The capability to increase the robustness to scattering has become a crucial request for communication protocols and imaging systems. Here we perform a complete analysis regarding the spatial features and the polarization of structured beams propagating in different scattering media. We observe different behaviors for structured light scattered by a solution of polystyrene latex beads in water and by tissue-mimicking phantom. The reported study can help in establishing a framework for the application of structured light illumination in imaging and diagnostic.
Scattering phenomena affect light propagation through any kind of medium from free space to biological tissues. Finding appropriate strategies to increase the robustness to scattering is the common requirement in developing both communication protocols and imaging systems. Recently, structured light has attracted attention due to its seeming scattering resistance in terms of transmissivity and spatial behavior. Moreover, correlation between optical polarization and orbital angular momentum (OAM), which characterizes the so-called vector vortex beam (VVB) states, seems to allow for the preservation of the polarization pattern. We extend the analysis by investigating both the spatial features and the polarization structure of vectorial optical vortexes propagating in scattering media with different concentrations. Among the observed features, we find a sudden swift decrease in contrast ratio for Gaussian, OAM, and VVB modes for concentrations of the adopted scattering media exceeding 0.09%. Our analysis provides a more general and complete study on the propagation of structured light in dispersive and scattering media.
With the increasing amount of available medical data, computing power and network speed, modern medical imaging is facing an unprecedented amount of data to analyze and interpret. Phenomena such as Big Data-omics stemming from several diagnostic procedures and novel multi-parametric imaging modalities tend to produce almost unmanageable quantities of data. The paper addresses the aforementioned context by assuming that a novel paradigm in massive data processing and automation becomes necessary in order to improve diagnostics and facilitate personalized and precision medicine for each patient. Traditional machine learning concepts have demonstrated many shortcomings when it comes to correctly diagnose fatal diseases. At the same time static graph networks are unable to capture the fluctuations in brain processing and monitor disease evolution. Therefore, artificial intelligence and deep learning are increasingly applied in oncologic medical imaging because they excel at providing quantitative assessments of biomedical imaging characteristics. On the other hand, novel concepts borrowed from modern control have paved the path for a dynamic graph theory that can predict neurodegenerative disease evolution and replace longitudinal studies. We chose two important topics, brain data processing and oncologic imaging to show the relevance of these concepts. We believe that these novel paradigms will impact multiple facets of radiology but are convinced that it is unlikely that they will replace radiologists any time in the near future since there are still many challenges in the clinical implementation.
KEYWORDS: Medical imaging, Magnetic resonance imaging, Analytics, Image compression, Image segmentation, Data storage, Machine learning, Evolutionary algorithms, Data modeling, Functional magnetic resonance imaging
Big data has been one of the hottest topics of scientific discussions in the recent years. In early 2000s, an industry analyst attempted to describe big data as the three Vs: Volume, Velocity, and Variability. With the new technologies such as Hadoop, it is now feasible to store and use extremely large volumes of data that comes in at an unprecedented velocity. The variability of this data can be large as it can come in different formats such as text documents, voice or video, and financial transactions. Big data analytics has been proven to be useful is various fields such as science, sports, advertising, health care, genomic sequence data, and medical imaging. This study presents a brief overview of big data analytics in medical imaging approaches with considering the importance of contemporary machine learning techniques such as deep learning.
Leader-follower controllability in brain networks which are affected neurodegenerative diseases can provide important biomarkers relevant for disease evolution. The brain network is viewed as a dynamic system where the nodes interact via neighbor-based Laplacian feedback rules. The network has cooperative connections between the nodes described by positive weights along with competitive connections which are described by negative connection weights. The nodes take the role of either leaders or followers, thus forming a leader-follower signed dynamic graph network. The results of this analysis can be easily generalized on unsigned brain networks. We apply the leader-follower concept to structural and functional brain networks with neurodegenerative diseases (dementia) and show that the found leaders represent important biomarkers for disease evolution. In other words, the leader nodes drive the network towards deteriorating cognitive states.
Reducing a graph model is extremely important for the dynamical analysis of large-scale networks. In order to approximate the behavior of such a system it is helpful to be able to simplify the model. In this paper, the graph reduction model is introduced. This method is based on removing edges that close independent cycles in the graph. We apply this novel model reduction paradigm to brain networks, and show the differences between the model approximation error for various brain network graphs ranging from those of healthy controls to those of Alzheimer's patients. The graph simplification for Alzheimer's brain networks yields the smallest approximation error, since the number of independent cycles is smaller than in either the healthy controls or mild cognitive impairment patients.
KEYWORDS: Machine learning, Tumors, Data analysis, Breast, Spatial resolution, Magnetic resonance imaging, Breast cancer, Computer aided diagnosis and therapy
Accurate methods for breast cancer diagnosis are of capital importance for selection and guidance of treatment and optimal patient outcomes. In dynamic contrast enhancing magnetic resonance imaging (DCE-MRI), the accurate differentiation of benign and malignant breast tumors that present as non-mass enhancing (NME) lesions is challenging, often resulting in unnecessary biopsies. Here we propose a new approach for the accurate diagnosis of such lesions with high resolution DCE-MRI by taking advantage of seven robust classification methods to discriminate between malignant and benign NME lesions using their dynamic curves at the voxel level, and test it in a manually delineated dataset. The tested approaches achieve a diagnostic accuracy up to 94% accuracy, sensitivity of 99 % and specificity of 90% respectively, with superiority of high temporal compared to high spatial resolution sequences.
KEYWORDS: Magnetic resonance imaging, Breast, Tumors, Drug discovery, Feature extraction, Image segmentation, Computer aided diagnosis and therapy, Imaging systems, Independent component analysis, Breast cancer
Diagnostically challenging breast tumors and Non-Mass-Enhancing (NME) lesions are often characterized by spatial and temporal heterogeneity, thus difficult to detect and classify. Differently from mass enhancing tumors they have an atypical temporal enhancement behavior that does not enable a straight-forward lesion classification into benign or malignant. The poorly defined margins do not support a concise shape description thus impacting morphological characterizations. A multi-level analysis strategy capturing the features of Non-Mass- Like-Enhancing (NMLEs) is shown to be superior to other methods relying only on morphological and kinetic information. In addition to this, the NMLE features such as NMLE distribution types and NMLE enhancement pattern, can be employed in radomics analysis to make robust models in the early prediction of the response to neo-adjuvant chemotherapy in breast cancer. Therefore, this could predict treatment response early in therapy to identify women who do not benefit from cytotoxic therapy.
Imaging connectomics emerged as an important field in modern neuroimaging to represent the interaction of structural and functional brain areas. Static graph networks are the mathematical structure to capture these interactions modeled by Pearson correlations between the representative area signals. Dynamical functional resting state networks seen in most fMRI experiments can not be represented by the classic correlation graph network. The changes in brain connectivity observed in many neuro-degenerative diseases in longitudinal data series suggest that more sophisticated graph networks to capture the dynamical properties of the brain networks are required. Furthermore, certain brain areas seem to act as ”disease epicenters” being responsible for the spread of neuro-degenerative diseases. To mathematically describe these aspects, we propose a novel framework based on pinning controllability applied to dynamic graphs and seek to determine the changes in the ”driver nodes” during the course of the disease. In contrast to other current research in pinning controllability, we aim to identify the best driver nodes describing disease evolution with respect to connectivity changes and location of the best driver nodes in functional 18F-Fluorodeoxyglucose Positron Emission Tomography (18FDG-PET) and structural Magnetic Resonance Imaging (MRI) connectivity graphs in healthy controls (CN), and patients with mild cognitive impairment (MCI), and Alzheimer’s disease (AD). We present the theoretical framework for determining the best driver nodes in connectivity graphs and their relation to disease evolution in dementia. We revolutionize the current graph analysis in brain networks and apply the concept of dynamic graph theory in connection with pinning controllability to reveal differences in the location of ”disease epicenters” that play an important role in the temporal evolution of dementia. The described research will constitute a leap in biomedical research related to novel disease prediction trajectories and precision dementia therapies.
KEYWORDS: Magnetic resonance imaging, Breast cancer, Feature extraction, Decision support systems, Lawrencium, Diffusion weighted imaging, Data conversion, Data modeling, Cancer, Machine learning
Neo-adjuvant chemotherapy (NAC) is the treatment of choice in patients with locally advanced breast cancer to reduce tumor burden, and potentially enable breast conservation. Response to treatment is assessed by histopathology from surgical specimen, a pathological complete response (pCR), or a minimal residual disease are associated with an improved disease-free, and overall survival. Early identification of non-responders is crucial as these patients might require different, or more aggressive treatment. Multi-parametric magnetic resonance imaging (mpMRI) using different morphological and functional MRI parameters such as T2-weighted, dynamic contrast-enhanced (DCE) MRI, and diffusion weighted imaging (DWI) has emerged as the method of choice for the early response assessments to NAC. Although, mpMRI is superior to conventional mammography for predicting treatment response, and evaluating residual disease, yet there is still room for improvement. In the past decade, the field of medical imaging analysis has grown exponentially, with an increased numbers of pattern recognition tools, and an increase in data sizes. These advances have heralded the field of radiomics. Radiomics allows the high-throughput extraction of the quantitative features that result in the conversion of images into mineable data, and the subsequent analysis of the data for an improved decision support with response monitoring during NAC being no exception. In this paper, we determine the importance and ranking of the extracted parameters from mpMRI using T2-weighted, DCE, and DWI for prediction of pCR and patient outcomes with respect to metastases and disease-specific death.
An important problem in modern therapeutics at the proteomic level remains to identify therapeutic targets in a plentitude of high-throughput data from experiments relevant to a variety of diseases. This paper presents the application of novel modern control concepts, such as pinning controllability and observability applied to the glioma cancer stem cells (GSCs) protein graph network with known and novel association to glioblastoma (GBM). The theoretical frameworks provides us with the minimal number of "driver nodes", which are necessary, and their location to determine the full control over the obtained graph network in order to provide a change in the network’s dynamics from an initial state (disease) to a desired state (non-disease). The achieved results will provide biochemists with techniques to identify more metabolic regions and biological pathways for complex diseases, to design and test novel therapeutic solutions.
Graph network models in dementia have become an important computational technique in neuroscience to study fundamental organizational principles of brain structure and function of neurodegenerative diseases such as dementia. The graph connectivity is reflected in the connectome, the complete set of structural and functional connections of the graph network, which is mostly based on simple Pearson correlation links.
In contrast to simple Pearson correlation networks, the partial correlations (PC) only identify direct correlations while indirect associations are eliminated. In addition to this, the state-of-the-art techniques in brain research are based on static graph theory, which is unable to capture the dynamic behavior of the brain connectivity, as it alters with disease evolution. We propose a new research avenue in neuroimaging connectomics based on combining dynamic graph network theory and modeling strategies at different time scales. We present the theoretical framework for area aggregation and time-scale modeling in brain networks as they pertain to disease evolution in dementia. This novel paradigm is extremely powerful, since we can derive both static parameters pertaining to node and area parameters, as well as dynamic parameters, such as system’s eigenvalues. By implementing and analyzing dynamically both disease driven PC-networks and regular concentration networks, we reveal differences in the structure of these network that play an important role in the temporal evolution of this disease. The described research is key to advance biomedical research on novel disease prediction trajectories and dementia therapies.
Glioma-derived cancer stem cells (GSCs) are tumor-initiating cells and may be refractory to radiation and
chemotherapy and thus have important implications for tumor biology and therapeutics. The analysis and interpretation
of large proteomic data sets requires the development of new data mining and visualization approaches.
Traditional techniques are insufficient to interpret and visualize these resulting experimental data. The emphasis
of this paper lies in the application of novel approaches for the visualization, clustering and projection representation
to unveil hidden data structures relevant for the accurate interpretation of biological experiments.
These qualitative and quantitative methods are applied to the proteomic analysis of data sets derived from the
GSCs. The achieved clustering and visualization results provide a more detailed insight into the protein-level
fold changes and putative upstream regulators for the GSCs. However the extracted molecular information
is insufficient in classifying GSCs and paving the pathway to an improved therapeutics of the heterogeneous
glioma.
Diagnostically challenging lesions pose a challenge both for the radiological reading and also for current CAD systems. They are not well-defined in both morphology (geometric shape) and kinetics (temporal enhancement) and pose a problem to lesion detection and classification. Their strong phenotypic differences can be visualized by MRI. Radiomics represents a novel approach to achieve a detailed quantification of the tumour phenotypes by analyzing a large number of image descriptors. In this paper, we apply a quantitative radiomics approach based on shape, texture and kinetics tumor features and evaluate it in comparison to a reduced-order feature approach in a computer-aided diagnosis system applied to diagnostically challenging lesions.
Chromosome 19 is known to be linked to neurodegeneration and many cancers. Glioma-derived cancer stem
cells (GSCs) are tumor-initiating cells and may be refractory to radiation and chemotherapy and thus have
important implications for tumor biology and therapeutics. The analysis and interpretation of large proteomic
data sets requires the development of new data mining and visualization approaches. Traditional techniques
are insufficient to interpret and visualize these resulting experimental data. The emphasis of this paper lies
in the presentation of novel approaches for the visualization, clustering and projection representation to unveil
hidden data structures relevant for the accurate interpretation of biological experiments. These qualitative and
quantitative methods are applied to the proteomic analysis of data sets derived from the GSCs. The achieved
clustering and visualization results provide a more detailed insight into the expression patterns for chromosome
19 proteins.
An important problem in modern therapeutics at the metabolomic, transcriptomic or phosphoproteomic level remains to identify therapeutic targets in a plentitude of high-throughput data from experiments relevant to a variety of diseases. This paper presents the application of novel graph algorithms and modern control solutions applied to the graph networks resulting from specific experiments to discover disease-related pathways and drug targets in glioma cancer stem cells (GSCs). The theoretical frameworks provides us with the minimal number of ”driver nodes” necessary to determine the full control over the obtained graph network in order to provide a change in the network’s dynamics from an initial state (disease) to a desired state (non-disease). The achieved results will provide biochemists with techniques to identify more metabolic regions and biological pathways for complex diseases, and design and test novel therapeutic solutions.
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