In cancer treatment, understanding the aggressiveness of the tumor is essential in therapy planning and patient follow-up. In this article, we present a novel method for quantifying the speed of invasion of gliomas in white and grey matter from time series of magnetic resonance (MR) images. The proposed approach is based on mathematical tumor growth models using the reaction-diffusion formalism. The quantification process is formulated by an inverse problem and solved using anisotropic fast marching method yielding an efficient algorithm.
In radiotherapy, the constant margin taken around the visible tumor is a very coarse approximation of the invasion margin of cancerous cells. In this article, a new formulation to estimate the invasion margin of a tumor by extrapolating low tumor densities in magnetic resonance images (MRIs) is proposed. The current imaging techniques are able to show parts of the tumor where cancerous cells are dense enough. However, tissue parts containing small number of tumor cells are not enhanced in images. We propose a way to estimate these parts using the tumor mass visible in the image.
Methods that leverage neighbourhood structures in high-dimensional image spaces have recently attracted attention. These approaches extract information from a new image using its "neighbours" in the image space equipped with an application-specific distance. Finding the neighbourhood of a given image is challenging due to large dataset sizes and costly distance evaluations. Furthermore, automatic neighbourhood search for a new image is currently not possible when the distance is based on ground truth annotations.
We present a method for automatic segmentation of high-grade gliomas and their subregions from multi-channel MR images. Besides segmenting the gross tumor, we also differentiate between active cells, necrotic core, and edema. Our discriminative approach is based on decision forests using context-aware spatial features, and integrates a generative model of tissue appearance, by using the probabilities obtained by tissue-specific Gaussian mixture models as additional input for the forest.
We propose a general database-driven framework for coherent synthesis of subject-specific scans of desired modality, which adopts and generalizes the patch-based label propagation (LP) strategy. While modality synthesis has received increased attention lately, current methods are mainly tailored to specific applications. On the other hand, the LP framework has been extremely successful for certain segmentation tasks, however, so far it has not been used for estimation of entities other than categorical segmentation labels.
Language is an essential higher cognitive function supported by large-scale brain networks. In this study, we investigated functional connectivity changes in the left frontoparietal network (LFPN), a language-cognition related brain network in aphasic patients. We enrolled 13 aphasic patients who had undergone a stroke in the left hemisphere and age-, gender-, educational level-matched controls and analyzed the data by integrating independent component analysis (ICA) with a network connectivity analysis method.
Human and animal studies suggest that acupuncture produces many beneficial effects through the central nervous system. However, the neural substrates of acupuncture actions are not completely clear to date. fMRI studies at Hegu (LI4) and Zusanli (ST36) indicated that the limbic system may play an important role for acupuncture effects. To test if this finding applies to other major classical acupoints, fMRI was performed on 10 healthy adults during manual acupuncture at Taichong (LV3), Xingjian (LV2), Neiting (ST44), and a sham point on the dorsum of the left foot.
UNLABELLED: Acupuncture is a therapeutic treatment that is defined as the insertion of needles into the body at specific points (ie, acupoints). Advances in functional neuroimaging have made it possible to study brain responses to acupuncture; however, previous studies have mainly concentrated on acupoint specificity. We wanted to focus on the functional brain responses that occur because of needle insertion into the body.
Those with high baseline stress levels are more likely to develop mild cognitive impairment (MCI) and Alzheimer's Disease (AD). While meditation may reduce stress and alter the hippocampus and default mode network (DMN), little is known about its impact in these populations. Our objective was to conduct a "proof of concept" trial to determine whether Mindfulness Based Stress Reduction (MBSR) would improve DMN connectivity and reduce hippocampal atrophy among adults with MCI. 14 adults with MCI were randomized to MBSR vs.
PURPOSE: To compare and contrast the pattern and characteristics of the cerebral blood volume (CBV) response to ethanol (EtOH) in rats under awake and anesthetized conditions.
