BRAHMA: Population specific T1, T2, and FLAIR weighted brain templates and their impact in structural and functional imaging studies

Differences in brain morphology across population groups necessitate creation of population-specific Magnetic Resonance Imaging (MRI) brain templates for interpretation of neuroimaging data. Variations in the neuroanatomy in a genetically heterogeneous population make the development of a population-specific brain template for the Indian subcontinent imperative. A dataset of high-resolution 3D T1, T2-weighted, and FLAIR images acquired from a group of 113 volunteers (M/F - 56/57, mean age-28.96 ± 7.80 years) are used to construct T1, T2-weighted, and FLAIR templates, collectively referred to as Indian Brain Template, “BRAHMA”. A processing pipeline is developed and implemented in a MATLAB based toolbox for template construction and generation of tissue probability maps and segmentation atlases, with additional labels for deep brain regions such as the Substantia Nigra generated from the T2-weighted and FLAIR templates. The use of BRAHMA template for analysis of structural and functional neuroimaging data obtained from Indian participants, provides improved accuracy with statistically significant results over that obtained using the ICBM-152 (International Consortium for Brain Mapping) template. Our results indicate that segmentations generated on structural images are closer in volume to those obtained from registration to the BRAHMA template than to the ICBM-152. Furthermore, functional MRI data obtained for Working Memory and Finger Tapping paradigms processed using the BRAHMA template show a significantly higher percentage of the activation area than ICBM-152 in relevant brain regions, i.e. the left middle frontal gyrus, and the left and right precentral gyri, respectively. The availability of different image contrasts, tissue maps, and segmentation atlases makes the BRAHMA template a comprehensive tool for multi-modal image analysis in laboratory and clinical settings.     

Microtesla MRI of the human brain combined with MEG

One of the challenges in functional brain imaging is integration of complementary imaging modalities, such as magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). MEG, which uses highly sensitive superconducting quantum interference devices (SQUIDs) to directly measure magnetic fields of neuronal currents, cannot be combined with conventional high-field MRI in a single instrument. Indirect matching of MEG and MRI data leads to significant co-registration errors. A recently proposed imaging method--SQUID-based microtesla MRI--can be naturally combined with MEG in the same system to directly provide structural maps for MEG-localized sources. It enables easy and accurate integration of MEG and MRI/fMRI, because microtesla MR images can be precisely matched to structural images provided by high-field MRI and other techniques. Here we report the first images of the human brain by microtesla MRI, together with auditory MEG (functional) data, recorded using the same seven-channel SQUID system during the same imaging session. The images were acquired at 46 microT measurement field with pre-polarization at 30 mT. We also estimated transverse relaxation times for different tissues at microtesla fields. Our results demonstrate feasibility and potential of human brain imaging by microtesla MRI. They also show that two new types of imaging equipment--low-cost systems for anatomical MRI of the human brain at microtesla fields, and more advanced instruments for combined functional (MEG) and structural (microtesla MRI) brain imaging--are practical.     

Curvilinear reconstruction of 3D magnetic resonance imaging in patients with partial epilepsy: A pilot study

In an attempt to better delineate the abnormalities associated with focal cortical dysgenesis, we performed curvilinear reformatting of the cortex from 3D magnetic resonance (MR) images. Illustrative patients with partial seizures and conventional orthogonal MRI evaluation show that small regions of cortical thickening suggestive of focal dysplastic lesions may not be recognized. In three such patients the curvilinear reformatting demonstrated two additional focal abnormalities of the cortical gyri and better defined the two focal lesions found on conventional orthogonal MR images. This method promises to a be useful tool in the evaluation of epileptic patients with proven or suspected subtle structural cortical abnormalities, particularly focal neuronal migration disorders where cortical thickening, abnormal gyral pattern, and poor delineation of the gray-white matter transition are the main findings.     

Cerebral perfusion alterations in epileptic patients during peri-ictal and post-ictal phase: PASL vs DSC-MRI

Non-invasive pulsed arterial spin labeling (PASL) MRI is a method to study brain perfusion that does not require the administration of a contrast agent, which makes it a valuable diagnostic tool as it reduces cost and side effects. The purpose of the present study was to establish the viability of PASL as an alternative to dynamic susceptibility contrast (DSC-MRI) and other perfusion imaging methods in characterizing changes in perfusion patterns caused by seizures in epileptic patients. We evaluated 19 patients with PASL. Of these, the 9 affected by high-frequency seizures were observed during the peri-ictal period (within 5 hours since the last seizure), while the 10 patients affected by low-frequency seizures were observed in the post-ictal period. For comparison, 17/19 patients were also evaluated with DSC-MRI and CBF/CBV. PASL imaging showed focal vascular changes, which allowed the classification of patients in three categories: 8 patients characterized by increased perfusion, 4 patients with normal perfusion and 7 patients with decreased perfusion. PASL perfusion imaging findings were comparable to those obtained by DSC-MRI. Since PASL is a) sensitive to vascular alterations induced by epileptic seizures, b) comparable to DSC-MRI for detecting perfusion asymmetries, c) potentially capable of detecting time-related perfusion changes, it can be recommended for repeated evaluations, to identify the epileptic focus, and in follow-up and/or therapy-response assessment.     

Detection of heterotopic gray matter in children by magnetic resonance imaging

Heterotopic gray matter results from abnormal brain development and is a recognized focus of seizures. It may be associated with mental retardation and/or severe malformations of the brain. Three patients with heterotopia of gray matter were identified by magnetic resonance imaging (MRI). CT failed to detect the heterotopic gray matter in each case. One child was referred for removal of a neoplasm based on CT studies until MRI demonstrated the developmental nature of his condition. One infant had severely dysplastic left cerebral hemisphere associated with heterotopic gray matter and the syndrome of Hypomelanosis of Ito. All three children suffered from seizures and/or mental retardation. MRI provided important information in the management of each case and appears to be the imaging method of choice in evaluating children with seizures or retardation for heterotopic gray matter in the brain.     

An algorithm to estimate anatomical connectivity between brain regions using diffusion MRI

The study of anatomical connectivity is essential for interpreting functional MRI data and for establishing how brain areas are linked together into networks to support higher-order functions. Diffusion-weighted MR images (DWI) and tractography provide a unique noninvasive tool to explore the connectional architecture of the brain. The identification of anatomical circuits associated with a specific function can be better accomplished by the joint application of diffusion and functional MRI. In this article, we propose a simple algorithm to identify the set of pathways between two regions of interest. The method is based upon running deterministic tractography from all possible starting positions in the brain and selecting trajectories that intersect both regions. We compare results from single-fiber tractography using diffusion tensor imaging and from multi-fiber tractography using reduced-encoding persistent angular structure (PAS) MRI on standard DWI datasets from healthy human volunteers. Our results show that, in comparison with single-fiber tractography, the multi-fiber technique reveals additional putative routes of connection. We demonstrate highly consistent results of the proposed technique over a cohort of 16 healthy subjects.     

Assessing functional connectivity in the human brain by fMRI

Functional magnetic resonance imaging (fMRI) is widely used to detect and delineate regions of the brain that change their level of activation in response to specific stimuli and tasks. Simple activation maps depict only the average level of engagement of different regions within distributed systems. FMRI potentially can reveal additional information about the degree to which components of large-scale neural systems are functionally coupled together to achieve specific tasks. In order to better understand how brain regions contribute to functionally connected circuits, it is necessary to record activation maps either as a function of different conditions, at different times or in different subjects. Data obtained under different conditions may then be analyzed by a variety of techniques to infer correlations and couplings between nodes in networks. Several multivariate statistical methods have been adapted and applied to analyze variations within such data. An approach of particular interest that is suited to studies of connectivity within single subjects makes use of acquisitions of runs of MRI images obtained while the brain is in a so-called steady state, either at rest (i.e., without any specific stimulus or task) or in a condition of continuous activation. Interregional correlations between fluctuations of MRI signal potentially reveal functional connectivity. Recent studies have established that interregional correlations between different components of circuits in each of the visual, language, motor and working memory systems can be detected in the resting state. Correlations at baseline are changed during the performance of a continuous task. In this review, various methods available for assessing connectivity are described and evaluated.     

Cerebral abnormalities in Wilson''s disease as evaluated by ultra-low-field magnetic resonance imaging and computerized image processing

The cerebral involvement of a 13-yr-old boy with Wilson's disease was serially evaluated during the first 18 mo of D-penicillamine treatment. An ultra-low-field magnetic resonance imaging (ULF MRI) system, operating at 0.02 T, with computerized image processing was used. The half-yr period prior to the clinical diagnosis was set, the patient had showed poor school performance, emotional lability, deteriorating handwriting, progressively slow, gross, and fine motor functions, and a fixed rigid smile. No overt signs of liver disease were found. With D-penicillamine treatment (1–1.5 g/d) a continuous improvement was seen. The pretreatment MRI investigation showed pronounced pathological transformation in the basal ganglia. However, changes were seen also in most other parts of the brain indicating diffuse involvement. During treatment the computerized MR images became gradually more normal. The current magnetic resonance imaging system with computerized image processing is a sensitive and simple method for evaluation of subtle parenchymal changes of the brain.     

Clinical applications: MRI, SPECT, and PET

MRI, PET, and SPECT are all used to image abnormalities in the epileptic brain. Comparison of the techniques is difficult because they measure different aspects of the epileptic process—structure, metabolism, and perfusion. SPECT is the only one that can be systematically applied during seizures, while all three are used to image interictal abnormalities. Literature review suggests that of interictal techniques, PET has the highest diagnostic sensitivity in temporal lobe epilepsy (TLE) (84% vs. 66% for SPECT, 55% for qualitative MRI, 71% for quantitative MRI) while SPECT has the highest sensitivity in extratemporal epilepsy (ETE) (60% vs. 43% for MRI and 33% for PET). The highest diagnostic sensitivity and specificity were achieved by ictal imaging with SPECT (90% in TLE, 81% in ETE). The techniques, however, were not always redundant. One reason for the wide discrepancy of results in TLE and ETE might be the differing pathologic substrates. A literature review of imaging findings associated with mesial temporal sclerosis (MTS), developmental lesion or tumor as the underlying abnormality associated with epilepsy supports this explantion. PET and MRI are much more sensitive to MTS than SPECT (100%, 95% vs. 70%). On the other hand, in developmental lesions the three techniques are equally sensitive (88–92%) and in tumors, MRI was most sensitive (96%) and SPECT least (82%). A study at NIH explains the differing sensitivities: using PET to measure both blood flow and metabolism revealed discrepant findings in the same patients. Preliminary evidence also indicates that the distribution of hyperperfusion on ictal SPECT can differentiate subtypes of TLE. Combining the results of refined imaging techniques holds great promise in epilepsy localization and diagnosis.     

Increased differentiation of intracranial white matter lesions by multispectral 3D-tissue segmentation: preliminary results

MRI is a very sensitive imaging modality, however with relatively low specificity. The aim of this work was to determine the potential of image post-processing using 3D-tissue segmentation technique for identification and quantitative characterization of intracranial lesions primarily in the white matter. Forty subjects participated in this study: 28 patients with brain multiple sclerosis (MS), 6 patients with subcortical ischemic vascular dementia (SIVD), and 6 patients with lacunar white matter infarcts (LI). In routine MR imaging these pathologies may be almost indistinguishable. The 3D-tissue segmentation technique used in this study was based on three input MR images (T(1), T(2)-weighted, and proton density). A modified k-Nearest-Neighbor (k-NN) algorithm optimized for maximum computation speed and high quality segmentation was utilized. In MS lesions, two very distinct subsets were classified using this procedure. Based on the results of segmentation one subset probably represent gliosis, and the other edema and demyelination. In SIVD, the segmented images demonstrated homogeneity, which differentiates SIVD from the heterogeneity observed in MS. This homogeneity was in agreement with the general histological findings. The LI changes pathophysiologically from subacute to chronic. The segmented images closely correlated with these changes, showing a central area of necrosis with cyst formation surrounded by an area that appears like reactive gliosis. In the chronic state, the cyst intensity was similar to that of CSF, while in the subacute stage, the peripheral rim was more prominent. Regional brain lesion load were also obtained on one MS patient to demonstrate the potential use of this technique for lesion load measurements. The majority of lesions were identified in the parietal and occipital lobes. The follow-up study showed qualitatively and quantitatively that the calculated MS load increase was associated with brain atrophy represented by an increase in CSF volume as well as decrease in "normal" brain tissue volumes. Importantly, these results were consistent with the patient's clinical evolution of the disease after a six-month period. In conclusion, these results show there is a potential application for a 3D tissue segmentation technique to characterize white matter lesions with similar intensities on T(2)-weighted MR images. The proposed methodology warrants further clinical investigation and evaluation in a large patient population.     

10B-editing 1H-detection and 19F MRI strategies to optimize boron neutron capture therapy

Boron neutron capture therapy (BNCT) is a binary radiation therapy used to treat malignant brain tumours. It is based on the nuclear reaction (10B + n th --> [11B*] --> alpha + 7Li + 2.79 MeV) that occurs when 10B captures a thermal neutron to yield alpha particles and recoiling 7Li nuclei, both responsible of tumour cells destruction by short range and high ionization energy release. The clinical success of the therapy depends on the selective accumulation of the 10B carriers in the tumour and on the high thermal neutron capture cross-section of 10B. Magnetic resonance imaging (MRI) methods provide the possibility of monitoring, through 10B nuclei, the metabolic and physiological processes suitable to optimize the BNCT procedure. In this study, spatial distribution mapping of borocaptate (BSH) and 4-borono-phenylalanine (BPA), the two boron carriers used in clinical trials, has been obtained. The BSH map in excised rat brain and the 19F-BPA image in vivo rat brain, representative of BPA spatial distribution, were reported. The BSH image was obtained by means of double-resonance 10B-editing 1H-detection sequence, named M-Bend, exploiting the J-coupling interaction between 10B and 1H nuclei. Conversely, the BPA map was obtained by 19F-BPA using 19F-MRI. Both images were obtained at 7 T, in C6 glioma-bearing rat brain. Our results demonstrate the powerful of non conventional MRI techniques to optimize the BNCT procedure.     

多孔岩芯渗透率与孔隙度的MRI研究

运用自旋回波序列,得到一系列用盐水饱和的多孔岩芯的磁共振自旋密度象,可以直观、准确地观察岩芯的结构,并对象的信号强度与渗透率和孔隙度之间的相关关系进行了分析,发现它们之间存在良好的定性关系,得到了较好的结果.     

基于AI+MRI的影像诊断的样本增广与批量标注方法

训练样本是所有领域人工智能（AI）研发的关键因素.目前，基于人工智能+磁共振成像（AI+MRI）的影像诊断存在着训练样本的有效标注数量和类型无法满足研发需求的瓶颈问题.本文利用临床MRI设备对志愿者或阳性病例进行正常或重点病灶区的定量扫描，获取高分辨率各向同性的纵向弛豫时间（T₁）、横向弛豫时间（T₂）、质子密度（Pd）和表观扩散系数（ADC）等物理信息的多维数据矩阵，作为原始数据.开发虚拟MRI技术平台，对原始数据（相当于数字人体样本）进行虚拟扫描，实现不同序列不同参数下的多种类磁共振图像输出.选择感兴趣组织具有最好边界区分度的图像种类，经有经验的影像医生对其进行手动勾画并轨迹跟踪形成三维MASK标注矩阵，作为其他种类图像的图像勾画标注模板，从而实现低成本、高效率的MRI样本增广和批量标注.该平台以临床少量阳性病例作为输入，进行样本增广和标注，极大地减少AI对实际扫描样本的要求，降低了影像医生的精力和时间投入，极大地节省了成本，并输出了数量足够的磁共振图像，为基于AI+MRI的影像诊断研发提供低成本的训练数据解决方案.     

A statistical fMRI model for differential T2* contrast incorporating T1 and T2* of gray matter

Relaxation parameter estimation and brain activation detection are two main areas of study in magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI). Relaxation parameters can be used to distinguish voxels containing different types of tissue whereas activation determines voxels that are associated with neuronal activity. In fMRI, the standard practice has been to discard the first scans to avoid magnetic saturation effects. However, these first images have important information on the MR relaxivities for the type of tissue contained in voxels, which could provide pathological tissue discrimination. It is also well-known that the voxels located in gray matter (GM) contain neurons that are to be active while the subject is performing a task. As such, GM MR relaxivities can be incorporated into a statistical model in order to better detect brain activation. Moreover, although the MR magnetization physically depends on tissue and imaging parameters in a nonlinear fashion, a linear model is what is conventionally used in fMRI activation studies. In this study, we develop a statistical fMRI model for Differential T₂^? ConTrast Incorporating T₁ and T₂^? of GM, so-called DeTeCT-ING Model, that considers the physical magnetization equation to model MR magnetization; uses complex-valued time courses to estimate T₁ and T₂^? for each voxel; then incorporates gray matter MR relaxivities into the statistical model in order to better detect brain activation, all from a single pulse sequence by utilizing the first scans.     

Continuous EEG-fMRI in patients with partial epilepsy and focal interictal slow-wave discharges on EEG

Purpose

To verify whether in patients with partial epilepsy and routine electroenecephalogram (EEG) showing focal interictal slow-wave discharges without spikes combined EEG–functional magnetic resonance imaging (fMRI) would localize the corresponding epileptogenic focus, thus providing reliable information on the epileptic source.

Methods

Eight patients with partial epileptic seizures whose routine scalp EEG recordings on presentation showed focal interictal slow-wave activity underwent EEG–fMRI. EEG data were continuously recorded for 24 min (four concatenated sessions) from 18 scalp electrodes, while fMRI scans were simultaneously acquired with a 1.5-Tesla magnetic resonance imaging (MRI) scanner. After recording sessions and MRI artefact removal, EEG data were analyzed offline. We compared blood oxygen level-dependent (BOLD) signal changes on fMRI with EEG recordings obtained at rest and during activation (with and without focal interictal slow-wave discharges).

Results

In all patients, when the EEG tracing showed the onset of focal slow-wave discharges on a few lateralized electrodes, BOLD-fMRI activation in the corresponding brain area significantly increased. We detected significant concordance between focal EEG interictal slow-wave discharges and focal BOLD activation on fMRI. In patients with lesional epilepsy, the epileptogenic area corresponded to the sites of increased focal BOLD signal.

Conclusions

Even in patients with partial epilepsy whose standard EEGs show focal interictal slow-wave discharges without spikes, EEG–fMRI can visualize related focal BOLD activation thus providing useful information for pre-surgical planning.     

Multispectral MR images segmentation based on fuzzy knowledge and modified seeded region growing

Magnetic resonance imaging (MRI) is a valuable diagnostic tool in medical science due to its capability for soft-tissue characterization and three-dimensional visualization. One potential application of MRI in clinical practice is brain parenchyma classification and segmentation. Based on fuzzy knowledge and modified seeded region growing, this work proposes a novel image segmentation method, called Fuzzy Knowledge-Based Seeded Region Growing (FKSRG), for multispectral MR images. In this work, fuzzy knowledge includes the fuzzy edge, fuzzy similarity and fuzzy distance, which are obtained from relationships between pixels in multispectral MR images and are applied to the modified seeded regions growing process. In conventional regions merging, the final number of regions is unknown. Therefore, a Target Generation Process is proposed and applied to support conventional regions merging, such that the FKSRG method does not over- or undersegment images. Finally, two image sets, namely, computer-generated phantom images and real MR images, are used in experiments to assess the effectiveness of the proposed FKSRG method. Experimental results demonstrate that the FKSRG method segments multispectral MR images much more effectively than the Functional MRI of the Brain Automated Segmentation Tool, K-means and Support Vector Machine methods.     

Brain MRI tissue classification based on local Markov random fields

A new method for tissue classification of brain magnetic resonance images (MRI) of the brain is proposed. The method is based on local image models where each models the image content in a subset of the image domain. With this local modeling approach, the assumption that tissue types have the same characteristics over the brain needs not to be evoked. This is important because tissue type characteristics, such as T1 and T2 relaxation times and proton density, vary across the individual brain and the proposed method offers improved protection against intensity non-uniformity artifacts that can hamper automatic tissue classification methods in brain MRI. A framework in which local models for tissue intensities and Markov Random Field (MRF) priors are combined into a global probabilistic image model is introduced. This global model will be an inhomogeneous MRF and it can be solved by standard algorithms such as iterative conditional modes. The division of the whole image domain into local brain regions possibly having different intensity statistics is realized via sub-volume probabilistic atlases. Finally, the parameters for the local intensity models are obtained without supervision by maximizing the weighted likelihood of a certain finite mixture model. For the maximization task, a novel genetic algorithm almost free of initialization dependency is applied. The algorithm is tested on both simulated and real brain MR images. The experiments confirm that the new method offers a useful improvement of the tissue classification accuracy when the basic tissue characteristics vary across the brain and the noise level of the images is reasonable. The method also offers better protection against intensity non-uniformity artifact than the corresponding method based on a global (whole image) modeling scheme.     

Signal characteristics of FLAIR related to water content: comparison with conventional spin echo imaging in infarcted rat brain

To examine the correlation between tissue water content and signal intensity on fluid-attenuated inversion recovery (FLAIR) images, we analyzed infarcted rat brain, verified the results by theoretical simulation, and compared them with conventional spin-echo images. We produced brain infarction with cavitation in five rats by middle cerebral artery occlusion. After in vivo MRI, histologic sections of the MRI plane were obtained. We measured the signal intensity of regions on FLAIR and spin-echo images, and measured the area of cavitation on histologic sections. We plotted curves of cavity percentage to signal intensity. Theoretical values were calculated using a two-compartment model. On the curve of cavity area to signal intensity, the signal on FLAIR images peaked in tissues with 20% to 30% area of cavitation. On the theoretical curve, the signal on FLAIR images peaked at 90% tissue water content. These results seem to be characteristic of FLAIR.     

Velocity measurement of clay intrusion through a sudden contraction step using a tagging pulse sequence

Magnetic resonance imaging (MRI) with a spatial tagging sequence was used to measure the velocity distribution of clay that was forced past a sudden contraction. A spatial tagging sequence provided magnetic resonance images of clay that allowed measurement of the velocity distribution in the clay, which can provide profound insights on the deformation process of clay during the intrusion process. The experiments were conducted using a specially-designed vessel that could operate at up to 30 MPa. The vessel offers a rectangle test section with a sudden contraction step that had a ratio of contraction of 2:1. The vessel was installed into a commercial magnetic resonance imaging equipment and then the fluid motion of clay flowing into the narrow contracted channel was quantitatively investigated to examine behaviors of flowing clay as non-Newtonian fluid. MRI results are compared with those obtained by computational fluid dynamics (CFD) calculation. Velocity distributions obtained from each tag displacement did not well agree with those predicted by CFD results near the contraction step where the fluid accelerated rapidly. However, a post-processing on calculation results, in which virtual tag displacement is calculated, gave better agreement with experiment and enabled us to compare MRI results with CFD results.     

Evaluation of discrete orthogonal versus polar Stockwell Transform for local multi-resolution texture analysis using brain MRI of multiple sclerosis patients

The Stockwell Transform has the potential to perform multi-resolution texture analysis in magnetic resonance imaging (MRI). However, it is computationally intensive and memory demanding. The polar Stockwell Transform (PST) is rotation-invariant and relatively memory efficient, but still computationally demanding. The new Discrete Orthogonal Stockwell Transform (DOST) appears to have addressed both the computation and storage challenges; however, its utility in localized texture analysis remains unclear. Our goal was to investigate the theory and texture analysis ability of the DOST versus PST using both synthetic and MR images, and explore the relative importance of the associated texture features using a simple classification example based on clinical brain MRI of six multiple sclerosis patients. MRI texture analysis focused on FLAIR images, and the classification used a machine learning algorithm, random forest, that differentiated regions of interest (ROIs) into 2 classes: white matter lesions, and the contralateral normal-appearing white matter (control). Our results showed that the PST features had a greater ability in detecting subtle changes in image structure than the DOST and polar-index DOST (PDOST). Quantitatively, based on 187 lesion and 187 control ROIs, both the PST and the rotation-invariant radial PST performed better in the classification than the DOST and PDOST, where the latter were no better than guessing (p = 0.65 and 0.98). Further analysis using a hierarchical random forest showed that combining MRI signal intensity with the PST or DOST predictions increased the classification performance, with the accuracy, sensitivity, and specificity all improved to >85% in the tests. Collectively, the DOST is less competitive than the PST in localized image texture analysis. The PST features may help with texture-based lesion classification in MS based on clinical brain MRI scans following further verification.