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Proceedings of SPIE

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https://www.readbyqxmd.com/read/29674804/three-material-decomposition-in-multi-energy-ct-impact-of-prior-information-on-noise-and-bias
#1
Liqiang Ren, Cynthia H McCollough, Lifeng Yu
In order to perform material decomposition for a three-material mixture, dual-energy CT (DECT) has to incorporate an additional condition, typically the prior information related to certain physical constraints such as volume or mass conservation. With the introduction of photon-counting CT and other multi-energy CT (MECT) platform, more than 2 energy bins can be simultaneously acquired, which in principle can solve a three-material problem without the need of additional prior information. The purpose of this work was to investigate the impact of prior information on noise and bias properties of three-material decomposition in both DECT and MECT, and to evaluate if the prior information is still needed in MECT...
March 2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29643571/a-general-ct-reconstruction-algorithm-for-model-based-material-decomposition
#2
Steven Tilley, Wojciech Zbijewski, Jeffrey H Siewerdsen, J Webster Stayman
Material decomposition in CT has the potential to reduce artifacts and improve quantitative accuracy by utilizing spectral models and multi-energy scans. In this work we present a novel Model-Based Material Decomposition (MBMD) method based on an existing iterative reconstruction algorithm derived from a general non-linear forward model. A digital water phantom with inserts containing different concentrations of calcium was scanned on a kV switching system. We used the presented method to simultaneously reconstruct water and calcium material density images, and compared the results to an image domain and a projection domain decomposition method...
March 2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29622855/prospective-image-quality-analysis-and-control-for-prior-image-based-reconstruction-of-low-dose-ct
#3
Hao Zhang, Grace J Gang, Hao Dang, Marc S Sussman, Cheng Ting Lin, Jeffrey H Siewerdsen, J Webster Stayman
Purpose: Prior-image-based reconstruction (PIBR) is a powerful tool for low-dose CT, however, the nonlinear behavior of such approaches are generally difficult to predict and control. Similarly, traditional image quality metrics do not capture potential biases exhibited in PIBR images. In this work, we identify a new bias metric and construct an analytical framework for prospectively predicting and controlling the relationship between prior image regularization strength and this bias in a reliable and quantitative fashion...
March 2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29780198/sva-shape-variation-analyzer
#4
Priscille de Dumast, Clement Mirabel, Beatriz Paniagua, Marilia Yatabe, Antonio Ruellas, Nina Tubau, Martin Styner, Lucia Cevidanes, Juan C Prieto
Temporo-mandibular osteo arthritis (TMJ OA) is characterized by progressive cartilage degradation and subchondral bone remodeling. The causes of this pathology remain unclear. Current research efforts are concentrated in finding new biomarkers that will help us understand disease progression and ultimately improve the treatment of the disease. In this work, we present Shape Variation Analyzer (SVA), the goal is to develop a noninvasive technique to provide information about shape changes in TMJ OA. SVA uses neural networks to classify morphological variations of 3D models of the mandibular condyle...
February 2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29780197/trafic-fiber-tract-classification-using-deep-learning
#5
Prince D Ngattai Lam, Gaetan Belhomme, Jessica Ferrall, Billie Patterson, Martin Styner, Juan C Prieto
We present TRAFIC, a fully automated tool for the labeling and classification of brain fiber tracts. TRAFIC classifies new fibers using a neural network trained using shape features computed from previously traced and manually corrected fiber tracts. It is independent from a DTI Atlas as it is applied to already traced fibers. This work is motivated by medical applications where the process of extracting fibers from a DTI atlas, or classifying fibers manually is time consuming and requires knowledge about brain anatomy...
February 2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29769755/automatic-quantification-framework-to-detect-cracks-in-teeth
#6
Hina Shah, Pablo Hernandez, Francois Budin, Deepak Chittajallu, Jean-Baptiste Vimort, Rick Walters, André Mol, Asma Khan, Beatriz Paniagua
Studies show that cracked teeth are the third most common cause for tooth loss in industrialized countries. If detected early and accurately, patients can retain their teeth for a longer time. Most cracks are not detected early because of the discontinuous symptoms and lack of good diagnostic tools. Currently used imaging modalities like Cone Beam Computed Tomography (CBCT) and intraoral radiography often have low sensitivity and do not show cracks clearly. This paper introduces a novel method that can detect, quantify, and localize cracks automatically in high resolution CBCT (hr-CBCT) scans of teeth using steerable wavelets and learning methods...
February 2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29769754/detection-of-bone-loss-via-subchondral-bone-analysis
#7
Jean-Baptiste Vimort, Antonio Ruellas, Jack Prothero, J S Marron, Matthew McCormick, Lucia Cevidanes, Erika Benavides, Beatriz Paniagua
To date, there is no single sign, symptom, or test that can clearly diagnose early stages of Temporomandibular Joint Osteoarthritis (TMJ OA). However, it has been observed that changes in the bone occur in early stages of this disease, involving structural changes both in the texture and morphometry of the bone marrow and the subchondral cortical plate. In this paper we present a tool to detect and highlight subtle variations in subchondral bone structure obtained from high resolution Cone Beam Computed Tomography (hr-CBCT) in order to help with detecting early TMJ OA...
February 2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29706685/quantifying-predictive-capability-of-electronic-health-records-for-the-most-harmful-breast-cancer
#8
Yirong Wu, Jun Fan, Peggy Peissig, Richard Berg, Ahmad Pahlavan Tafti, Jie Yin, Ming Yuan, David Page, Jennifer Cox, Elizabeth S Burnside
Improved prediction of the "most harmful" breast cancers that cause the most substantive morbidity and mortality would enable physicians to target more intense screening and preventive measures at those women who have the highest risk; however, such prediction models for the "most harmful" breast cancers have rarely been developed. Electronic health records (EHRs) represent an underused data source that has great research and clinical potential. Our goal was to quantify the value of EHR variables in the "most harmful" breast cancer risk prediction...
February 2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29706684/strain-map-of-the-tongue-in-normal-and-als-speech-patterns-from-tagged-and-diffusion-mri
#9
Fangxu Xing, Jerry L Prince, Maureen Stone, Timothy G Reese, Nazem Atassi, Van J Wedeen, Georges El Fakhri, Jonghye Woo
Amyotrophic Lateral Sclerosis (ALS) is a neurological disease that causes death of neurons controlling muscle movements. Loss of speech and swallowing functions is a major impact due to degeneration of the tongue muscles. In speech studies using magnetic resonance (MR) techniques, diffusion tensor imaging (DTI) is used to capture internal tongue muscle fiber structures in three-dimensions (3D) in a non-invasive manner. Tagged magnetic resonance images (tMRI) are used to record tongue motion during speech. In this work, we aim to combine information obtained with both MR imaging techniques to compare the functionality characteristics of the tongue between normal and ALS subjects...
February 2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29681676/tilted-light-sheet-microscopy-with-3d-point-spread-functions-for-single-molecule-super-resolution-imaging-in-mammalian-cells
#10
Anna-Karin Gustavsson, Petar N Petrov, Maurice Y Lee, Yoav Shechtman, W E Moerner
To obtain a complete picture of subcellular nanostructures, cells must be imaged with high resolution in all three dimensions (3D). Here, we present tilted light sheet microscopy with 3D point spread functions (TILT3D), an imaging platform that combines a novel, tilted light sheet illumination strategy with engineered long axial range point spread functions (PSFs) for low-background, 3D super localization of single molecules as well as 3D super-resolution imaging in thick cells. TILT3D is built upon a standard inverted microscope and has minimal custom parts...
February 2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29622857/spatial-resolution-and-noise-prediction-in-flat-panel-cone-beam-ct-penalized-likelihood-reconstruction
#11
W Wang, G J Gang, J H Siewerdsen, J W Stayman
Purpose: Model based iterative reconstruction (MBIR) algorithms such as penalized-likelihood (PL) methods have data-dependent and shift-variant image properties. Predictors of local reconstructed noise and resolution have found application in a number of methods that seek to understand, control, and optimize CT data acquisition and reconstruction parameters in a prospective fashion (as opposed to studies based on exhaustive evaluation). However, previous MBIR prediction methods have relied on idealized system models...
February 2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29622856/joint-optimization-of-fluence-field-modulation-and-regularization-for-multi-task-objectives
#12
Grace J Gang, J Webster Stayman
This work investigates task-driven optimization of fluence field modulation (FFM) and regularization for model-based iterative reconstruction (MBIR) when different imaging tasks are presented by different organs. Example applications of the design framework were demonstrated in an abdomen phantom where the task of interest in the liver is a low-contrast, low-frequency detection task while that in the kidney is a high-contrast, high-frequency discrimination task. The global performance objective is based on maximizing local detectability index ( d ') at a discrete set of locations...
February 2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29622854/dynamic-beam-filtering-for-miscentered-patients
#13
Andrew Mao, William Shyr, Grace J Gang, J Webster Stayman
Purpose: Accurate centering of the patient within the bore of a CT scanner takes time and is often difficult to achieve precisely. Patient miscentering can result in significant dose and image noise penalties with the use of traditional bowtie filters. This work describes a system to dynamically position an x-ray beam filter during image acquisition to enable more consistent image performance and potentially lower dose needed for CT imaging. Methods: We propose a new approach in which two orthogonal low-dose scout images are used to estimate a parametric model of the object describing its shape, size, and location within the field of view (FOV)...
February 2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29568146/voxel-based-plaque-classification-in-coronary-intravascular-optical-coherence-tomography-images-using-decision-trees
#14
Chaitanya Kolluru, David Prabhu, Yazan Gharaibeh, Hao Wu, David L Wilson
Intravascular Optical Coherence Tomography (IVOCT) is a high contrast, 3D microscopic imaging technique that can be used to assess atherosclerosis and guide stent interventions. Despite its advantages, IVOCT image interpretation is challenging and time consuming with over 500 image frames generated in a single pullback volume. We have developed a method to classify voxel plaque types in IVOCT images using machine learning. To train and test the classifier, we have used our unique database of labeled cadaver vessel IVOCT images accurately registered to gold standard cryo-images...
February 2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29780196/light-fluence-dosimetry-in-lung-simulating-cavities
#15
Timothy C Zhu, Michele M Kim, Jonah Padawer, Andreea Dimofte, Mary Potasek, Karl Beeson, Evgueni Parilov
Accurate light dosimery is critical to ensure consistent outcome for pleural photodynamic therapy (pPDT). Ellipsoid shaped cavities with different sizes surrounded by turbid medium are used to simulate the intracavity lung geometry. An isotropic light source is introduced and surrounded by turbid media. Direct measurements of light fluence rate were compared to Monte Carlo simulated values on the surface of the cavities for various optical properties. The primary component of the light was determined by measurements performed in air in the same geometry...
January 2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29755164/mass-measurements-of-focal-adhesions-in-single-cells-using-high-resolution-surface-plasmon-resonance-microscopy
#16
Alexander W Peterson, Michael Halter, Alessandro Tona, Anne L Plant, John T Elliott
Surface plasmon resonance microscopy (SPRM) is a powerful label-free imaging technique with spatial resolution approaching the optical diffraction limit. The high sensitivity of SPRM to small changes in index of refraction at an interface allows imaging of dynamic protein structures within a cell. Visualization of subcellular features, such as focal adhesions (FAs), can be performed on live cells using a high numerical aperture objective lens with a digital light projector to precisely position the incident angle of the excitation light...
2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29551853/deep-learning-and-texture-based-semantic-label-fusion-for-brain-tumor-segmentation
#17
L Vidyaratne, M Alam, Z Shboul, K M Iftekharuddin
Brain tumor segmentation is a fundamental step in surgical treatment and therapy. Many hand-crafted and learning based methods have been proposed for automatic brain tumor segmentation from MRI. Studies have shown that these approaches have their inherent advantages and limitations. This work proposes a semantic label fusion algorithm by combining two representative state-of-the-art segmentation algorithms: texture based hand-crafted, and deep learning based methods to obtain robust tumor segmentation. We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation challenge dataset...
2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29497230/image-guided-removal-of-interproximal-lesions-with-a-co-2-laser
#18
Albert Ngo, Kenneth H Chan, Oanh Le, Jacob C Simon, Daniel Fried
Recent studies have shown that near-IR (NIR) imaging methods such as NIR reflectance can be used to image lesions on proximal surfaces, and optical coherence tomography (OCT) can be used to measure the depth of those lesions below the tooth surface. These imaging modalities can be used to acquire high contrast images of demineralized tooth surfaces, and 2-D and 3-D images can be extracted from this data. At NIR wavelengths longer than 1200-nm, there is no interference from stains and the contrast is only due to the increased light scattering of the demineralization...
January 2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29497229/multispectral-near-infrared-reflectance-and-transillumination-imaging-of-occlusal-carious-lesions-variation-in-lesion-contrast-with-lesion-depth
#19
Jacob C Simon, Donald A Curtis, Cynthia L Darling, Daniel Fried
In vivo and in vitro studies have demonstrated that near-infrared (NIR) light at λ=1300-1700-nm can be used to acquire high contrast images of enamel demineralization without interference of stains. The objective of this study was to determine if a relationship exists between the NIR image contrast of occlusal lesions and the depth of the lesion. Extracted teeth with varying amounts of natural occlusal decay were measured using a multispectral-multimodal NIR imaging system which captures λ=1300-nm occlusal transillumination, and λ=1500-1700-nm cross-polarized reflectance images...
January 2018: Proceedings of SPIE
https://www.readbyqxmd.com/read/29263566/deep-learning-based-classification-of-fdg-pet-data-for-alzheimers-disease-categories
#20
Shibani Singh, Anant Srivastava, Liang Mi, Richard J Caselli, Kewei Chen, Dhruman Goradia, Eric M Reiman, Yalin Wang
Fluorodeoxyglucose (FDG) positron emission tomography (PET) measures the decline in the regional cerebral metabolic rate for glucose, offering a reliable metabolic biomarker even on presymptomatic Alzheimer's disease (AD) patients. PET scans provide functional information that is unique and unavailable using other types of imaging. However, the computational efficacy of FDG-PET data alone, for the classification of various Alzheimers Diagnostic categories, has not been well studied. This motivates us to correctly discriminate various AD Diagnostic categories using FDG-PET data...
October 2017: Proceedings of SPIE
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