163 research outputs found

    Methods for Improving MRI-Based Conductivity Mapping

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    The electrical properties - permittivity and conductivity - of a material describe how electromagnetic waves behave in that material. Electrical properties are frequency-dependent parameters and, for a liquid sample, are measured with a dielectric probe and a network analyzer. This measurement technique is not feasible in vivo, but methods have been developed to make these measurements using magnetic resonance imaging (MRI). This work focuses on measuring conductivity, or the ability to conduct electric current. Mapping the electrical properties within the human body can provide important information for MRI safety and diagnostic applications. First, the specific absorption rate (SAR) in an MRI scan is proportional to conductivity, and limited to minimize the risk of heating in a subject. Knowledge of subject-specific conductivity maps could lead to better, subject-specific SAR estimation. Second, several small studies in recent years have shown that conductivity is elevated in malignant tumors as compared to healthy tissue. There are open research questions regarding the correlation between conductivity and other diagnostic metrics. Both of these applications benefit from accurate conductivity maps. In this work we describe three different methods for improving the accuracy of conductivity maps. The first is a novel regularized, model-based approach which we refer to as the Inverse Laplacian method. The Inverse Laplacian method resulted in lower reconstruction bias and error due to noise in simulations than the conventional filtering method. The Inverse Laplacian method also produced conductivity maps closer to the measured values in a phantom and with reduced noise in the human brain, as compared to the filtering method. The second is a method for combining multi-coil MRI data for conductivity mapping, because the use of multi-coil receivers can drastically improve the SNR in conductivity maps. The noise in the combined phase data using the proposed method was slightly elevated as compared to the optimal combination method, but the conductivity uniformity in a uniform gel phantom was greater than that of the optimal combination method. Furthermore, by visual inspection, the human brain conductivity calculated from data combined using the proposed method had minimal bias and noise amplification. Finally, we present a method for mapping conductivity tensors, as opposed to scalar values, which provides an additional layer of information to conductivity maps. Our proposed mathematical framework yields accurate tensor quantities provided the object can rotate 90 degrees in any direction. However, restricting the object rotation to mimic the constraints on a human subject yields slightly inaccurate results. We also present a dictionary-based approach to tensor calculations to try to improve the tensor estimates using restricted rotations.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144027/1/kropella_1.pd

    Uncertainty propagation in phaseless electric properties tomography

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    Uncertainty propagation in a phaseless magnetic resonance-based electric properties tomography technique is investigated using the Monte Carlo method. The studied inverse method, which recovers the electric properties distribution at radiofrequency inside a scatterer irradiated by the coils of a magnetic resonance imaging scanner, is based on the contrast source inversion technique adapted to process phaseless input data.Comment: 4 pages, 6 figures. 2019 International Conference on Electromagnetics in Advanced Applications (ICEAA

    Electrical properties tomography: a methodological review

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    Electrical properties tomography (EPT) is an imaging method that uses a magnetic resonance (MR) system to non-invasively determine the spatial distribution of the conductivity and permittivity of the imaged object. This manuscript starts by providing clear definitions about the data required for, and acquired in, EPT, followed by comprehensively formulating the physical equations underlying a large number of analytical EPT techniques. This thorough mathematical overview of EPT harmonizes several EPT techniques in a single type of formulation and gives insight into how they act on the data and what their data requirements are. Furthermore, the review describes machine learning-based algorithms. Matlab code of several differential and iterative integral methods is available upon request.Imaging- and therapeutic targets in neoplastic and musculoskeletal inflammatory diseas

    Design, Implementation, and Evaluation of a Fluorescence Laminar Optical Tomography Scanner for Brain Imaging

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    Implementation of new instrumentation and techniques for neuroscience research in recent years has opened new avenues in the study of the dynamics of large-scale neural networks such as the brain. In many of these techniques, including fluorescence recordings and optogenetic stimulation, a combination of photonics and molecular genetic methods are exploited to manipulate and monitor neural activities. Such techniques have been proven to be highly efficient in unraveling the mysteries of data processing in the micro circuits of the brain and as a result these techniques are widely used nowadays in most neuroscience labs. In optogenetics, cell-types of interest are genetically modified by expressing light-sensitive proteins adapted from microbial opsin. Once these proteins are expressed, we are able to use light of appropriate wavelengths to manipulate, increase or suppress neural activity of specific neurons on command. With a high temporal resolution (in the order of milliseconds) and cell-type-specific precision, optogenetics is able to probe how the nervous system functions in real-time, even in freely-moving animals. Currently, whenever genetic modifications are employed in the study of nervous systems, fluorescence proteins are also co-expressed in the same cells as biological markers to visualize the induced changes in the targeted cells. Despite its importance to trace the signal of such markers in-vivo, capabilities of the developed fluorescence tomography instrumentation are still limited and researchers mostly document the fluorescence distribution and expression of proteins of interest after euthanizing the animal and dissection of the tissue. In this project, we present our effort in implementing a fluorescence laminar optical tomography (FLOT) system which is specifically designed for non-invasive three dimensional imaging of fluorescence proteins within the brain of rodents. The application of the developed technology is not limited to optogenetics, but it can be used as a powerful tool to help improving the precision and accuracy of neuroscience and optogenetic experiments. In this design, a set of galvanometer mirrors are employed for realization of a fast and flexible scanner while a highly sensitive camera records the produced fluorescence signals. Fluorescence laminar optical tomography (FLOT) scanner has shown promising results in imaging superficial areas up to 2mm deep from the surface, with the resolution of ~200Āµm. Details of the design of the hardware and reconstruction algorithms are discussed and samples of experimental results are presented

    Imaging Electrical Properties Using MRI and In Vivo Applications

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    University of Minnesota Ph.D. dissertation. November 2015. Major: Biomedical Engineering. Advisor: Bin He. 1 computer file (PDF); viii, 137 pages.Electrical properties, namely conductivity and permittivity, describe the interaction of materials with the surrounding electromagnetic field. The electrical properties of biological tissue are associated with many fundamental aspects of tissue, such as cellular and molecular structure, ion concentration, cell membrane permeability, etc. Electrical properties of tissue in vivo can be used as biomarkers to characterize cancerous tissue or provide useful information in applications involving tissue and electromagnetic field. Among many related electrical-property imaging technologies, electrical properties tomography (EPT) is a promising one that noninvasively extracts the in vivo electrical properties with high spatial resolution based on measured B1 field using magnetic resonance imaging (MRI). In this thesis, advanced EPT methods have been developed to improve the imaging quality of conventional EPT. First of all, a multi-channel EPT framework was introduced to release its dependency on a B1 phase assumption and expand its application under high field strength. Secondly, a gradient-based EPT (gEPT) approach was proposed and implemented, showing enhanced robustness against effect of measurement noise and improved performance near tissue boundaries. Using gEPT, high resolution in vivo electrical-property images of healthy human brain were obtained, and an imaging system for rat tumor models was also developed. As a result of malignancy, increased conductivity was captured in tumors using the in vivo animal imaging system. Thirdly, based on EPT theory, quantitative water proton density imaging was proposed using measured B1 field information, provide a new way for estimating water content in tissue for diagnostic and research purpose

    Advanced tomographic image reconstruction algorithms for Diffuse Optical Imaging

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    Diļ¬€use Optical Imaging is relatively new set of imaging modality that use infrared and near infrared light to characterize the optical properties of biological tissue. The technology used is less expensive than other imaging modalities such as X-ray mammography, it is portable and can be used to monitor brain activation and cancer diagnosis, besides to aid to other imaging modalities and therapy treatments in the characterization of diseased tissue, i. e. X-ray, Magnetic Resonance Imaging and Radio Frequency Ablation. Due the optical properties of biological tissue near-infrared light is highly scattered, as a consequence, a limited amount of light is propagated thus making the image reconstruction process very challenging. Typically, diļ¬€use optical image reconstructions require from several minutes to hours to produce an accurate image from the interaction of the photons and the chormophores of the studied medium. To this day, this limitation is still under investigation and there are several approaches that are close to the real-time image reconstruction operation. Diļ¬€use Optical Imaging includes a variety of techniques such as functional Near-Infrared Spectroscopy (fNIRS), Diļ¬€use Optical Tomography (DOT), Fluorescence Diļ¬€use Optical Tomography (FDOT) and Spatial Frequency Domain imaging (SFDI). These emerging image reconstruction modalities aim to become routine modalities for clinical applications. Each technique presents their own advantages and limitations, but they have been successfully used in clinical trials such as brain activation analysis and breast cancer diagnosis by mapping the response of the vascularity within the tissue through the use of models that relate the interaction between the tissue and the path followed by the photons. One way to perform the image reconstruction process is by separating it in two stages: the forward problem and the inverse problem; the former is used to describe light propagation inside a medium and the latter is related to the reconstruction of the spatio-temporal distribution of the photons through the tissue. Iterative methods are used to solve both problems but the intrinsic complexity of photon transport in biological tissue makes the problem time-consuming and computationally expensive. The aim of this research is to apply a fast-forward solver based on reduced order models to Fluorescence Diļ¬€use Optical Tomography and Spatial Frequency Domain Imaging to contribute to these modalities in their application of clinical trials. Previous work showed the capabilities of the reduced order models for real-time reconstruction of the absorption parameters in the brain of mice. Results demonstrated insigniļ¬cant loss of quantitative and qualitative accuracy and the reconstruction was performed in a fraction of the time normally required on this kind of studies. The forward models proposed in this work, oļ¬€er the capability to run three-dimensional image reconstructions in CPU-based computational systems in a fraction of the time required by image reconstructions methods that use meshes generated using the Finite Element Method. In the case of SFMI, the proposed approach is fused with the approach of the virtual sensor for CCD cameras to reduce the computational burden and to generate a three-dimensional map of the distribution of tissue optical properties. In this work, the use case of FDOT focused on the thorax of a mouse model with tumors in the lungs as the medium under investigation. The mouse model was studied under two- and three- dimension conditions. The two-dimensional case is presented to explain the process of creating the Reduced-Order Models. In this case, there is not a signiļ¬cant improvement in the reconstruction considering NIRFAST as the reference. The proposed approach reduced the reconstruction time to a quarter of the time required by NIRFAST, but the last one performed it in a couple of seconds. In contrast, the three-dimensional case exploited the capabilities of the Reduced-Order Models by reducing the time of the reconstruction from a couple of hours to several seconds, thus allowing a closer real-time reconstruction of the ļ¬‚uorescent properties of the interrogated medium. In the case of Spatial Frequency Domain Imaging, the use case considered a three-dimensional section of a human head that is analysed using a CCD camera and a spatially modulated light source that illuminates the mentioned head section. Using the principle of the virtual sensor, diļ¬€erent regions of the CCD camera are clustered and then Reduced Order Models are generated to perform the image reconstruction of the absorption distribution in a fraction of the time required by the algorithm implemented on NIRFAST. The ultimate goal of this research is to contribute to the ļ¬eld of Diļ¬€use Optical Imaging and propose an alternative solution to be used in the reconstruction process to those models already used in three-dimensional reconstructions of Fluorescence Diļ¬€use Optical Tomography and Spatial Frequency Domain Imaging, thus oļ¬€ering the possibility to continuously monitor tissue obtaining results in a matter of seconds
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