Multimodal Brain Magnetic Resonance Imaging Research Articles

Regional Strength of Large-Scale Functional Brain Networks is Associated with Regional Volumes in Older Adults and in Alzheimer's Disease.

Background: The association between regional volumes and resting-state functional networks was tested within the default-mode network (DMN), influenced by Alzheimer pathology, salience network (SalN), not under similar pathological influence, and sensorimotor network (SMN), usually spared by pathology. Methods: A total of 148 participants, with Alzheimer's disease (AD) dementia, mild cognitive impairment (MCI), and healthy controls underwent multimodal brain magnetic resonance imaging (MRI). Functional network identification was achieved with group-level independent-component analysis of functional MRI (fMRI) scans. T1 weighted images were also analyzed. Ten regions of interest (ROI) were defined in core hubs of the three networks. Gray-matter volume/functional network strength association was tested within-ROI and cross-ROI in each group by using partial-correlation models and ROI-to-ROI, ROI-to-voxel, and voxel-to-voxel correlations. Results: In controls, a negative association was found between right inferior-parietal volumes and SMN expression in the left precentral gyrus, as revealed by ROI-to-ROI models. In AD, DMN expression was positively associated with the volume of the left insula and the right inferior parietal lobule, and SalN expression was positively associated with volume of the left inferior parietal lobule. ROI-to-voxel models revealed significant associations between the volume of the posterior cingulate cortex and SMN expression in sensorimotor and premotor regions. No significant findings emerged in the MCI nor from voxel-to-voxel analyses. Discussion: Regional volumes of main network hubs are significantly associated with hemodynamic network expression, although patterns are intricate and dependent on diagnostic status. Since distinct networks are differentially influenced by Alzheimer pathology, it appears that pathology plays a significant role in influencing the association between regional volumes and regional functional network strength.

Multimodal MRI synthesis using unified generative adversarial networks.

Complementary information obtained from multiple contrasts of tissue facilitates physicians assessing, diagnosing and planning treatment of a variety of diseases. However, acquiring multiple contrasts magnetic resonance images (MRI) for every patient using multiple pulse sequences is time-consuming and expensive, where, medical image synthesis has been demonstrated as an effective alternative. The purpose of this study is to develop a unified framework for multimodal MR image synthesis. A unified generative adversarial network consisting of only a single generator and a single discriminator was developed to learn the mappings among images of four different modalities. The generator took an image and its modality label as inputs and learned to synthesize the image in the target modality, while the discriminator was trained to distinguish between real and synthesized images and classify them to their corresponding modalities. The network was trained and tested using multimodal brain MRI consisting of four different contrasts which are T1-weighted (T1), T1-weighted and contrast-enhanced (T1c), T2-weighted (T2), and fluid-attenuated inversion recovery (Flair). Quantitative assessments of our proposed method were made through computing normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), structural similarity index measurement (SSIM), visual information fidelity (VIF), and naturalness image quality evaluator (NIQE). The proposed model was trained and tested on a cohort of 274 glioma patients with well-aligned multi-types of MRI scans. After the model was trained, tests were conducted by using each of T1, T1c, T2, Flair as a single input modality to generate its respective rest modalities. Our proposed method shows high accuracy and robustness for image synthesis with arbitrary MRI modality that is available in the database as input. For example, with T1 as input modality, the NMAEs for the generated T1c, T2, Flair respectively are 0.034±0.005, 0.041±0.006, and 0.041±0.006, the PSNRs respectively are 32.353±2.525dB, 30.016±2.577dB, and 29.091±2.795dB, the SSIMs are 0.974±0.059, 0.969±0.059, and 0.959±0.059, the VIF are 0.750±0.087, 0.706±0.097, and 0.654±0.062, and NIQE are 1.396±0.401, 1.511±0.460, and 1.259±0.358, respectively. We proposed a novel multimodal MR image synthesis method based on a unified generative adversarial network. The network takes an image and its modality label as inputs and synthesizes multimodal images in a single forward pass. The results demonstrate that the proposed method is able to accurately synthesize multimodal MR images from a single MR image.

The Role of Cerebral Hypoperfusion in Multiple Sclerosis (ROCHIMS) Trial in Multiple Sclerosis: Insights From Negative Results.

Background: Accumulating evidence indicates that mitochondrial energy failure is involved in the progressive axonal degeneration in multiple sclerosis (MS). In patients with MS, it has been shown that both levels of N-acetylaspartate (NAA), which is a marker of axonal mitochondrial energy, and cerebral blood flow (CBF) are reduced in cerebral normal appearing white matter (NAWM). The latter is likely due to the vasoconstrictive action of endothelin-1 (ET-1) produced by reactive astrocytes, which is triggered by local proinflammatory cytokines. A preliminary study in patients with MS showed that CBF could be restored to normal values after a single dose of 62.5 mg of the ET-1 antagonist bosentan.Objective: To investigate whether restoring CBF in patients with relapsing remitting MS (RRMS) increases levels of NAA in cerebral NAWM and improves clinical symptoms.Methods: 27 RRMS patients were included in a 4 weeks proof-of-concept, randomized, double-blind placebo-controlled trial (ROCHIMS) to investigate whether bosentan 62.5 mg twice daily could increase the NAA/creatine (NAA/Cr) ratio in NAWM of the centrum semiovale. Magnetic resonance imaging (MRI) assessing CBF and NAA/Cr, and clinical evaluations were performed at baseline and at end of study. Separately from the clinical trial, 10 healthy controls underwent the same baseline multimodal brain MRI protocol as the MS patients.Results: Eleven patients in the bosentan arm and thirteen patients in the placebo arm completed the study. Bosentan did not increase CBF. However, we found that CBF in the patients was not different from that of the healthy controls. There were no effects on NAA levels and clinical symptoms.Conclusions: Our study showed that CBF in RRMS patients is not always decreased and that bosentan has no effect when CBF values are within the normal range. We hypothesize that in our patients there was no significant astrocytic production of ET-1 because they had a mild disease course, with minimal local inflammatory activity. Future studies with bosentan in MS should focus on patients with elevated ET-1 levels in cerebrospinal fluid or blood.

533-P: Predicting Treatment Response in Painful Diabetic Neuropathy Using Magnetic Resonance Brain Imaging

Background: Painful diabetic polyneuropathy (DPN) is difficult to manage as treatment response is often varied. The aim of this study was to explore the central mechanisms to explain the variance in treatment response. Methods: Forty-five consecutive patients who received intravenous lidocaine treatment for painful DPN were screened. All subjects completed a treatment efficacy questionnaire. Twenty-nine patients (responders n=14 and non-responders n=15) along with 26 healthy controls subsequently underwent multimodal brain magnetic resonance (MR) imaging to acquire anatomical and resting state functional data (3T, Achieva, Phillips Healthcare). Results: There was no significant difference in age (p=0.53), pain duration (p=0.89), diabetes duration (p=0.19) and clinical parameters of neuropathy (TCNS, p=0.16 and NTSS-6, p=0.61) between responders and non-responders to lidocaine treatment. Neither was there a difference in the use of concurrent neuropathic pain treatments. Non-responders to lidocaine had significantly lower mean S1 cortical volumes and mean number of vertices compared to responders and healthy controls (ANOVA p=0.02 and 0.02 respectively). There was no significant difference in S1 cortical volumes or vertices between responders and healthy controls. There was significantly greater resting state functional connectivity in lidocaine responders between the insula cortex [F(4)(13)=10.33; p-FDR=0.01] and the orbital frontal cortex [T(20)=3.88; p-FDR=0.02], amygdala [T(20)=3.65; p-FDR=0.02] and anterior cingulate gyrus [T(20)=4.04; p-FDR=0.01]. Conclusion: To the best of our knowledge, this is the first assessment of CNS mechanisms of treatment response in painful DPN. Key nociceptive processing areas showed decreased grey matter density (S1 cortex) and reduced functional connectivity (insula) relating to treatment response. MR neuroimaging may serve as a Central Pain Signature which could be used to predict treatment response. Disclosure K. Teh: None. I.D. Wilkinson: None. G.P. Sloan: None. S. Tesfaye: Advisory Panel; Self; Mitsubishi Tanabe Pharma Corporation, Wörwag Pharma. Speaker’s Bureau; Self; Abbott, AstraZeneca, Grunenthal Group, Napp Pharmaceuticals, Novo Nordisk Inc., Pfizer Inc. D. Selvarajah: None. Funding European Foundation for the Study of Diabetes; University of Sheffield

Impaired orthostatic heart rate recovery is associated with smaller thalamic volume: Results from The Irish Longitudinal Study on Aging (TILDA).

The thalamus is a central hub of the autonomic network and thalamic volume has been associated with high‐risk phenotypes for sudden cardiac death. Heart rate response to physiological stressors (e.g., standing) and the associated recovery patterns provide reliable indicators of both autonomic function and cardiovascular risk. Here we examine if thalamic volume may be a risk marker for impaired heart rate recovery in response to orthostatic challenge. The Irish Longitudinal Study on Aging involves a nationally representative sample of older individuals aged ≥50 years. Multimodal brain magnetic resonance imaging and orthostatic heart rate recovery were available for a cross‐sectional sample of 430 participants. Multivariable regression and linear mixed‐effects models were adjusted for head size, age, sex, education, body mass index, blood pressure, history of cardiovascular diseases and events, cardiovascular medication, diabetes mellitus, smoking, alcohol intake, timed up‐and‐go (a measure of physical frailty), physical exercise and depression. Smaller thalamic volume was associated with slower heart rate recovery (−1.4 bpm per 1 cm3 thalamic volume, 95% CI −2.01 to −0.82; p < .001). In multivariable analysis, participants with smaller thalamic volumes had a mean heart rate recovery −2.7 bpm slower than participants with larger thalamic volumes (95% CI −3.89 to −1.61; p < .001). Covariates associated with smaller thalamic volume included age, history of diabetes, and heavy alcohol consumption. Thalamic volume may be an indicator of the structural integrity of the central autonomic network. It may be a clinical biomarker for stratification of individuals at risk of autonomic dysfunction, cardiovascular events, and sudden cardiac death.

Segmenting Brain Tumor Using Cascaded V-Nets in Multimodal MR Images.

In this work, we propose a novel cascaded V-Nets method to segment brain tumor substructures in multimodal brain magnetic resonance imaging. Although V-Net has been successfully used in many segmentation tasks, we demonstrate that its performance could be further enhanced by using a cascaded structure and ensemble strategy. Briefly, our baseline V-Net consists of four levels with encoding and decoding paths and intra- and inter-path skip connections. Focal loss is chosen to improve performance on hard samples as well as balance the positive and negative samples. We further propose three preprocessing pipelines for multimodal magnetic resonance images to train different models. By ensembling the segmentation probability maps obtained from these models, segmentation result is further improved. In other hand, we propose to segment the whole tumor first, and then divide it into tumor necrosis, edema, and enhancing tumor. Experimental results on BraTS 2018 online validation set achieve average Dice scores of 0.9048, 0.8364, and 0.7748 for whole tumor, tumor core and enhancing tumor, respectively. The corresponding values for BraTS 2018 online testing set are 0.8761, 0.7953, and 0.7364, respectively. We also evaluate the proposed method in two additional data sets from local hospitals comprising of 28 and 28 subjects, and the best results are 0.8635, 0.8036, and 0.7217, respectively. We further make a prediction of patient overall survival by ensembling multiple classifiers for long, mid and short groups, and achieve accuracy of 0.519, mean square error of 367240 and Spearman correlation coefficient of 0.168 for BraTS 2018 online testing set.

Survival prediction models in motor neuron disease.

This study aimed to assess the predictive value of multimodal brain magnetic resonance imaging (MRI) on survival in a large cohort of patients with motor neuron disease (MND), in combination with clinical and cognitive features. Two hundred MND patients were followed up prospectively for a median of 4.13years. At baseline, subjects underwent neurological examination, cognitive assessment and brain MRI. Grey matter volumes of cortical and subcortical structures and diffusion tensor MRI metrics of white matter tracts were obtained. A multivariable Royston-Parmar survival model was created using clinical and cognitive variables. The increase of survival prediction accuracy provided by MRI variables was assessed. The multivariable clinical model included predominant upper or lower motor neuron presentations and diagnostic delay as significant prognostic predictors, reaching an area under the receiver operating characteristic curve (AUC) of a 4-year survival prediction of 0.79. The combined clinical and MRI model including selected grey matter fronto-temporal volumes and diffusion tensor MRI metrics of the corticospinal and extra-motor tracts reached an AUC of 0.89. Considering amyotrophic lateral sclerosis patients only, the clinical model including diagnostic delay and semantic fluency scores provided an AUC of 0.62, whereas the combined clinical and MRI model reached an AUC of 0.77. Our study demonstrated that brain MRI measures of motor and extra-motor structural damage, when combined with clinical and cognitive features, are useful predictors of survival in patients with MND, particularly when a diagnosis of amyotrophic lateral sclerosis is made.

Multi-branch convolutional neural network for multiple sclerosis lesion segmentation

In this paper, we present an automated approach for segmenting multiple sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our method is based on a deep end-to-end 2D convolutional neural network (CNN) for slice-based segmentation of 3D volumetric data. The proposed CNN includes a multi-branch downsampling path, which enables the network to encode information from multiple modalities separately. Multi-scale feature fusion blocks are proposed to combine feature maps from different modalities at different stages of the network. Then, multi-scale feature upsampling blocks are introduced to upsize combined feature maps to leverage information from lesion shape and location. We trained and tested the proposed model using orthogonal plane orientations of each 3D modality to exploit the contextual information in all directions. The proposed pipeline is evaluated on two different datasets: a private dataset including 37 MS patients and a publicly available dataset known as the ISBI 2015 longitudinal MS lesion segmentation challenge dataset, consisting of 14 MS patients. Considering the ISBI challenge, at the time of submission, our method was amongst the top performing solutions. On the private dataset, using the same array of performance metrics as in the ISBI challenge, the proposed approach shows high improvements in MS lesion segmentation compared with other publicly available tools.

Deep/mixed cerebral microbleeds are associated with cognitive dysfunction through thalamocortical connectivity disruption: The Taizhou Imaging Study.

BackgroundCerebral microbleeds (CMBs) are considered to be risk factors for cognitive dysfunction. The specific pathology and clinical manifestations of CMBs are different based on their locations. We investigated the association between CMBs at different locations and cognitive dysfunction and explored the potential underlying pathways in a rural Han Chinese population.MethodsWe used baseline data from 562 community-dwelling adults (55–65 years old) in the Taizhou Imaging Study between 2013 and 2015. All individuals underwent multimodal brain magnetic resonance imaging (MRI) and 444 subjects completed neuropsychological tests: the Mini-Mental Status Examination and the Montreal Cognitive Assessment. Multinomial logistic regression was used to estimate the association between CMBs and cognitive dysfunction. The volume of brain regions and white matter microstructure were analyzed using Freesurfer and tract-based spatial statistics, respectively.ResultsCMBs were detected in 104 individuals (18.5%) in our study. Multinomial logistic regression found deep/mixed CMBs were associated with global cognitive dysfunction (OR 3.52; 95% CI 1.21 to 10.26), whereas lobar CMBs (OR 1.76; 95% CI 0.56 to 5.53) were not. Quantification of multimodal brain MRI showed that deep/mixed CMBs were accompanied by decreased thalamic volume and loss of fractional anisotropy of bilateral anterior thalamic radiations.ConclusionDeep/mixed CMBs were associated with cognitive dysfunction in this Chinese cross-sectional study. Disruption of thalamocortical connectivity might be a potential pathway underlying this relationship.

Multimodal Imaging of Retired Professional Contact Sport Athletes Does Not Provide Evidence of Structural and Functional Brain Damage.

Long-term consequences of playing professional football and hockey on brain function and structural neuronal integrity are unknown. To investigate multimodal metabolic and structural brain magnetic resonance imaging (MRI) differences in retired professional contact sport athletes compared with noncontact sport athletes. Twenty-one male contact sport athletes and 21 age-matched noncontact sport athletes were scanned on a 3 tesla (3T) MRI using a multimodal imaging approach. The MRI outcomes included presence, number, and volume of focal white matter signal abnormalities, volumes of global and regional tissue-specific brain structures, diffusion-tensor imaging tract-based spatial statistics measures of mean diffusivity and fractional anisotropy, quantitative susceptibility mapping of deep gray matter, presence, number, and volume of cerebral microbleeds, MR spectroscopy N-acetyl-aspartate, glutamate, and glutamine concentrations relative to creatine and phosphor creatine of the corpus callosum, and perfusion-weighted imaging mean transit time, cerebral blood flow, and cerebral blood volume outcomes. Subjects were also classified as having mild cognitive impairment. No significant differences were found for structural or functional MRI measures between contact sport athletes and noncontact sport athletes. This multimodal imaging study did not show any microstructural, metabolic brain tissue injury differences in retired contact versus non-contact sport athletes.

Multi-modal Brain MRI in Subjects with PD and iRBD.

Idiopathic rapid eye movement sleep behavior disorder (iRBD) is a condition that often evolves into Parkinson's disease (PD). Therefore, by monitoring iRBD it is possible to track the neurodegeneration of individuals who may progress to PD. Here we aimed at piloting the characterization of brain tissue properties in mid-brain subcortical regions of 10 healthy subjects, 8 iRBD, and 9 early-diagnosed PD. We used a battery of magnetic resonance imaging (MRI) contrasts at 3 T, including adiabatic and non-adiabatic rotating frame techniques developed by our group, along with diffusion tensor imaging (DTI) and resting-state fMRI. Adiabatic T1ρ and T2ρ, and non-adiabatic RAFF4 (Relaxation Along a Fictitious Field in the rotating frame of rank 4) were found to have lower coefficient of variations and higher sensitivity to detect group differences as compared to DTI parameters such as fractional anisotropy and mean diffusivity. Significantly longer T1ρ were observed in the amygdala of PD subjects vs. controls, along with a trend of lower functional connectivity as measured by regional homogeneity, thereby supporting the notion that amygdalar dysfunction occurs in PD. Significant abnormalities in reward networks occurred in iRBD subjects, who manifested lower network strength of the accumbens. In agreement with previous studies, significantly longer T1ρ occurred in the substantia nigra compacta of PD vs. controls, indicative of neuronal degeneration, while regional homogeneity was lower in the substantia nigra reticulata. Finally, other trend-level findings were observed, i.e., lower RAFF4 and T2ρ in the midbrain of iRBD subjects vs. controls, possibly indicating changes in non-motor features as opposed to motor function in the iRBD group. We conclude that rotating frame relaxation methods along with functional connectivity measures are valuable to characterize iRBD and PD subjects, and with proper validation in larger cohorts may provide pathological signatures of iRBD and PD.

Cortical Thickness and Microstructural White Matter Changes Detect Amnestic Mild Cognitive Impairment.

Both the apolipoprotein E (APOE) ɛ4 allele and amnestic mild cognitive impairment (aMCI) are considered to be risk factors for Alzheimer's disease (AD). The primary aim of this study was to determine whether the aMCI-related abnormality in gray matter (GM) cortical thickness and white matter (WM) tracts integrity would be modified by the APOE genotype. A total of 146 older adults, including 64 aMCI patients (28 ɛ4 carriers and 36 non-carriers) and 82 healthy controls (39 ɛ4 carriers and 43 non-carriers), underwent a standardized clinical interview, neuropsychological battery assessment, and multi-modal brain magnetic resonance imaging scans. Compared with control subjects, the patients with aMCI showed significantly reduced cortical thickness bilaterally in the parahippocampal gyrus and disrupted WM integrity in the limbic tracts (e.g., increased mean diffusivity in the right parahippocampal cingulum and bilateral uncinate fasciculus). However, no significant main effects of the APOE genotype and diagnosis-by-genotype interaction on GM thickness and WM integrity were observed. Further, diffusivity measures of the limbic WM tracts were significantly correlated with the parahippocampal atrophy in aMCI. Importantly, the parahippocampal thickness and diffusivity measures of the limbic WM tracts were significantly correlated with the cognitive performance (i.e., episodic memory Z score) in patients with aMCI. These results demonstrate that WM microstructural disruptions in the limbic tracts are present at the early stage of AD in an APOE-independent manner; and this degeneration may occur progressively, in parallel with parahippocampal atrophy, and may specifically contribute to early initial impairment in episodic memory.

Progression of Brain Network Alterations in Cerebral Amyloid Angiopathy.

We recently showed that cerebral amyloid angiopathy (CAA) is associated with functionally relevant brain network impairments, in particular affecting posterior white matter connections. Here we examined how these brain network impairments progress over time. Thirty-three patients with probable CAA underwent multimodal brain magnetic resonance imaging at 2 time points (mean follow-up time: 1.3±0.4 years). Brain networks of the hemisphere free of intracerebral hemorrhages were reconstructed using fiber tractography and graph theory. The global efficiency of the network and mean fractional anisotropies of posterior-posterior, frontal-frontal, and posterior-frontal network connections were calculated. Patients with moderate versus severe CAA were defined based on microbleed count, dichotomized at the median (median=35). Global efficiency of the intracerebral hemorrhage-free hemispheric network declined from baseline to follow-up (-0.008±0.003; P=0.029). The decline in global efficiency was most pronounced for patients with severe CAA (group×time interaction P=0.03). The decline in global network efficiency was associated with worse executive functioning (β=0.46; P=0.03). Examination of subgroups of network connections revealed a decline in fractional anisotropies of posterior-posterior connections at both levels of CAA severity (-0.006±0.002; P=0.017; group×time interaction P=0.16). The fractional anisotropies of posterior-frontal and frontal-frontal connections declined in patients with severe but not moderate CAA (group×time interaction P=0.007 and P=0.005). Associations were independent of change in white matter hyperintensity volume. Brain network impairment in patients with CAA worsens measurably over just 1.3-year follow-up and seem to progress from posterior to frontal connections with increasing disease severity.

Do white matter hyperintensities mediate the association between brain iron deposition and cognitive abilities in older people?

Background and purposeSeveral studies have reported associations between brain iron deposits (IDs), white matter hyperintensities (WMHs) and cognitive ability in older individuals. Whether the association between brain IDs and cognitive abilities in older people is mediated by or independent of total brain tissue damage represented by WMHs visible on structural magnetic resonance imaging (MRI) was examined.MethodsData from 676 community‐dwelling individuals from the Lothian Birth Cohort 1936, with Mini‐Mental State Examination scores >24, who underwent detailed cognitive testing and multimodal brain MRI at mean age 72.7 years were analysed. Brain IDs were assessed automatically following manual editing. WMHs were assessed semi‐automatically. Brain microbleeds were visually counted. Structural equation modelling was used to test for mediation.ResultsOverall, 72.8% of the sample had IDs with a median total volume of 0.040 ml (i.e. 0.004% of the total brain volume). The total volume of IDs, significantly and negatively associated with general cognitive function (standardized β = −0.17, P < 0.01), was significantly and positively associated with WMH volume (std β = 0.13, P = 0.03). WMH volume had a significant negative association with general cognitive function, independent of IDs (std β = −0.13, P < 0.01). The association between cognition and IDs in the brain stem (and minimally the total brain iron load) was partially and significantly mediated by WMH volume (P = 0.03).ConclusionsThe negative association between brain IDs and cognitive ability in the elderly is partially mediated by WMHs, with this mediation mainly arising from the iron deposition load in the brain stem. IDs might be an indicator of small vessel disease that predisposes to white matter damage, affecting the neuronal networks underlying higher cognitive functioning.

Association of Magnetic Resonance Imaging Markers of Cerebrovascular Disease Burden and Cognition.

The present study sought to examine the association between the burden of cerebrovascular disease (CeVD) as assessed by multimodal magnetic resonance imaging and neurocognitive function. Cognitively impaired patients and controls were tested on an extensive neuropsychological battery and underwent multimodal brain magnetic resonance imaging. CeVD markers determined from magnetic resonance imaging included the presence of multiple lacunes, multiple cerebral microbleeds, and moderate or severe white matter hyperintensities as markers for small-vessel disease and cortical stroke and intracranial stenosis as markers for large-vessel disease. A weighted CeVD burden score was constructed, and its association with global and domain-specific cognitive performance was investigated. A total of 305 cases and 94 controls were included in the analysis. A graded association of CeVD burden with neurocognitive function was found. Moreover, a clear threshold of CeVD burden was associated with severe impairment. White matter hyperintensities was associated with global neurocognitive deficits, whereas microbleeds were associated with domain-specific impairments. The weighted CeVD burden score comprising markers of both small- and large-vessel diseases were associated with deficits in both global and domain-specific neurocognitive function. Additional studies are needed to validate the use of this CeVD burden score for the prediction of dementia.

Automatic Diagnosis of Brain Magnetic Resonance Images based on Riemannian Geometry

Automated brain tumor detection and segmentation, from medical images, is one of the most challenging. The authors present, in this paper, an automatic diagnosis of brain magnetic resonance image. The goal is to prepare the image of the human brain to locate the existence of abnormal tissues in multi-modal brain magnetic resonance images. The authors start from the image acquisition, reduce information, brain extraction, and then brain region diagnosis. Brain extraction is the most important preprocessing step for automatic brain image analysis. The authors consider the image as residing in a Riemannian space and they based on Riemannian manifold to develop an algorithm to extract brain regions, these regions used in other algorithm to brain tumor detection, segmentation and classification. Riemannian Manifolds show the efficient results to brain extraction and brain analysis for multi-modal resonance magnetic images.

Multimodal brain magnetic resonance imaging and perfusion computer assisted tomography scanning in evaluation of cerebroprotective efficacy of antihypertensive therapy

A multimodal diagnostic study of the brain was carried out in 22 patients with arterial hypertension (mean systolic blood pressure 152,8 ± 7,6 mm Hg, mean diastolic blood pressure 94,6 ± 5,2 mm Hg), without cardiovascular events in anamnesis. Magnetic resonance imaging (MRI) imaging and dynamic contrast-enhanced perfusion X-ray computer assisted tomography scan (DynCT) of the brain were performed at admission and after six months of antihypertensive treatment. Based on the MRI and DynCT visual data the extent of periventricular oedema, dimensions of liquor system and regional cerebral blood flow (as ml/min/100 g tissue) were quantified. The quantitative MRI and DynCT indices were analyzed regarding the decrease of blood pressure. Significant decrease of periventricular oedema and improvement in perfusion of basal ganglii area were observed in patients demonstrated decrease in systolic blood pressure for 12-28 mm Hg. The degree of the blood pressure decrease was not associated with the significant MRI and DynCT data improvement. Hencefore, we conclude that the brain MRI and perfusion DynCT data can be employed for evaluation of cerebroprotective effects of antihypertensive therapy.

Simulation of brain tumors in MR images for evaluation of segmentation efficacy.

Obtaining validation data and comparison metrics for segmentation of magnetic resonance images (MRI) are difficult tasks due to the lack of reliable ground truth. This problem is even more evident for images presenting pathology, which can both alter tissue appearance through infiltration and cause geometric distortions. Systems for generating synthetic images with user-defined degradation by noise and intensity inhomogeneity offer the possibility for testing and comparison of segmentation methods. Such systems do not yet offer simulation of sufficiently realistic looking pathology. This paper presents a system that combines physical and statistical modeling to generate synthetic multi-modal 3D brain MRI with tumor and edema, along with the underlying anatomical ground truth, Main emphasis is placed on simulation of the major effects known for tumor MRI, such as contrast enhancement, local distortion of healthy tissue, infiltrating edema adjacent to tumors, destruction and deformation of fiber tracts, and multi-modal MRI contrast of healthy tissue and pathology. The new method synthesizes pathology in multi-modal MRI and diffusion tensor imaging (DTI) by simulating mass effect, warping and destruction of white matter fibers, and infiltration of brain tissues by tumor cells. We generate synthetic contrast enhanced MR images by simulating the accumulation of contrast agent within the brain. The appearance of the the brain tissue and tumor in MRI is simulated by synthesizing texture images from real MR images. The proposed method is able to generate synthetic ground truth and synthesized MR images with tumor and edema that exhibit comparable segmentation challenges to real tumor MRI. Such image data sets will find use in segmentation reliability studies, comparison and validation of different segmentation methods, training and teaching, or even in evaluating standards for tumor size like the RECIST criteria (response evaluation criteria in solid tumors).