Proceedings - Society of Photo-Optical Instrumentation Engineers

ApoliopoproteinE ε4 (ApoE-ε4) polymorphism is the most well known genetic risk factor for developing Alzheimers Disease. The exact mechanism through which ApoE ε4 increases AD risk is not fully known, but may be related to decreased clearance and increased oligomerization of Aβ. By making measurements of white matter integrity via diffusion MR and correlating the metrics in a voxel-based statistical analysis with ApoE-ε4 genotype (whilst controlling for vascular risk factor, gender, cognitive status and age) we are able to identify changes in white matter associated with carrying an ApoE ε4 allele...

F1-ATPase is the soluble portion of the membrane-embedded enzyme FoF1-ATP synthase that catalyzes the production of adenosine triphosphate in eukaryotic and eubacterial cells. In reverse, the F1 part can also hydrolyze ATP quickly at three catalytic binding sites. Therefore, catalysis of 'non-productive' ATP hydrolysis by F1 (or FoF1) must be minimized in the cell. In bacteria, the ε subunit is thought to control and block ATP hydrolysis by mechanically inserting its C-terminus into the rotary motor region of F1...

The image shape and texture (appearance) estimation designed for facial recognition is a novel and promising approach for application in breast imaging. The purpose of this study was to apply a shape and appearance model to automatically estimate percent breast fibroglandular volume (%FGV) using digital mammograms. We built a shape and appearance model using 2000 full-field digital mammograms from the San Francisco Mammography Registry with known %FGV measured by single energy absorptiometry method. An affine transformation was used to remove rotation, translation and scale...

Longitudinal analysis of medical imaging data has become central to the study of many disorders. Unfortunately, various constraints (study design, patient availability, technological limitations) restrict the acquisition of data to only a few time points, limiting the study of continuous disease/treatment progression. Having the ability to produce a sensible time interpolation of the data can lead to improved analysis, such as intuitive visualizations of anatomical changes, or the creation of more samples to improve statistical analysis...

The sparse regression framework has been widely used in medical image processing and analysis. However, it has been rarely used in anatomical studies. We present a sparse shape modeling framework using the Laplace-Beltrami (LB) eigenfunctions of the underlying shape and show its improvement of statistical power. Traditionally, the LB-eigenfunctions are used as a basis for intrinsically representing surface shapes as a form of Fourier descriptors. To reduce high frequency noise, only the first few terms are used in the expansion and higher frequency terms are simply thrown away...

Techniques in medical image analysis are many times used for the comparison or regression on the intensities of images. In general, the domain of the image is a given Cartesian grids. Shape analysis, on the other hand, studies the similarities and differences among spatial objects of arbitrary geometry and topology. Usually, there is no function defined on the domain of shapes. Recently, there has been a growing needs for defining and analyzing functions defined on the shape space, and a coupled analysis on both the shapes and the functions defined on them...

Brain tumor segmentation in brain MRI volumes is used in neurosurgical planning and illness staging. It is important to explore the tumor shape and necrosis regions at different points of time to evaluate the disease progression. We propose an algorithm for semi-automatic tumor segmentation and necrosis detection. Our algorithm consists of three parts: conversion of MRI volume to a probability space based on the on-line learned model, tumor probability density estimation, and adaptive segmentation in the probability space...

A cochlear implant (CI) is a device that restores hearing using an electrode array that is surgically placed in the cochlea. After implantation, the CI is programmed to attempt to optimize hearing outcome. Currently, we are testing an image-guided CI programming (IGCIP) technique we recently developed that relies on knowledge of relative position of intracochlear anatomy to implanted electrodes. IGCIP is enabled by a number of algorithms we developed that permit determining the positions of electrodes relative to intra-cochlear anatomy using a pre- and a post-implantation CT...

Prediction of Alzheimers disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end we combine a predictive multi-task machine learning method(1) with novel MR-based multivariate morphometric surface map of the hippocampus(2) to predict future cognitive scores of patients. Previous work by Zhou et al.(1) has shown that a multi-task learning framework that performs prediction of all future time points (or tasks) simultaneously can be used to encode both sparsity as well as temporal smoothness...

We propose a novel multi-atlas segmentation method that employs a group-wise image registration method for the brain segmentation on rodent magnetic resonance (MR) images. The core element of the proposed segmentation is the use of a particle-guided image registration method that extends the concept of particle correspondence into the volumetric image domain. The registration method performs a group-wise image registration that simultaneously registers a set of images toward the space defined by the average of particles...

Label fusion is a critical step in many image segmentation frameworks (e.g., multi-atlas segmentation) as it provides a mechanism for generalizing a collection of labeled examples into a single estimate of the underlying segmentation. In the multi-label case, typical label fusion algorithms treat all labels equally - fully neglecting the known, yet complex, anatomical relationships exhibited in the data. To address this problem, we propose a generalized statistical fusion framework using hierarchical models of rater performance...

Spleen segmentation on clinically acquired CT data is a challenging problem given the complicity and variability of abdominal anatomy. Multi-atlas segmentation is a potential method for robust estimation of spleen segmentations, but can be negatively impacted by registration errors. Although labeled atlases explicitly capture information related to feasible organ shapes, multi-atlas methods have largely used this information implicitly through registration. We propose to integrate a level set shape model into the traditional label fusion framework to create a shape-constrained multi-atlas segmentation framework...

In this paper, we present a new approach to calculate 2D strain through the registration of the pre- and post-compression (deformation) B-mode image sequences based on an intensity-based non-rigid registration algorithm (INRA). Compared with the most commonly used cross-correlation (CC) method, our approach is not constrained to any particular set of directions, and can overcome displacement estimation errors introduced by incoherent motion and variations in the signal under high compression. This INRA method was tested using phantom and in vivo data...

Contrast enhancement on cardiac CT provides valuable information about myocardial perfusion and methods have been proposed to assess perfusion with static and dynamic acquisitions. There is a lack of knowledge and consensus on the appropriate approach to ensure 1) sufficient diagnostic accuracy for clinical decisions and 2) low radiation doses for patient safety. This work developed a thorough dynamic CT simulation and several accepted blood flow estimation techniques to evaluate the performance of perfusion assessment across a range of acquisition and estimation scenarios...

Anti-scatter grids are used in fluoroscopic systems to improve image quality by absorbing scattered radiation. A stationary Smit Rontgen X-ray grid (line density: 70 lines/cm, grid ratio: 13:1) was used with a flat panel detector (FPD) of pixel size 194 micron and a high-resolution CMOS detector, the Dexela 1207 with pixel size of 75 microns. To investigate the effectiveness of the grid, a simulated artery block was placed in a modified uniform frontal head phantom and imaged with both the FPD and the Dexela for an approximately 15 × 15 cm field of view (FOV)...

Endovascular image-guided intervention (EIGI) has become the primary interventional therapy for the most widespread vascular diseases. These procedures involve the insertion of a catheter into the femoral artery, which is then threaded under fluoroscopic guidance to the site of the pathology to be treated. Flat Panel Detectors (FPDs) are normally used for EIGIs; however, once the catheter is guided to the pathological site, high-resolution imaging capabilities can be used for accurately guiding a successful endovascular treatment...

Relative object detectability (ROD) quantifies the relative performance of two image detectors for a specified object of interest by taking the following ratio: the integral of detective quantum efficiency of a detector weighted by the frequency spectrum of the object divided by that for a second detector. Four different detectors, namely the micro-angiographic fluoroscope (MAF), the Dexela Model 1207 (Dex) and Hamamatsu Model C10901D-40 (Ham) CMOS x-ray detectors, and a flat-panel detector (FPD) were compared...

Endovascular-Image-Guided-Interventional (EIGI) treatment of neuro-vascular conditions such as aneurysms, stenosed arteries, and vessel thrombosis make use of treatment devices such as stents, coils, and balloons which have very small feature sizes, 10's of microns to a few 100's of microns, and hence demand a high resolution imaging system. The current state-of-the-art flat panel detector (FPD) has about a 200-um pixel size with the Nyquist of 2.5 lp/mm. For higher-resolution imaging a charge-coupled device (CCD) based Micro-Angio -Fluoroscope (MAF-CCD) with a pixel size of 35um (Nyquist of 11 lp/mm) was developed and previously reported...

Compton scatter is the main interaction of x-rays with objects undergoing radiographic and fluoroscopic imaging procedures. Such scatter is responsible for reducing image signal to noise ratio which can negatively impact object detection especially for low contrast objects. To reduce scatter, possible methods are smaller fields-of-view, larger air gaps and the use of an anti-scatter grid. Smaller fields of view may not be acceptable and scanned-beam radiography is not practical for real-time imaging. Air gaps can increase geometric unsharpness and thus degrade image resolution...

Neuro-EIGIs require visualization of very small endovascular devices and small vessels. A Microangiographic Fluoroscope (MAF) x-ray detector was developed to improve on the standard flat panel detector's (FPD's) ability to visualize small objects during neuro-EIGIs. To compare the performance of FPD and MAF imaging systems, specific imaging tasks related to those encountered during neuro-EIGIs were used to assess contrast to noise ratio (CNR) of different objects. A bar phantom and a stent were placed at a fixed distance from the x-ray focal spot to mimic a clinical imaging geometry and both objects were imaged by each detector system...