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Proceedings of SPIE

Lifu Xiao, Sian Sloan-Dennison, Zachary D Schultz
Our lab has shown that nanoparticles functionalized with short peptides can selectively bind to receptor proteins in vitro . Our results indicate that the Raman signals observed from purified receptors in surface enhanced Raman scattering (SERS) experiments match those observed with tip-enhanced Raman scattering (TERS) experiments performed on membrane receptors in intact cell membranes. Analysis of the observed Raman signals suggest the signals arise from the amino-acids in the protein receptor responsible for binding and recognition of the ligand attached to the nanoparticle probe...
August 2018: Proceedings of SPIE
Mahmoud Mostapha, Mark D Shen, SunHyung Kim, Meghan Swanson, D Louis Collins, Vladimir Fonov, Guido Gerig, Joseph Piven, Martin A Styner
The quantification of cerebrospinal fluid (CSF) in the human brain has shown to play an important role in early postnatal brain developmental. Extr a-axial fluid (EA-CSF), which is characterized by the CSF in the subarachnoid space, is promising in the early detection of children at risk for neurodevelopmental disorders. Currently, though, there is no tool to extract local EA-CSF measurements in a way that is suitable for localized analysis. In this paper, we propose a novel framework for the localized, cortical surface based analysis of EA-CSF...
March 2, 2018: Proceedings of SPIE
Duo Wang, Rui Zhang, Jin Zhu, Zhongzhao Teng, Yuan Huang, Filippo Spiga, Michael Hong-Fei Du, Jonathan H Gillard, Qingsheng Lu, Pietro LiĆ²
Medical imaging examination on patients usually involves more than one imaging modalities, such as Computed Tomography (CT), Magnetic Resonance (MR) and Positron Emission Tomography(PET) imaging. Multimodal imaging allows examiners to benefit from the advantage of each modalities. For example, for Abdominal Aortic Aneurysm, CT imaging shows calcium deposits in the aorta clearly while MR imaging distinguishes thrombus and soft tissues better.1 Analysing and segmenting both CT and MR images to combine the results will greatly help radiologists and doctors to treat the disease...
March 2, 2018: Proceedings of SPIE
Yurui Gao, Kurt G Schilling, Iwona Stepniewska, Junzhong Xu, Bennett A Landman, Benoit M Dawant, Adam W Anderson
Background: Clustering thalamic nuclei is important for both research and clinical purposes. For example, ventral intermediate nuclei in thalami serve as targets in both deep brain stimulation neurosurgery and radiosurgery for treating patients suffering from movement disorders (e.g., Parkinson's disease and essential tremor). Diffusion magnetic resonance imaging (dMRI) is able to reflect tissue microstructure in the central nervous system via fitting different models, such as, the diffusion tensor (DT), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI), diffusion kurtosis imaging (DKI) and the spherical mean technique (SMT)...
March 2018: Proceedings of SPIE
Srijata Chakravorti, Victoria L Morgan, Paula Trujillo-Diaz, Raul Wirz, Benoit M Dawant
The pulvinar of the thalamus is a higher-order thalamic nucleus that is responsible for gating information flow to the cortical regions of the brain. It is involved in several cortico-thalamocortical relay circuits and is known to be affected in a number of neurological disorders. Segmenting the pulvinar in clinically acquired images is important to support studies exploring its role in brain function. In recent years, we have proposed an active shape model method to segment multiple thalamic nuclei, including the pulvinar...
March 2018: Proceedings of SPIE
Ilwoo Lyu, Jonathan Perdomo, Gabriel S Yapuncich, Beatriz Paniagua, Doug M Boyer, Martin A Styner
We present a group-wise shape correspondence method for analyzing variable and complex objects in a population study. The proposed method begins with the standard spherical harmonics (SPHARM) point distribution models (PDM) with their spherical mappings. In case of complex and variable objects, the equal area spherical mapping based SPHARM correspondence is imperfect. For such objects, we present here a novel group-wise correspondence. As an example dataset, we use 12 second mandibular molars representing 6 living or fossil euarchontan species...
March 2018: Proceedings of SPIE
Kishore Rajendran, Shengzhen Tao, Dilbar Abdurakhimova, Shuai Leng, Cynthia McCollough
Photon-counting detector based CT (PCD-CT) enables dose efficient high resolution imaging, in addition to providing multi-energy information. This allows better delineation of anatomical structures crucial for several clinical applications ranging from temporal bone imaging to pulmonary nodule visualization. Due to the smaller detector pixel sizes required for high resolution imaging, the PCD-CT images suffer from higher noise levels. The image quality is further degraded in narrow energy bins as a consequence of low photon counts...
March 2018: Proceedings of SPIE
Wei Zhou, Dilbar Abdurakhimova, Michael Bruesewitz, Ahmed Halaweish, Cynthia H McCollough, Shuai Leng
The purpose of this study is to determine the optimal iodine contrast-to-noise ratio (CNR) achievable for different patient sizes using virtual-monoenergetic-images (VMIs) and a universal acquisition protocol on photon-counting-detector CT (PCD-CT), and to compare results to those from single-energy (SE) and dual-source-dual-energy (DSDE) CT. Vials containing 3 concentrations of iodine were placed in torso-shaped water phantoms of 5 sizes and scanned on a 2nd generation DSDE scanner with both SE and DE modes...
March 2018: Proceedings of SPIE
Wei Zhou, Rachel Schornak, Gregory Michalak, Jayse Weaver, Dilbar Abdurakhimova, Andrea Ferrero, Kenneth A Fetterly, Cynthia H McCollough, Shuai Leng
Photon counting detector (PCD) based multi-energy CT is able to generate different types of images such as virtual monoenergetic images (VMIs) and material specific images (e.g., iodine maps) in addition to the conventional single energy images. The purpose of this study is to determine the image type that has optimal iodine detection and to determine the lowest detectable iodine concentration using a PCD-CT system. A 35 cm body phantom with iodine inserts of 4 concentrations and 2 sizes was scanned on a research PCD-CT system...
March 2018: Proceedings of SPIE
Yuankai Huo, Shunxing Bao, Prasanna Parvathaneni, Bennett A Landman
Whole brain segmentation and cortical surface parcellation are essential in understanding the brain anatomical-functional relationship. Multi-atlas segmentation has been regarded as one of the leading segmentation methods for the whole brain segmentation. In our recent work, the multi-atlas technique has been adapted to surface reconstruction using a method called Multi-atlas CRUISE (MaCRUISE). The MaCRUISE method not only performed the consistent volume-surface analyses but also shown advantages on robustness compared with FreeSurfer method...
March 2018: Proceedings of SPIE
Shunxing Bao, Yuankai Huo, Prasanna Parvathaneni, Andrew J Plassard, Camilo Bermudez, Yuang Yao, Ilwoo Lyu, Aniruddha Gokhale, Bennett A Landman
When processing large medical imaging studies, adopting high performance grid computing resources rapidly becomes important. We recently presented a "medical image processing-as-a-service" grid framework that offers promise in utilizing the Apache Hadoop ecosystem and HBase for data colocation by moving computation close to medical image storage. However, the framework has not yet proven to be easy to use in a heterogeneous hardware environment. Furthermore, the system has not yet validated when considering variety of multi-level analysis in medical imaging...
March 2018: Proceedings of SPIE
Yuankai Huo, Zhoubing Xu, Shunxing Bao, Camilo Bermudez, Andrew J Plassard, Jiaqi Liu, Yuang Yao, Albert Assad, Richard G Abramson, Bennett A Landman
Spleen volume estimation using automated image segmentation technique may be used to detect splenomegaly (abnormally enlarged spleen) on Magnetic Resonance Imaging (MRI) scans. In recent years, Deep Convolutional Neural Networks (DCNN) segmentation methods have demonstrated advantages for abdominal organ segmentation. However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods. In this paper, we propose the Splenomegaly Segmentation Network (SSNet) to address spatial variations when segmenting extraordinarily large spleens...
March 2018: Proceedings of SPIE
Meg F Bobo, Shunxing Bao, Yuankai Huo, Yuang Yao, Jack Virostko, Andrew J Plassard, Ilwoo Lyu, Albert Assad, Richard G Abramson, Melissa A Hilmes, Bennett A Landman
Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans...
March 2018: Proceedings of SPIE
Prasanna Parvathaneni, Ilwoo Lyu, Yuankai Huo, Justin Blaber, Allison E Hainline, Hakmook Kang, Neil D Woodward, Bennett A Landman
The choice of surface template plays an important role in cross-sectional subject analyses involving cortical brain surfaces because there is a tendency toward registration bias given variations in inter-individual and inter-group sulcal and gyral patterns. In order to account for the bias and spatial smoothing, we propose a feature-based unbiased average template surface. In contrast to prior approaches, we factor in the sample population covariance and assign weights based on feature information to minimize the influence of covariance in the sampled population...
March 2018: Proceedings of SPIE
Ilwoo Lyu, Hakmook Kang, Neil D Woodward, Bennett A Landman
Sulcal depth is an important marker of brain anatomy in neuroscience/neurological function. Previously, sulcal depth has been explored at the region-of-interest (ROI) level to increase statistical sensitivity to group differences. In this paper, we present a fully automated method that enables inferences of ROI properties from a sulcal region-focused perspective consisting of two main components: 1) sulcal depth computation and 2) sulcal curve-based refined ROIs. In conventional statistical analysis, the average sulcal depth measurements are employed in several ROIs of the cortical surface...
March 2018: Proceedings of SPIE
Allison E Hainline, Vishwesh Nath, Prasanna Parvathaneni, Justin Blaber, Baxter Rogers, Allen Newton, Jeffrey Luci, Heidi Edmonson, Hakmook Kang, Bennett A Landman
An understanding of the bias and variance of diffusion weighted magnetic resonance imaging (DW-MRI) acquisitions across scanners, study sites, or over time is essential for the incorporation of multiple data sources into a single clinical study. Studies that combine samples from various sites may be introducing confounding due to site-specific artifacts and patterns. Differences in bias and variance across sites may render the scans incomparable, and, without correction, any inferences obtained from these data are misleading...
March 2018: Proceedings of SPIE
Vishwesh Nath, Kurt G Schilling, Allison E Hainline, Prasanna Parvathaneni, Justin A Blaber, Ilwoo Lyu, Adam W Anderson, Hakmook Kang, Allen T Newton, Baxter P Rogers, Bennett A Landman
High Angular Resolution Diffusion Imaging (HARDI) models are used to capture complex intra-voxel microarchitectures. The magnetic resonance imaging sequences that are sensitized to diffusion are often highly accelerated and prone to motion, physiologic, and imaging artifacts. In diffusion tensor imaging, robust statistical approaches have been shown to greatly reduce these adverse factors without human intervention. Similar approaches would be possible with HARDI methods, but robust versions of each distinct HARDI approach would be necessary...
March 2018: Proceedings of SPIE
Shikha Chaganti, Bennett A Landman
Image registration involves identification of a transformation to fit a target image to a reference image space. The success of the registration process is vital for correct interpretation of the results of many medical image-processing applications, including multi-atlas segmentation. While there are several validation metrics employed in rigid registration to examine the accuracy of the method, non-rigid registrations (NRR) are validated subjectively in most cases, validated in offline cases, or based on image similarity metrics, all of which have been shown to poorly correlate with true registration quality...
March 2018: Proceedings of SPIE
Camilo Bermudez, Andrew J Plassard, Taylor L Davis, Allen T Newton, Susan M Resnick, Bennett A Landman
An important task in image processing and neuroimaging is to extract quantitative information from the acquired images in order to make observations about the presence of disease or markers of development in populations. Having a low-dimensional manifold of an image allows for easier statistical comparisons between groups and the synthesis of group representatives. Previous studies have sought to identify the best mapping of brain MRI to a low-dimensional manifold, but have been limited by assumptions of explicit similarity measures...
March 2018: Proceedings of SPIE
Baxter P Rogers, Justin Blaber, Allen T Newton, Colin B Hansen, E Brian Welch, Adam W Anderson, Jeffrey J Luci, Carlo Pierpaoli, Bennett A Landman
Gradient coils in magnetic resonance imaging do not produce perfectly linear gradient fields. For diffusion imaging, the field nonlinearities cause the amplitude and direction of the applied diffusion gradients to vary over the field of view. This leads to site- and scan-specific systematic errors in estimated diffusion parameters such as diffusivity and anisotropy, reducing reliability especially in studies that take place over multiple sites. These errors can be substantially reduced if the actual scanner-specific gradient coil magnetic fields are known...
March 2018: Proceedings of SPIE
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