IUPUI Imaging Research Initiative: Research Center for Quantitative Renal Imaging

poster abstractMission: The overall mission of the Research Center for Quantitative Renal Imaging is to provide a focused research environment and resource for the development, implementation, and dissemination of innovative, quantitative imaging methods designed to assess the status of and mechanisms associated with acute and chronic kidney disease and evaluate efficacy of therapeutic interventions. Currently, there is no comprehensive research center within the United States that is solely dedicated to the development of quantitative imaging methods specifically designed to diagnose kidney disease, monitor its progression, and evaluate efficacy of therapeutic interventions. The Research Center for Quantitative Renal Imaging represents a very unique resource within the nephrology and medical imaging communities that is distinctly associated with IUPUI and the IU School of Medicine. Nature of the Center: Our plan is to build upon the individually successful research programs and infrastructure that currently exist within our institution and weave these individual components into a new, unified, and unique Research Center focused on developing novel and innovative methods for quantitative imaging of the kidney. Goals: The Research Center for Quantitative Renal Imaging will achieve its mission by: • Identifying, developing, and implementing innovative imaging methods that provide quantitative imaging biomarkers for assessing and inter-relating renal structure, function, hemodynamics and underlying tissue microenvironmental factors contributing to kidney disease. • Establishing an environment that facilitates and encourages interdisciplinary collaborations among investigators, helps advance the research careers of junior faculty, and offers research support to investigators focused on developing and utilizing innovative quantitative imaging methods in support of kidney disease research. • Providing a resource to inform the greater research and healthcare communities of advances in quantitative renal imaging and its potential for enhanced patient management and care. • Offering an imaging research resource to pharmaceutical companies and medical device manufacturers engaged in product development associated with the diagnosis and treatment of kidney disease

An approach to the synthesis of biological tissue

Mathematical phantoms developed to synthesize realistic complex backgrounds such as those obtained when imaging biological tissue, play a key role in the quantitative assessment of image quality for medical and biomedical imaging. We present a modeling framework for the synthesis of realistic tissue samples. The technique is demonstrated using radiological breast tissue. The model employs a two-component image decomposition consisting of a slowly, spatially varying mean-background and a residual texture image. Each component is synthesized independently. The approach and results presented here constitute an important step towards developing methods for the quantitative assessment of image quality in medical and biomedical imaging, and more generally image science

Management of COPD:Is there a role for quantitative imaging?

While the recent development of quantitative imaging methods have led to their increased use in the diagnosis and management of many chronic diseases, medical imaging still plays a limited role in the management of chronic obstructive pulmonary disease (COPD). In this review we highlight three pulmonary imaging modalities: computed tomography (CT), magnetic resonance imaging (MRI) and optical coherence tomography (OCT) imaging and the COPD biomarkers that may be helpful for managing COPD patients. We discussed the current role imaging plays in COPD management as well as the potential role quantitative imaging will play by identifying imaging phenotypes to enable more effective COPD management and improved outcomes

Validation of fluorescence transition probability calculations

A systematic and quantitative validation of the K and L shell X-ray transition probability calculations according to different theoretical methods has been performed against experimental data. This study is relevant to the optimization of data libraries used by software systems, namely Monte Carlo codes, dealing with X-ray fluorescence. The results support the adoption of transition probabilities calculated according to the Hartree-Fock approach, which manifest better agreement with experimental measurements than calculations based on the Hartree-Slater method.Comment: 8 pages, 21 figures and images, 3 tables, to appear in proceedings of the Nuclear Science Symposium and Medical Imaging Conference 2009, Orland

QUANTITATIVE METHODS AND DETECTION TECHNIQUES IN HYPERSPECTRAL IMAGING INVOLVING MEDICAL AND OTHER APPLICATIONS

This research using Hyperspectral imaging involves recognizing targets through spatial and spectral matching and spectral un-mixing of data ranging from remote sensing to medical imaging kernels for clinical studies based on Hyperspectral data-sets generated using the VFTHSI [Visible Fourier Transform Hyperspectral Imager], whose high resolution Si detector makes the analysis achievable. The research may be broadly classified into (I) A Physically Motivated Correlation Formalism (PMCF), which places both spatial and spectral data on an equivalent mathematical footing in the context of a specific Kernel and (II) An application in RF plasma specie detection during carbon nanotube growing process. (III) Hyperspectral analysis for assessing density and distribution of retinopathies like age related macular degeneration (ARMD) and error estimation enabling the early recognition of ARMD, which is treated as an ill-conditioned inverse imaging problem. The broad statistical scopes of this research are two fold- target recognition problems and spectral unmixing problems. All processes involve experimental and computational analysis of Hyperspectral data sets is presented, which is based on the principle of a Sagnac Interferometer, calibrated to obtain high SNR levels. PMCF computes spectral/spatial/cross moments and answers the question of how optimally the entire hypercube should be sampled and finds how many spatial-spectral pixels are required precisely for a particular target recognition. Spectral analysis of RF plasma radicals, typically Methane plasma and Argon plasma using VFTHSI has enabled better process monitoring during growth of vertically aligned multi-walled carbon nanotubes by instant registration of the chemical composition or density changes temporally, which is key since a significant correlation can be found between plasma state and structural properties. A vital focus of this thesis is towards medical Hyperspectral imaging applied to retinopathies like age related macular degeneration targets taken with a Fundus imager, which is akin to the VFTHSI. Detection of the constituent components in the diseased hyper-pigmentation area is also computed. The target or reflectance matrix is treated as a highly ill-conditioned spectral un-mixing problem, to which methodologies like inverse techniques, principal component analysis (PCA) and receiver operating curves (ROC) for precise spectral recognition of infected area. The region containing ARMD was easily distinguishable from the spectral mesh plots over the entire band-pass area. Once the location was detected the PMCF coefficients were calculated by cross correlating a target of normal oxygenated retina with the de-oxygenated one. The ROCs generated using PMCF shows 30% higher detection probability with improved accuracy than ROCs based on Spectral Angle Mapper (SAM). By spectral unmixing methods, the important endmembers/carotenoids of the MD pigment were found to be Xanthophyl and lutein, while β-carotene which showed a negative correlation in the unconstrained inverse problem is a supplement given to ARMD patients to prevent the disease and does not occur in the eye. Literature also shows degeneration of meso-zeaxanthin. Ophthalmologists may assert the presence of ARMD and commence the diagnosis process if the Xanthophyl pigment have degenerated 89.9%, while the lutein has decayed almost 80%, as found deduced computationally. This piece of current research takes it to the next level of precise investigation in the continuing process of improved clinical findings by correlating the microanatomy of the diseased fovea and shows promise of an early detection of this disease

Medical Image Segmentation using Deep Convolutional Neural Networks

Deep learning (DL) has been evolved in many forms in recent years, with applications not only limited to the Computer Vision tasks, expanded towards Autonomous Driving, Medical Imaging, Bio-Medical Imaging including Digital Pathology Image Analysis (DPIA), and in many other forms. Deep Convolutional Neural Network (DCNN) methods such as LeNet, AlexNet, GoogleNet, VGGNet, ResidulaNet, DenseNet, and CapsuleNet within the DL has been very successful in object classification and detection problems on a very large scale publicly available data set. Due to the great success of these DCNN methods, researchers have explored these methods to other imaging areas such as medical imaging problems, where there is a greater need for automated computer algorithms to make the diagnosis quick and cost-efficient, specifically for image classification, segmentation, detection, registration, and medical image data processing. Several state of art methods that provided superior performance in medical image segmentation such as Fully Connect Networks (FCN), SegNet, DeepLabs, U-Net, V-Net, and R2U-Net have outperformed hand-crafted machine learning algorithms. These models have been tested on several medical imaging and DPIA data sets but have not been explored on multi-organ segmentation, so the primary goal of this proposal is to explore more on these state of art models and test on several publicly available multi-organ segmentation data sets. The quantitative and qualitative performance will be evaluated against existing models using different performance metrics including, Accuracy, Sensitivity, Specificity, F1-score, Receiver Operating Characteristics (ROC) curve, dice coefficient (DC), and Mean Squared Error (MSE).https://ecommons.udayton.edu/stander_posters/2666/thumbnail.jp