Matching Items (24)
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- All Subjects: Biomedical Engineering
- Creators: Towe, Bruce
Description
Computed tomography (CT) is one of the essential imaging modalities for medical diagnosis. Since its introduction in 1972, CT technology has been improved dramatically, especially in terms of its acquisition speed. However, the main principle of CT which consists in acquiring only density information has not changed at all until recently. Different materials may have the same CT number, which may lead to uncertainty or misdiagnosis. Dual-energy CT (DECT) was reintroduced recently to solve this problem by using the additional spectral information of X-ray attenuation and aims for accurate density measurement and material differentiation. However, the spectral information lies in the difference between two low and high energy images or measurements, so that it is difficult to acquire the accurate spectral information due to amplification of high pixel noise in the resulting difference image. In this work, a new model and an image enhancement technique for DECT are proposed, based on the fact that the attenuation of a high density material decreases more rapidly as X-ray energy increases. This fact has been previously ignored in most of DECT image enhancement techniques. The proposed technique consists of offset correction, spectral error correction, and adaptive noise suppression. It reduced noise, improved contrast effectively and showed better material differentiation in real patient images as well as phantom studies.
ContributorsPark, Kyung Kook (Author) / Metin, Akay (Thesis advisor) / Pavlicek, William (Committee member) / Akay, Yasemin (Committee member) / Towe, Bruce (Committee member) / Muthuswamy, Jitendran (Committee member) / Arizona State University (Publisher)
Created2011
Description
Phase contrast magnetic resonance angiography (PCMRA) is a non-invasive imaging modality that is capable of producing quantitative vascular flow velocity information. The encoding of velocity information can significantly increase the imaging acquisition and reconstruction durations associated with this technique. The purpose of this work is to provide mechanisms for reducing the scan time of a 3D phase contrast exam, so that hemodynamic velocity data may be acquired robustly and with a high sensitivity. The methods developed in this work focus on the reduction of scan duration and reconstruction computation of a neurovascular PCMRA exam. The reductions in scan duration are made through a combination of advances in imaging and velocity encoding methods. The imaging improvements are explored using rapid 3D imaging techniques such as spiral projection imaging (SPI), Fermat looped orthogonally encoded trajectories (FLORET), stack of spirals and stack of cones trajectories. Scan durations are also shortened through the use and development of a novel parallel imaging technique called Pretty Easy Parallel Imaging (PEPI). Improvements in the computational efficiency of PEPI and in general MRI reconstruction are made in the area of sample density estimation and correction of 3D trajectories. A new method of velocity encoding is demonstrated to provide more efficient signal to noise ratio (SNR) gains than current state of the art methods. The proposed velocity encoding achieves improved SNR through the use of high gradient moments and by resolving phase aliasing through the use measurement geometry and non-linear constraints.
ContributorsZwart, Nicholas R (Author) / Frakes, David H (Thesis advisor) / Pipe, James G (Thesis advisor) / Bennett, Kevin M (Committee member) / Debbins, Josef P (Committee member) / Towe, Bruce (Committee member) / Arizona State University (Publisher)
Created2011
Description
A noninvasive optical method is developed to monitor rapid changes in blood glucose levels in diabetic patients. The system depends on an optical cell built with a LED that emits light of wavelength 535nm that is a peak absorbance of hemoglobin. As the glucose concentration in the blood decreases, its osmolarity also decreases and the RBCs swell and decrease the path length absorption coefficient. Decreasing absorption coefficient increases the transmission of light through the whole blood. The system was tested with a constructed optical cell that held whole blood in a capillary tube. As expected the light transmitted to the photodiode increases with decreasing glucose concentration. The average response time of the system was between 30-40 seconds. The changes in size of the RBC cells in response to glucose concentration changes were confirmed using a cell counter and also visually under microscope. This method does not allow measuring the glucose concentration with an absolute concentration calibration. It is directed towards development of a device to monitor the changes in glucose concentration as an aid to diabetic management. This method might be improvised for precision and resolution and be developed as a ring or an earring that patients can wear.
ContributorsRajan, Shiny Amala Priya (Author) / Towe, Bruce (Thesis advisor) / Muthuswamy, Jitendran (Committee member) / LaBelle, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2013
Description
Image resolution limits the extent to which zooming enhances clarity, restricts the size digital photographs can be printed at, and, in the context of medical images, can prevent a diagnosis. Interpolation is the supplementing of known data with estimated values based on a function or model involving some or all of the known samples. The selection of the contributing data points and the specifics of how they are used to define the interpolated values influences how effectively the interpolation algorithm is able to estimate the underlying, continuous signal. The main contributions of this dissertation are three fold: 1) Reframing edge-directed interpolation of a single image as an intensity-based registration problem. 2) Providing an analytical framework for intensity-based registration using control grid constraints. 3) Quantitative assessment of the new, single-image enlargement algorithm based on analytical intensity-based registration. In addition to single image resizing, the new methods and analytical approaches were extended to address a wide range of applications including volumetric (multi-slice) image interpolation, video deinterlacing, motion detection, and atmospheric distortion correction. Overall, the new approaches generate results that more accurately reflect the underlying signals than less computationally demanding approaches and with lower processing requirements and fewer restrictions than methods with comparable accuracy.
ContributorsZwart, Christine M. (Author) / Frakes, David H (Thesis advisor) / Karam, Lina (Committee member) / Kodibagkar, Vikram (Committee member) / Spanias, Andreas (Committee member) / Towe, Bruce (Committee member) / Arizona State University (Publisher)
Created2013
Description
Coronary computed tomography angiography (CTA) has a high negative predictive value for ruling out coronary artery disease with non-invasive evaluation of the coronary arteries. My work has attempted to provide metrics that could increase the positive predictive value of coronary CTA through the use of dual energy CTA imaging. After developing an algorithm for obtaining calcium scores from a CTA exam, a dual energy CTA exam was performed on patients at dose levels equivalent to levels for single energy CTA with a calcium scoring exam. Calcium Agatston scores obtained from the dual energy CTA exam were within ±11% of scores obtained with conventional calcium scoring exams. In the presence of highly attenuating coronary calcium plaques, the virtual non-calcium images obtained with dual energy CTA were able to successfully measure percent coronary stenosis within 5% of known stenosis values, which is not possible with single energy CTA images due to the presence of the calcium blooming artifact. After fabricating an anthropomorphic beating heart phantom with coronary plaques, characterization of soft plaque vulnerability to rupture or erosion was demonstrated with measurements of the distance from soft plaque to aortic ostium, percent stenosis, and percent lipid volume in soft plaque. A classification model was developed, with training data from the beating heart phantom and plaques, which utilized support vector machines to classify coronary soft plaque pixels as lipid or fibrous. Lipid versus fibrous classification with single energy CTA images exhibited a 17% error while dual energy CTA images in the classification model developed here only exhibited a 4% error. Combining the calcium blooming correction and the percent lipid volume methods developed in this work will provide physicians with metrics for increasing the positive predictive value of coronary CTA as well as expanding the use of coronary CTA to patients with highly attenuating calcium plaques.
ContributorsBoltz, Thomas (Author) / Frakes, David (Thesis advisor) / Towe, Bruce (Committee member) / Kodibagkar, Vikram (Committee member) / Pavlicek, William (Committee member) / Bouman, Charles (Committee member) / Arizona State University (Publisher)
Created2013
Description
Millions of Americans live with motor impairments resulting from a stroke and the best way to administer rehabilitative therapy to achieve recovery is not well understood. Adaptive mixed reality rehabilitation (AMRR) is a novel integration of motion capture technology and high-level media computing that provides precise kinematic measurements and engaging multimodal feedback for self-assessment during a therapeutic task. The AMRR system was evaluated in a small (N=3) cohort of stroke survivors to determine best practices for administering adaptive, media-based therapy. A proof of concept study followed, examining changes in clinical scale and kinematic performances among a group of stroke survivors who received either a month of AMRR therapy (N = 11) or matched dosing of traditional repetitive task therapy (N = 10). Both groups demonstrated statistically significant improvements in Wolf Motor Function Test and upper-extremity Fugl-Meyer Assessment scores, indicating increased function after the therapy. However, only participants who received AMRR therapy showed a consistent improvement in their kinematic measurements, including those measured in the trained reaching task (reaching to grasp a cone) and in an untrained reaching task (reaching to push a lighted button). These results suggest that that the AMRR system can be used as a therapy tool to enhance both functionality and reaching kinematics that quantify movement quality. Additionally, the AMRR concepts are currently being transitioned to a home-based training application. An inexpensive, easy-to-use, toolkit of tangible objects has been developed to sense, assess and provide feedback on hand function during different functional activities. These objects have been shown to accurately and consistently track hand function in people with unimpaired movements and will be tested with stroke survivors in the future.
ContributorsDuff, Margaret Rose (Author) / Rikakis, Thanassis (Thesis advisor) / He, Jiping (Thesis advisor) / Herman, Richard (Committee member) / Kleim, Jeffrey (Committee member) / Santos, Veronica (Committee member) / Towe, Bruce (Committee member) / Arizona State University (Publisher)
Created2012
Description
Electrical neural activity detection and tracking have many applications in medical research and brain computer interface technologies. In this thesis, we focus on the development of advanced signal processing algorithms to track neural activity and on the mapping of these algorithms onto hardware to enable real-time tracking. At the heart of these algorithms is particle filtering (PF), a sequential Monte Carlo technique used to estimate the unknown parameters of dynamic systems. First, we analyze the bottlenecks in existing PF algorithms, and we propose a new parallel PF (PPF) algorithm based on the independent Metropolis-Hastings (IMH) algorithm. We show that the proposed PPF-IMH algorithm improves the root mean-squared error (RMSE) estimation performance, and we demonstrate that a parallel implementation of the algorithm results in significant reduction in inter-processor communication. We apply our implementation on a Xilinx Virtex-5 field programmable gate array (FPGA) platform to demonstrate that, for a one-dimensional problem, the PPF-IMH architecture with four processing elements and 1,000 particles can process input samples at 170 kHz by using less than 5% FPGA resources. We also apply the proposed PPF-IMH to waveform-agile sensing to achieve real-time tracking of dynamic targets with high RMSE tracking performance. We next integrate the PPF-IMH algorithm to track the dynamic parameters in neural sensing when the number of neural dipole sources is known. We analyze the computational complexity of a PF based method and propose the use of multiple particle filtering (MPF) to reduce the complexity. We demonstrate the improved performance of MPF using numerical simulations with both synthetic and real data. We also propose an FPGA implementation of the MPF algorithm and show that the implementation supports real-time tracking. For the more realistic scenario of automatically estimating an unknown number of time-varying neural dipole sources, we propose a new approach based on the probability hypothesis density filtering (PHDF) algorithm. The PHDF is implemented using particle filtering (PF-PHDF), and it is applied in a closed-loop to first estimate the number of dipole sources and then their corresponding amplitude, location and orientation parameters. We demonstrate the improved tracking performance of the proposed PF-PHDF algorithm and map it onto a Xilinx Virtex-5 FPGA platform to show its real-time implementation potential. Finally, we propose the use of sensor scheduling and compressive sensing techniques to reduce the number of active sensors, and thus overall power consumption, of electroencephalography (EEG) systems. We propose an efficient sensor scheduling algorithm which adaptively configures EEG sensors at each measurement time interval to reduce the number of sensors needed for accurate tracking. We combine the sensor scheduling method with PF-PHDF and implement the system on an FPGA platform to achieve real-time tracking. We also investigate the sparsity of EEG signals and integrate compressive sensing with PF to estimate neural activity. Simulation results show that both sensor scheduling and compressive sensing based methods achieve comparable tracking performance with significantly reduced number of sensors.
ContributorsMiao, Lifeng (Author) / Chakrabarti, Chaitali (Thesis advisor) / Papandreou-Suppappola, Antonia (Thesis advisor) / Zhang, Junshan (Committee member) / Bliss, Daniel (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Doppler radar can be used to measure respiration and heart rate without contact and through obstacles. In this work, a Doppler radar architecture at 2.4 GHz and a new signal processing algorithm to estimate the respiration and heart rate are presented. The received signal is dominated by the transceiver noise, LO phase noise and clutter which reduces the signal-to-noise ratio of the desired signal. The proposed architecture and algorithm are used to mitigate these issues and obtain an accurate estimate of the heart and respiration rate. Quadrature low-IF transceiver architecture is adopted to resolve null point problem as well as avoid 1/f noise and DC offset due to mixer-LO coupling. Adaptive clutter cancellation algorithm is used to enhance receiver sensitivity coupled with a novel Pattern Search in Noise Subspace (PSNS) algorithm is used to estimate respiration and heart rate. PSNS is a modified MUSIC algorithm which uses the phase noise to enhance Doppler shift detection. A prototype system was implemented using off-the-shelf TI and RFMD transceiver and tests were conduct with eight individuals. The measured results shows accurate estimate of the cardio pulmonary signals in low-SNR conditions and have been tested up to a distance of 6 meters.
ContributorsKhunti, Hitesh Devshi (Author) / Kiaei, Sayfe (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Bliss, Daniel (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2013
Description
This research investigated using impedance as a minimally invasive oral cancer-screening tool by modeling healthy and diseased tissue. This research developed an ultra-structurally based tissue model for oral mucosa that is versatile enough to be easily modified to mimic the passive electrical impedance responses of multiple benign and cancerous tissue types. This new model provides answers to biologically meaningful questions related to the impedance response of healthy and diseased tissues. This model breaks away from the old empirical top down "black box" Thèvinin equivalent model. The new tissue model developed here was created from a bottom up perspective resulting in a model that is analogous to having a "Transparent Box" where each network element relating to a specific structural component is known. This new model was developed starting with sub cellular ultra-structural components such as membranes, proteins and electrolytes. These components formed the basic network elements and topology of the organelles. The organelle networks combine to form the cell networks. The cell networks combine to make networks of cell layers and the cell layers were combined into tissue networks. This produced the complete "Transparent Box" model for normal tissue. This normal tissue model was modified for disease based on the ultra-structural pathology of each disease. The diseased tissues evaluated include cancers type one through type three; necrotic-inflammation, hyperkeratosis and the compound condition of hyperkeratosis over cancer type two. The impedance responses for each of the disease were compared side by side with the response of normal healthy tissue. Comparative evidence from the models showed the structural changes in cancer produce a unique identifiable impedance "finger print." The evaluation of the "Transparent Box" model for normal tissues and diseased tissues show clear support for using comparative impedance measurements as a clinical tool for oral cancer screening.
ContributorsPelletier, Peter Robert (Author) / Kozicki, Michael (Thesis advisor) / Towe, Bruce (Committee member) / Saraniti, Marco (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2012
Description
Multiple Sclerosis, an autoimmune disease, is one of the most common neurological disorder in which demyelinating of the axon occurs. The main symptoms of MS disease are fatigue, vision problems, stability issue, balance problems. Unfortunately, currently available treatments for this disease do not always guarantee the improvement of the condition of the MS patient and there has not been an accurate mechanism to measure the effectiveness of the treatment due to inter-patient heterogeneity. The factors that count for varying the performance of MS patients include environmental setting, weather, psychological status, dressing style and more. Also, patients may react differently while examined at specially arranged setting and this may not be the same while he/she is at home. Hence, it becomes a major problem for MS patients that how effectively a treatment slows down the progress of the disease and gives a relief for the patient. This thesis is trying to build a reliable system to estimate how good a treatment is for MS patients. Here I study the kinematic variables such as velocity of walking, stride length, variability and so on to find and compare the variations of the patient after a treatment given by the doctor, and trace these parameters for some patients after the treatment effect subdued.
ContributorsYin, Siyang (Author) / He, Jiping (Thesis advisor) / Pizziconi, Vincent (Committee member) / Towe, Bruce (Committee member) / Arizona State University (Publisher)
Created2012