RANDOM FOREST FLAIR RECONSTRUCTION FROM T1, T2, AND PD -WEIGHTED MRI

Fluid Attenuated Inversion Recovery (FLAIR) is a commonly acquired pulse sequence for multiple sclerosis (MS) patients. MS white matter lesions appear hyperintense in FLAIR images and have excellent contrast with the surrounding tissue. Hence, FLAIR images are commonly used in automated lesion segmentation algorithms to easily and quickly delineate the lesions. This expedites the lesion load computation and correlation with disease progression. Unfortunately for numerous reasons the acquired FLAIR images can be of a poor quality and suffer from various artifacts. In the most extreme cases the data is absent, which poses a problem when consistently processing a large data set. We propose to fill in this gap by reconstructing a FLAIR image given the corresponding T₁-weighted, T₂-weighted, and P_D -weighted images of the same subject using random forest regression. We show that the images we produce are similar to true high quality FLAIR images and also provide a good surrogate for tissue segmentation.

MR to CT Registration of Brains using Image Synthesis

Computed tomography (CT) is the standard imaging modality for patient dose calculation for radiation therapy. Magnetic resonance (MR) imaging (MRI) is used along with CT to identify brain structures due to its superior soft tissue contrast. Registration of MR and CT is necessary for accurate delineation of the tumor and other structures, and is critical in radiotherapy planning. Mutual information (MI) or its variants are typically used as a similarity metric to register MRI to CT. However, unlike CT, MRI intensity does not have an accepted calibrated intensity scale. Therefore, MI-based MR-CT registration may vary from scan to scan as MI depends on the joint histogram of the images. In this paper, we propose a fully automatic framework for MR-CT registration by synthesizing a synthetic CT image from MRI using a co-registered pair of MR and CT images as an atlas. Patches of the subject MRI are matched to the atlas and the synthetic CT patches are estimated in a probabilistic framework. The synthetic CT is registered to the original CT using a deformable registration and the computed deformation is applied to the MRI. In contrast to most existing methods, we do not need any manual intervention such as picking landmarks or regions of interests. The proposed method was validated on ten brain cancer patient cases, showing 25% improvement in MI and correlation between MR and CT images after registration compared to state-of-the-art registration methods.

Example Based Lesion Segmentation

Automatic and accurate detection of white matter lesions is a significant step toward understanding the progression of many diseases, like Alzheimer's disease or multiple sclerosis. Multi-modal MR images are often used to segment T₂ white matter lesions that can represent regions of demyelination or ischemia. Some automated lesion segmentation methods describe the lesion intensities using generative models, and then classify the lesions with some combination of heuristics and cost minimization. In contrast, we propose a patch-based method, in which lesions are found using examples from an atlas containing multi-modal MR images and corresponding manual delineations of lesions. Patches from subject MR images are matched to patches from the atlas and lesion memberships are found based on patch similarity weights. We experiment on 43 subjects with MS, whose scans show various levels of lesion-load. We demonstrate significant improvement in Dice coefficient and total lesion volume compared to a state of the art model-based lesion segmentation method, indicating more accurate delineation of lesions.

An adaptive grid for graph-based segmentation in retinal OCT

Graph-based methods for retinal layer segmentation have proven to be popular due to their efficiency and accuracy. These methods build a graph with nodes at each voxel location and use edges connecting nodes to encode the hard constraints of each layer's thickness and smoothness. In this work, we explore deforming the regular voxel grid to allow adjacent vertices in the graph to more closely follow the natural curvature of the retina. This deformed grid is constructed by fixing node locations based on a regression model of each layer's thickness relative to the overall retina thickness, thus we generate a subject specific grid. Graph vertices are not at voxel locations, which allows for control over the resolution that the graph represents. By incorporating soft constraints between adjacent nodes, segmentation on this grid will favor smoothly varying surfaces consistent with the shape of the retina. Our final segmentation method then follows our previous work. Boundary probabilities are estimated using a random forest classifier followed by an optimal graph search algorithm on the new adaptive grid to produce a final segmentation. Our method is shown to produce a more consistent segmentation with an overall accuracy of 3.38 μm across all boundaries.

Subject Specific Sparse Dictionary Learning for Atlas based Brain MRI Segmentation

Quantitative measurements from segmentations of soft tissues from magnetic resonance images (MRI) of human brains provide important biomarkers for normal aging, as well as disease progression. In this paper, we propose a patch-based tissue classification method from MR images using sparse dictionary learning from an atlas. Unlike most atlas-based classification methods, deformable registration from the atlas to the subject is not required. An "atlas" consists of an MR image, its tissue probabilities, and the hard segmentation. The "subject" consists of the MR image and the corresponding affine registered atlas probabilities (or priors). A subject specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches. The same sparse combination is applied to the segmentation patches of the atlas to generate tissue memberships of the subject. The novel combination of prior probabilities in the example patches enables us to distinguish tissues having similar intensities but having different spatial location. We show that our method outperforms two state-of-the-art whole brain tissue segmentation methods. We experimented on 12 subjects having manual tissue delineations, obtaining mean Dice coefficients of 0:91 and 0:87 for cortical gray matter and cerebral white matter, respectively. In addition, experiments on subjects with ventriculomegaly shows significantly better segmentation using our approach than the competing methods.

Automatic method for thalamus parcellation using multi-modal feature classification

Segmentation and parcellation of the thalamus is an important step in providing volumetric assessment of the impact of disease n brain structures. Conventionally, segmentation is carried out on T1-weighted magnetic resonance (MR) images and nuclear parcellation using diffusion weighted MR images. We present the first fully automatic method that incorporates both tissue contrasts and several derived fea-fractional anisotrophy, fiber orientation from the 5D Knutsson representation of the principal eigenvectors, and connectivity between the thalamus and the cortical lobes, as features. Combining these multiple information sources allows us to identify discriminating dimensions and thus parcellate the thalamic nuclei. A hierarchical random forest framework with a multidimensional feature per voxel, first distinguishes thalamus from background, and then separates each group of thalamic nuclei. Using a leave one out cross-validation on 12 subjects we have a mean Dice score of 0.805 and 0.799 for the left and right thalami, respectively. We also report overlap for the thalamic nuclear groups.

Deformation field correction for spatial normalization of PET images using a population-derived partial least squares model

Spatial normalization of positron emission tomography (PET) images is essential for population studies, yet work on anatomically accurate PET-to-PET registration is limited. We present a method for the spatial normalization of PET images that improves their anatomical alignment based on a deformation correction model learned from structural image registration. To generate the model, we first create a population-based PET template with a corresponding structural image template. We register each PET image onto the PET template using deformable registration that consists of an affine step followed by a diffeomorphic mapping. Constraining the affine step to be the same as that obtained from the PET registration, we find the diffeomorphic mapping that will align the structural image with the structural template. We train partial least squares (PLS) regression models within small neighborhoods to relate the PET intensities and deformation fields obtained from the diffeomorphic mapping to the structural image deformation fields. The trained model can then be used to obtain more accurate registration of PET images to the PET template without the use of a structural image. A cross validation based evaluation on 79 subjects shows that our method yields more accurate alignment of the PET images compared to deformable PET-to-PET registration as revealed by 1) a visual examination of the deformed images, 2) a smaller error in the deformation fields, and 3) a greater overlap of the deformed anatomical labels with ground truth segmentations.

Reconstruction of the human cerebral cortex robust to white matter lesions: method and validation

Cortical atrophy has been reported in a number of diseases, such as multiple sclerosis and Alzheimer's disease, that are also associated with white matter (WM) lesions. However, most cortical reconstruction techniques do not account for these pathologies, thereby requiring additional processing to correct for the effect of WM lesions. In this work, we introduce CRUISE(+), an automated process for cortical reconstruction from magnetic resonance brain images with WM lesions. The process extends previously well validated methods to allow for multichannel input images and to accommodate for the presence of WM lesions. We provide new validation data and tools for measuring the accuracy of cortical reconstruction methods on healthy brains as well as brains with multiple sclerosis lesions. Using this data, we validate the accuracy of CRUISE(+) and compare it to another state-of-the-art cortical reconstruction tool. Our results demonstrate that CRUISE(+) has superior performance in the cortical regions near WM lesions, and similar performance in other regions.

COVARIANCE SHRINKING IN ACTIVE SHAPE MODELS WITH APPLICATION TO GYRAL LABELING OF THE CEREBRAL CORTEX

Active shape models (ASMs) have been widely used in segmentation tasks in medical image analysis. Complex structures and a limited number of training samples can, however, result in the failure to capture the complete range of shape variations. Various modifications to the point distribution model (PDM) have been proposed to increase the flexibility of the model. Still model parameters are often determined empirically without respect to the underlying data structure. We explore shrinkage covariance estimation in building a PDM by combining the sample covariance matrix with a target covariance matrix estimated from a low-dimensional constrained model. Instead of using a global shrinkage intensity, we apply a spatially varying shrinkage intensity field to better adapt to the spatially varying characteristic of a complex shape. The parameters of the constrained model and the amount of shrinkage are determined in a data-driven fashion, so that the resulting distribution is optimized in representing the underlying data. The PDM, which we call SC-PDM, shows an increased flexibility in fitting new shapes and at the same time, is robust to noise. We demonstrate the effectiveness of using SC-PDM to label gyral regions on the human cerebral cortex.

Magnetic Resonance Image Example-Based Contrast Synthesis

The performance of image analysis algorithms applied to magnetic resonance images is strongly influenced by the pulse sequences used to acquire the images. Algorithms are typically optimized for a targeted tissue contrast obtained from a particular implementation of a pulse sequence on a specific scanner. There are many practical situations, including multi-institution trials, rapid emergency scans, and scientific use of historical data, where the images are not acquired according to an optimal protocol or the desired tissue contrast is entirely missing. This paper introduces an image restoration technique that recovers images with both the desired tissue contrast and a normalized intensity profile. This is done using patches in the acquired images and an atlas containing patches of the acquired and desired tissue contrasts. The method is an example-based approach relying on sparse reconstruction from image patches. Its performance in demonstrated using several examples, including image intensity normalization, missing tissue contrast recovery, automatic segmentation, and multimodal registration. These examples demonstrate potential practical uses and also illustrate limitations of our approach.

Retinal layer segmentation of macular OCT images using boundary classification

Optical coherence tomography (OCT) has proven to be an essential imaging modality for ophthalmology and is proving to be very important in neurology. OCT enables high resolution imaging of the retina, both at the optic nerve head and the macula. Macular retinal layer thicknesses provide useful diagnostic information and have been shown to correlate well with measures of disease severity in several diseases. Since manual segmentation of these layers is time consuming and prone to bias, automatic segmentation methods are critical for full utilization of this technology. In this work, we build a random forest classifier to segment eight retinal layers in macular cube images acquired by OCT. The random forest classifier learns the boundary pixels between layers, producing an accurate probability map for each boundary, which is then processed to finalize the boundaries. Using this algorithm, we can accurately segment the entire retina contained in the macular cube to an accuracy of at least 4.3 microns for any of the nine boundaries. Experiments were carried out on both healthy and multiple sclerosis subjects, with no difference in the accuracy of our algorithm found between the groups.

MAGNETIC RESONANCE IMAGE SYNTHESIS THROUGH PATCH REGRESSION

Magnetic resonance imaging (MRI) is widely used for analyzing human brain structure and function. MRI is extremely versatile and can produce different tissue contrasts as required by the study design. For reasons such as patient comfort, cost, and improving technology, certain tissue contrasts for a cohort analysis may not have been acquired during the imaging session. This missing pulse sequence hampers consistent neuroanatomy research. One possible solution is to synthesize the missing sequence. This paper proposes a data-driven approach to image synthesis, which provides equal, if not superior synthesis compared to the state-of-the-art, in addition to being an order of magnitude faster. The synthesis transformation is done on image patches by a trained bagged ensemble of regression trees. Validation was done by synthesizing T₂-weighted contrasts from T₁-weighted scans, for phantoms and real data. We also synthesized 3 Tesla T₁-weighted magnetization prepared rapid gradient echo (MPRAGE) images from 1.5 Tesla MPRAGEs to demonstrate the generality of this approach.

PATCH BASED INTENSITY NORMALIZATION OF BRAIN MR IMAGES

Magnetic resonance (MR) imaging (MRI) is widely used to study the structure of human brains. Unlike computed tomography (CT), MR image intensities do not have a tissue specific interpretation. Thus images of the same subject obtained with either the same imaging sequence on different scanners or with differing parameters have widely varying intensity scales. This inconsistency introduces errors in segmentation, and other image processing tasks, thus necessitating image intensity standardization. Compared to previous intensity normalization methods using histogram transformations-which try to find a global one-to-one intensity mapping based on histograms-we propose a patch based generative model for intensity normalization between images acquired under different scanners or different pulse sequence parameters. Our method outperforms histogram based methods when normalizing phantoms simulated with various parameters. Additionally, experiments on real data, acquired under a variety of scanners and acquisition parameters, have more consistent segmentations after our normalization.

DEFORMABLE REGISTRATION OF MACULAR OCT USING A-MODE SCAN SIMILARITY

Optical coherence tomography (OCT) of the macular cube has become an increasingly important tool for investigating and managing retinal pathology. One important new area of investigation is the analysis of anatomic variably across a population. Such an analysis on the retina requires the construction of a normalized space, which is generally created through deformable registration of each subject into a common template. Unfortunately, state-of-the-art 3D registration tools fail to adequately spatially normalize retinal OCT images. This work proposes a new deformable registration algorithm for OCT images using the similarity between pairs of A-mode scans. First, a retinal OCT specific affine step is presented, which uses automated landmarks to perform global translations and individual rescaling of all the subject's A-mode scans. Then, a deformable registration using regularized one-dimensional radial basis functions is applied to further align the retinal layers. Results on 15 subjects show the improved accuracy of this approach in comparison to state of the art methods with respect to registration for labeling. Additional results show the ability to generate stereotaxic spaces for retinal OCT.

LONGITUDINAL INTENSITY NORMALIZATION IN THE PRESENCE OF MULTIPLE SCLEROSIS LESIONS

This paper proposes a longitudinal intensity normalization algorithm for T₁-weighted magnetic resonance images of human brains in the presence of multiple sclerosis lesions, aiming towards stable and consistent longitudinal segmentations. Unlike previous longitudinal segmentation methods, we propose a 4D intensity normalization that can be used as a preprocessing step to any segmentation method. The variability in intensities arising from the relapsing and remitting nature of the multiple sclerosis lesions is modeled into an otherwise smooth intensity transform based on first order autoregressive models, resulting in smooth changes in segmentation statistics of normal tissues, while keeping the lesion information unaffected. We validated our method on both simulated and real longitudinal normal subjects and on multiple sclerosis subjects.

Voxel-Wise Displacement as Independent Features in Classification of Multiple Sclerosis

We present a method that utilizes registration displacement fields to perform accurate classification of magnetic resonance images (MRI) of the brain acquired from healthy individuals and patients diagnosed with multiple sclerosis (MS). Contrary to standard approaches, each voxel in the displacement field is treated as an independent feature that is classified individually. Results show that when used with a simple linear discriminant and majority voting, the approach is superior to using the displacement field with a single classifier, even when compared against more sophisticated classification methods such as adaptive boosting, random forests, and support vector machines. Leave-one-out cross-validation was used to evaluate this method for classifying images by disease, MS subtype (Acc: 77%-88%), and age (Acc: 96%-100%).

Segmentation of retinal OCT images using a random forest classifier

Optical coherence tomography (OCT) has become one of the most common tools for diagnosis of retinal abnormalities. Both retinal morphology and layer thickness can provide important information to aid in the differential diagnosis of these abnormalities. Automatic segmentation methods are essential to providing these thickness measurements since the manual delineation of each layer is cumbersome given the sheer amount of data within each OCT scan. In this work, we propose a new method for retinal layer segmentation using a random forest classifier. A total of seven features are extracted from the OCT data and used to simultaneously classify nine layer boundaries. Taking advantage of the probabilistic nature of random forests, probability maps for each boundary are extracted and used to help refine the classification. We are able to accurately segment eight retinal layers with an average Dice coefficient of 0.79 ± 0.13 and a mean absolute error of 1.21 ± 1.45 pixels for the layer boundaries.

Automatic Cell Segmentation in Fluorescence Images of Confluent Cell Monolayers Using Multi-object Geometric Deformable Model

With the rapid development of microscopy for cell imaging, there is a strong and growing demand for image analysis software to quantitatively study cell morphology. Automatic cell segmentation is an important step in image analysis. Despite substantial progress, there is still a need to improve the accuracy, efficiency, and adaptability to different cell morphologies. In this paper, we propose a fully automatic method for segmenting cells in fluorescence images of confluent cell monolayers. This method addresses several challenges through a combination of ideas. 1) It realizes a fully automatic segmentation process by first detecting the cell nuclei as initial seeds and then using a multi-object geometric deformable model (MGDM) for final segmentation. 2) To deal with different defects in the fluorescence images, the cell junctions are enhanced by applying an order-statistic filter and principal curvature based image operator. 3) The final segmentation using MGDM promotes robust and accurate segmentation results, and guarantees no overlaps and gaps between neighboring cells. The automatic segmentation results are compared with manually delineated cells, and the average Dice coefficient over all distinguishable cells is 0.88.

Pulse Sequence based Multi-acquisition MR Intensity Normalization

Intensity normalization is an important preprocessing step in magnetic resonance (MR) image analysis. In MR images (MRI), the observed intensities are primarily dependent on (1) intrinsic magnetic resonance properties of the tissues such as proton density (P_D ), longitudinal and transverse relaxation times (T₁ and T₂ respectively), and (2) the scanner imaging parameters like echo time (TE), repeat time (TR), and flip angle (α). We propose a method which utilizes three co-registered images with different contrast mechanisms (PD-weighted, T2-weighted and T1-weighted) to first estimate the imaging parameters and then estimate P_D , T₁, and T₂ values. We then normalize the subject intensities to a reference by simply applying the pulse sequence equation of the reference image to the subject tissue parameters. Previous approaches to solve this problem have primarily focused on matching the intensity histograms of the subject image to a reference histogram by different methods. The fundamental drawback of these methods is their failure to respect the underlying imaging physics and tissue biology. Our method is validated on phantoms and we show improvement of normalization on real images of human brains.

AUTOMATED ANATOMICAL LABELING OF THE CEREBRAL ARTERIES USING BELIEF PROPAGATION

Labeling of cerebral vasculature is important for characterization of anatomical variation, quantification of brain morphology with respect to specific vessels, and inter-subject comparisons of vessel properties and abnormalities. We propose an automated method to label the anterior portion of cerebral arteries using a statistical inference method on the Bayesian network representation of the vessel tree. Our approach combines the likelihoods obtained from a random forest classifier trained using vessel centerline features with a belief propagation method integrating the connection probabilities of the cerebral artery network. We evaluate our method on 30 subjects using a leave-one-out validation, and show that it achieves an average correct vessel labeling rate of over 92%.

Longitudinal Intensity Normalization of Magnetic Resonance Images using Patches

This paper presents a patch based method to normalize temporal intensities from longitudinal brain magnetic resonance (MR) images. Longitudinal intensity normalization is relevant for subsequent processing, such as segmentation, so that rates of change of tissue volumes, cortical thickness, or shapes of brain structures becomes stable and smooth over time. Instead of using intensities at each voxel, we use patches as image features as a patch encodes neighborhood information of the center voxel. Once all the time-points of a longitudinal dataset are registered, the longitudinal intensity change at each patch is assumed to follow an auto-regressive (AR(1)) process. An estimate of the normalized intensities of a patch at every time-point are generated from a hidden Markov model, where the hidden states are the unobserved normalized patches and the outputs are the observed patches. A validation study on a phantom dataset shows good segmentation overlap with the truth, and an experiment with real data shows more stable rates of change for tissue volumes with the temporal normalization than without.

System for integrated neuroimaging analysis and processing of structure

Mapping brain structure in relation to neurological development, function, plasticity, and disease is widely considered to be one of the most essential challenges for opening new lines of neuro-scientific inquiry. Recent developments with MRI analysis of structural connectivity, anatomical brain segmentation, cortical surface parcellation, and functional imaging have yielded fantastic advances in our ability to probe the neurological structure-function relationship in vivo. To date, the image analysis efforts in each of these areas have typically focused on a single modality. Here, we extend the cortical reconstruction using implicit surface evolution (CRUISE) methodology to perform efficient, consistent, and topologically correct analyses in a natively multi-parametric manner. This effort combines and extends state-of-the-art techniques to simultaneously consider and analyze structural and diffusion information alongside quantitative and functional imaging data. Robust and consistent estimates of the cortical surface extraction, cortical labeling, diffusion-inferred contrasts, diffusion tractography, and subcortical parcellation are demonstrated in a scan-rescan paradigm. Accompanying this demonstration, we present a fully automated software system complete with validation data.

Brain metabolite alterations and cognitive dysfunction in early Huntington's disease

Huntington's disease (HD) is a neurodegenerative disorder characterized by early cognitive decline that progresses at later stages to dementia and severe movement disorder. HD is caused by a cytosine-adenine-guanine triplet-repeat expansion mutation in the Huntingtin gene, allowing early diagnosis by genetic testing. This study aimed to identify the relationship of N-acetylaspartate and other brain metabolites to cognitive function in HD-mutation carriers by using high-field-strength magnetic resonance spectroscopy (MRS) at 7 Tesla. Twelve individuals with the HD mutation in premanifest or early-stage disease versus 12 healthy controls underwent (1)H magnetic resonance spectroscopy (7.2 mL voxel in the posterior cingulate cortex) at 7 Tesla, and also T1-weighted structural magnetic resonance imaging. All participants received standardized tests of cognitive functioning including the Montreal Cognitive Assessment and standardized quantified neurological examination within an hour before scanning. Individuals with the HD mutation had significantly lower posterior cingulate cortex N-acetylaspartate (-9.6%, P = .02) and glutamate (-10.1%, P = .02) levels than did controls. In contrast, in this small group, measures of brain morphology including striatal and ventricle volumes did not differ significantly. Linear regression with Montreal Cognitive Assessment scores revealed significant correlations with N-acetylaspartate (r(2) = 0.50, P = .01) and glutamate (NAA) (r(2) = 0.64, P = .002) in HD subjects. Our data suggest a relationship between reduced N-acetylaspartate and glutamate levels in the posterior cingulate cortex with cognitive decline in the early stages of HD. N-acetylaspartate and glutamate magnetic resonance spectroscopy signals of the posterior cingulate cortex region may serve as potential biomarkers of disease progression or treatment outcome in HD and other neurodegenerative disorders with early cognitive dysfunction, when structural brain changes are still minor.

Automatic Sulcal Curve Extraction with MRF Based Shape Prior

Extracting and labeling sulcal curves on the human cerebral cortex is important for many neuroscience studies, however manually annotating the sulcal curves is a time-consuming task. In this paper, we present an automatic sulcal curve extraction method by registering a set of dense landmark points representing the sulcal curves to the subject cortical surface. A Markov random field is used to model the prior distribution of these landmark points, with short edges in the graph preserving the curve structure and long edges modeling the global context of the curves. Our approach is validated using a leave-one-out strategy of training and evaluation on fifteen cortical surfaces, and a quantitative error analysis on the extracted major sulcal curves.

Simultaneous Cortical Surface Labeling and Sulcal Curve Extraction

Automatic labeling of the gyri and sulci on the cortical surface is important for studying cortical morphology and brain functions within populations. A method to simultaneously label gyral regions and extract sulcal curves is proposed. Assuming that the gyral regions parcellate the whole cortical surface into contiguous regions with certain fixed topology, the proposed method labels the subject cortical surface by deformably registering a network of curves that form the boundary of gyral regions to the subject cortical surface. In the registration process, the curves are encouraged to follow the fine details of the sulcal geometry and to observe the shape statistics learned from training data. Using the framework of probabilistic point set registration methods, the proposed algorithm finds the sulcal curve network that maximizes the posterior probability by Expectation-Maximization (EM). The automatic labeling method was evaluated on 15 cortical surfaces using a leave-one-out strategy. Quantitative error analysis is carried out on both labeled regions and major sulcal curves.