Assessing Midbrain Abnormalities In Parkinson’S Disease Using Magnetic Resonance Imaging,
2021
Wayne State University
Assessing Midbrain Abnormalities In Parkinson’S Disease Using Magnetic Resonance Imaging, Kiarash Ghassaban
Wayne State University Dissertations
Diagnosing early-stage Parkinson’s disease (PD) and its manifestations is still a clinical challenge. Previous imaging studies using iron, neuromelanin (NM) and the Nigrosome-1 (N1) measures in the substantia nigra (SN) by themselves have been unable to provide sufficiently high diagnostic performance for these methods to be adopted clinically. In this dissertation, we start by studying idiopathic PD patients at their intermediate stages of the disease to evaluate the role of global and regional iron in the major deep gray matter nuclei. Then, we only focus on the NM complex in the midbrain and how neuronal loss interact with iron overload …
Imaging Potential In Saturation Recovery Methods For Sarcoidosis Patients With Medical Devices,
2021
Virginia Commonwealth University
Imaging Potential In Saturation Recovery Methods For Sarcoidosis Patients With Medical Devices, Samantha Zhao
Theses and Dissertations
Cardiovascular magnetic resonance (CMR) imaging is a preferred imaging methodology due to its lack of ionizing radiation and ability to detect myocardial inflammation and fibrosis using quantitative T1 mapping techniques. Cardiac sarcoidosis (CS) is characterized as the formation of granulomas in the myocardium. Current methods for detection include measuring non-cardiac specific C-reactive protein (CRP) levels, or PET imaging, which uses ionizing radiation, therefore CMR would make an ideal imaging option. However, many CS patients have implanted cardiac devices which can cause degradation in image. The modified Look-Locker inversion recovery (MOLLI) method is widely used in quantitative T1 mapping with high …
Characterization & Calibration Of Foresight Ice,
2021
Western University
Characterization & Calibration Of Foresight Ice, Hareem Nisar, Terry M Peters, Elvis C.S. Chen
Robarts Imaging Publications
No abstract provided.
Clinical Applications And Feasibility Of Proton Ct And Proton Radiography,
2021
Northern Illinois University
Clinical Applications And Feasibility Of Proton Ct And Proton Radiography, Christina Marie Sarosiek
Graduate Research Theses & Dissertations
Proton therapy is a form of radiation treatment for cancer that utilizes the Bragg peak to create conformal high dose regions around the tumor volume. However, the use of x-ray computed tomography (CT) and x-ray radiography for treatment planning and pre-treatment quality assurance procedures improves the achievable effectiveness of proton treatment plans (using proton CT) and the pretreatment verification (using proton radiography). Errors in the conversion from x-ray Hounsfield units (HU) to proton relative stopping powers (RSP) leads to errors in the predicted proton range. To account for the errors, 3.5% margins are included in the treatment plan. This means …
Perceptually Improved Medical Image Translations Using Conditional Generative Adversarial Networks,
2021
Bucknell University
Perceptually Improved Medical Image Translations Using Conditional Generative Adversarial Networks, Anurag Vaidya
Honors Theses
Magnetic resonance imaging (MRI) can help visualize various brain regions. Typical MRI sequences consist of T1-weighted sequence (favorable for observing large brain structures), T2-weighted sequence (useful for pathology), and T2-FLAIR scan (useful for pathology with suppression of signal from water). While these different scans provide complementary information, acquiring them leads to acquisition times of ~1 hour and an average cost of $2,600, presenting significant barriers. To reduce these costs associated with brain MRIs, we present pTransGAN, a generative adversarial network capable of translating both healthy and unhealthy T1 scans into T2 scans. We show that the addition of non-adversarial …
Development Of Light Actuated Chemical Delivery Platform On A 2-D Array Of Micropore Structure,
2021
CUNY City College
Development Of Light Actuated Chemical Delivery Platform On A 2-D Array Of Micropore Structure, Hojjat Rostami Azmand, Hojjat Rostami Azmand
Dissertations and Theses
Localized chemical delivery plays an essential role in the fundamental information transfers within biological systems. Thus, the ability to mimic the natural chemical signal modulation would provide significant contributions to understand the functional signaling pathway of biological cells and develop new prosthetic devices for neurological disorders. In this paper, we demonstrate a light-controlled hydrogel platform that can be used for localized chemical delivery in a high spatial resolution. By utilizing the photothermal behavior of graphene-hydrogel composites confined within micron-sized fluidic channels, patterned light illumination creates the parallel and independent actuation of chemical release in a group of fluidic ports. The …
Fast And Accurate Autofocus Control Using Guassian Standard Deviation And Gradient-Based Binning,
2021
University of Massachusetts Amherst
Fast And Accurate Autofocus Control Using Guassian Standard Deviation And Gradient-Based Binning, Peter Dimeo, Lu Sun, Xian Du
Mechanical and Industrial Engineering Faculty Publication Series
We propose a fast and accurate autofbcus algorithm using Gaussian standard deviation and gradient-based binning. Rather than iteratively searching for the optimal focus using an optimization process, the proposed algorithm directly calculates the mean of the Gaussian shaped focus measure (FM) curve to find the optimal focus location and uses the FM curve standard deviation to adapt the motion step size. The calculation only requires 3-4 defocused images to identify the center location of the FM curve. Furthermore, by assigning motion step sizes based on the FM curve standard deviation, the magnitude of the motion step is adaptively controlled according …
Combining Cryo-Em Density Map And Residue Contact For Protein Secondary Structure Topologies,
2021
Old Dominion University
Combining Cryo-Em Density Map And Residue Contact For Protein Secondary Structure Topologies, Maytha Alshammari, Jing He
Computer Science Faculty Publications
Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. A topology of secondary structures defines the mapping between a set of sequence segments and a set of traces of secondary structures in three-dimensional space. In order to enhance accuracy in ranking secondary structure topologies, we explored a method that combines three sources of information: a set …
Fmri Feature Extraction Model For Adhd Classification Using Convolutional Neural Network,
2021
Old Dominion University
Fmri Feature Extraction Model For Adhd Classification Using Convolutional Neural Network, Senuri De Silva, Sanuwani Udara Dayarathna, Gangani Ariyarathne, Dulani Meedeniya, Sampath Jayarathna
Computer Science Faculty Publications
Biomedical intelligence provides a predictive mechanism for the automatic diagnosis of diseases and disorders. With the advancements of computational biology, neuroimaging techniques have been used extensively in clinical data analysis. Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder, with the symptomology of inattention, impulsivity, and hyperactivity, in which early diagnosis is crucial to prevent unwelcome outcomes. This study addresses ADHD identification using functional magnetic resonance imaging (fMRI) data for the resting state brain by evaluating multiple feature extraction methods. The features of seed-based correlation (SBC), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) are comparatively applied to …
Bibliometric Review On Liver And Tumour Segmentation Using Deep Learning,
2021
University of Nebraska - Lincoln
Bibliometric Review On Liver And Tumour Segmentation Using Deep Learning, Jayant Jagtap, Aamir Habeeb, Avinash Jha, Shrey Aggarwal, Khushi Gupta
Library Philosophy and Practice (e-journal)
One of the major organs in the body is liver where tumors occur often. Malignant liver tumors pose a serious hazard to human life and health. Manual segmentation of the liver organ and tumor from computed tomography (CT) scans is difficult, time-consuming, and skewed to the clinician's experience, yet it is essential for hepatic surgical planning. However, due to the following considerations, segmenting liver tumors from computed tomography (CT) images is difficult: In CT pictures, the contrast between the liver tumor and healthy tissues is low, and the boundary is indistinct; the picture of the liver tumor is confusing, with …
Bibliometric Review On Applications Of Disease Detection Using Digital Image Processing Techniques,
2021
University of Nebraska - Lincoln
Bibliometric Review On Applications Of Disease Detection Using Digital Image Processing Techniques, Jayant Jagtap, Rahil Sharma, Aryan Sinha, Nikhil Panda, Amulya Reddy
Library Philosophy and Practice (e-journal)
Advances around the field of deep learning and cognitive computing have allowed mankind to look and solve the problems of the world in a completely new way. Deep learning has been making huge advancements in the field of healthcare, which most importantly focuses upon disease detection and disease prediction. Techniques such as these have been conceptualized the idea of early detection and economical ways of treating the predicted disease in particular. Still, it has been observed that there seems to be no change in the way diagnosis of a particular disease takes place even in the 21st generation of …
Approaches To Studying Bacterial Biofilms In The Bioeconomy With Nanofabrication Techniques And Engineered Platforms.,
2020
University of Tennessee, Knoxville
Approaches To Studying Bacterial Biofilms In The Bioeconomy With Nanofabrication Techniques And Engineered Platforms., Michelle Caroline Halsted
Doctoral Dissertations
Studies that estimate more than 90% of bacteria subsist in a biofilm state to survive environmental stressors. These biofilms persist on man-made and natural surfaces, and examples of the rich biofilm diversity extends from the roots of bioenergy crops to electroactive biofilms in bioelectrochemical reactors. Efforts to optimize microbial systems in the bioeconomy will benefit from an improved fundamental understanding of bacterial biofilms. An understanding of these microbial systems shows promise to increase crop yields with precision agriculture (e.g. biosynthetic fertilizer, microbial pesticides, and soil remediation) and increase commodity production yields in bioreactors. Yet conventional laboratory methods investigate these micron-scale …
Network-Level Mechanisms Underlying Effects Of Transcranial Direct Current Stimulation (Tdcs) On Visuomotor Learning,
2020
Columbia University
Network-Level Mechanisms Underlying Effects Of Transcranial Direct Current Stimulation (Tdcs) On Visuomotor Learning, Pejman Sehatpour, Clément Dondé, Matthew J. Hoptman, Johanna Kreither, Devin Adair, Elisa Dias, Blair Vail, Stephanie Rohrig, Gail Silipo, Javier Lopez-Calderon, Antigona Martinez, Daniel C. Javitt
Publications and Research
Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation approach in which low level currents are administered over the scalp to influence underlying brain function. Prevailing theories of tDCS focus on modulation of excitation-inhibition balance at the local stimulation location. However, network level effects are reported as well, and appear to depend upon differential underlying mechanisms. Here, we evaluated potential network-level effects of tDCS during the Serial Reaction Time Task (SRTT) using convergent EEG- and fMRI-based connectivity approaches. Motor learning manifested as a significant (p <.0001) shift from slow to fast responses and corresponded to a significant increase in beta-coherence (p <.0001) and fMRI connectivity (p <.01) particularly within the visual-motor pathway. Differential patterns of tDCS effect were observed within different parametric task versions, consistent with network models. Overall, these findings demonstrate objective physiological effects of tDCS at the network level that result in effective behavioral modulation when tDCS parameters are matched to network-level requirements of the underlying task.
Multiphoton Microscopy And Deep Learning Neural Networks For The Automated Quantification Of In Vivo, Label-Free Optical Biomarkers Of Skin Wound Healing,
2020
University of Arkansas, Fayetteville
Multiphoton Microscopy And Deep Learning Neural Networks For The Automated Quantification Of In Vivo, Label-Free Optical Biomarkers Of Skin Wound Healing, Jake D. Jones
Graduate Theses and Dissertations
Non-healing ulcerative wounds that occur frequently in diseases such as diabetes are challenging to diagnose and treat due to numerous possible etiologies and the variable efficacy of wound care products. With advanced age, skin wound healing is often delayed, leaving elderly patients at high risk for developing these chronic injuries. As it is challenging to discriminate age-related delays from disease-related chronicity, there is a critical need to develop new quantitative biomarkers that are sensitive to wound status. Multiphoton microscopy (MPM) techniques are well-suited for 3D imaging of epithelia and are capable of non-invasively detecting metabolic cofactors (NADH and FAD) without …
3-D Fabry–Pérot Cavities Sculpted On Fiber Tips Using A Multiphoton Polymerization Process,
2020
Air Force Institute of Technology
3-D Fabry–Pérot Cavities Sculpted On Fiber Tips Using A Multiphoton Polymerization Process, Jonathan W. Smith, Jeremiah C. Williams, Joseph S. Suelzer, Nicholas G. Usechak, Hengky Chandrahalim
Faculty Publications
This paper presents 3-D Fabry–Pérot (FP) cavities fabricated directly onto cleaved ends of low-loss optical fibers by a two-photon polymerization (2PP) process. This fabrication technique is quick, simple, and inexpensive compared to planar microfabrication processes, which enables rapid prototyping and the ability to adapt to new requirements. These devices also utilize true 3-D design freedom, facilitating the realization of microscale optical elements with challenging geometries. Three different device types were fabricated and evaluated: an unreleased single-cavity device, a released dual-cavity device, and a released hemispherical mirror dual-cavity device. Each iteration improved the quality of the FP cavity's reflection spectrum. The …
Understanding Radiation Resistance In Head And Neck Tumor Xenografts Using Diffuse Reflectance And Raman Spectroscopy,
2020
University of Arkansas, Fayetteville
Understanding Radiation Resistance In Head And Neck Tumor Xenografts Using Diffuse Reflectance And Raman Spectroscopy, Sina Dadgar
Graduate Theses and Dissertations
Each year, 800,000 new patients are diagnosed with head and neck squamous cell carcinoma (HNSCC), a majority of whom are treated with a combination of daily fractions of radiation and weekly chemotherapy sessions for up to seven weeks. Current methods to evaluate treatment response of individual patients are limited to anatomical measurements of tumor burden using CT scan or MRI 4-8 weeks after completion of treatment. However, earlier knowledge of radiation-response prior to or at early days after commencement of therapy can aid oncologist with escalating and de-escalating treatment plans for exceptionally non-responding and responding patients. Such a knowledge can …
Imparting 3d Representations To Artificial Intelligence For A Full Assessment Of Pressure Injuries.,
2020
University of Louisville
Imparting 3d Representations To Artificial Intelligence For A Full Assessment Of Pressure Injuries., Sofia Zahia
Electronic Theses and Dissertations
During recent decades, researches have shown great interest to machine learning techniques in order to extract meaningful information from the large amount of data being collected each day. Especially in the medical field, images play a significant role in the detection of several health issues. Hence, medical image analysis remarkably participates in the diagnosis process and it is considered a suitable environment to interact with the technology of intelligent systems. Deep Learning (DL) has recently captured the interest of researchers as it has proven to be efficient in detecting underlying features in the data and outperformed the classical machine learning …
Multimodal Computational Modeling Of Visual Object Recognition Deficits But Intact Repetition Priming In Schizophrenia,
2020
Columbia University
Multimodal Computational Modeling Of Visual Object Recognition Deficits But Intact Repetition Priming In Schizophrenia, Pejman Sehatpour, Anahita Bassir Nia, Devin Adair, Zhishun Wang, Heloise M. Debaun, Gail Silipo, Antigona Martinez, Daniel C. Javitt
Publications and Research
The term perceptual closure refers to the neural processes responsible for “filling-in” missing information in the visual image under highly adverse viewing conditions such as fog or camouflage. Here we used a closure task that required the participants to identify barely recognizable fragmented line-drawings of common objects. Patients with schizophrenia have been shown to perform poorly on this task. Following priming, controls and importantly patients can complete the line-drawings at greater levels of fragmentation behaviorally, suggesting an improvement in their ability to performthe task. Closure phenomena have been shown to involve a distributed network of cortical regions, notably the lateral …
Deep Reinforcement Learning In Medical Object Detection And Segmentation,
2020
The University of Western Ontario
Deep Reinforcement Learning In Medical Object Detection And Segmentation, Dong Zhang
Electronic Thesis and Dissertation Repository
Medical object detection and segmentation are crucial pre-processing steps in the clinical workflow for diagnosis and therapy planning. Although deep learning methods have achieved considerable performance in this field, they impose several shortcomings, such as computational limitations, sub-optimal parameter optimization, and weak generalization. Deep reinforcement learning as the newest artificial intelligence algorithm has great potential to address the limitation of traditional deep learning methods, as well as obtaining accurate detection and segmentation results. Deep reinforcement learning has a cognitive-like process to propose the area of desirable objects, thereby facilitating accurate object detection and segmentation. In this thesis, we deploy deep …
Single‐Molecule 3d Orientation Imaging Reveals Nanoscale Compositional Heterogeneity In Lipid Membranes,
2020
Washington University in St. Louis
Single‐Molecule 3d Orientation Imaging Reveals Nanoscale Compositional Heterogeneity In Lipid Membranes, Jin Lu, Hesam Mazidi, Tianben Ding, Oumeng Zhang, Matthew D. Lew
Electrical & Systems Engineering Publications and Presentations
In soft matter, thermal energy causes molecules to continuously translate and rotate, even in crowded environments, thereby impacting the spatial organization and function of most molecular assemblies, such as lipid membranes. Directly measuring the orientation and spatial organization of large collections (>3000 molecules μm−2) of single molecules with nanoscale resolution remains elusive. In this paper, we utilize SMOLM, single‐molecule orientation localization microscopy, to directly measure the orientation spectra (3D orientation plus “wobble”) of lipophilic probes transiently bound to lipid membranes, revealing that Nile red's (NR) orientation spectra are extremely sensitive to membrane chemical composition. SMOLM images resolve …
