Retrieving medical images that present similar diseases is an active research area for diagnostics and therapy. However, it can be problematic given the visual variations between anatomical structures. In this paper, we propose a new feature extraction method for similarity computation in medical imaging. Instead of the low-level visual appearance, we design a CCA-PairLDA feature representation method to capture the similarity between images with high-level semantics. First, we extract the PairLDA topics to represent an image as a mixture of latent semantic topics in an image pair context. Second, we generate a CCA-correlation model to represent the semantic association between an image pair for similarity computation. While PairLDA adjusts the latent topics for all image pairs, CCA-correlation helps to associate an individual image pair. In this way, the semantic descriptions of an image pair are closely correlated, and naturally correspond to similarity computation between images. We evaluated our method on two public medical imaging datasets for image retrieval and showed improved performance.

Understanding how the structure of cognition arises from the topographical organization of the cortex is a primary goal in neuroscience. Previous work has described local functional gradients extending from perceptual and motor regions to cortical areas representing more abstract functions, but an overarching framework for the association between structure and function is still lacking. Here, we show that the principal gradient revealed by the decomposition of connectivity data in humans and the macaque monkey is anchored by, at one end, regions serving primary sensory/motor functions and at the other end, transmodal regions that, in humans, are known as the default-mode network (DMN). These DMN regions exhibit the greatest geodesic distance along the cortical surface-and are precisely equidistant-from primary sensory/motor morphological landmarks. The principal gradient also provides an organizing spatial framework for multiple large-scale networks and characterizes a spectrum from unimodal to heteromodal activity in a functional metaanalysis. Together, these observations provide a characterization of the topographical organization of cortex and indicate that the role of the DMN in cognition might arise from its position at one extreme of a hierarchy, allowing it to process transmodal information that is unrelated to immediate sensory input.

In recent years evidence has accumulated to suggest that neuroinflammation might be an early pathology of schizophrenia that later leads to neurodegeneration, yet the exact role in the etiology, as well as the source of neuroinflammation, are still not known. The hypothesis of neuroinflammation involvement in schizophrenia is quickly gaining popularity, and thus it is imperative that we have reliable and reproducible tools and measures that are both sensitive, and, most importantly, specific to neuroinflammation. The development and use of appropriate human in vivo imaging methods can help in our understanding of the location and extent of neuroinflammation in different stages of the disorder, its natural time-course, and its relation to neurodegeneration. Thus far, there is little in vivo evidence derived from neuroimaging methods. This is likely the case because the methods that are specific and sensitive to neuroinflammation are relatively new or only just being developed. This paper provides a methodological review of both existing and emerging positron emission tomography and magnetic resonance imaging techniques that identify and characterize neuroinflammation. We describe \how these methods have been used in schizophrenia research. We also outline the shortcomings of existing methods, and we highlight promising future techniques that will likely improve state-of-the-art neuroimaging as a more refined approach for investigating neuroinflammation in schizophrenia.

Matching the bolus arrival time (BAT) of the arterial input function (AIF) and tissue residue function (TRF) is necessary for accurate pharmacokinetic (PK) modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We investigated the sensitivity of volume transfer constant ([Formula: see text]) and extravascular extracellular volume fraction ([Formula: see text]) to BAT and compared the results of four automatic BAT measurement methods in characterization of prostate and breast cancers. Variation in delay between AIF and TRF resulted in a monotonous change trend of [Formula: see text] and [Formula: see text] values. The results of automatic BAT estimators for clinical data were all comparable except for one BAT estimation method. Our results indicate that inaccuracies in BAT measurement can lead to variability among DCE-MRI PK model parameters, diminish the quality of model fit, and produce fewer valid voxels in a region of interest. Although the selection of the BAT method did not affect the direction of change in the treatment assessment cohort, we suggest that BAT measurement methods must be used consistently in the course of longitudinal studies to control measurement variability.

PURPOSE: The aim of this study was to present a tractography algorithm using a two-tensor unscented Kalman filter (UKF) to improve the modeling of the corticospinal tract (CST) by tracking through regions of peritumoral edema and crossing fibers. METHODS: Ten patients with brain tumors in the vicinity of motor cortex and evidence of significant peritumoral edema were retrospectively selected for the study. All patients underwent 3-T magnetic resonance imaging (MRI) including functional MRI (fMRI) and a diffusion-weighted data set with 31 directions. Fiber tracking was performed using both single-tensor streamline and two-tensor UKF tractography methods. A two-region-of-interest approach was used to delineate the CST. Results from the two tractography methods were compared visually and quantitatively. fMRI was applied to identify the functional fiber tracts. RESULTS: Single-tensor streamline tractography underestimated the extent of tracts running through the edematous areas and could only track the medial projections of the CST. In contrast, two-tensor UKF tractography tracked fanning projections of the CST despite peritumoral edema and crossing fibers. Based on visual inspection, the two-tensor UKF tractography delineated tracts that were closer to motor fMRI activations, and it was apparently more sensitive than single-tensor streamline tractography to define the tracts directed to the motor sites. The volume of the CST was significantly larger on two-tensor UKF than on single-tensor streamline tractography ([Formula: see text]). CONCLUSION: Two-tensor UKF tractography tracks a larger volume CST than single-tensor streamline tractography in the setting of peritumoral edema and crossing fibers in brain tumor patients.

Patient-specific biomechanical models have been advocated as a tool for predicting deformations of soft body organs/tissue for medical image registration (aligning two sets of images) when differences between the images are large. However, complex and irregular geometry of the body organs makes generation of patient-specific biomechanical models very time-consuming. Meshless discretisation has been proposed to solve this challenge. However, applications so far have been limited to 2D models and computing single organ deformations. In this study, 3D comprehensive patient-specific nonlinear biomechanical models implemented using meshless Total Lagrangian explicit dynamics algorithms are applied to predict a 3D deformation field for whole-body image registration. Unlike a conventional approach that requires dividing (segmenting) the image into non-overlapping constituents representing different organs/tissues, the mechanical properties are assigned using the fuzzy c-means algorithm without the image segmentation. Verification indicates that the deformations predicted using the proposed meshless approach are for practical purposes the same as those obtained using the previously validated finite element models. To quantitatively evaluate the accuracy of the predicted deformations, we determined the spatial misalignment between the registered (i.e. source images warped using the predicted deformations) and target images by computing the edge-based Hausdorff distance. The Hausdorff distance-based evaluation determines that our meshless models led to successful registration of the vast majority of the image features. Copyright 2016 John Wiley & Sons, Ltd.

BACKGROUND: There is growing evidence to suggest that delusions associated with schizophrenia arise from altered structural brain connectivity. The present study investigated whether structural changes in three major fasciculi that interconnect the limbic system - the cingulum bundle, uncinate fasciculus and fornix - are associated with delusions in chronic schizophrenia patients. METHODS: Free-water corrected Diffusion Tensor Imaging was used to investigate the association between delusions and both microstructural changes within these three fasciculi and extracellular changes in the surrounding free-water. Clinical data and diffusion MRI scans were obtained from 28 healthy controls and 86 schizophrenia patients, of whom 34 had present state delusions, 35 had a lifetime history but currently remitted delusions, and 17 had never experienced delusions. RESULTS: While present state and remitted delusions were found to be associated with reduced free-water corrected fractional anisotropy (FAT) and increased free-water corrected radial diffusivity (RDT) in the cingulum bundle bilaterally, extracellular free-water (FW) in the left cingulum bundle was found to be specifically associated with present state delusions in chronic schizophrenia. No changes were observed in the remaining tracts. CONCLUSIONS: These findings suggest that state and trait delusions in chronic schizophrenia are associated with microstructural processes, such as myelin abnormalities (as indicated by decreased FAT and increased RDT) in the cingulum bundle and that state delusions are additionally associated with extracellular processes such as neuroinflammation or atrophy (as indicated by increased FW) in the left cingulum bundle.

BACKGROUND: The nigrosome-1 region of the substantia nigra (SN) undergoes the greatest and earliest dopaminergic neuron loss in Parkinson’s disease (PD). As T2-weighted magnetic resonance imaging (MRI) scans are often collected with routine clinical MRI protocols, this investigation aims to determine whether T2-imaging changes in the nigrosome-1 are related to clinical measures of PD and to assess their potential as a more clinically accessible biomarker for PD. METHODS: Voxel intensity ratios were calculated for T2-weighted MRI scans from 47 subjects from the Parkinson’s Progression Markers Initiative database. Three approaches were used to delineate the SN and nigrosome-1: (1) manual segmentation, (2) automated segmentation, and (3) area voxel-based morphometry. Voxel intensity ratios were calculated from voxel intensity values taken from the nigrosome-1 and two areas of the remaining SN. Linear regression analyses were conducted relating voxel intensity ratios with the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) sub-scores for each subject.

Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic. The latent topics are learnt, based on the relationship between the images and words, and are employed to bridge the gap between low-level visual features and high-level semantics. These latent topics describe the images and words semantically and can thus facilitate more meaningful comparisons between the words. Second, we compute an overall-word significance value to evaluate the significance of a visual word within the entire dictionary. We designed an iterative ranking method to measure overall-word significance by considering the relationship between all latent topics and words. The words with higher values are considered meaningful with more significant discriminative power in differentiating medical images. We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency.

PURPOSE: Tractography is the most anatomically accurate method for delineating white matter tracts in the brain, yet few studies have examined multiple tracts using tractography in patients with schizophrenia (SCZ). We analyze 5 white matter connections important in the pathophysiology of SCZ: uncinate fasciculus, cingulum bundle (CB), inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus, and arcuate fasciculus (AF). Additionally, we investigate the relationship between diffusion tensor imaging (DTI) markers and neuropsychological measures. METHODS: High-resolution DTI data were acquired on a 3 Tesla scanner in 30 patients with early-course SCZ and 30 healthy controls (HC) from the Boston Center for Intervention Development and Applied Research study. After manually guided tracts delineation, fractional anisotropy (FA), trace, radial diffusivity (RD), and axial diffusivity (AD) were calculated and averaged along each tract. The association of DTI measures with the Scales for the Assessment of Negative and Positive Symptoms and neuropsychological measures was evaluated. RESULTS: Compared to HC, patients exhibited reduced FA and increased trace and RD in the right AF, CB, and ILF. A discriminant analysis showed the possible use of FA of these tracts for better future group membership classifications. FA and RD of the right ILF and AF were associated with positive symptoms while FA and RD of the right CB were associated with memory performance and processing speed. CONCLUSION: We observed white matter alterations in the right CB, ILF, and AF, possibly caused by myelin disruptions. The structural abnormalities interact with cognitive performance, and are linked to clinical symptoms.