecently, there has been an increasing interest in “chronic enlarging” or “chronic active” multiple sclerosis (MS) lesions that are associated with clinical disability. However, investigation of dynamic lesion volume changes requires longitudinal MRI data from two or more time points. The aim of this study was to investigate the application of texture analysis (TA) on baseline T1-weighted 3D magnetization-prepared rapid acquisition gradient-echo (MPRAGE) images to differentiate chronic active from chronic stable MS lesions.Material and methodsTo identify chronic active lesions as compared to non-enhancing stable lesions, two MPRAGE datasets acquired on a 3 T MRI at baseline and after 12 months follow-up were applied to the Voxel-Guided Morphometry (VGM) algorithm. TA was performed on the baseline MPRAGE images, 36 texture features were extracted for each lesion.ResultsOverall, 374 chronic MS lesions (155 chronic active and 219 chronic stable lesions) from 60 MS patients were included in the final analysis. Multiple texture features including “DISCRETIZED_HISTO_Energy”, “GLCM_Energy”, “GLCM_Contrast” and “GLCM_Dissimilarity” were significantly higher in chronic active as compared to chronic stable lesions. Partial least squares regression yielded an area under the curve of 0.7 to differentiate both lesion types.ConclusionOur results suggest that multiple texture features extracted from MPRAGE images indicate higher intralesional heterogeneity, however they demonstrate only a fair accuracy to differentiate chronic active from chronic stable MS lesions.     

Distinct patterns of active and non-active plaques using texture analysis on brain NMR images in multiple sclerosis patients: preliminary results

The benefits of texture analysis of magnetic resonance images have been assessed in multiple sclerosis (MS) patients. Out of thirty-two lesions identified in eight MS patients, nine were considered active, judging from their gadolinium uptake. Texture analysis allowed to obtain forty-two characterizing parameters for each lesion. Using discriminant analysis as a statistical method allowed to classify the lesions into two groups: active or non-active. An attempt to classify their level of activity by using only co-occurrence matrices was unsuccessful. Alternately, the same type of analysis performed on runlength analysis criteria allowed the accurate classification of 88% of active lesions and 96% of non-active lesions. Using incremental discriminate analysis can reduce the number of useful parameters. This method showed that among the 42 parameters, 8 only were highly significant and permitted an accurate classification. Five of these parameters are runlength parameters, and three others are more directly related to the global distribution. The main interest of runlength parameters is that they allowed to demonstrate that the lesion structure was different in active and non-active plaques. This preliminary work suggests that using texture analysis could be of interest in the follow-up of MS patients because it provides an opportunity to identify active lesions without frequent gadolinium injections.     

Spatial normalization of multiple sclerosis brain MRI data depends on analysis method and software package

BackgroundSpatially normalizing brain MRI data to a template is commonly performed to facilitate comparisons between individuals or groups. However, the presence of multiple sclerosis (MS) lesions and other MS-related brain pathologies may compromise the performance of automated spatial normalization procedures. We therefore aimed to systematically compare five commonly used spatial normalization methods for brain MRI – including linear (affine), and nonlinear MRIStudio (LDDMM), FSL (FNIRT), ANTs (SyN), and SPM (CAT12) algorithms – to evaluate their performance in the presence of MS-related pathologies.Methods3 Tesla MRI images (T1-weighted and T2-FLAIR) were obtained for 20 participants with MS from an ongoing cohort study (used to assess a real dataset) and 1 healthy control participant (used to create a simulated lesion dataset). Both raw and lesion-filled versions of each participant's T1-weighted brain images were warped to the Montreal Neurological Institute (MNI) template using all five normalization approaches for the real dataset, and the same procedure was then repeated using the simulated lesion dataset (i.e., total of 400 spatial normalizations). As an additional quality-assurance check, the resulting deformations were also applied to the corresponding lesion masks to evaluate how each processing pipeline handled focal white matter lesions. For each normalization approach, inter-subject variability (across normalized T1-weighted images) was quantified using both mutual information (MI) and coefficient of variation (COV), and the corresponding normalized lesion volumes were evaluated using paired-sample t-tests.ResultsAll four nonlinear warping methods outperformed conventional linear normalization, with SPM (CAT12) yielding the highest MI values, lowest COV values, and proportionately-scaled lesion volumes. Although lesion-filling improved spatial normalization accuracy for each of the methods tested, these effects were small compared to differences between normalization algorithms.ConclusionsSPM (CAT12) warping, ideally combined with lesion-filling, is recommended for use in future MS brain imaging studies requiring spatial normalization.     

Quantification of multiple sclerosis lesion load and brain tissue volumetry using multiparameter MRI: methodology and reproducibility

Quantitative characterization of multiple sclerosis (MS) lesion load is of considerable interest to clinical follow-up studies. Based on fuzzy clustering of multiparameter magnetic resonance images, we have developed a computer-assisted system for volumetric quantification of brain tissue. Tests on patient data show that the system is very efficient, and volumetric measurements characterized are highly reproducible. The high reproducibility and efficiency offer the potential of routine laboratory and clinical use for quantification of MS lesion load.     

How does brain MRI lesion volume change on serial scans in patients with multiple sclerosis?

Although lesion load changes on conventional T₂-weighted brain magnetic resonance imaging (MRI) scans from patients with multiple sclerosis (MS) are used to monitor the effect of treatment, there is no clear definition of how lesion load changes over years according to the lesion load present at a baseline evaluation. In the present study, we evaluated the relationship between lesion load changes over time and lesion load at a baseline evaluation in a group of untreated patients with MS. We scanned nineteen patients on two separate occasions with a mean interval 16.4 months between the two examinations. In each scanning session, a scan with forty contiguous 3-mm-thick axial slices was acquired. We assessed MRI lesion loads using a semi-automated local thresholding technique. Both a linear (p < 0.0001) and a quadratic component (p = 0.0008) of the baseline volume were significant in describing the follow-up volume. The equation to model this finding was as follows: V_f = β₀ V_b + β₁ (V_b)², where V_f is the lesion volume at follow-up, V_b is the lesion volume at baseline, β₀ = 0.834 (SE = 0.098), and β₁ = 0.014 (SE = 0.003) (mL)⁻¹. Our data indicate that lesion volume changes detectable on serial brain MRI studies from patients with MS are dependent on the extent of lesion burden present on the baseline MRI scans. This finding has to be considered when planning phase III trials.     

Optimizing brain MRI protocols in the follow-up of patients with multiple sclerosis T2-weighted MRI of the brain after the administration of gadopentetate dimeglumine

The aim of our study was to determine whether T2-weighted (T2w) MRI of the brain could be performed immediately after the administration of gadopentetate dimeglumine (gadolinium DTPA) in patients with multiple sclerosis (MS) without a loss in image quality or diagnostic reliability. Sixteen patients with clinically diagnosed MS were included in the study. Twenty-four patients with various cerebral pathologies (14 patients with multiple lacunar lesions) were examined in order to exclude masking of T2 hyperintense lesions other than MS lesions. Images of 10 patients without pathological changes served as a control condition for the qualitative analysis. In these 50 patients, T1w and T2w MRI was performed before and after the administration of gadolinium DTPA. Signal intensities were measured within T2 hyperintense cerebral lesions, in T1-enhancing lesions and in normal appearing brain tissue on T2w turbo spin-echo (TSE) sequences. Both quantitative and qualitative analysis did not show significant differences between T2w pre- and postcontrast series. T2w MRI performed prior to and after the administration of gadolinium DTPA provides similar information in patients with MS. With a TR of 3.2 s, not a single lesion was obscured on T2w postcontrast series. Acquisition of T2w MR images immediately after the administration of gadolinium DTPA allows for shorter examination time and assures sufficient time for contrast enhancement in cerebral lesions with a disrupted blood-brain barrier.     

Acoustic analysis of voice in multiple sclerosis patients

The objective is to investigate the presence of dysphonic symptoms in multiple sclerosis (MS) patients and to compare quantitative acoustic parameters in multiple sclerosis patients and normal individuals. The method of study was an 8-month controlled cross-sectional that was carried out with 106 individuals (30 MS, 76 controls). Both groups included males and females from 20 to 55 years. Exclusion criteria were prior vocal disorder, laryngeal microsurgery, recent endotracheal intubation, tumors, laryngeal, lung or mediastinal metastases, respiratory disease, and other associated neurological diagnoses. For dysphonic symptoms (qualitative variables), associations were assessed using Mantel-Haenszel's chi2 test, with Yates correction or the Fisher exact test when necessary. Statistical significance was set at p< or =0.05. Dysphonia was observed in 70% of MS individuals versus 33% of controls (p=0.01). Association was found between MS and dysphonia (OR: 2.2, CI 95%: 1.13-4.25). Fundamental frequency was higher among MS patients (p=0.01). Fundamental frequency deviation was significantly higher in MS women (but not men) than controls (p=0.00). Jitter was higher in MS men than in all other groups (p=0.00). Results suggest that evaluation and treatment of MS patients should be revised, evaluating voice alterations in relation to other signs. MS seems to intensify gender effect on fundamental frequency deviation, noise, and jitter, with MS women presenting fewer voice variations than men.     

Normal appearing brain white matter changes in relapsing multiple sclerosis: Texture image and classification analysis in serial MRI scans

ObjectiveThere is a clinical interest in identifying normal appearing white matter (NAWM) areas in brain T2-weighted (T₂W) MRI scans in multiple sclerosis (MS) subjects. These areas are susceptible to disease development and areas need to be studied in order to find potential associations between texture feature changes and disease progression.MethodsThe subjects investigated had a first demyelinating event (Clinically Isolated Syndrome-CIS) at baseline (Time₀), and the NAWM₀ (i.e. NAWM at Time₀) of the brain tissue was subsequently converted to demyelinating plaques (as evaluated in a follow up MRI at Time_6–12). 38 untreated subjects that had developed a CIS, had brain MRI scans within an interval of 6–12 months (Time_6–12 at follow-up). An experienced MS neurologist manually delineated the demyelinating lesions at Time₀ (L₀) and at Time_6–12 (L_6–12). Areas in the Time_6–12 MRI scans, where new lesions had been developed, were mapped back to their corresponding NAWM areas on the Time₀ MR scans (ROIS₀). In addition, contralateral ROIs of similar size and shape were segmented on the same images at Time₀ (ROIS_C0) to form an intra-subject control group. Following that, texture features were extracted from all prescribed areas and MS lesions.ResultsTexture features were used as input into Support Vector Machine (SVM) models to differentiate between the following: NAWM₀ vs ROIS_C0, NAWM₀ vs NAWM_6–12, NAWM₀ vs L₀, NAWM_6–12 vs L_6–12, ROIS₀ vs L₀, ROIS₀ vs L_6–12 and ROIS₀ vs ROIS_C0, where the corresponding % correct classifications scores were 89%, 95%, 98%, 92%, 85%, 90% and 65% respectively.ConclusionsTexture features may provide complementary information for following up the development and progression of MS disease. Future work will investigate the proposed method on more subjects.     

Optimization and numerical evaluation of multi-compartment diffusion MRI using the spherical mean technique for practical multiple sclerosis imaging

BackgroundThe multi-compartment diffusion MRI using the spherical mean technique (SMT) has been suggested to enhance the pathological specificity to tissue injury in multiple sclerosis (MS) imaging, but its accuracy and precision have not been comprehensively evaluated.MethodsA Cramer-Rao Lower Bound method was used to optimize an SMT protocol for MS imaging. Finite difference computer simulations of spins in packed cylinders were then performed to evaluate the influences of five realistic pathological features in MS lesions: axon diameter, axon density, free water fraction, axonal crossing, dispersion, and undulation.ResultsSMT derived metrics can be biased by some confounds of pathological variations, such as axon size and free water fraction. However, SMT in general provides valuable information to characterize pathological features in MS lesions with a clinically feasible protocol.ConclusionSMT may be used as a practical MS imaging method and should be further improved in clinical MS imaging.     

Unsupervised MRI segmentation of brain tissues using a local linear model and level set

Real-world magnetic resonance imaging of the brain is affected by intensity nonuniformity (INU) phenomena which makes it difficult to fully automate the segmentation process. This difficult task is accomplished in this work by using a new method with two original features: (1) each brain tissue class is locally modeled using a local linear region representative, which allows us to account for the INU in an implicit way and to more accurately position the region's boundaries; and (2) the region models are embedded in the level set framework, so that the spatial coherence of the segmentation can be controlled in a natural way. Our new method has been tested on the ground-truthed Internet Brain Segmentation Repository (IBSR) database and gave promising results, with Tanimoto indexes ranging from 0.61 to 0.79 for the classification of the white matter and from 0.72 to 0.84 for the gray matter. To our knowledge, this is the first time a region-based level set model has been used to perform the segmentation of real-world MRI brain scans with convincing results.     

Lesion load quantification on fast-FLAIR, rapid acquisition relaxation-enhanced, and gradient spin echo brain MRI scans from multiple sclerosis patients.

Previous studies have addressed the issue of the usefullness of fast fluid-attenuated (fast-FLAIR), rapid acquisition relaxation-enhanced (RARE), and gradient spin echo (GRASE) sequences in small groups of patients with multiple sclerosis (MS). The aim of this study was to assess and compare the lesion volumes and the intra-rater reproducibility of such measurements using fast-FLAIR, dual echo RARE, and dual echo GRASE brain scans from a large sample of MS patients. Using a 1.5 Tesla scanner, fast-FLAIR, dual echo RARE, and dual echo GRASE scans (24 axial, 5-mm thick contiguous interleaved slices) of the brain were obtained from 50 MS patients. Total lesion loads (TLL) were assessed twice using a semi-automated local thresholding segmentation technique by the same rater from the scans obtained with the three techniques. Mean TLL were 20.3 mL for fast-FLAIR, 16.6 mL for RARE, and 17.6 mL for GRASE sequences. Mean TLL detected by the three techniques were significantly heterogeneous (p < 0.001); at post-hoc analysis, the mean lesion volume detected on fast-FLAIR images was significantly higher than that on both RARE and GRASE images (p < 0.001) and the mean TLL on GRASE scans was significantly higher than that on RARE scans (p = 0.001). The mean values of intra-observer coefficient of variation for TLL measurements were similar for the three techniques (2.69% for fast-FLAIR, 2.33% for RARE, and 2.65% for GRASE). Our results confirm that fast-FLAIR sequences detect higher lesion volumes than those detected by other magnetic resonance imaging (MRI) sequences with shorter acquisition times. However, the reproducibility of TLL measurements is comparable among fast-FLAIR, RARE, and GRASE. This suggests that when assessing MS disease burden with MRI, the choice of the pulse sequence to be used should be dictated by the clinical setting.     

Correlation between enhancing lesion number and volume on standard and triple dose gadolinium-enhanced brain MRI scans from patients with multiple sclerosis.

We investigated the correlations between numbers and volumes of multiple sclerosis (MS) lesions enhancing on standard dose (SD) and triple dose (TD) gadolinium (Gd)-enhanced brain magnetic resonance imaging (MRI) scans, to clarify whether the measurement of enhancing lesion volumes or the use of TD MRI give additional information which can not be obtained by counting enhancing lesions on SD scans. SD and TD Gd-enhanced brain MRI scans were obtained every month for three months from 40 MS patients. The numbers of total and new enhancing lesions were counted, and the total volumes of enhancing lesions were measured from each of the four scans obtained with the two techniques. Univariate correlations between enhancing lesion numbers and volumes were assessed. The numbers of total and new enhancing lesions seen either on SD or TD scans were significantly correlated (r = 0.91 and 0.93, respectively). The numbers and volumes of total enhancing lesions were significantly correlated on both SD (r = 0.90), and TD (r = 0.89) scans. Moderate correlations were found between the total number of enhancing lesions on SD scans and the average difference between TD and SD scans for total enhancing lesion number (r = 0.66), and between the number of new enhancing lesions on SD scans and the average difference between TD and SD scans for new enhancing lesion number (r = 0.50). Our findings indicate that, both on SD and TD MRI, the counts and the volumes of total and new enhancing lesions are highly correlated, and that lesion counting may suffice to monitor MS activity. On the contrary, this study confirms the usefulness of TD MRI for a more complete assessment of the acute changes occurring in MS patients.     

Pattern recognition system for the discrimination of multiple sclerosis from cerebral microangiopathy lesions based on texture analysis of magnetic resonance images

In this study, a pattern recognition system has been developed for the discrimination of multiple sclerosis (MS) from cerebral microangiopathy (CM) lesions based on computer-assisted texture analysis of magnetic resonance images. Twenty-three textural features were calculated from MS and CM regions of interest, delineated by experienced radiologists on fluid attenuated inversion recovery images and obtained from 11 patients diagnosed with clinically definite MS and from 18 patients diagnosed with clinically definite CM. The probabilistic neural network classifier was used to construct the proposed pattern recognition system and the generalization of the system to unseen data was evaluated using an external cross validation process. According to the findings of the present study, statistically significant differences exist in the values of the textural features between CM and MS: MS regions were darker, of higher contrast, less homogeneous and rougher as compared to CM.     

Statistical analysis of discrete control systems with varying parameters using the method of orthogonal expansions. II

The determination of the mathematical expectation and correlation function of the output signal of discrete automatic control systems with varying parameters is considered. The statistical characteristics of the output signal are determined by means of an expansion in a certain specially formulated orthonormalized system of functions. The computing algorithm may be realized on a digital computer.Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Radiofizika, Vol. 15, No. 3, pp. 438–447, March, 1972.     

In vivo magnetic resonance diffusion measurement in the brain of patients with multiple sclerosis.

Measurement of water self-diffusion in the brain in 25 patients with multiple sclerosis was performed by magnetic resonance imaging. Quantitative diffusion measurements were obtained using single spin-echo pulse sequences with pulsed magnetic field gradients of different magnitude. Twenty-two of these patients also underwent measurement of the transverse relaxation time (T2). Only one plaque was evaluated in each patient. Based on prior knowledge, 12 plaques were classified as being 3 mo or less in age, and 7 plaques were classified as being more than 3 mo old. In all 25 plaques, water self-diffusion was found to be higher than in apparently normal white matter. Furthermore, water self-diffusion was found to be higher in acute plaques compared with chronic plaques. Finally, a slight tendency toward a relationship between the diffusion capability and T2 was found. We believe that an increased diffusion capability signifies an increase of the extracellular water space, which probably is related to the degree of demyelination. Thus, measurement of water self-diffusion in multiple sclerosis plaques may contribute to the study of pathogenesis of demyelination.     

In vivo determination of multiexponential T2 relaxation in the brain of patients with multiple sclerosis

In vivo measurement of T2 relaxation times in multiple sclerosis (MS) lesions by magnetic resonance imaging (MRI) is potentially useful for the evaluation of the disease activity. Seven patients with definite MS were investigated over a period of three years (19 examinations), using a whole-body MRI scanner operating at 0.15 T with a specially designed high-power radio-frequency head coil. A modified CPMG sequence with a 180 degree pulse interval of TE = 6 msec and 128 echoes was used for the T2 relaxation measurement of the areas of increased signal (AIS) and white matter (WM). A biexponential T2 analysis of each pixel of the spin-echo images was computed. The T2 relaxation processes were found to be a monoexponential function in WM. The T2 relaxation times of apparently normal white matter in MS patients was significantly longer than in control subjects. The T2 relaxation curves of the AIS were found in most cases to fit a biexponential function characterized by a short and a long T2. T2 long relaxation times of AIS were spread out over a wide range (150-560 msec). The study of T2 long histograms shows that some AIS can be divided into two or three parts depending on the T2 long values. Each of these parts may correspond to a pathological process such as edema, demyelination and gliosis. Evolution of T2 relaxation times over a period of time cannot as yet be correlated with modifications in the clinical state.     

Application of spherical harmonics derived space rotation invariant indices to the analysis of multiple sclerosis lesions' geometry by MRI

In the longitudinal study of multiple sclerosis (MS) lesions, varying position of the patient inside the MRI scanner is one of the major sources of assessment errors. We propose to use analytical indices that are invariant to spatial orientation to describe the lesions, rather than focus on patient repositioning or image realignment. Studies were made on simulated lesions systematically rotated, from in vitro MS lesions scanned on different days, and from in vivo MS lesions from a patient that was scanned five times the same day with short intervals of time between scans. Each of the lesions' 3D surfaces was approximated using spherical harmonics, from which indices that are invariant to space rotation were derived. From these indices, an accurate and highly reproducible volume estimate can be derived, which is superior to the common approach of 2D slice stacking. The results indicate that the suggested approach is useful in reducing part of the errors that affect the analysis of changes of MS lesions during follow-up studies. In conclusion, our proposed method circumvents the need for precise patient repositioning and can be advantageous in MRI longitudinal studies of MS patients.     

A comparison of the sensitivity of MRI after double- and triple-dose Gd-DTPA for detecting enhancing lesions in multiple sclerosis

We compared the number and volume of enhancing lesions detected in patients with multiple sclerosis (MS) seen on post-contrast T(1)-weighted scans obtained after the injection of different gadolinium-DTPA (Gd) doses. Enhanced magnetic resonance imaging (MRI) scans were obtained from 16 patients with relapsing remitting or secondary progressive MS on two different occasions separated by an interval of approximately 24 h. On the first occasion, enhanced scans were obtained 15 min after the injection of a double dose of Gd (0.2 mmol/Kg), on the second 15 min after the injection of a triple dose (0.3 mmol/Kg) of Gd. Scans were assessed by consensus in a random order by two observers unaware of the dose of Gd used. We counted the same 30 enhancing lesions on both double dose and triple dose scans from 9 patients. The mean (SD) volumes of enhancing lesions were 1.7 (2.7) mL on double dose and 1.9 (3.4) mL on triple-dose scans. This difference was not statistically significant. This study demonstrated that double dose of Gd has a sensitivity for detecting MS activity similar to that of a triple dose, with the advantage of a significant cost saving.     

In vivo evaluation of the reproducibility of T1 and T2 measured in the brain of patients with multiple sclerosis.

The precision (reproducibility) of relaxation times derived from magnetic resonance images of patients with multiple sclerosis (MS) were investigated. Measurements of 10 MS patients were performed at 1.5 T on two occasions within 1 wk. T1 and T2 was measured using a partial saturation inversion recovery sequence (6 points) and a Carr-Purcell-Meiboom-Gill phase alternating-phase shift multiple spin-echo sequence with 32 echoes. Regions of interest (ROI) were placed both in apparently normal white matter and plaques. The precision (+/- 1.96 SD) and the confidence intervals for T1 and T2 for white matter and plaques were calculated. The precision of T1 for white matter and plaques was respectively +/- 94 msec and +/- 208 msec. The precision of T2 for white matter and plaques was respectively +/- 18 msec and +/- 26 msec. For all measurements the coefficient of variation was about 9%. Judging from our own study and others as well, a precision better than 10% for T1 and T2 would seem unrealistic at present.     

Utililization of experimental animal model for correlative multispectral MRI and pathological analysis of brain tumors

Magnetic resonance imaging is the method of choice for non-invasive detection and evaluation of tumors of the central nervous system. However, discrimination of tumor boundaries from normal tissue, and the evaluation of heterogeneous lesions have proven to be limitations in traditional magnetic resonance imaging. The use of post-image acquisition processing techniques, such as multispectral tissue segmentation analysis, may provide more accurate clinical information. In this report, we have employed an experimental animal model for brain tumors induced by glial cells transformed by the human neurotropic JC virus to examine the utility of multispectral tissue segmentation for tumor cell identification. Six individual tissue types were discriminated by segmentation analysis, including heterogeneous tumor tissue, a clear demarcation of the boundary between tumor and non-tumor tissue, deep and cortical gray matter, and cerebrospinal fluid. Furthermore, the segmentation analysis was confirmed by histopathological evaluation. The use of multispectral tissue segmentation analysis may optimize the non-invasive determination and volumetric analysis of CNS neoplasms, thus providing improved clinical evaluation of tumor growth and evaluation of the effectiveness of therapeutic treatments.