Series Of Brain Images Research Articles

Brain Tumour Temporal Monitoring of Interval Change Using Digital Image Subtraction Technique.

A process that involves the registration of two brain Magnetic Resonance Imaging (MRI) acquisitions is proposed for the subtraction between previous and current images at two different follow-up (FU) time points. Brain tumours can be non-cancerous (benign) or cancerous (malignant). Treatment choices for these conditions rely on the type of brain tumour as well as its size and location. Brain cancer is a fast-spreading tumour that must be treated in time. MRI is commonly used in the detection of early signs of abnormality in the brain area because it provides clear details. Abnormalities include the presence of cysts, haematomas or tumour cells. A sequence of images can be used to detect the progression of such abnormalities. A previous study on conventional (CONV) visual reading reported low accuracy and speed in the early detection of abnormalities, specifically in brain images. It can affect the proper diagnosis and treatment of the patient. A digital subtraction technique that involves two images acquired at two interval time points and their subtraction for the detection of the progression of abnormalities in the brain image was proposed in this study. MRI datasets of five patients, including a series of brain images, were retrieved retrospectively in this study. All methods were carried out using the MATLAB programming platform. ROI volume and diameter for both regions were recorded to analyse progression details, location, shape variations and size alteration of tumours. This study promotes the use of digital subtraction techniques on brain MRIs to track any abnormality and achieve early diagnosis and accuracy whilst reducing reading time. Thus, improving the diagnostic information for physicians can enhance the treatment plan for patients.

QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent Brain.

Transgenic animal models are invaluable research tools for elucidating the pathways and mechanisms involved in the development of neurodegenerative diseases. Mechanistic clues can be revealed by applying labelling techniques such as immunohistochemistry or in situ hybridisation to brain tissue sections. Precision in both assigning anatomical location to the sections and quantifying labelled features is crucial for output validity, with a stereological approach or image-based feature extraction typically used. However, both approaches are restricted by the need to manually delineate anatomical regions. To circumvent this limitation, we present the QUINT workflow for quantification and spatial analysis of labelling in series of rodent brain section images based on available 3D reference atlases. The workflow is semi-automated, combining three open source software that can be operated without scripting knowledge, making it accessible to most researchers. As an example, a brain region-specific quantification of amyloid plaques across whole transgenic Tg2576 mouse brain series, immunohistochemically labelled for three amyloid-related antigens is demonstrated. First, the whole brain image series were registered to the Allen Mouse Brain Atlas to produce customised atlas maps adapted to match the cutting plan and proportions of the sections (QuickNII software). Second, the labelling was segmented from the original images by the Random Forest Algorithm for supervised classification (ilastik software). Finally, the segmented images and atlas maps were used to generate plaque quantifications for each region in the reference atlas (Nutil software). The method yielded comparable results to manual delineations and to the output of a stereological method. While the use case demonstrates the QUINT workflow for quantification of amyloid plaques only, the workflow is suited to all mouse or rat brain series with labelling that is visually distinct from the background, for example for the quantification of cells or labelled proteins.

The Utility of Applying Various Image Preprocessing Strategies to Reduce the Ambiguity in Deep Learning-based Clinical Image Diagnosis.

Purpose:A general problem of machine-learning algorithms based on the convolutional neural network (CNN) technique is that the reason for the output judgement is unclear. The purpose of this study was to introduce a strategy that may facilitate better understanding of how and why a specific judgement was made by the algorithm. The strategy is to preprocess the input image data in different ways to highlight the most important aspects of the images for reaching the output judgement.Materials and Methods:T2-weighted brain image series falling into two age-ranges were used. Classifying each series into one of the two age-ranges was the given task for the CNN model. The images from each series were preprocessed in five different ways to generate five different image sets: (1) subimages from the inner area of the brain, (2) subimages from the periphery of the brain, (3–5) subimages of brain parenchyma, gray matter area, and white matter area, respectively, extracted from the subimages of (2). The CNN model was trained and tested in five different ways using one of these image sets. The network architecture and all the parameters for training and testing remained unchanged.Results:The judgement accuracy achieved by training was different when the image set used for training was different. Some of the differences was statistically significant. The judgement accuracy decreased significantly when either extra-parenchymal or gray matter area was removed from the periphery of the brain (P < 0.05).Conclusion:The proposed strategy may help visualize what features of the images were important for the algorithm to reach correct judgement, helping humans to understand how and why a particular judgement was made by a CNN.

Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

Active contour model (ACM) has been one of the most widely utilized methods in magnetic resonance (MR) brain image segmentation because of its ability of capturing topology changes. However, most of the existing ACMs only consider single-slice information in MR brain image data, i.e., the information used in ACMs based segmentation method is extracted only from one slice of MR brain image, which cannot take full advantage of the adjacent slice images’ information, and cannot satisfy the local segmentation of MR brain images. In this paper, a novel ACM is proposed to solve the problem discussed above, which is based on multi-variate local Gaussian distribution and combines the adjacent slice images’ information in MR brain image data to satisfy segmentation. The segmentation is finally achieved through maximizing the likelihood estimation. Experiments demonstrate the advantages of the proposed ACM over the single-slice ACM in local segmentation of MR brain image series.

Linear-fitting-based similarity coefficient map for tissue dissimilarity analysis in -w magnetic resonance imaging**Project supported in part by the National High Technology Research and Development Program of China (Grant Nos. 2015AA043203 and 2012AA02A604), the National Natural Science Foundation of China (Grant Nos. 81171402, 61471349, and 81501463), the Innovative Research Team Program of Guangdong Province, China (Grant No. 2011S013), the Science and

Similarity coefficient mapping (SCM) aims to improve the morphological evaluation of weighted magnetic resonance imaging However, how to interpret the generated SCM map is still pending. Moreover, is it probable to extract tissue dissimilarity messages based on the theory behind SCM? The primary purpose of this paper is to address these two questions. First, the theory of SCM was interpreted from the perspective of linear fitting. Then, a term was embedded for tissue dissimilarity information. Finally, our method was validated with sixteen human brain image series from multi-echo . Generated maps were investigated from signal-to-noise ratio (SNR) and perceived visual quality, and then interpreted from intra- and inter-tissue intensity. Experimental results show that both perceptibility of anatomical structures and tissue contrast are improved. More importantly, tissue similarity or dissimilarity can be quantified and cross-validated from pixel intensity analysis. This method benefits image enhancement, tissue classification, malformation detection and morphological evaluation.

Constrained multiplicative graph cuts based active contour model for magnetic resonance brain image series segmentation

Graph cuts-based active contour model (GCACM) is often used in image segmentation, which can be categorized into additive GCACM and multiplicative GCACM. However, both the additive GCACM and multiplicative GCACM are insufficient for magnetic resonance (MR) brain image series segmentation. Considering the effectiveness of the multiplicative GCACM over the additive GCACM in local segmentation, we propose a new constrained multiplicative GCACM (CM-GCACM) for MR brain image series segmentation, in which, the constraint term is built based on the signed distance function, and can make the segmentation results obtained around the initialized contour. Generally, the deformations between adjacent slices in MR brain series are small, so we only need to give the initialized contour in one selected slice for constrained segmentation, and then the selected slice segmentation result can spread to the adjacent slices, in which case, the segmentation result of the current slicer can be served as the initialized contour for adjacent slices, and the constrained segmentation can be obtained again. By that analogy, we can realize the series segmentation. Experiments on putamen and caudate nucleus segmentation in MR brain image series demonstrate the effectiveness of proposed CM-GCACM over additive GCACM and multiplicative GCACM.

A Hierarchical Model for Probabilistic Independent Component Analysis of Multi-Subject fMRI Studies

An important goal in fMRI studies is to decompose the observed series of brain images to identify and characterize underlying brain functional networks. Independent component analysis (ICA) has been shown to be a powerful computational tool for this purpose. Classic ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix. Existing group ICA methods generally concatenate observed fMRI data across subjects on the temporal domain and then decompose multi-subject data in a similar manner to single-subject ICA. The major limitation of existing methods is that they ignore between-subject variability in spatial distributions of brain functional networks in group ICA. In this article, we propose a new hierarchical probabilistic group ICA method to formally model subject-specific effects in both temporal and spatial domains when decomposing multi-subject fMRI data. The proposed method provides model-based estimation of brain functional networks at both the population and subject level. An important advantage of the hierarchical model is that it provides a formal statistical framework to investigate similarities and differences in brain functional networks across subjects, for example, subjects with mental disorders or neurodegenerative diseases such as Parkinson's as compared to normal subjects. We develop an EM algorithm for model estimation where both the E-step and M-step have explicit forms. We compare the performance of the proposed hierarchical model with that of two popular group ICA methods via simulation studies. We illustrate our method with application to an fMRI study of Zen meditation.

A method to assess spatially variant noise in dynamic MR image series

Accurate measurement of spatially variant noise in MR images acquired using parallel imaging techniques is challenging. Image-based noise measurement methods such as the subtraction method proposed by the National Electrical Manufacturers Association or the multiple acquisition method often cannot be applied in vivo due to motion and/or dynamic contrast changes. Based on the Karhunen-Loeve transform and random matrix theory, we propose a novel method to accurately assess the noise variance in image series bearing temporal redundancy. The method fits the probability density function of eigenvalues from the temporal covariance matrix of the image series to the Marcenko-Pastur distribution. The accuracy of our method was validated using numerical simulation and an MR noise measurement experiment. The ability of this method to derive the g-factor map of a static phantom was validated against the multiple acquisition method. The method was applied to in vivo cardiac and brain image series and the results agreed with subtraction and multiple acquisition methods, respectively. This new image-based noise measurement method provides a practical means of retrospectively evaluating the noise level and/or g-factor map from multiframe image series.

Excitation UNFOLD (XUNFOLD) to improve the temporal resolution of multishot tailored RF pulses.

An extension of the "UNaliasing by Fourier encoding the Overlaps using the temporaL Dimension" (UNFOLD) method to the excitation domain (XUNFOLD) is presented to improve the temporal resolution of multishot tailored RF (TRF) pulses. Multishot three-dimensional TRF pulses were designed to produce a time series of images with periodically aliased excitation profiles. The XUNFOLD method is shown to remove the excitation profile aliasing from the dynamic imaging data by filtering in the temporal frequency dimension. The technique is demonstrated to improve the temporal resolution of simulated functional MRI (fMRI) activation in a time series of brain images.

Analyzing fMRI experiments with structural adaptive smoothing procedures

Data from functional magnetic resonance imaging (fMRI) consist of time series of brain images that are characterized by a low signal-to-noise ratio. In order to reduce noise and to improve signal detection, the fMRI data are spatially smoothed. However, the common application of a Gaussian filter does this at the cost of loss of information on spatial extent and shape of the activation area. We suggest to use the propagation–separation procedures introduced by Polzehl, J., Spokoiny, V. (2006). Propagation–separation approach for local likelihood estimation. Probab. Theory Relat. Fields, in print. http://www.springerlink.com/link.asp?id=r57kl83111n7302u instead. We show that this significantly improves the information on the spatial extent and shape of the activation region with similar results for the noise reduction. To complete the statistical analysis, signal detection is based on thresholds defined by random field theory. Effects of adaptive and non-adaptive smoothing are illustrated by artificial examples and an analysis of experimental data.

Magnetic field changes in the human brain due to swallowing or speaking.

Variations in the magnetic field in the human brain caused by the processes of swallowing or speaking are measured. In both processes, motion of the pharyngeal muscles, especially the tongue and jaw, alter the susceptibility-induced magnetic field distribution at the brain slice being imaged. This leads to image warping, compromising the analysis of a time series of images, such as in functional magnetic resonance imaging (fMRI). These dynamic changes are assessed by acquiring a time series of images using a gradient-echo asymmetric-spin-echo sequence (GREASE), a technique in which two images are acquired for each excitation--one during the gradient echo, and one during the latter part of the spin echo. The NMR phase difference between the two images is a measure of the magnetic field distribution. A series of brain images, acquired with this sequence while the subject either swallows or speaks, indicated negative magnetic field changes of up to 0.087 ppm in the inferior region of the brain for both speaking and swallowing, and in some speech, additional positive changes of up to 0.056 ppm in the frontal region of the brain were indicated.