A Unique Method for 3D MRI Segmentation

Segmentation of white matter, grey matter and cerebrospinal fluid from brain MR images using a modified FCM based on double estimation

Segmentation of brain magnetic resonance imaging (MRI) data plays an important role in the computer-aided diagnosis and neuroscience research. Fuzzy c-means (FCM) clustering algorithm is one of the most usually used techniques for brain MRI image segmentation because of its fuzzy nature. However, the conventional FCM method fails to carry out segmentation well enough due to intensity inhomogeneity in MRI data. To overcome this issue, we propose an improved algorithm based on FCM clustering for segmentation of brain MRI data. Specifically, we modify the conventional FCM algorithm to allow for intensity inhomogeneity by introducing the regularization of the neighborhood influence and bias field. Results show that our proposed algorithm obtains reasonable segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) from MRI data, which is superior to the expectation-maximization (EM) and conventional FCM methods.

The aims of this study were (1) to design a mathematical segmentation technique to allow extraction of grey matter, white matter and cerebral spinal fluid volumes from paired high resolution MR images and (2) to document the statistical accuracy of the method with different image combinations. A series of linear equations were derived that describe proportional tissue volumes in individual image voxels. The equations use estimates of pure tissue values to derive the proportion of each tissue within a single voxel. Repeatability of manual estimations of pure tissue values was assessed both using regions of interest and thresholding techniques. Statistical accuracy of tissue estimations for a variety of image pairs was assessed from measurements of root-mean-square noise and mean grey level intensity. The technique was used to produce parametric images of grey and white matter distribution. The segmentation technique showed greatest statistical accuracy when the first image has high grey/white matter contrast and the second image has little contrast or the rank order of the signal intensities from pure tissue is reversed. A combination of inversion recovery fast spin echo and fast FLAIR images produced a statistical error of 11% for grey matter and 10% for white matter for any given voxel. The effect of increasing sample size improves both of these figures to give a 1% statistical error on a 100 pixel sample.

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a non-invasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. Significant partial volume effects (PVEs) are present in the DW signal due to relatively large voxel sizes. These PVEs can be caused by both non-WM tissue, such as gray matter (GM) and cerebrospinal fluid (CSF), and by multiple non-parallel WM fiber populations. High angular resolution diffusion imaging (HARDI) methods have been developed to correctly characterize complex WM fiber configurations, but to date, many of the HARDI methods do not account for non-WM PVEs. In this work, we investigated the isotropic PVEs caused by non-WM tissue in WM voxels on fiber orientations extracted with constrained spherical deconvolution (CSD). Experiments were performed on simulated and real DW-MRI data. In particular, simulations were performed to demonstrate the effects of varying the diffusion weightings, signal-to-noise ratios (SNRs), fiber configurations, and tissue fractions. Our results show that the presence of non-WM tissue signal causes a decrease in the precision of the detected fiber orientations and an increase in the detection of false peaks in CSD. We estimated 35–50% of WM voxels to be affected by non-WM PVEs. For HARDI sequences, which typically have a relatively high degree of diffusion weighting, these adverse effects are most pronounced in voxels with GM PVEs. The non-WM PVEs become severe with 50% GM volume for maximum spherical harmonics orders of 8 and below, and already with 25% GM volume for higher orders. In addition, a low diffusion weighting or SNR increases the effects. The non-WM PVEs may cause problems in connectomics, where reliable fiber tracking at the WM–GM interface is especially important. We suggest acquiring data with high diffusion-weighting 2500–3000 s/mm2, reasonable SNR (~30) and using lower SH orders in GM contaminated regions to minimize the non-WM PVEs in CSD.

The correct interpretation of clinical positron emission tomography (PET) data depends largely on the physical limits of the PET scanner. The partial volume effect (PVE) is related to the size of the studied object compared to the spatial resolution. It represents one of the most important limiting factors in quantitative data analysis. This effect is increased in the case of atrophy, as in patients with Alzheimer disease (AD), and it influences measurement of the metabolic reduction generally seen in cerebral degeneration. In this case, interpretation can be biased, because cortical activity will be underestimated due to the atrophy. In general, anatomical images of AD patients have shown diffuse atrophy, while PET studies have found widespread hypometabolism affecting the parietal and temporal lobes. Although hypometabolic areas usually correspond to atrophic regions, they also occur without such changes. Thus, the aim is to differentiate authentic hypometabolism (decrease of glucose consumption per unit volume of gray matter) from that due to PVE from atrophy (cell loss). Consequently, we are using a method for three-dimensional (3D) correction of human PET data with 3D magnetic resonance imaging (MRI). We measured atrophy and metabolism by using both T1-weighted MR images and high and medium resolution PET scans. We injected 12 patients and controls with [18F]fluorodeoxyglucose for glucose consumption measurements. Atrophy was estimated in the following way. We isolated the cerebral structures, using a segmentation technique on the MRI scans, into gray matter (GM), white matter, and cerebrospinal fluid. We superimposed the PET images onto the MR images to obtain anatomo-functional correlations. We degraded the segmented MR images to the resolution of the PET images by a convolution process to create a PET image correction map. We corrected the metabolic PET data for the PVE. We studied the cerebral metabolic rate of glucose in the GM where metabolic variation is the most relevant to AD. By dealing with problems relating to the sensitivity to the segmentation and to the PET-MRI coregistration, computation of MRI convolution processes provided the degree of PVE on a pixel-by-pixel basis, allowing correction of hypometabolisms contained in GM PET values. Global cortical metabolism increased after correction for PVE by, on average, 29 and 24% for tomographs acquired with medium (TTV03 LETI) and high (ECAT 953B CTI/Siemens) resolution, respectively, whereas the cortical metabolism increased by 75 and 65% for the respective tomographs in AD patients. The difference of metabolism between scans after correction for PVE was less than before correction, decreasing from 31 to 17%. This difference was most marked in the frontal and temporal lobes. Fusion imaging allowed correction for PVE in metabolic data using 3D MRI and determination of whether a change in the apparent radiotracer concentration in PET data reflected an alteration in GM volume, a change in radiotracer concentration per unit volume of GM, or both.

Cranial Ultrasonography

MP2RAGE can generate uniform T1 -weighted images, which have been used for brain segmentation. However, there remain concerns about carrying out fast brain segmentation. To propose an acquisition-based method for fast segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) of healthy human brains with fluid and white matter suppression (FLAWS) using MP2RAGE. Prospective. 12 volunteers (23-28 years, seven subjects; 51-62 years, five subjects). 3T/3D MPRAGE and FLAWS. The proposed method was evaluated by calculating tissue volumes and the spatial overlap with the segmentation results from FSL and SPM12. The processing time was recorded. A paired t-test was used to compare the tissue volumes of the proposed method with those from other segmentation methods RESULTS: For the 12 subjects, the tissue volume difference between the proposed and SPM12 were 3.2 ± 2.8%, 4.2 ± 2.5%, 18.2 ± 13.1% for GM, WM, and CSF, respectively. The relative difference between the proposed and FSL was over 14% for all tissue classes. The spatial overlap between the proposed and other methods were 87-94% for GM and WM and less than 80% for CSF. The GM and WM volumes of the proposed method were not significantly different from those of SPM12 using MPRAGE as the input (P = 0.5540 and P = 0.3115, respectively). The rest of the comparisons all showed significant differences between the proposed and other methods. Statistical analysis of the two subgroups yielded similar results. The mean processing time of one subject was 6.5, 185, and 165 seconds for the proposed method, FSL, and SPM12, respectively. Our method may be accurate for the segmentation of most brain structures using FLAWS. In addition, the proposed method is fast and applicable to the two distinct age ranges. 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;48:1540-1550.

This study investigates the value of magnetic resonance imaging (MRI) based on a deep learning algorithm in the diagnosis of diabetic macular edema (DME) patients. A total of 96 patients with DME were randomly divided into the experimental group (N = 48) and the control group (N = 48). A deep learning 3D convolutional neural network (3D-CNN) algorithm for MRI images of patients with DME was designed. The application value of this algorithm was comprehensively evaluated by MRI image segmentation Dice value, sensitivity, specificity, and other indicators and diagnostic accuracy. The results showed that the quality of MRI images processed by the 3D-CNN algorithm based on deep learning was significantly improved, and the Dice value, sensitivity, and specificity index data were significantly better than those of the traditional CNN algorithm (P < 0.05). In addition, the diagnostic accuracy of MRI images processed by this algorithm was 93.78 ± 5.32%, which was significantly better than the diagnostic accuracy of 64.25 ± 10.24% of traditional MRI images in the control group (P < 0.05). In summary, the 3D-CNN algorithm based on deep learning can significantly improve the accuracy and sensitivity of MRI image recognition and segmentation in patients with DME, can significantly improve the diagnostic accuracy of MRI in patients with DME, and has a good clinical application value.

Commentary on “Diagnosis of Alzheimer's disease: Two decades of progress.” Central role of technology in the treatment and prevention of Alzheimer's disease

A method for quantitative analysis of multiple sclerosis (MS) was presented. An automatic self-adaptive image segmentation algorithm was first employed to classify voxels in multi- spectral magnetic resonance (MR) images. The segmentation results from multi-spectral MR images were then combined to obtain reliable results. The volumes of brain tissues and cerebral spinal fluid (CSF) were finally extracted. Since it is fully automated, the results of the segmentation algorithm are completely reproducible. The repeatability of the presented method was evaluated on volunteer data sets. The variation is less than 0.2% for the intra-cranial volume, the whole brain volume, the central CSF, the white matter (WM) and the gray matter (GM). The variation of 3% for the entire CSF is mainly due to the peripheral CSF part, which has more partial volume effect and is less important than the central one. Methods for minimizing this variation are under investigation. These measurements demonstrate the potential for study on whole brain atrophy and cerebral atrophy. Feasibility studies on 14 MS patients were performed. The results are promising.

An Enhanced Fuzzy Segmentation Framework for extracting white matter from T1-weighted MR images

The Expectation Maximization algorithm is a powerful probabilistic tool for brain tissue segmentation. The framework is based on the Gaussian mixture model in MRI, and employs a probabilistic brain atlas as a prior to produce a segmentation of white matter, grey matter and cerebro-spinal fluid (CSF). However, several artifacts can alter the segmentation process. For example, CSF is not a well defined class because of the large quantity of voxels affected by the partial volume effect which alters segmentation results and volume computation. In this study, we show that ignoring vessel segmentation when handling partial volume effect can also lead to false results, more specifically to an over-estimation of the CSF variance in the intensity space. We also propose a more versatile method to improve tissue classification, without a requirement of any outlier class, so that brain tissues, especially the cerebro-spinal fluid, follows the Gaussian noise model in MRI correctly.KeywordsGrey MatterGaussian Mixture ModelGaussian Probability Density FunctionJoint HistogramPure ClassisThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Differentiation of absolute metabolite concentrations between gray and white matter in the occipital region of normal human brain was performed by in vivo localized single-voxel 1H magnetic resonance spectroscopy at 1.5 Tesla with long echo time (136 ms). With the combination of image segmentation between white and gray matter and cerebrospinal fluid, signal compensation of T1 and T2 effects, tissue water signal as the internal concentration reference, as well as compensation by different water contents in gray and white matters, it was determined that the levels of N-acetylaspartate (NAA), creatine and/or phosphocreatine (Cr), and choline-containing compounds (Cho) in gray matter were significantly higher than in white matter. The averaged NAA, Cr, and Cho concentrations in gray matter were 11.0, 9.7, and 1.9 mM/liter, respectively, in comparison with 7.5, 5.2, and 1.6 mM/liter in white matter. These results suggest that precise composition of white and gray matter and cerebrospinal fluid is necessary to avoid partial voluming effect in a single voxel and to accurately quantify the metabolite concentrations.

Not Just Blood: Brain Fluid Systems and Their Relevance to Cerebrovascular Diseases.

Sports-related concussions may cause white matter injuries and persistent post-concussive symptoms (PPCS). We hypothesized that athletes with PPCS would have neurocognitive impairments and white matter abnormalities that could be revealed by advanced neuroimaging using ultra-high field strength diffusion tensor (DTI) and diffusion kurtosis (DKI) imaging metrics and cerebrospinal fluid (CSF) biomarkers. A cohort of athletes with PPCS severity limiting the ability to work/study and participate in sport school and/or social activities for ≥6 months completed 7T magnetic resonance imaging (MRI) (morphological T1-weighed volumetry, DTI and DKI), extensive neuropsychological testing, symptom rating, and CSF biomarker sampling. Twenty-two athletes with PPCS and 22 controls were included. Concussed athletes performed below norms and significantly lower than controls on all but one of the psychometric neuropsychology tests. Supratentorial white and gray matter, as well as hippocampal volumes did not differ between concussed athletes and controls. However, of the 72 examined white matter tracts, 16% of DTI and 35% of DKI metrics (in total 28%) were significantly different between concussed athletes and controls. DKI fractional anisotropy and axial kurtosis were increased, and DKI radial diffusivity and radial kurtosis decreased in concussed athletes when compared with controls. CSF neurofilament light (NfL; an axonal injury marker), although not glial fibrillary acidic protein, correlated with several diffusion metrics. In this first 7T DTI and DKI study investigating PPCS, widespread microstructural alterations were observed in the white matter, correlating with CSF markers of axonal injury. More white matter changes were observed using DKI than using DTI. These white matter alterations may indicate persistent pathophysiological processes following concussion in sport.