MRI texture analysis on texture test objects, normal brain and intracranial tumors

Texture analysis was performed in three different MRI units on T1 and T2-weighted MR images from 10 healthy volunteers and 63 patients with histologically confirmed intracranial tumors. The goal of this study was a multicenter evaluation of the usefulness of this quantitative approach for the characterization of healthy and pathologic human brain tissues (white matter, gray matter, cerebrospinal fluid, tumors and edema). Each selected brain region of interest was characterized with both its mean gray level values and several texture parameters. Multivariate statistical analyses were then applied in order to discriminate each brain tissue type represented by its own set of texture parameters. Texture analysis was previously performed on test objects to evaluate the method dependence on acquisition parameters and consequently the interest of a multicenter evaluation. Even obtained on different sites with their own acquisition routine protocol, MR brain images contain textural features that can reveal discriminant factors for tissue classification and image segmentation. It can also offer additional information in case of undetermined diagnosis or to develop a more accurate tumor grading.     

State of the art survey on MRI brain tumor segmentation

Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema, and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain tumor segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation. Semiautomatic and fully automatic techniques are emphasized.     

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.     

In vivo NMR T2 relaxation of experimental brain tumors in the cat: a multiparameter tissue characterization.

Experimental gliomas (F98) were inoculated in cat brain for the systematic study of their in vivo T₂ relaxation time behavior. With a CPMG multi-echo imaging sequence, a train of 16 echoes was evaluated to obtain the transverse relaxation time and the magnetization M(0) at time T = 0. The magnetization decay curves were analyzed for biexponentiality. All tissues showed monoexponential T₂, only that of the ventricular fluid and part of the vital tumor tissue were biexponential. Based on these NMR relaxation parameters the tissues were characterized, their correct assignment being assured by comparison with histological slices. T₂ of normal grey and white matter was 74 ± 6 and 72 ± 6 msec, respectively. These two tissue types were distinguished through M(0) which for white matter was only 0.88 of the intensity of grey matter in full agreement with water content, determined from tissue specimens. At the time of maximal tumor growth and edema spread a tissue differentiation was possible in NMR relaxation parameter images. Separation of the three tissue groups of normal tissue, tumor and edema was based on T₂ with T_2(normal) < T_2(tumor) < T_2(edema). Using M(0) as a second parameter the differentiation was supported, in particular between white matter and tumor or edema. Animals were studied at 1–4 wk after tumor implantation to study tumor development. The magnetization M(0) of both tumor and peritumoral edema went through a maximum between the second and third week of tumor growth. T₂ of edema was maximal at the same time with 133 ± 4 msec, while the relaxation time of tumor continued to increase during the whole growth period, reaching values of 114 ± 12 msec at the fourth week. Thus, a complete characterization of pathological tissues with NMR relaxometry must include a detailed study of the developmental changes of these tissues to assure correct experimental conditions for the goal of optimal contrast between normal and pathological regions in the NMR images.     

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.     

Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques

The aim of this study was to evaluate the contribution of diffusion and perfusion MR metrics in the discrimination of intracranial brain lesions at 3T MRI, and to investigate the potential diagnostic and predictive value that pattern recognition techniques may provide in tumor characterization using these metrics as classification features. Conventional MRI, diffusion weighted imaging (DWI), diffusion tensor imaging (DTI) and dynamic-susceptibility contrast imaging (DSCI) were performed on 115 patients with newly diagnosed intracranial tumors (low-and- high grade gliomas, meningiomas, solitary metastases). The Mann–Whitney U test was employed in order to identify statistical differences of the diffusion and perfusion parameters for different tumor comparisons in the intra-and peritumoral region. To assess the diagnostic contribution of these parameters, two different methods were used; the commonly used receiver operating characteristic (ROC) analysis and the more sophisticated SVM classification, and accuracy, sensitivity and specificity levels were obtained for both cases. The combination of all metrics provided the optimum diagnostic outcome. The highest predictive outcome was obtained using the SVM classification, although ROC analysis yielded high accuracies as well. It is evident that DWI/DTI and DSCI are useful techniques for tumor grading. Nevertheless, cellularity and vascularity are factors closely correlated in a non-linear way and thus difficult to evaluate and interpret through conventional methods of analysis. Hence, the combination of diffusion and perfusion metrics into a sophisticated classification scheme may provide the optimum diagnostic outcome. In conclusion, machine learning techniques may be used as an adjunctive diagnostic tool, which can be implemented into the clinical routine to optimize decision making.     

In vivo NMR T2 relaxation of experimental brain tumors in the cat: A multiparameter tissue characterization

Experimental gliomas (F98) were inoculated in cat brain for the systematic study of their in vivo T₂ relaxation time behavior. With a CPMG multi-echo imaging sequence, a train of 16 echoes was evaluated to obtain the transverse relaxation time and the magnetization M(0) at time t = 0. The magnetization decay curves were analyzed for biexponentiality. All tissues showed monoexponential T₂, only that of the ventricular fluid and part of the vital tumor tissue were biexponential. Based on these NMR relaxation parameters the tissues were characterized, their correct assignment being assured by comparison with histological slices. T₂ of normal grey and white matter was 74 ± 6 and 72 ± 6 msec, respectively. These two tissue types were distinguished through M(0) which for white matter was only 0.88 of the intensity of grey matter in full agreement with water content, determined from tissue specimens. At the time of maximal tumor growth and edema spread a tissue differentiation was possible in NMR relaxation parameter images. Separation of the three tissue groups of normal tissue, tumor and edema was based on T₂ with T_2(normal) < T_2(tumor) < T_2(edema). Using M(0) as a second parameter the differentiation was supported, in particular between white matter and tumor or edema. Animals were studied at 1–4 wk after tumor implantation to study tumor development. The magnetization M(0) of both tumor and peritumoral edema went through a maximum between the second and third week of tumor growth. T₂ of edema was maximal at the same time with 133 ± 4 msec, while the relaxation time of tumor continued to increase during the whole growth period, reaching values of 114 ± 12 msec at the fourth week. Thus, a complete characterization of pathological tissues with NMR relaxometry must include a detailed study of the developmental changes of these tissues to assure correct experimental conditions for the goal of optimal contrast between normal and pathological regions in the NMR images.     

Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods

Three-dimensional (3D) texture analysis of volumetric brain magnetic resonance (MR) images has been identified as an important indicator for discriminating among different brain pathologies. The purpose of this study was to evaluate the efficiency of 3D textural features using a pattern recognition system in the task of discriminating benign, malignant and metastatic brain tissues on T1 postcontrast MR imaging (MRI) series. The dataset consisted of 67 brain MRI series obtained from patients with verified and untreated intracranial tumors. The pattern recognition system was designed as an ensemble classification scheme employing a support vector machine classifier, specially modified in order to integrate the least squares features transformation logic in its kernel function. The latter, in conjunction with using 3D textural features, enabled boosting up the performance of the system in discriminating metastatic, malignant and benign brain tumors with 77.14%, 89.19% and 93.33% accuracy, respectively. The method was evaluated using an external cross-validation process; thus, results might be considered indicative of the generalization performance of the system to "unseen" cases. The proposed system might be used as an assisting tool for brain tumor characterization on volumetric MRI series.     

Differentiation of recurrent brain tumor versus radiation injury using diffusion tensor imaging in patients with new contrast-enhancing lesions

BACKGROUND AND PURPOSE: The purpose of this study was to assess the use of diffusion tensor imaging (DTI) in the evaluation of new contrast-enhancing lesions and perilesional edema in patients previously treated for brain neoplasm in the differentiation of recurrent neoplasm from treatment-related injury. METHODS: Twenty-eight patients with new contrast-enhancing lesions and perilesional edema at the site of previously treated brain neoplasms were retrospectively reviewed. Nine directional echoplanar DTIs with b=1000 s/mm(2) were obtained using a single-shot spin-echo echoplanar imaging. Standardized regions of interest were manually drawn in several regions. Mean apparent diffusion coefficient (ADC), fractional anisotropy (FA) and eigenvalue indices (lambda( parallel) and lambda( perpendicular)) and their ratios relative to the contralateral side were compared in patients with recurrent neoplasm versus patients with radiation injury, as established by histological examination or by clinical course, including long-term imaging studies and magnetic resonance spectroscopy. RESULTS: The ADC values in the contrast-enhancing lesions were significantly higher (P=.01) for the recurrence group (range=1.01 x 10(-3) to 1.66 x 10(-3) mm(2)/s; mean+/-S.D.=1.27+/-0.15) than for the nonrecurrence group (range=0.9 x 10(-3) to 1.31 x 10(-3) mm(2)/s; mean+/-S.D.=1.12+/-0.14). The ADC ratios in the white matter tracts in perilesional edema trended higher (P=.09) in treatment-related injury than in recurrent neoplasm (mean+/-S.D.=1.85+/-0.30 vs. 1.60+/-0.27, respectively). FA ratios were significantly higher in normal-appearing white matter (NAWM) tracts adjacent to the edema in the nonrecurrence group (mean+/-S.D.=0.89+/-0.15) than in those in the recurrence group (mean+/-S.D.=0.74+/-0.14; P=.03). Both eigenvalue indices lambda( parallel) and lambda( perpendicular) were significantly higher in contrast-enhancing lesions in the recurrence group than in those in the nonrecurrence group (P=.02). As well, both eigenvalue indices lambda( parallel) and lambda( perpendicular) were significantly higher in perilesional edema than in normal white matter (P<.01 and P<.001, respectively) in both groups. CONCLUSION: The assessment of diffusion properties, especially ADC values and ADC ratios, in contrast-enhancing lesions, perilesional edema and NAWM adjacent to the edema in the follow-up of new contrast-enhancing lesions at the site of previously treated brain neoplasms may add to the information obtained by other imaging techniques in the differentiation of radiation injury from tumor recurrence.     

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

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

一种快速提取连续CT脑肿瘤病灶区的算法

脑肿瘤图像提取就是将肿瘤病灶区域(水肿、坏死、癌变)从正常的脑部组织(灰质、白质、脑脊液)分开,精确的脑肿瘤分割对脑瘤的诊断、研究和治疗有重要的临床意义。针对传统脑部CT肿瘤病灶提取的缺点,即需要耗费大量时间并且分割精度不高的问题,提出一种综合了形态学重建、分水岭分割和改进的区域生长算法。先用形态学重建进行去噪,再用结合多尺度梯度分水岭分割提取整个图像的边界,然后在肿瘤病灶区域内选取种子点进行区域生长,提取肿瘤区域轮廓,滤除其他封闭区域,得到的图像作为改进的区域生长法的初始分割区域,使用改进的区域生长法,滤除过分割区域。实验结果显示该算法分割出的结果有效区域大,分割精度高。结论：该算法提高了分割精度,由于不用匹配结构参数,加快了分割速度,具有一定的临床价值。     

Perfusion and diffusion in meningioma tumors: a preliminary multiparametric analysis with Dynamic Susceptibility Contrast and IntraVoxel Incoherent Motion MRI

Multiparametric MRI is a remarkable imaging method for the assessment of patho-physiological processes. In particular, brain tumor characterization has taken advantage of the development of advanced techniques such as Diffusion- (DWI) and Perfusion- (PWI) Weighted Imaging, but a thorough analysis of meningiomas is still lacking despite the variety of computational methods proposed.We compute perfusion and diffusion parametric maps relying on a well-defined methodological workflow, investigating possible correlations between pure and diffusion-based perfusion parameters in a cohort of 26 patients before proton therapy. A preliminary investigation of meningioma staging biomarkers based on IntraVoxel Incoherent Motion and Dynamic Susceptibility Contrast is also reported. We observed significant differences between the gross target volume and the normal appearing white matter for every investigated parameter, confirming the higher vascularization of the neoplastic tissue. DWI and PWI parameters appeared to be weakly correlated and we found that diffusion parameters – the perfusion fraction in particular – could be promising biomarkers for tumor staging.     

3D spin-lock imaging of human gliomas

We investigated whether the simultaneous use of paramagnetic contrast medium and 3D on-resonance spin lock (SL) imaging could improve the contrast of enhancing brain tumors at 0.1 T. A phantom containing serial concentrations of gadopentetate dimeglumine (Gd-DTPA) in cross-linked bovine serum albumin (BSA) was imaged. Eleven patients with histologically verified glioma were also studied. T₁-weighted 3D gradient echo images with and without SL pulse were acquired before and after a Gd-DTPA injection. SL effect, contrast, and contrast-to-noise ratio (CNR) were calculated for each patient. In the glioma patients, the SL effect was significantly smaller in the tumor than in the white and gray matter both before (p = 0.001, p = 0.025, respectively), and after contrast medium injection (p < 0.001, p < 0.001, respectively). On post-contrast images, SL imaging significantly improved tumor contrast (p = 0.001) whereas tumor CNR decreased slightly (p = 0.024). The combined use of SL imaging and paramagnetic Gd-DTPA contrast agent offers a modality for improving tumor contrast in magnetic resonance imaging (MRI) of enhancing brain tumors. 3D gradient echo SL imaging has also shown potential to increase tissue characterization properties of MR imaging of human gliomas.     

大鼠创伤性脑水肿模型中近红外光有效检测深度研究

采用近红外光谱技术实现颅脑损伤的无损监测过程中,存在着检测深度不明确的问题.利用Monte Carlo模拟光子在生物组织中的传输过程,建立有效检测深度模型,对光纤探头在大鼠创伤性脑水肿模型中的有效检测深度规律进行了研究.采用不依赖于模板的阈值分割和窄带水平集分割方法,将大鼠头部MRI图像分为头皮、头骨、脑脊液、灰质和白质五部分,建立真实的大鼠脑组织三维模型,使Monte Carlo仿真结果更加准确.改进复杂组织光场分布仿真的tMCimg软件,使其能够实时记录光子在组织中的位置和光子被检测器接收时的能量,从而计算出探头在组织中的有效检测深度.分析了不同光源和检测器的中心距、光源芯径对有效检测深度的影响,结果表明光在大鼠脑组织中的有效检测深度小于或者等于光源和检测器中心距的一半,并随光源芯径的增大逐渐增大.建立大鼠脑水肿模型,验证了仿真结果的正确性.研究结果对于无创脑水肿模型的光纤探头的设计和脑水肿区域的判定有着重要的意义.     

Automatic segmentation of brain white matter and white matter lesions in normal aging: comparison of five multispectral techniques

White matter loss, ventricular enlargement and white matter lesions are common findings on brain scans of older subjects. Accurate assessment of these different features is therefore essential for normal aging research. Recently, we developed a novel unsupervised classification method, named ‘Multispectral Coloring Modulation and Variance Identification’ (MCMxxxVI), that fuses two different structural magnetic resonance imaging (MRI) sequences in red/green color space and uses Minimum Variance Quantization (MVQ) as the clustering technique to segment different tissue types. Here we investigate how this method performs compared with several commonly used supervised image classifiers in segmenting normal-appearing white matter, white matter lesions and cerebrospinal fluid in the brains of 20 older subjects with a wide range of white matter lesion load and brain atrophy. The three tissue classes were segmented from T₁-, T₂-, T₂?- and fluid attenuation inversion recovery (FLAIR)-weighted structural MRI data using MCMxxxVI and the four supervised multispectral classifiers available in the Analyze package, namely, Back-Propagated Neural Networks, Gaussian classifier, Nearest Neighbor and Parzen Windows. Bland–Altman analysis and Jaccard index values indicated that, in general, MCMxxxVI performed better than the supervised multispectral classifiers in identifying the three tissue classes, although final manual editing was still required to deliver radiologically acceptable results. These analyses show that MVQ, as implemented in MCMxxxVI, has the potential to provide quick and accurate white matter segmentations in the aging brain, although further methodological developments are still required to automate fully this technique.     

Comparison of diffusion tensor,dynamic susceptibility contrast MRI and Tc-Tetrofosmin brain SPECT for the detection of recurrent high-grade glioma

Introduction

Treatment induced necrosis is a relatively frequent finding in patients treated for high-grade glioma. Differentiation by imaging modalities between glioma recurrence and treatment induced necrosis is not always straightforward. This is a comparative study of diffusion tensor imaging (DTI), dynamic susceptibility contrast MRI and ^99mTc-Tetrofosmin brain single-photon emission computed tomography (SPECT) for differentiation of recurrent glioma from treatment induced necrosis.

Methods

A prospective study was made of 30 patients treated for high-grade glioma who had suspected recurrent tumor on follow-up MRI. All had been treated by surgical resection of the tumor followed by standard postoperative radiotherapy with chemotherapy. No residual tumor had been found on brain imaging immediately after the initial treatment. All the patients were studied with dynamic susceptibility contrast brain MRI and, within a week, ^99mTc-Tetrofosmin brain SPECT.

Results

Both ^99mTc-Tetrofosmin brain SPECT and dynamic susceptibility contrast MRI could discriminate between tumor recurrence and treatment induced necrosis with 100% sensitivity and 100% specificity. An apparent diffusion coefficient (ADC) ratio cut-off value of 1.27 could differentiate recurrence from treatment induced necrosis with 65% sensitivity and 100% specificity and a fractional anisotropy (FA) ratio cut-off value of 0.47 could differentiate recurrence from treatment induced necrosis with 57% sensitivity and 100% specificity. A significant correlation was demonstrated between ^99mTc-Tetrofosmin uptake ratio and rCBV (P = 0.003).

Conclusions

Dynamic susceptibility contrast MRI and brain SPECT with ^99mTc-Tetrofosmin had the same accuracy and may be used to detect recurrent tumor following treatment for glioma. DTI also showed promise for the detection of recurrent tumor, but was inferior to both dynamic susceptibility contrast MRI and brain SPECT.     

Significance of proton relaxation time measurement in brain edema, cerebral infarction and brain tumors

We examined the proton relaxation times in vitro in various neurological diseases using experimental and clinical materials, and consequently obtained significant results for making a fundamental analysis of magnetic resonance imaging (MRI) as followings. 1) In the brain edema and cerebral infarction, T1 prolonged and T2 separated into two components, one fast and one slow. Prolongation of T1 referred to the volume of increased water in tissue. The slow component of T2 reflects both the volume and the content of increased edema fluid in tissue. 2) In the edematous brain tissue with the damaged Blood-Brain-Barrier (BBB), the slow component of T2 became shorter after the injection of Mn-EDTA. Paramagnetic ion could be used as an indicator to demonstrate the destruction of BBB in the brain. 3) After the i.v. injection of glycerol, the slow component of T2 became shorter in the edematous brain with the concomitant decrease of water content. The effects of therapeutic drug could be evaluated by the measurement of proton relaxation times. 4) Almost all tumor tissue showed a longer T1 and T2 values than the normal rat brain, and many of them showed two components in T2. It was difficult to determine the histology of tumor tissue by the relaxation time alone because of an overlap of T1 and T2 values occurred among various types of brain tumors. 5) In vivo T1 values of various brain tumor were calculated from the data of MRIs by zero-crossing method, and they were compared with the in vitro T1 values which were measured immediately after the surgical operation. Though the absolute value did not coincide with each other due to differences in magnetic field strength, the tendency of the changes was the same among all kinds of tumors. It is concluded that the fundamental analysis of proton relaxation times is essentially important not only for the study of pathophysiology in many diseases but also for the interpretation of clinical MRI.     

Magnetization transfer MRI of mouse brain reveals areas of high neural density

Extending applications of magnetization transfer contrast (MTC) in magnetic resonance imaging (MRI) of the human central nervous system, this work quantitatively describes MTC of the murine brain. As a novel finding, complementing T₁- and T₂-weighted MRI, MTC allows for the distinction of densely packed gray matter from normal gray and white matter. Examples include the Purkinje cell layer and the granular cell layer in the mouse cerebellum as well as the delineation of the CA3 subfield of the hippocampus relative to surrounding hippocampal gray matter and white matter tracts such as the hippocampal fimbria. Using a kainate lesion model, the CA3 hyperintensities in MTC and T₁-weighted MRI are assigned to the densely packed somata of pyramidal cells.     

Segmenting brain white matter, gray matter and cerebro-spinal fluid using diffusion tensor-MRI derived indices.

Based on its ability to provide quantitative information about tissue microstructure, diffusion tensor magnetic resonance imaging (DT-MRI) might be a valuable approach to improve the reliability of segmentation of the various brain tissues.In this study, a fully automated and easy-to-implement technique based on 2D histogram analysis of DT-MRI derived images was used to segment white and gray matter of the brain from 10 healthy subjects (aged = 27-56 years). The results obtained with this novel segmentation strategy were compared to those achieved by two experienced observers using an operator-dependent segmentation on the dual-echo scans.Visual inspection of the segmented tissues from a third senior observer disclosed that the automated technique worked properly on all images from all subjects and was more accurate than the human raters in defining thalamus white and gray matter portions as well as in tissue classification at the external brain edge. In addition, this segmentation technique resulted in an average gray/white matter ratio similar to that reported by post-mortem assessment. The application of the operator-dependent segmentation strategy was extremely time-consuming and the two observers achieved poorly reproducible results.Segmenting brain white and gray matter using information from DT-MRI proved to be an accurate approach with the potential for improving the understanding of the pathophysiology of many neurologic conditions.     

Visualizing iron in multiple sclerosis

Magnetic resonance imaging (MRI) protocols that are designed to be sensitive to iron typically take advantage of (1) iron effects on the relaxation of water protons and/or (2) iron-induced local magnetic field susceptibility changes. Increasing evidence sustains the notion that imaging iron in brain of patients with multiple sclerosis (MS) may add some specificity toward the identification of the disease pathology. The present review summarizes currently reported in vivo and post mortem MRI evidence of (1) iron detection in white matter and gray matter of MS brains, (2) pathological and physiological correlates of iron as disclosed by imaging and (3) relations between iron accumulation and disease progression as measured by clinical metrics.