Texture analysis of MR images of patients with mild traumatic brain injury

Bilateral internal carotid artery hypoplasia presenting with watershed territory infarcts

Internal carotid artery hypoplasia is a rare vascular anomaly that can lead to various neurological symptoms due to altered cerebral blood flow. We present a case of a 36 years old female who presented to us with forgetfulness and right sided weakness. She was ultimately diagnosed with bilateral internal carotid artery hypoplasia through imaging studies. This case highlights the importance of considering vascular anomalies in patients presenting with neurological symptoms and the significance of comprehensive diagnostic evaluation for appropriate management.

Persistent fatigue in post-acute COVID syndrome is associated with altered T1 MRI texture in subcortical structures: a preliminary investigation

Post-acute COVID syndrome (PACS) is a global health concern and is often associated with debilitating symptoms. Post-COVID fatigue is a particularly frequent and troubling issue, and its underlying mechanisms remain incompletely understood. One potential contributor is micropathological injury of subcortical and brainstem structures, as has been identified in other patient populations. Texture-based analysis (TA) may be used to measure such changes in anatomical MRI data. The present study develops a methodology of voxel-wise TA mapping in subcortical and brainstem regions, which is then applied to T1-weighted MRI data from a cohort of 48 individuals who had PACS (32 with and 16 without ongoing fatigue symptoms) and 15 controls who had cold and flu-like symptoms but tested negative for COVID-19. Both groups were assessed an average of 4-5 months post-infection. There were no significant differences between PACS and control groups, but significant differences were observed within the PACS groups, between those with and without fatigue symptoms. This included reduced texture energy and increased entropy, along with reduced texture correlation, cluster shade and profile in the putamen, pallidum, thalamus and brainstem. These findings provide new insights into the neurophysiological mechanisms that underlie PACS, with altered tissue texture as a potential biomarker of this debilitating condition.

Whole-brain traumatic controlled cortical impact to the left frontal lobe: Magnetic resonance image-based texture analysis

This research assesses the capability of texture analysis (TA) derived from high-resolution (HR) T2-weighted magnetic resonance imaging to identify primary sequelae following 1-5 hours of controlled cortical impact mild or severe traumatic brain injury (TBI) to the left frontal cortex (focal impact) and secondary (diffuse) sequelae in the right frontal cortex, bilateral corpus callosum, and hippocampus in rats. The TA technique comprised first-order (histogram-based) and second-order statistics (including gray-level co-occurrence matrix, gray-level run length matrix, and neighborhood gray-level difference matrix). Edema in the left frontal impact region developed within 1 hour and continued throughout the 5-hour assessments. The TA features from HR images confirmed the focal injury. There was no significant difference among radiomics features between the left and right corpus callosum or hippocampus from 1 to 5 hours following a mild or severe impact. The adjacent corpus callosum region and the distal hippocampus region (s), showed no diffuse injury 1-5 hours after mild or severe TBI. These results suggest that combining HR images with TA may enhance detection of early primary and secondary sequelae following TBI.

Spatial mapping of tumor heterogeneity in whole-body PET-CT: a feasibility study

BACKGROUND

Tumor heterogeneity is recognized as a predictor of treatment response and patient outcome. Quantification of tumor heterogeneity across all scales may therefore provide critical insight that ultimately improves cancer management.

METHODS

An image registration-based framework for the study of tumor heterogeneity in whole-body images was evaluated on a dataset of 490 FDG-PET-CT images of lung cancer, lymphoma, and melanoma patients. Voxel-, lesion- and subject-level features were extracted from the subjects' segmented lesion masks and mapped to female and male template spaces for voxel-wise analysis. Resulting lesion feature maps of the three subsets of cancer patients were studied visually and quantitatively. Lesion volumes and lesion distances in subject spaces were compared with resulting properties in template space. The strength of the association between subject and template space for these properties was evaluated with Pearson's correlation coefficient.

RESULTS

Spatial heterogeneity in terms of lesion frequency distribution in the body, metabolic activity, and lesion volume was seen between the three subsets of cancer patients. Lesion feature maps showed anatomical locations with low versus high mean feature value among lesions sampled in space and also highlighted sites with high variation between lesions in each cancer subset. Spatial properties of the lesion masks in subject space correlated strongly with the same properties measured in template space (lesion volume, R = 0.986, p < 0.001; total metabolic volume, R = 0.988, p < 0.001; maximum within-patient lesion distance, R = 0.997, p < 0.001). Lesion volume and total metabolic volume increased on average from subject to template space (lesion volume, 3.1 ± 52 ml; total metabolic volume, 53.9 ± 229 ml). Pair-wise lesion distance decreased on average by 0.1 ± 1.6 cm and maximum within-patient lesion distance increased on average by 0.5 ± 2.1 cm from subject to template space.

CONCLUSIONS

Spatial tumor heterogeneity between subsets of interest in cancer cohorts can successfully be explored in whole-body PET-CT images within the proposed framework. Whole-body studies are, however, especially prone to suffer from regional variation in lesion frequency, and thus statistical power, due to the non-uniform distribution of lesions across a large field of view.

Influence of maternal psychological distress during COVID-19 pandemic on placental morphometry and texture

The Coronavirus Disease 2019 (COVID-19) pandemic has been accompanied by increased prenatal maternal distress (PMD). PMD is associated with adverse pregnancy outcomes which may be mediated by the placenta. However, the potential impact of the pandemic on in vivo placental development remains unknown. To examine the impact of the pandemic and PMD on in vivo structural placental development using advanced magnetic resonance imaging (MRI), acquired anatomic images of the placenta from 63 pregnant women without known COVID-19 exposure during the pandemic and 165 pre-pandemic controls. Measures of placental morphometry and texture were extracted. PMD was determined from validated questionnaires. Generalized estimating equations were utilized to compare differences in PMD placental features between COVID-era and pre-pandemic cohorts. Maternal stress and depression scores were significantly higher in the pandemic cohort. Placental volume, thickness, gray level kurtosis, skewness and run length non-uniformity were increased in the pandemic cohort, while placental elongation, mean gray level and long run emphasis were decreased. PMD was a mediator of the association between pandemic status and placental features. Altered in vivo placental structure during the pandemic suggests an underappreciated link between disturbances in maternal environment and perturbed placental development. The long-term impact on offspring is currently under investigation.

Combined Features in Region of Interest for Brain Tumor Segmentation

Diagnosis of brain tumor gliomas is a challenging task in medical image analysis due to its complexity, the less regularity of tumor structures, and the diversity of tissue textures and shapes. Semantic segmentation approaches using deep learning have consistently outperformed the previous methods in this challenging task. However, deep learning is insufficient to provide the required local features related to tissue texture changes due to tumor growth. This paper designs a hybrid method arising from this need, which incorporates machine-learned and hand-crafted features. A semantic segmentation network (SegNet) is used to generate the machine-learned features, while the grey-level co-occurrence matrix (GLCM)-based texture features construct the hand-crafted features. In addition, the proposed approach only takes the region of interest (ROI), which represents the extension of the complete tumor structure, as input, and suppresses the intensity of other irrelevant area. A decision tree (DT) is used to classify the pixels of ROI MRI images into different parts of tumors, i.e. edema, necrosis and enhanced tumor. The method was evaluated on BRATS 2017 dataset. The results demonstrate that the proposed model provides promising segmentation in brain tumor structure. The F-measures for automatic brain tumor segmentation against ground truth are 0.98, 0.75 and 0.69 for whole tumor, core and enhanced tumor, respectively.

Post-concussive mTBI in Student Athletes: MRI Features and Machine Learning

Purpose: To determine and characterize the radiomics features from structural MRI (MPRAGE) and Diffusion Tensor Imaging (DTI) associated with the presence of mild traumatic brain injuries on student athletes with post-concussive syndrome (PCS).

Material and Methods: 122 student athletes (65 M, 57 F), median (IQR) age 18.8 (15–20) years, with a mixed level of play and sports activities, with a known history of concussion and clinical PCS, and 27 (15 M, 12 F), median (IQR) age 20 (19, 21) years, concussion free athlete subjects were MRI imaged in a clinical MR machine. MPRAGE and DTI-FA and DTI-ADC images were used to extract radiomic features from white and gray matter regions within the entire brain (2 ROI) and the eight main lobes of the brain (16 ROI) for a total of 18 analyzed regions. Radiomic features were divided into five different data sets used to train and cross-validate five different filter-based Support Vector Machines. The top selected features of the top model were described. Furthermore, the test predictions of the top four models were ensembled into a single average prediction. The average prediction was evaluated for the association to the number of concussions and time from injury.

Results: Ninety-one PCS subjects passed inclusion criteria (91 Cases, 27 controls). The average prediction of the top four models had a sensitivity of 0.80, 95% CI: [0.71, 0.88] and specificity of 0.74 95%CI [0.54, 0.89] for distinguishing subjects from controls. The white matter features were strongly associated with mTBI, while the whole-brain analysis of gray matter showed the worst association. The predictive index was significantly associated with the number of concussions (p < 0.0001) and associated with the time from injury (p < 0.01).

Conclusion: MRI Radiomic features are associated with a history of mTBI and they were successfully used to build a predictive machine learning model for mTBI for subjects with PCS associated with a history of one or more concussions.

Texture analysis of early cerebral tissue damage in magnetic resonance imaging of patients with lung cancer

Primary tumors can secrete many cytokines, inducing tissue damage or microstructural changes in distant organs. The purpose of this study was to investigate changes in texture features in the cerebral tissue of patients with lung cancer without brain metastasis. In this study, 50 patients with lung cancers underwent 3.0-T magnetic resonance imaging (MRI) within 2 weeks of being diagnosed with lung cancer. Texture analysis (TA) was carried out in 8 gray matter areas, including bilateral frontal cortices, parietal cortices, occipital cortices and temporal cortices, as well as 2 areas of bilateral frontoparietal white matter. The same procedure was performed for 57 healthy controls. A total of 32 texture parameters were separately compared between the patients and controls in the different cerebral tissue sites. Texture features among patients based on histological type and clinical stage were also compared. Of the 32 texture parameters, 27 showed significant differences between patients with lung cancer and healthy controls. There were significant differences in cerebral tissue, both gray matter and white matter between patients and controls, especially in several wavelet-based parameters. However, there were no significant differences between tissue at homologous sites in bilateral hemispheres, either in patients or controls. TA detected overt changes in the texture features of cerebral tissue in patients with lung cancer without brain metastasis compared with those of healthy controls. TA may be considered as a novel and adjunctive approach to conventional brain MRI to reveal cerebral tissue changes invisible on MRI alone in patients with lung cancer.

Radiomics from magnetic resonance imaging may be used to predict the progression of white matter hyperintensities and identify associated risk factors

OBJECTIVE

The progression of white matter hyperintensities (WMH) varies considerably in adults. In this study, we aimed to predict the progression and related risk factors of WMH based on the radiomics of whole-brain white matter (WBWM).

METHODS

A retrospective analysis was conducted on 141 patients with WMH who underwent two consecutive brain magnetic resonance (MR) imaging sessions from March 2014 to May 2018. The WBWM was segmented to extract and score the radiomics features at baseline. Follow-up images were evaluated using the modified Fazekas scale, with progression indicated by scores ≥ 1. Patients were divided into progressive (n = 65) and non-progressive (n = 76) groups. The progressive group was subdivided into any WMH (AWMH), periventricular WMH (PWMH), and deep WMH (DWMH). Independent risk factors were identified using logistic regression.

RESULTS

The area under the curve (AUC) values for the radiomics signatures of the training sets were 0.758, 0.749, and 0.775 for AWMH, PWMH, and DWMH, respectively. The AUC values of the validation set were 0.714, 0.697, and 0.717, respectively. Age and hyperlipidemia were independent predictors of progression for AWMH. Age and body mass index (BMI) were independent predictors of progression for DWMH, while hyperlipidemia was an independent predictor of progression for PWMH. After combining clinical factors and radiomics signatures, the AUC values were 0.848, 0.863, and 0.861, respectively, for the training set, and 0.824, 0.818, and 0.833, respectively, for the validation set.

CONCLUSIONS

MRI-based radiomics of WBWM, along with specific risk factors, may allow physicians to predict the progression of WMH.

KEY POINTS

• Radiomics features detected by magnetic resonance imaging may be used to predict the progression of white matter hyperintensities. • Radiomics may be used to identify risk factors associated with the progression of white matter hyperintensities. • Radiomics may serve as non-invasive biomarkers to monitor white matter status.

Influence of Acquisition Time on MR Image Quality Estimated with Nonparametric Measures Based on Texture Features

Correlation of parametrized image texture features (ITF) analyses conducted in different regions of interest (ROIs) overcomes limitations and reliably reflects image quality. The aim of this study is to propose a nonparametrical method and classify the quality of a magnetic resonance (MR) image that has undergone controlled degradation by using textural features in the image. Images of 41 patients, 17 women and 24 men, aged between 23 and 56 years were analyzed. T2-weighted sagittal sequences of the lumbar spine, cervical spine, and knee and T2-weighted coronal sequences of the shoulder and wrist were generated. The implementation of parallel imaging with the use of GRAPPA2, GRAPPA3, and GRAPPA4 led to a substantial reduction in the scanning time but also degraded image quality. The number of degraded image textural features was correlated with the scanning time. Longer scan times correlated with markedly higher ITF image persistence in comparison with images computed with reduced scan times. Higher ITF preservation was observed in images of bones in the spine and femur as compared to images of soft tissues, i.e., tendons and muscles. Finally, a nonparametrized image quality assessment based on an analysis of the ITF, computed for different tissues, correlating with the changes in acquisition time of the MR images, was successfully developed. The correlation between acquisition time and the number of reproducible features present in an MR image was found to yield the necessary assumptions to calculate the quality index.

Radiomics nomogram based on MRI for predicting white matter hyperintensity progression in elderly adults

BACKGROUND

White matter hyperintensity (WMH) is widely observed in aging brain and is associated with various diseases. A pragmatic and handy method in the clinic to assess and follow up white matter disease is strongly in need.

PURPOSE

To develop and validate a radiomics nomogram for the prediction of WMH progression.

STUDY TYPE

Retrospective.

POPULATION

Brain images of 193 WMH patients from the Picture Archiving and Communication Systems (PACS) database in the A Medical Center (Zhejiang Provincial People's Hospital). MRI data of 127 WMH patients from the PACS database in the B Medical Center (Zhejiang Lishui People's Hospital) were included for external validation. All of the patients were at least 60 years old.

FIELD STRENGTH/SEQUENCE

T₁ -fluid attenuated inversion recovery images were acquired using a 3T scanner.

ASSESSMENT

WMH was evaluated utilizing the Fazekas scale based on MRI. WMH progression was assessed with a follow-up MRI using a visual rating scale. Three neuroradiologists, who were blinded to the clinical data, assessed the images independently. Moreover, interobserver and intraobserver reproducibility were performed for the regions of interest for segmentation and feature extraction.

STATISTICAL TESTS

A receiver operating characteristic (ROC) curve, the area under the curve (AUC) of the ROC was calculated, along with sensitivity and specificity. Also, a Hosmer-Lemeshow test was performed.

RESULTS

The AUC of radiomics signature in the primary, internal validation cohort, external validation cohort were 0.886, 0.816, and 0.787, respectively; the specificity were 71.79%, 72.22%, and 81%, respectively; the sensitivity were 92.68%, 87.94% and 78.3%, respectively. The radiomics nomogram in the primary cohort (AUC = 0.899) and the internal validation cohort (AUC = 0.84). The Hosmer-Lemeshow test showed no significant difference between the primary cohort and the internal validation cohort (P > 0.05). The AUC of the radiomics nomogram, radiomics signature, and hyperlipidemia in all patients from the primary and internal validation cohort was 0.878, 0.848, and 0.626, respectively.

DATA CONCLUSION

This multicenter study demonstrated the use of a radiomics nomogram in predicting the progression of WMH with elderly adults (an age of at least 60 years) based on conventional MRI.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:535-546.

Prospective study of myelin water fraction changes after mild traumatic brain injury in collegiate contact sports

OBJECTIVE

Mild traumatic brain injury (mTBI) in athletes, including concussion, is increasingly being found to have long-term sequelae. Current imaging techniques have not been able to identify early damage caused by mTBI that is predictive of long-term symptoms or chronic traumatic encephalopathy. In this preliminary feasibility study, the authors investigated the use of an emerging magnetic resonance imaging (MRI) technique, multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT), in visualizing acute and chronic white matter changes after mTBI in collegiate football and rugby players.

METHODS

This study was a nonrandomized, nonblinded prospective trial designed to quantify changes in the myelin water fraction (MWF), used as a surrogate MRI measure of myelin content, in a group of male collegiate football and rugby players, classified here as a contact sport player (CSP) cohort, at the time of mTBI diagnosis and 3 months after injury when the acute symptoms of the injury had resolved. In addition, differences in the MWF between the CSP cohort and a control cohort of noncontact sport players (NCSPs) were quantified. T-tests and a threshold-free cluster enhancement (TFCE) statistical analysis technique were used to identify brain structures with significant changes in the MWF between the CSP and NCSP cohorts and between immediately postinjury and follow-up images obtained in the CSP cohort.

RESULTS

Brain MR images of 12 right-handed male CSPs were analyzed and compared with brain images of 10 right-handed male NCSPs from the same institution. A comparison of CSP and NCSP baseline images using TFCE showed significantly higher MWFs in the bilateral basal ganglia, anterior and posterior corpora callosa, left corticospinal tract, and left anterior and superior temporal lobe (p < 0.05). At the 3-month follow-up examination, images from the CSP cohort still showed significantly higher MWFs than those identified on baseline images from the NCSP cohort in the bilateral basal ganglia, anterior and posterior corpora callosa, and left anterior temporal lobe, and also in the bilateral corticospinal tracts, parahippocampal gyrus, and bilateral juxtapositional (previously known as supplemental motor) areas (p < 0.05). In the CSP cohort, a t-test comparing the MWF at the time of injury and 3 months later showed a significant increase in the overall MWF at follow-up (p < 0.005). These increases were greatest in the bilateral basal ganglia and deep white matter. MWF decreases were seen in more superficial white matter (p < 0.005).

CONCLUSIONS

In this preliminary study, MWF was found to be increased in the brains of CSPs compared with the brains of controls, suggesting acute/chronic MWF alterations in CSPs from previous injuries. Increases in the MWF were also demonstrated in the brains of CSPs 3 months after the players sustained an mTBI. The full clinical significance of an increased MWF and whether this reflects axon neuropathology or disorderly remyelination leading to hypermyelination has yet to be determined.

Role of texture analysis in breast MRI as a cancer biomarker: A review

Breast cancer is a known heterogeneous disease. Current clinically utilized histopathologic biomarkers may undersample tumor heterogeneity, resulting in higher rates of misdiagnosis for breast cancer. MRI can provide a whole-tumor sampling of disease burden and is widely utilized in clinical care. Texture analysis can provide a localized description of breast cancer, with particular emphasis on quantifying breast lesion heterogeneity. The object of this review is to provide an overview of texture analysis applications towards breast cancer diagnosis, prognosis, and treatment response evaluation and review the role of image-based texture features as noninvasive prognostic and predictive biomarkers. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:927-938.

Standardized quality metric system for structural brain magnetic resonance images in multi-center neuroimaging study

BACKGROUND

Multi-site neuroimaging offer several benefits and poses tough challenges in the drug development process. Although MRI protocol and clinical guidelines developed to address these challenges recommend the use of good quality images, reliable assessment of image quality is hampered by the several shortcomings of existing techniques.

METHODS

Given a test image two feature images are extracted. They are grayscale and contrast feature images. Four binary images are generated by setting four different global thresholds on the feature images. Image quality is predicted by measuring the structural similarity between appropriate pairs of binary images. The lower and upper limits of the quality index are 0 and 1. Quality prediction is based on four quality attributes; luminance contrast, texture, texture contrast and lightness.

RESULTS

Performance evaluation on test data from three multi-site clinical trials show good objective quality evaluation across MRI sequences, levels of distortion and quality attributes. Correlation with subjective evaluation by human observers is ≥ 0.6.

CONCLUSION

The results are promising for the evaluation of MRI protocols, specifically the standardization of quality index, designed to overcome the challenges encountered in multi-site clinical trials.

Texture analysis of magnetic resonance T1 mapping with dilated cardiomyopathy: A machine learning approach

The diagnosis of dilated cardiomyopathy (DCM) remains a challenge in clinical radiology. This study aimed to investigate whether texture analysis (TA) parameters on magnetic resonance T1 mapping can be helpful for the diagnosis of DCM.A total of 50 DCM cases were retrospectively screened and 24 healthy controls were prospectively recruited between March 2015 and July 2017. T1 maps were acquired using the Modified Look-Locker Inversion Recovery (MOLLI) sequence at a 3.0 T MR scanner. The endocardium and epicardium were drawn on the short-axis slices of the T1 maps by an experienced radiologist. Twelve histogram parameters and 5 gray-level co-occurrence matrix (GLCM) features were extracted during the TA. Differences in texture features between DCM patients and healthy controls were evaluated by t test. Support vector machine (SVM) was used to calculate the diagnostic accuracy of those texture parameters.Most histogram features were higher in the DCM group when compared to healthy controls, and 9 of these had significant differences between the DCM group and healthy controls. In terms of GLCM features, energy, correlation, and homogeneity were higher in the DCM group, when compared with healthy controls. In addition, entropy and contrast were lower in the DCM group. Moreover, entropy, contrast, and homogeneity had significant differences between these 2 groups. The diagnostic accuracy when using the SVM classifier with all these histogram and GLCM features was 0.85 ± 0.07.A computer-based TA and machine learning approach of T1 mapping can provide an objective tool for the diagnosis of DCM.

Multi-institutional validation of a novel textural analysis tool for preoperative stratification of suspected thyroid tumors on diffusion-weighted MRI

PURPOSE

Ultrasound-guided fine needle aspirate cytology fails to diagnose many malignant thyroid nodules; consequently, patients may undergo diagnostic lobectomy. This study assessed whether textural analysis (TA) could noninvasively stratify thyroid nodules accurately using diffusion-weighted MRI (DW-MRI).

METHODS

This multi-institutional study examined 3T DW-MRI images obtained with spin echo echo planar imaging sequences. The training data set included 26 patients from Cambridge, United Kingdom, and the test data set included 18 thyroid cancer patients from Memorial Sloan Kettering Cancer Center (New York, New York, USA). Apparent diffusion coefficients (ADCs) were compared over regions of interest (ROIs) defined on thyroid nodules. TA, linear discriminant analysis (LDA), and feature reduction were performed using the 21 MaZda-generated texture parameters that best distinguished benign and malignant ROIs.

RESULTS

Training data set mean ADC values were significantly different for benign and malignant nodules (P = 0.02) with a sensitivity and specificity of 70% and 63%, respectively, and a receiver operator characteristic (ROC) area under the curve (AUC) of 0.73. The LDA model of the top 21 textural features correctly classified 89/94 DW-MRI ROIs with 92% sensitivity, 96% specificity, and an AUC of 0.97. This algorithm correctly classified 16/18 (89%) patients in the independently obtained test set of thyroid DW-MRI scans.

CONCLUSION

TA classifies thyroid nodules with high sensitivity and specificity on multi-institutional DW-MRI data sets. This method requires further validation in a larger prospective study. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance.

High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features

Statistical features are widely used in radiology for tumor heterogeneity assessment using magnetic resonance (MR) imaging technique. In this paper, feature selection based on decision tree is examined to determine the relevant subset of glioblastoma (GBM) phenotypes in the statistical domain. To discriminate between active tumor (vAT) and edema/invasion (vE) phenotype, we selected the significant features using analysis of variance (ANOVA) with p value < 0.01. Then, we implemented the decision tree to define the optimal subset features of phenotype classifier. Naïve Bayes (NB), support vector machine (SVM), and decision tree (DT) classifier were considered to evaluate the performance of the feature based scheme in terms of its capability to discriminate vAT from vE. Whole nine features were statistically significant to classify the vAT from vE with p value < 0.01. Feature selection based on decision tree showed the best performance by the comparative study using full feature set. The feature selected showed that the two features Kurtosis and Skewness achieved a highest range value of 58.33–75.00% accuracy classifier and 73.88–92.50% AUC. This study demonstrated the ability of statistical features to provide a quantitative, individualized measurement of glioblastoma patient and assess the phenotype progression.

Diffusion tensor imaging findings in semi-acute mild traumatic brain injury

The past 10 years have seen a rapid increase in the use of diffusion tensor imaging to identify biomarkers of traumatic brain injury (TBI). Although the literature generally indicates decreased anisotropic diffusion at more chronic injury periods and in more severe injuries, considerable debate remains regarding the direction (i.e., increased or decreased) of anisotropic diffusion in the acute to semi-acute phase (here defined as less than 3 months post-injury) of mild TBI (mTBI). A systematic review of the literature was therefore performed to (1) determine the prevalence of different anisotropic diffusion findings (increased, decreased, bidirectional, or null) during the semi-acute injury phase of mTBI and to (2) identify clinical (e.g., age of injury, post-injury scan time, etc.) and experimental factors (e.g., number of unique directions, field strength) that may influence these findings. Results from the literature review indicated 31 articles with independent samples of semi-acute mTBI patients, with 13 studies reporting decreased anisotropic diffusion, 11 reporting increased diffusion, 2 reporting bidirectional findings, and 5 reporting null findings. Chi-squared analyses indicated that the total number of diffusion-weighted (DW) images was significantly associated with findings of either increased (DW ≥ 30) versus decreased (DW ≤ 25) anisotropic diffusion. Other clinical and experimental factors were not statistically significant for direction of anisotropic diffusion, but these results may have been limited by the relatively small number of studies within each domain (e.g., pediatric studies). In summary, current results indicate roughly equivalent number of studies reporting increased versus decreased anisotropic diffusion during semi-acute mTBI, with the number of unique diffusion images being statistically associated with the direction of findings.

A T1 and DTI fused 3D Corpus Callosum analysis in pre- vs. post-season contact sports players

Sports related traumatic brain injury (TBI) is a worldwide public health issue, and damage to the corpus callosum (CC) has been considered as an important indicator of TBI. However, contact sports players suffer repeated hits to the head during the course of a season even in the absence of diagnosed concussion, and less is known about their effect on callosal anatomy. In addition, T1-weighted and diffusion tensor brain magnetic resonance images (DTI) have been analyzed separately, but a joint analysis of both types of data may increase statistical power and give a more complete understanding of anatomical correlates of subclinical concussions in these athletes. Here, for the first time, we fuse T1 surface-based morphometry and a new DTI analysis on 3D surface representations of the CCs into a single statistical analysis on these subjects. Our new combined method successfully increases detection power in detecting differences between pre- vs. post-season contact sports players. Alterations are found in the ventral genu, isthmus, and splenium of CC. Our findings may inform future health assessments in contact sports players. The new method here is also the first truly multimodal diffusion and T1-weighted analysis of the CC in TBI, and may be useful to detect anatomical changes in the corpus callosum in other multimodal datasets.

Evaluation of Axonal Strain as a Predictor for Mild Traumatic Brain Injuries Using Finite Element Modeling

Finite element (FE) models are often used to study the biomechanical effects of traumatic brain injury (TBI). Measures based on mechanical responses, such as principal strain or invariants of the strain tensor, are used as a metric to predict the risk of injury. However, the reliability of inferences drawn from these models depends on the correspondence between the mechanical measures and injury data, as well as the establishment of accurate thresholds of tissue injury. In the current study, a validated anisotropic FE model of the human head is used to evaluate the hypothesis that strain in the direction of fibers (axonal strain) is a better predictor of TBI than maximum principal strain (MPS), anisotropic equivalent strain (AESM) and cumulative strain damage measure (CSDM). An analysis of head kinematics-based metrics, such as head injury criterion (HIC) and brain injury criterion (BrIC), is also provided. Logistic regression analysis is employed to compare binary injury data (concussion/no concussion) with continuous strain/kinematics data. The threshold corresponding to 50% of injury probability is determined for each parameter. The predictive power (area under the ROC curve, AUC) is calculated from receiver operating characteristic (ROC) curve analysis. The measure with the highest AUC is considered to be the best predictor of mTBI. Logistic regression shows a statistical correlation between all the mechanical predictors and injury data for different regions of the brain. Peaks of axonal strain have the highest AUC and determine a strain threshold of 0.07 for corpus callosum and 0.15 for the brainstem, in agreement with previously experimentally derived injury thresholds for reversible axonal injury. For a data set of mild TBI from the national football league, the strain in the axonal direction is found to be a better injury predictor than MPS, AESM, CSDM, BrIC and HIC.

Correlation of quantitative texture analysis of cranial ultrasound with later neurobehavior in preterm infants

The purpose of the study was to evaluate the association between a quantitative texture analysis of early neonatal brain ultrasound images and later neurobehavior in preterm infants. A prospective cohort study including 120 preterm (<33 wk of gestational age) infants was performed. Cranial ultrasound images taken early after birth were analyzed in six regions of interest using software based on texture analysis. The resulting texture scores were correlated with the Neonatal Behavioural Assessment Scale (NBAS) at term-equivalent age. The ability of texture scores, in combination with clinical data and standard ultrasound findings, to predict the NBAS results was evaluated. Texture scores were significantly associated with all but one NBAS domain and better predicted NBAS results than clinical data and standard ultrasound findings. The best predictive value was obtained by combining texture scores with clinical information and ultrasound standard findings (area under the curve = 0.94). We conclude that texture analysis of neonatal cranial ultrasound-extracted quantitative features that correlate with later neurobehavior has a higher predictive value than the combination of clinical data with abnormalities in conventional cranial ultrasound.

Detection of exercise load-associated differences in hip muscles by texture analysis

We examined whether specific physical exercise loading is associated with texture parameters from hip muscles scanned with magnetic resonance imaging (MRI). Ninety-one female athletes representing five distinct exercise-loading groups (high-impact, odd-impact, low-impact, nonimpact and high-magnitude) and 20 nonathletic female controls underwent MRI of the hip. Texture parameters were computed from the MRI images of four hip muscles (gluteus maximus, gluteus medius, iliopsoas and obturator internus). Differences in muscle texture between the athlete groups and the controls were evaluated using Mann-Whitney U-test. Significant (P < 0.05) textural differences were found between the high-impact (triple and high jumpers) and the control group in gluteus medius, iliopsoas and obturator internus muscles. Texture of the gluteus maximus, gluteus medius and obturator internus muscles differed significantly between the odd impact (soccer and squash players) and the control group. Textures of all studied muscles differed significantly between the low impact (endurance runners) and the controls. Only the gluteus medius muscle differed significantly between the nonimpact (swimmers) and the controls. No significant difference in muscle texture was found between the high-magnitude (powerlifters) and the control group. In conclusion, MRI texture analysis provides a quantitative method capable of detecting textural differences in hip muscles that are associated with specific types of long-term exercise loadings.

Structural integrity of medial temporal lobes of patients with acute mild traumatic brain injury

Post-traumatic amnesia (PTA) is an acute characteristic of traumatic brain injury (TBI) and the duration of PTA is commonly used to estimate the severity of brain injury. In the context of mild traumatic brain injury (MTBI), PTA is an essential part of the routine clinical assessment. Macroscopic lesions in temporal lobes, especially hippocampal regions, are thought to be connected to memory loss. However, conventional neuroimaging has failed to reveal neuropathological correlates of PTA in MTBI. Texture analysis (TA) is an image analysis technique that quantifies the minor MRI signal changes among image pixels and, therefore, the variations in intensity patterns within the image. The objective of this work was to apply the TA technique to MR images of MTBI patients and control subjects, and to assess the microstructural damage in medial temporal lobes of patients with MTBI with definite PTA. TA was performed for fluid-attenuated inversion recovery (FLAIR) images of 50 MTBI patients and 50 age- and gender-matched controls in the regions of the amygdala, hippocampus, and thalamus. It was hypothesized that 1) there would be statistically significant differences in TA parameters between patients with MTBIs and controls, and 2) the duration of PTA would be related to TA parameters in patients with MTBI. No significant textural differences were observed between patients and controls in the regions of interest (p>0.01). No textural features were observed to correlate with the duration of PTA. Subgroup analyses were conducted on patients with PTA of>1 h, (n=33) and compared the four TA parameters to the age- and gender-matched controls (n=33). The findings were similar. This study did not reveal significant textural changes in medial temporal structures that could be related to the duration of PTA.

Visual and Contextual Modeling for the Detection of Repeated Mild Traumatic Brain Injury

Currently, there is a lack of computational methods for the evaluation of mild traumatic brain injury (mTBI) from magnetic resonance imaging (MRI). Further, the development of automated analyses has been hindered by the subtle nature of mTBI abnormalities, which appear as low contrast MR regions. This paper proposes an approach that is able to detect mTBI lesions by combining both the high-level context and low-level visual information. The contextual model estimates the progression of the disease using subject information, such as the time since injury and the knowledge about the location of mTBI. The visual model utilizes texture features in MRI along with a probabilistic support vector machine to maximize the discrimination in unimodal MR images. These two models are fused to obtain a final estimate of the locations of the mTBI lesion. The models are tested using a novel rodent model of repeated mTBI dataset. The experimental results demonstrate that the fusion of both contextual and visual textural features outperforms other state-of-the-art approaches. Clinically, our approach has the potential to benefit both clinicians by speeding diagnosis and patients by improving clinical care.

Automatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehavior

Background

We tested the hypothesis whether texture analysis (TA) from MR images could identify patterns associated with an abnormal neurobehavior in small for gestational age (SGA) neonates.

Methods

Ultrasound and MRI were performed on 91 SGA fetuses at 37 weeks of GA. Frontal lobe, basal ganglia, mesencephalon and cerebellum were delineated from fetal MRIs. SGA neonates underwent NBAS test and were classified as abnormal if ≥1 area was <5^th centile and as normal if all areas were >5^th centile. Textural features associated with neurodevelopment were selected and machine learning was used to model a predictive algorithm.

Results

Of the 91 SGA neonates, 49 were classified as normal and 42 as abnormal. The accuracies to predict an abnormal neurobehavior based on TA were 95.12% for frontal lobe, 95.56% for basal ganglia, 93.18% for mesencephalon and 83.33% for cerebellum.

Conclusions

Fetal brain MRI textural patterns were associated with neonatal neurodevelopment. Brain MRI TA could be a useful tool to predict abnormal neurodevelopment in SGA.

Abnormalities in fronto-striatal connectivity within language networks relate to differences in grey-matter heterogeneity in Asperger syndrome

Asperger syndrome (AS) is an Autism Spectrum Disorder (ASD) characterised by qualitative impairment in the development of emotional and social skills with relative preservation of general intellectual abilities, including verbal language. People with AS may nevertheless show atypical language, including rate and frequency of speech production. We previously observed that abnormalities in grey matter homogeneity (measured with texture analysis of structural MR images) in AS individuals when compared with controls are also correlated with the volume of caudate nucleus. Here, we tested a prediction that these distributed abnormalities in grey matter compromise the functional integrity of brain networks supporting verbal communication skills. We therefore measured the functional connectivity between caudate nucleus and cortex during a functional neuroimaging study of language generation (verbal fluency), applying psycho-physiological interaction (PPI) methods to test specifically for differences attributable to grey matter heterogeneity in AS participants. Furthermore, we used dynamic causal modelling (DCM) to characterise the causal directionality of these differences in interregional connectivity during word production. Our results revealed a diagnosis-dependent influence of grey matter heterogeneity on the functional connectivity of the caudate nuclei with right insula/inferior frontal gyrus and anterior cingulate, respectively with the left superior frontal gyrus and right precuneus. Moreover, causal modelling of interactions between inferior frontal gyri, caudate and precuneus, revealed a reliance on bottom-up (stimulus-driven) connections in AS participants that contrasted with a dominance of top-down (cognitive control) connections from prefrontal cortex observed in control participants. These results provide detailed support for previously hypothesised central disconnectivity in ASD and specify discrete brain network targets for diagnosis and therapy in ASD.

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We used MRI techniques to assess the connectivity in language networks in AS.

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Grey-matter heterogeneity of MR images correlated with volume of caudate in AS.

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Hence, caudate nuclei were used as seed ROIs in connectivity analyses: PPI, DCM.

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Grey-matter heterogeneity differently tuned caudate connectivity in AS, controls.

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DCM of language circuitry featured bottom-up models in AS and top-down in controls.

Neuropathology of mild traumatic brain injury: relationship to neuroimaging findings

Neuroimaging identified abnormalities associated with traumatic brain injury (TBI) are but gross indicators that reflect underlying trauma-induced neuropathology at the cellular level. This review examines how cellular pathology relates to neuroimaging findings with the objective of more closely relating how neuroimaging findings reveal underlying neuropathology. Throughout this review an attempt will be made to relate what is directly known from post-mortem microscopic and gross anatomical studies of TBI of all severity levels to the types of lesions and abnormalities observed in contemporary neuroimaging of TBI, with an emphasis on mild traumatic brain injury (mTBI). However, it is impossible to discuss the neuropathology of mTBI without discussing what occurs with more severe injury and viewing pathological changes on some continuum from the mildest to the most severe. Historical milestones in understanding the neuropathology of mTBI are reviewed along with implications for future directions in the examination of neuroimaging and neuropathological correlates of TBI.

A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury

Mild traumatic brain injury (mTBI), also referred to as concussion, remains a controversial diagnosis because the brain often appears quite normal on conventional computed tomography (CT) and magnetic resonance imaging (MRI) scans. Such conventional tools, however, do not adequately depict brain injury in mTBI because they are not sensitive to detecting diffuse axonal injuries (DAI), also described as traumatic axonal injuries (TAI), the major brain injuries in mTBI. Furthermore, for the 15 to 30 % of those diagnosed with mTBI on the basis of cognitive and clinical symptoms, i.e., the "miserable minority," the cognitive and physical symptoms do not resolve following the first 3 months post-injury. Instead, they persist, and in some cases lead to long-term disability. The explanation given for these chronic symptoms, i.e., postconcussive syndrome, particularly in cases where there is no discernible radiological evidence for brain injury, has led some to posit a psychogenic origin. Such attributions are made all the easier since both posttraumatic stress disorder (PTSD) and depression are frequently co-morbid with mTBI. The challenge is thus to use neuroimaging tools that are sensitive to DAI/TAI, such as diffusion tensor imaging (DTI), in order to detect brain injuries in mTBI. Of note here, recent advances in neuroimaging techniques, such as DTI, make it possible to characterize better extant brain abnormalities in mTBI. These advances may lead to the development of biomarkers of injury, as well as to staging of reorganization and reversal of white matter changes following injury, and to the ability to track and to characterize changes in brain injury over time. Such tools will likely be used in future research to evaluate treatment efficacy, given their enhanced sensitivity to alterations in the brain. In this article we review the incidence of mTBI and the importance of characterizing this patient population using objective radiological measures. Evidence is presented for detecting brain abnormalities in mTBI based on studies that use advanced neuroimaging techniques. Taken together, these findings suggest that more sensitive neuroimaging tools improve the detection of brain abnormalities (i.e., diagnosis) in mTBI. These tools will likely also provide important information relevant to outcome (prognosis), as well as play an important role in longitudinal studies that are needed to understand the dynamic nature of brain injury in mTBI. Additionally, summary tables of MRI and DTI findings are included. We believe that the enhanced sensitivity of newer and more advanced neuroimaging techniques for identifying areas of brain damage in mTBI will be important for documenting the biological basis of postconcussive symptoms, which are likely associated with subtle brain alterations, alterations that have heretofore gone undetected due to the lack of sensitivity of earlier neuroimaging techniques. Nonetheless, it is noteworthy to point out that detecting brain abnormalities in mTBI does not mean that other disorders of a more psychogenic origin are not co-morbid with mTBI and equally important to treat. They arguably are. The controversy of psychogenic versus physiogenic, however, is not productive because the psychogenic view does not carefully consider the limitations of conventional neuroimaging techniques in detecting subtle brain injuries in mTBI, and the physiogenic view does not carefully consider the fact that PTSD and depression, and other co-morbid conditions, may be present in those suffering from mTBI. Finally, we end with a discussion of future directions in research that will lead to the improved care of patients diagnosed with mTBI.

Microenvironment changes in mild traumatic brain injury

Traumatic brain injury (TBI) is a major public-health problem for which mild TBI (MTBI) makes up majority of the cases. MTBI is a poorly-understood health problem and can persist for years manifesting into neurological and non-neurological problems that can affect functional outcome. Presently, diagnosis of MTBI is based on symptoms reporting with poor understanding of ongoing pathophysiology, hence precluding prognosis and intervention. Other than rehabilitation, there is still no pharmacological treatment for the treatment of secondary injury and prevention of the development of cognitive and behavioural problems. The lack of external injuries and absence of detectable brain abnormalities lend support to MTBI developing at the cellular and biochemical level. However, the paucity of suitable and validated non-invasive methods for accurate diagnosis of MTBI poses as a substantial challenge. Hence, it is crucial that a clinically useful evaluation and management procedure be instituted for MTBI that encompasses both molecular pathophysiology and functional outcome. The acute microenvironment changes post-MTBI presents an attractive target for modulation of MTBI symptoms and the development of cognitive changes later in life.

Parkinson's disease: interhemispheric textural differences in MR images

RATIONALE AND OBJECTIVES

Early-stage diagnosis of Parkinson's disease (PD) is essential in making decisions related to treatment and prognosis. However, there is no specific diagnostic test for the diagnosis of PD. The aim of this study was to evaluate the role of texture analysis (TA) of magnetic resonance images in detecting subtle changes between the hemispheres in various brain structures in patients with early symptoms of parkinsonism. In addition, functional TA parameters for detecting textural changes are presented.

MATERIALS AND METHODS

Fifty-one patients with symptoms of PD and 20 healthy controls were imaged using a 3-T magnetic resonance device. Co-occurrence matrix-based TA was applied to detect changes in textures between the hemispheres in the following clinically interesting areas: dentate nucleus, basilar pons, substantia nigra, globus pallidus, thalamus, putamen, caudate nucleus, corona radiata, and centrum semiovale. The TA results were statistically evaluated using the Mann-Whitney U test.

RESULTS

The results showed interhemispheric textural differences among the patients, especially in the area of basilar pons and midbrain. Concentrating on this clinically interesting area, the four most discriminant parameters were defined: co-occurrence matrix correlation, contrast, difference variance, and sum variance. With these parameters, differences were also detected in the dentate nucleus, globus pallidus, and corona radiata.

CONCLUSIONS

On the basis of this study, interhemispheric differences in the magnetic resonance images of patients with PD can be identified by the means of co-occurrence matrix-based TA. The detected areas correlate with the current pathophysiologic and neuroanatomic knowledge of PD.

Correlation between a semiautomated method based on ultrasound texture analysis and standard ultrasound diagnosis using white matter damage in preterm neonates as a model

OBJECTIVES

Diagnosis of white matter damage by cranial ultrasound imaging is still subject to interobserver variability and has limited sensitivity for predicting abnormal neurodevelopment later in life. In this study, we evaluated the ability of a semiautomated method based on ultrasound texture analysis to identify patterns that correlate with the ultrasound diagnosis of white matter damage.

METHODS

The study included 44 very preterm neonates born at a median gestational age of 29 weeks 3 days (range, 26 weeks-31 weeks 6 days). Patients underwent cranial ultrasound scans within 1 week of birth and between 14 and 31 days of life. Periventricular leukomalacia was diagnosed by experienced clinicians on the 14- to 31-day scan according to standard criteria. To perform the texture analysis, 4 regions of interest were delineated in stored images: left and right periventricular areas and choroid plexuses. A classification algorithm was developed on the basis of the best combination of texture coefficients to correlate with the clinical diagnosis, and the ability of this algorithm to predict a later diagnosis of periventricular leukomalacia on the first scan was evaluated using a leave-one-out cross-validation.

RESULTS

Periventricular leukomalacia was diagnosed by the standard procedure in 14 of 44 neonates. The texture classification algorithm performed on the first scan could identify cases with a later diagnosis of periventricular leukomalacia with sensitivity of 100% and accuracy of 97.7%.

CONCLUSIONS

These data support the notion that semiautomated quantitative ultrasound analysis achieves early identification of changes in subclinical stages and warrant further investigation of the role of ultrasound texture analysis methods to improve early detection of neonatal brain damage.

A longitudinal proton magnetic resonance spectroscopy study of mild traumatic brain injury

Despite the prevalence and impact of mild traumatic brain injury (mTBI), common clinical assessment methods for mTBI have insufficient sensitivity and specificity. Moreover, few researchers have attempted to document underlying changes in physiology as a function of recovery from mTBI. Proton magnetic resonance spectroscopy (¹H-MRS) was used to assess neurometabolite concentrations in a supraventricular tissue slab in 30 individuals with semi-acute mTBI, and 30 sex-, age-, and education-matched controls. No significant group differences were evident on traditional measures of attention, memory, working memory, processing speed, and executive skills, though the mTBI group reported significantly more somatic, cognitive, and emotional symptoms. At a mean of 13 days post-injury, white matter concentrations of creatine (Cre) and phosphocreatine (PCre) and the combined glutamate-glutamine signal (Glx) were elevated in the mTBI group, while gray matter concentrations of Glx were reduced. Partial normalization of these three neurometabolites and N-acetyl aspartate occurred in the early days post-injury, during the semi-acute period of recovery. In addition, 17 mTBI patients (57%) returned for a follow-up evaluation (mean = 120 days post-injury). A significant group × time interaction indicated recovery in the mTBI group for gray matter Glx, and trends toward recovery in white matter Cre and Glx. An estimate of premorbid intelligence predicted the magnitude of neurometabolite normalization over the follow-up interval for the mTBI group, indicating that biological factors underlying intelligence may also be associated with more rapid recovery.

Synaptic behaviour of hexaploid wheat haploids with different effectiveness of the diploidizing mechanism

Haploids of three cultivars of Triticum aestivum (Thatcher, Chris, and Chinese Spring) were obtained from crosses with Zea mays. The level of chromosome pairing at metaphase I and the synaptic behaviour at prophase I was studied. There were differences in the meiotic behaviour of the haploids from different cultivars. Thatcher and Chris haploids had significantly higher levels of pairing at metaphase I than Chinese Spring haploids. This metaphase I pairing was correlated with higher levels of synapsis achieved in the Thatcher and Chris prophase I nuclei than in the Chinese Spring nuclei. Variation in the effectiveness of the diploidizing mechanism among cultivars of wheat is proposed to have a genetic origin and the role of the Ph1 locus in the different haploids is discussed.