Quantitative magnetization transfer imaging in relapsing-remitting multiple sclerosis: a systematic review and meta-analysis

Myelin-sensitive MRI such as magnetization transfer imaging has been widely used in multiple sclerosis. The influence of methodology and differences in disease subtype on imaging findings is, however, not well established. Here, we systematically review magnetization transfer brain imaging findings in relapsing-remitting multiple sclerosis. We examine how methodological differences, disease effects and their interaction influence magnetization transfer imaging measures. Articles published before 06/01/2021 were retrieved from online databases (PubMed, EMBASE and Web of Science) with search terms including 'magnetization transfer' and 'brain' for systematic review, according to a pre-defined protocol. Only studies that used human in vivo quantitative magnetization transfer imaging in adults with relapsing-remitting multiple sclerosis (with or without healthy controls) were included. Additional data from relapsing-remitting multiple sclerosis subjects acquired in other studies comprising mixed disease subtypes were included in meta-analyses. Data including sample size, MRI acquisition protocol parameters, treatments and clinical findings were extracted and qualitatively synthesized. Where possible, effect sizes were calculated for meta-analyses to determine magnetization transfer (i) differences between patients and healthy controls; (ii) longitudinal change and (iii) relationships with clinical disability in relapsing-remitting multiple sclerosis. Eighty-six studies met inclusion criteria. MRI acquisition parameters varied widely, and were also underreported. The majority of studies examined the magnetization transfer ratio in white matter, but magnetization transfer metrics, brain regions examined and results were heterogeneous. The analysis demonstrated a risk of bias due to selective reporting and small sample sizes. The pooled random-effects meta-analysis across all brain compartments revealed magnetization transfer ratio was 1.17 per cent units (95% CI -1.42 to -0.91) lower in relapsing-remitting multiple sclerosis than healthy controls (z-value: -8.99, P < 0.001, 46 studies). Linear mixed-model analysis did not show a significant longitudinal change in magnetization transfer ratio across all brain regions [β = 0.12 (-0.56 to 0.80), t-value = 0.35, P = 0.724, 14 studies] or normal-appearing white matter alone [β = 0.037 (-0.14 to 0.22), t-value = 0.41, P = 0.68, eight studies]. There was a significant negative association between the magnetization transfer ratio and clinical disability, as assessed by the Expanded Disability Status Scale [r = -0.32 (95% CI -0.46 to -0.17); z-value = -4.33, P < 0.001, 13 studies]. Evidence suggests that magnetization transfer imaging metrics are sensitive to pathological brain changes in relapsing-remitting multiple sclerosis, although effect sizes were small in comparison to inter-study variability. Recommendations include: better harmonized magnetization transfer acquisition protocols with detailed methodological reporting standards; larger, well-phenotyped cohorts, including healthy controls; and, further exploration of techniques such as magnetization transfer saturation or inhomogeneous magnetization transfer ratio.

Texture Analysis and its Applications in Biomedical Imaging: A Survey

Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This surveys emphasis is in collecting and categorising over five decades of active research on texture analysis. Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this surveys final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.

MRI Texture Analysis Reveals Brain Abnormalities in Medically Refractory Trigeminal Neuralgia

Background: Several neuroimaging studies report structural alterations of the trigeminal nerve in trigeminal neuralgia (TN). Less attention has been paid to structural brain changes occurring in TN, even though such changes can influence the development and response to treatment of other headache and chronic pain conditions. The purpose of this study was to apply a novel neuroimaging technique-texture analysis-to identify structural brain differences between classical TN patients and healthy subjects. Methods: We prospectively recruited 14 medically refractory classical TN patients and 20 healthy subjects. 3-Tesla T1-weighted brain MRI scans were acquired in all participants. Three texture features (autocorrelation, contrast, energy) were calculated within four a priori brain regions of interest (anterior cingulate, insula, thalamus, brainstem). Voxel-wise analysis was used to identify clusters of texture difference between TN patients and healthy subjects within regions of interest (p < 0.001, cluster size >20 voxels). Median raw texture values within clusters were also compared between groups, and further used to differentiate TN patients from healthy subjects (receiver-operator characteristic curve analysis). Median raw texture values were correlated with pain severity (visual analog scale, 1-100) and illness duration. Results: Several clusters of texture difference were observed between TN patients and healthy subjects: right-sided TN patients showed reduced autocorrelation in the left brainstem, increased contrast in the left brainstem and right anterior insula, and reduced energy in right and left anterior cingulate, right midbrain, and left brainstem. Within-cluster median raw texture values also differed between TN patients and healthy subjects: TN patients could be segregated from healthy subjects using brainstem autocorrelation (p = 0.0040, AUC = 0.84, sensitivity = 89%, specificity = 70%), anterior insula contrast (p = 0.0002, AUC = 0.92, sensitivity = 78%, specificity = 100%), and anterior cingulate energy (p = 0.0004, AUC = 0.92, sensitivity = 78%, specificity = 100%). Additionally, anterior insula contrast and duration of TN were inversely correlated (p = 0.030, Spearman r = -0.73). Conclusions: Texture analysis reveals distinct brain abnormalities in TN, which relate to clinical features such as duration of illness. These findings further implicate structural brain changes in the development and maintenance of TN.

Event-Related Potential Analysis of ADHD and Control Adults During a Sustained Attention Task

Background. Event-related potentials (ERPs) of attention deficit hyperactivity disorder (ADHD) population have been extensively studied using the time-domain representation of signals but time-frequency domain techniques are less explored. Although, adult ADHD is a proven disorder, most of the electrophysiological studies have focused only on children with ADHD. Methods. ERP data of 35 university students with ADHD and 35 control adults were recorded during visual continuous performance task (CPT). Gray level co-occurrence matrix-based texture features were extracted from time-frequency (t-f) images of event-related EEG epochs. Different ERP components measures, that is, amplitudes and latencies corresponding to N1, N2, and P3 components were also computed relative to standard and target stimuli. Results. Texture analysis has shown that the mean value of contrast, dissimilarity, and difference entropy is significantly reduced in adults with ADHD than in control adults. The mean correlation and homogeneity in adults with ADHD were significantly increased as compared with control adults. ERP components analysis has reported that adults with ADHD have reduced N1 amplitude to target stimuli, reduced N2 and P3 amplitude to both standard and target stimuli than controls. Conclusions. The differences in texture features obtained from t-f images of ERPs point toward altered information processing in adults with ADHD during a cognitive task. Findings of reduction in N1, N2, and P3 components highlight deficits of early sensory processing, stimulus categorization, and attentional resources, respectively, in adults with ADHD.

Texture Analysis of T1-Weighted and Fluid-Attenuated Inversion Recovery Images Detects Abnormalities That Correlate With Cognitive Decline in Small Vessel Disease

Supplemental Digital Content is available in the text.

Background and Purpose—

Magnetic resonance imaging may be useful to assess disease severity in cerebral small vessel disease (SVD), identify those individuals who are most likely to progress to dementia, monitor disease progression, and act as surrogate markers to test new therapies. Texture analysis extracts information on the relationship between signal intensities of neighboring voxels. A potential advantage over techniques, such as diffusion tensor imaging, is that it can be used on clinically obtained magnetic resonance sequences. We determined whether texture parameters (TP) were abnormal in SVD, correlated with cognitive impairment, predicted cognitive decline, or conversion to dementia.

Methods—

In the prospective SCANS study (St George’s Cognition and Neuroimaging in Stroke), we assessed TP in 121 individuals with symptomatic SVD at baseline, 99 of whom attended annual cognitive testing for 5 years. Conversion to dementia was recorded for all subjects during the 5-year period. Texture analysis was performed on fluid-attenuated inversion recovery and T1-weighted images. The TP obtained from the SVD cohort were cross-sectionally compared with 54 age-matched controls scanned on the same magnetic resonance imaging system.

Results—

There were highly significant differences in several TP between SVD cases and controls. Within the SVD population, TP were highly correlated to other magnetic resonance imaging parameters (brain volume, white matter lesion volume, lacune count). TP correlated with executive function and global function at baseline and predicted conversion to dementia, after controlling for age, sex, premorbid intelligence quotient, and magnetic resonance parameters.

Conclusions—

TP, which can be obtained from routine clinical images, are abnormal in SVD, and the degree of abnormality correlates with executive dysfunction and global cognition at baseline and decline during 5 years. TP may be useful to assess disease severity in clinically collected data. This needs testing in data clinically acquired across multiple sites.

Quantitative and textural analysis of magnetization transfer and diffusion images in the early detection of brain metastases

PURPOSE

The sensitivity of the magnetization transfer ratio (MTR) and apparent diffusion coefficient (ADC) for early detection of brain metastases was investigated in mice and humans.

METHODS

Mice underwent MRI twice weekly for up to 31 d following intracardiac injection of the brain-homing breast cancer cell line MDA-MB231-BR. Patients with small cell lung cancer underwent quarterly MRI for 1 year. MTR and ADC were measured in regions of metastasis and matched contralateral tissue at the final time point and in registered regions at earlier time points. Texture analysis and linear discriminant analysis were performed to detect metastasis-containing slices.

RESULTS

Compared with contralateral tissue, mouse metastases had significantly lower MTR and higher ADC at the final time point. Some lesions were visible at earlier time points on the MTR and ADC maps: 24% of these were not visible on corresponding T2 -weighted images. Texture analysis using the MTR maps showed 100% specificity and 98% sensitivity for metastasis at the final time point, with 77% sensitivity 2-4 d earlier and 46% 5-8 d earlier. Only 2 of 16 patients developed metastases, and their penultimate scans were normal.

CONCLUSIONS

Some brain metastases may be detected earlier on MTR than conventional T2 ; however, the small gain is unlikely to justify "predictive" MRI. Magn Reson Med 77:1987-1995, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cerebral Degeneration in Amyotrophic Lateral Sclerosis Revealed by 3-Dimensional Texture Analysis

Introduction: Routine MR images do not consistently reveal pathological changes in the brain in ALS. Texture analysis, a method to quantitate voxel intensities and their patterns and interrelationships, can detect changes in images not apparent to the naked eye. Our objective was to evaluate cerebral degeneration in ALS using 3-dimensional texture analysis of MR images of the brain.

Methods: In a case-control design, voxel-based texture analysis was performed on T1-weighted MR images of 20 healthy subjects and 19 patients with ALS. Four texture features, namely, autocorrelation, sum of squares variance, sum average, and sum variance were computed. Texture features were compared between the groups by statistical parametric mapping and correlated with clinical measures of disability and upper motor neuron dysfunction.

Results: Texture features were different in ALS in motor regions including the precentral gyrus and corticospinal tracts. To a lesser extent, changes were also found in the thalamus, cingulate gyrus, and temporal lobe. Texture features in the precentral gyrus correlated with disease duration, and in the corticospinal tract they correlated with finger tapping speed.

Conclusions: Changes in MR image textures are present in motor and non-motor regions in ALS and correlate with clinical features. Whole brain texture analysis has potential in providing biomarkers of cerebral degeneration in ALS.

Texture Analysis of T2-Weighted MR Images to Assess Acute Inflammation in Brain MS Lesions

Brain blood barrier breakdown as assessed by contrast-enhanced (CE) T1-weighted MR imaging is currently the standard radiological marker of inflammatory activity in multiple sclerosis (MS) patients. Our objective was to evaluate the performance of an alternative model assessing the inflammatory activity of MS lesions by texture analysis of T2-weighted MR images. Twenty-one patients with definite MS were examined on the same 3.0T MR system by T2-weighted, FLAIR, diffusion-weighted and CE-T1 sequences. Lesions and mirrored contralateral areas within the normal appearing white matter (NAWM) were characterized by texture parameters computed from the gray level co-occurrence and run length matrices, and by the apparent diffusion coefficient (ADC). Statistical differences between MS lesions and NAWM were analyzed. ROC analysis and leave-one-out cross-validation were performed to evaluate the performance of individual parameters, and multi-parametric models using linear discriminant analysis (LDA), partial least squares (PLS) and logistic regression (LR) in the identification of CE lesions. ADC and all but one texture parameter were significantly different within white matter lesions compared to within NAWM (p < 0.0167). Using LDA, an 8-texture parameter model identified CE lesions with a sensitivity Se = 70% and a specificity Sp = 76%. Using LR, a 10-texture parameter model performed better with Se = 86% / Sp = 84%. Using PLS, a 6-texture parameter model achieved the highest accuracy with Se = 88% / Sp = 81%. Texture parameter from T2-weighted images can assess brain inflammatory activity with sufficient accuracy to be considered as a potential alternative to enhancement on CE T1-weighted images.

Using Texture Analysis to Determine Human Papillomavirus Status of Oropharyngeal Squamous Cell Carcinomas on CT

BACKGROUND AND PURPOSE

Human papillomavirus-associated oropharyngeal squamous cell carcinoma is increasing in prevalence and typically occurs in younger patients than human papillomavirus-negative squamous cell carcinoma. While imaging features of human papillomavirus-positive versus human papillomavirus-negative squamous cell carcinoma nodal metastases have been described, characteristics distinguishing human papillomavirus-positive from human papillomavirus-negative primary squamous cell carcinomas have not been well established. The purpose of this project was to evaluate the use of texture features to distinguish human papillomavirus-positive and human papillomavirus-negative primary oropharyngeal squamous cell carcinoma.

MATERIALS AND METHODS

Following institutional review board approval, 40 patients with primary oropharyngeal squamous cell carcinoma and known human papillomavirus status who underwent contrast-enhanced CT between December 2009 and October 2013 were included in this study. Segmentation of the primary lesion was manually performed with a semiautomated graphical-user interface. Following segmentation, an in-house-developed texture analysis program extracted 42 texture features from each segmented volume. A t test was used to evaluate differences in texture parameters between human papillomavirus-positive and human papillomavirus-negative squamous cell carcinomas.

RESULTS

Of the 40 included patients, 29 had human papillomavirus-positive oropharyngeal squamous cell carcinoma and 11 had human papillomavirus-negative oropharyngeal squamous cell carcinoma. Significant differences were seen in the histogram parameters median (P = .006) and entropy (P = .016) and squamous cell carcinoma entropy (P = .043).

CONCLUSIONS

There are statistically significant differences in some texture features between human papillomavirus-positive and human papillomavirus-negative oropharyngeal tumors. Texture analysis may be considered an adjunct to the evaluation of human papillomavirus status and characterization of squamous cell carcinoma.

Application of Texture Analysis in Diagnosis of Multiple Sclerosis by Magnetic Resonance Imaging

Introduction:

Visual inspection by magnetic resonance (MR) images cannot detect microscopic tissue changes occurring in MS in normal appearing white matter (NAWM) and may be perceived by the human eye as having the same texture as normal white matter (NWM). The aim of the study was to evaluate computer aided diagnosis (CAD) system using texture analysis (TA) in MR images to improve accuracy in identification of subtle differences in brain tissue structure.

Material and Methods:

The MR image database comprised 50 MS patients and 50 healthy subjects. Up to 270 statistical texture features extract as descriptors for each region of interest. The feature reduction methods used were the Fisher method, the lowest probability of classification error and average correlation coefficients (POE+ACC) method and the fusion Fisher plus the POE+ACC (FFPA) to select the best, most effective features to differentiate between MS lesions, NWM and NAWM. The features parameters were used for texture analysis with principle component analysis (PCA) and linear discriminant analysis (LDA). Then first nearest-neighbour (1-NN) classifier was used for features resulting from PCA and LDA. Receiver operating characteristic (ROC) curve analysis was used to examine the performance of TA methods.

Results:

The highest performance for discrimination between MS lesions, NAWM and NWM was recorded for FFPA feature parameters using LDA; this method showed 100% sensitivity, specificity and accuracy and an area of A_z = 1 under the ROC curve.

Conclusion:

TA is a reliable method with the potential for effective use in MR imaging for the diagnosis and prediction of MS.

Voxel-based texture analysis of the brain

This paper presents a novel voxel-based method for texture analysis of brain images. Texture analysis is a powerful quantitative approach for analyzing voxel intensities and their interrelationships, but has been thus far limited to analyzing regions of interest. The proposed method provides a 3D statistical map comparing texture features on a voxel-by-voxel basis. The validity of the method was examined on artificially generated effects as well as on real MRI data in Alzheimer's Disease (AD). The artificially generated effects included hyperintense and hypointense signals added to T1-weighted brain MRIs from 30 healthy subjects. The AD dataset included 30 patients with AD and 30 age/sex matched healthy control subjects. The proposed method detected artificial effects with high accuracy and revealed statistically significant differences between the AD and control groups. This paper extends the usage of texture analysis beyond the current region of interest analysis to voxel-by-voxel 3D statistical mapping and provides a hypothesis-free analysis tool to study cerebral pathology in neurological diseases.

Quantitative texture analysis of brain white matter lesions derived from T2-weighted MR images in MS patients with clinically isolated syndrome

INTRODUCTION

This study investigates the application of texture analysis methods on brain T2-white matter lesions detected with magnetic resonance imaging (MRI) for the prognosis of future disability in subjects diagnosed with clinical isolated syndrome (CIS) of multiple sclerosis (MS).

METHODS

Brain lesions and normal appearing white matter (NAWM) from 38 symptomatic untreated subjects diagnosed with CIS as well as normal white matter (NWM) from 20 healthy volunteers, were manually segmented, by an experienced MS neurologist, on transverse T2-weighted images obtained from serial brain MR imaging scans (0 and 6-12 months). Additional clinical information in the form of the Expanded Disability Status Scale (EDSS), a scale from 0 to 10, which provides a way of quantifying disability in MS and monitoring the changes over time in the level of disability, were also provided. Shape and most importantly different texture features including GLCM and laws were then extracted for all above regions, after image intensity normalization.

RESULTS

The findings showed that: (i) there were significant differences for the texture futures extracted between the NAWM and lesions at 0 month and between NAWM and lesions at 6-12 months. However, no significant differences were found for all texture features extracted when comparing lesions temporally at 0 and 6-12 months with the exception of contrast (gray level difference statistics-GLDS) and difference entropy (spatial gray level dependence matrix-SGLDM); (ii) significant differences were found between NWM and NAWM for most of the texture features investigated in this study; (iii) there were significant differences found for the lesion texture features at 0 month for those with EDSS≤2 versus those with EDSS>2 (mean, median, inverse difference moment and sum average) and for the lesion texture features at 6-12 months with EDSS>2 and EDSS≤2 for the texture features (mean, median, entropy and sum average). It should be noted that whilst there were no differences in entropy at time 0 between the two groups, significant change was observed at 6-12 months, relating the corresponding features to the follow-up and disability (EDSS) progression. For the NAWM, significant differences were found between 0 month and 6-12 months with EDSS≤2 (contrast, inverse difference moment), for 6-12 months for EDSS>2 and 0 month with EDSS>2 (difference entropy) and for 6-12 months for EDSS>2 and EDSS≤2 (sum average); (iv) there was no significant difference for NAWM and the lesion texture features (for both 0 and 6-12 months) for subjects with no change in EDSS score versus subjects with increased EDSS score from 2 to 5 years.

CONCLUSIONS

The findings of this study provide evidence that texture features of T2 MRI brain white matter lesions may have an additional potential role in the clinical evaluation of MRI images in MS and perhaps may provide some prognostic evidence in relation to future disability of patients. However, a larger scale study is needed to establish the application in clinical practice and for computing shape and texture features that may provide information for better and earlier differentiation between normal brain tissue and MS lesions.

The prognostic utility of MRI in clinically isolated syndrome: a literature review

For patients presenting with clinically isolated syndrome, the treating clinician needs to advise the patient on the probability of conversion to clinically definite multiple sclerosis. MR imaging may give useful prognostic information, and there is large body of literature pertaining to the use of MR imaging in assessing patients presenting with clinically isolated syndrome. This literature review evaluates the accuracy of MR imaging in predicting which patients with clinically isolated syndrome will go on to develop long-term disease and/or disability. New and emerging MR imaging technologies and their applicability to patients with clinically isolated syndrome are also considered.

Clinical and magnetic resonance imaging (MRI) distinctions between tumefactive demyelination and brain tumors in children

Tumefactive demyelinating lesions can be difficult to distinguish from tumors. Clinical and magnetic resonance imaging features of children with tumefactive demyelination and supratentorial brain tumors were compared. Patients were identified through a 23-site national demyelinating disease study, and from a single-site neuroradiology database. For inclusion, lesions met at least 1 of 3 criteria: maximal cross-sectional diameter >20 mm, local or global cerebral mass effect, or presence of perilesional edema. Thirty-one children with tumefactive demyelination (5 with solitary lesions) were identified: 27 of 189 (14.3%) from the demyelinating disease study and 4 from the database. Thirty-three children with tumors were identified. Children with tumefactive demyelination were more likely to have an abnormal neurologic examination and polyfocal neurologic deficits compared to children with tumors. Tumefactive demyelination was distinguished from tumor by the presence of multiple lesions, absence of cortical involvement, and decrease in lesion size or detection of new lesions on serial imaging.

Pathological correlates of magnetic resonance imaging texture heterogeneity in multiple sclerosis

OBJECTIVE

To analyze the texture of T2-weighted magnetic resonance imaging (MRI) of postmortem multiple sclerosis (MS) brain, and to determine whether and how MRI texture correlates with tissue pathology.

METHODS

Ten brain samples from 3 subjects with MS were examined. Areas of complete, partial, or no loss of Luxol fast blue (myelin) and Bielschowsky (axons) staining were marked on histological images, and matched on corresponding MRI as lesions, diffusely abnormal white matter (DAWM), and normal-appearing white matter (NAWM). The number of CD45(+) cells (inflammation) was also counted. MRI texture was computed using polar Stockwell transform and compared to histology.

RESULTS

Thirty-four lesions, 17 DAWM regions, and 36 NAWM regions were identified. After mixed effects modeling, MRI texture heterogeneity was greater in lesions than in DAWM (p < 0.001) and NAWM (p < 0.001), and was greater in DAWM than in NAWM (p < 0.001); the number of CD45+ cells was greater in both lesions (p < 0.001) and DAWM (p = 0.005) than in NAWM. In MRI, a gradient of texture heterogeneity was detected in lesions, with gradual tapering toward perilesional NAWM. Moreover, besides univariate correlation with histological markers, texture heterogeneity correlated independently with normalized myelin density (p < 0.01) when random effects were considered. Within sample, MRI texture correlated with myelin and axonal density in 7 of 10 samples (p < 0.01).

INTERPRETATION

Texture analysis performed on routine clinical magnetic resonance images may be a potential measure of tissue integrity. Tissues with more severe myelin and axonal pathology are associated with greater texture heterogeneity.

MRI texture analysis in multiple sclerosis

Multiple sclerosis (MS) is a complicated disease characterized by heterogeneous pathology that varies across individuals. Accurate identification and quantification of pathological changes may facilitate a better understanding of disease pathogenesis and progression and help identify novel therapies for MS patients. Texture analysis evaluates interpixel relationships that generate characteristic organizational patterns in an image, many of which are beyond the ability of visual perception. Given its promise detecting subtle structural alterations texture analysis may be an attractive means to evaluate disease activity and evolution. It may also become a new tool to assess therapeutic efficacy if technique issues are resolved and pathological correlates are further confirmed. This paper describes the concept, strategies, and considerations of MRI texture analysis; summarizes applications of texture analysis in MS as a measure of tissue integrity and its clinical relevance; then discusses potentially future directions of texture analysis in MS.

Feasibility of texture analysis for the assessment of biochemical changes in meniscal tissue on T1 maps calculated from delayed gadolinium-enhanced magnetic resonance imaging of cartilage data: comparison with conventional relaxation time measurements

OBJECTIVES

To (1) establish the feasibility of texture analysis for the in vivo assessment of biochemical changes in meniscal tissue on delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC), and (2) compare textural with conventional T1 relaxation time measurements calculated from dGEMRIC data ("T1(Gd) relaxation times").

MATERIALS AND METHODS

We enrolled 10 asymptomatic volunteers (7 men and 3 women; mean age, 27.2 +/- 4.5 years), without a history of meniscus damage, in our study. MRI of the right knee was performed at 3.0 T. An isotropic, 3-dimensional (3D), double-echo steady-state sequences was used for morphologic evaluation, and a dual flip angle 3D gradient echo sequence was used for T1(Gd) mapping. All MRI scans were performed 90 minutes after injection of 0.2 mmol/kg of Gd-diethylenetriamine pentaacetic acid (DTPA), and subsequently, during application of a compressive force (50% of the body weight) in the axial direction. Regions of interest, covering the central portions of the posterior horn of the medial meniscus, were defined on 3 adjacent sagittal sections. Based on the relaxation time maps, mean T1(Gd), as well as the T1(Gd) texture features derived from the co-occurrence matrix (COC: Angular Second Moment, Entropy, Inverse Difference Moment) and wavelet transform (WAV: WavEnLL, WavEnHL, WavEnHH, WavEnLH), were calculated. Paired t tests were used to assess differences between baseline and compression, and intraclass correlation coefficients (ICC) were calculated to establish the intrarater reliability of the measurements.

RESULTS

Mean T1(Gd) (-67.3 ms, P = 0.011), Angular Second Moment (-0.0002, P = 0.009), Entropy (+0.033, P = 0.025), WavEnLL (+1011.16, P = 0.002), WavEnHL (+18.64, P = 0.012), and WavEnLH (+72.74, P = 0.035) differed significantly between baseline and compression. Intrarater reliability was substantial for mean T1(Gd) relaxation times (ICC = 0.99-1.0), and also for T1(Gd) co-occurrence matrix (ICC = 0.63-0.92) and WAV (ICC = 0.86-0.98) features.

CONCLUSIONS

Texture features extracted from T1 maps calculated from dGEMRIC data are feasible for the in vivo assessment of biochemical changes in the menisci, such as might be induced by mechanical loading. Thus, T1(Gd) texture features complement conventional relaxation time measurements. Further studies are necessary to determine whether the mechanical compression, or a prolonged Gd-DTPA uptake, or both, are responsible for the observed decrease in mean T1(Gd) relaxation times in the menisci.

Texture analysis differentiates persistent and transient T1 black holes at acute onset in multiple sclerosis: A preliminary study

Background and Objective: The persistence of new enhancing T1 hypointense lesions (acute black holes, ABHs) in multiple sclerosis (MS) cannot be predicted visually at lesion onset. Texture analysis using the polar Stockwell transform (PST) applied to conventional MR images however shows promise in quantifying tissue injury early. The objective of this study was to explore whether ABHs that persist (pABHs) differ from those that are transient (tABHs) using PST texture analysis.

Methods: Fifteen ABHs (8 pABHs; 7 tABHs) from 9 patients were analyzed on 3T images obtained during a clinical trial. Persistence was defined as remaining T1 hypointense 5–8 months later. NAWM regions were examined to control for changes unrelated to ABHs.

Results: At first appearance, there was higher coarse texture indicating greater tissue damage in the pABHs than in the tABHs ( p < 0.01). Both had greater coarse texture than the contralateral and general NAWM ( p ≤ 0.01). No difference was identified in normalized signal intensity between pABHs and tABHs and neither demonstrated location preference. While tABHs tended to be smaller than pABHs there was no correlation between lesion size and texture (r = 0.44, p > 0.05). Furthermore, coarse texture content appeared to predict persistence of individual lesions.

Conclusions: This preliminary study suggests that PST texture could predict persistence of tissue injury based on the severity of structural disorganization within acute lesions. While confirmation of this data is required texture analysis may prove to be a valuable tool to quantify tissue damage and predict recovery in proof-of-concept neuroprotection and repair trials.

Texture-based classification of focal liver lesions on MRI at 3.0 Tesla: a feasibility study in cysts and hemangiomas

PURPOSE

To determine the feasibility of texture analysis for the classification of liver cysts and hemangiomas, on nonenhanced, zero-fill interpolated T1- and T2-weighted MR images.

MATERIALS AND METHODS

Forty-five patients (26 women and 19 men; mean age, 58.1 +/- 16.9 years) with liver cysts or hemangiomas were enrolled in the study. After exclusion of images with artifacts, T1-weighted images of 42 patients, and T2-weighted images of 39 patients, obtained at 3.0 Tesla (T), were available for further analysis. Texture features derived from the gray-level histogram, co-occurrence and run-length matrix, gradient, autoregressive model, and wavelet transform were calculated. Fisher, probability of classification error and average correlation (POE+ACC), and mutual information coefficients were used to extract subsets of optimized texture features. Linear discriminant analysis (LDA) in combination with k nearest neighbor (k-NN) classification, and k-means clustering, were used for lesion classification.

RESULTS

LDA/k-NN produced misclassification rates of 16-18% on T1-weighted, and 12-18% on T2-weighted images. K-means clustering yielded misclassification rates of 15-23% on T1-weighted, and 15-25% on T2-weighted images.

CONCLUSION

Texture-based classification of liver cysts and hemangiomas is feasible on zero-fill interpolated MR images obtained at 3.0T. Further studies are warranted to investigate the value of texture-based classification of other liver lesions, such as hepatocellular and cholangiocellular carcinoma, on MRI.

Texture analysis: a review of neurologic MR imaging applications

Texture analysis describes a variety of image-analysis techniques that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Texture analysis may be particularly well-suited for lesion segmentation and characterization and for the longitudinal monitoring of disease or recovery. We begin this review by outlining the general procedure for performing texture analysis, identifying some potential pitfalls and strategies for avoiding them. We then provide an overview of some intriguing neuro-MR imaging applications of texture analysis, particularly in the characterization of brain tumors, prediction of seizures in epilepsy, and a host of applications to MS.