Medical Imaging, IEEE Transactions on - new TOC
TOC Alert for Publication# 42

Table of contents
1 Jun 2010 at 12:00am
IEEE Transactions on Medical Imaging publication information
1 Jun 2010 at 12:00am
Endoscopic Video Texture Mapping on Pre-Built 3-D Anatomical Objects Without ...
1 Jun 2010 at 12:00am
Traditional minimally invasive surgeries use a view port provided by an endoscope or laparoscope. We argue that a useful addition to typical endoscopic imagery would be a global 3-D view providing a wider field of view with explicit depth information for both the exterior and interior of target anatomy. One technical challenge of implementing such a view is finding efficient and accurate means of registering texture images from the laparoscope on prebuilt 3-D surface models of target anatomy derived from magnetic resonance (MR) or computed tomography (CT) images. This paper presents a novel method for addressing this challenge that differs from previous approaches, which depend on tracking the position of the laparoscope. We take advantage of the fact that neighboring frames within a video sequence usually contain enough coherence to allow a 2-D–2-D registration, which is a much more tractable problem. The texturing process can be bootstrapped by an initial 2-D–3-D user-assisted registration of the first video frame followed by mostly-automatic texturing of subsequent frames. We perform experiments on phantom and real data, validate the algorithm against the ground truth, and compare it with the traditional tracking method by simulations. Experiments show that our method improves registration performance compared to the traditional tracking approach.
Statistics of Optical Coherence Tomography Data From Human Retina
1 Jun 2010 at 12:00am
Optical coherence tomography (OCT) has recently become one of the primary methods for noninvasive probing of the human retina. The pseudoimage formed by OCT (the so-called B-scan) varies probabilistically across pixels due to complexities in the measurement technique. Hence, sensitive automatic procedures of diagnosis using OCT may exploit statistical analysis of the spatial distribution of reflectance. In this paper, we perform a statistical study of retinal OCT data. We find that the stretched exponential probability density function can model well the distribution of intensities in OCT pseudoimages. Moreover, we show a small, but significant correlation between neighbor pixels when measuring OCT intensities with pixels of about 5 $mu{rm m}$. We then develop a simple joint probability model for the OCT data consistent with known retinal features. This model fits well the stretched exponential distribution of intensities and their spatial correlation. In normal retinas, fit parameters of this model are relatively constant along retinal layers, but varies across layers. However, in retinas with diabetic retinopathy, large spikes of parameter modulation interrupt the constancy within layers, exactly where pathologies are visible. We argue that these results give hope for improvement in statistical pathology-detection methods even when the disease is in its early stages.
Spatio-Temporal Data Fusion for 3D+T Image Reconstruction in Cerebral Angiogr...
1 Jun 2010 at 12:00am
This paper provides a framework for generating high resolution time sequences of 3D images that show the dynamics of cerebral blood flow. These sequences have the potential to allow image feedback during medical procedures that facilitate the detection and observation of pathological abnormalities such as stenoses, aneurysms, and blood clots. The 3D time series is constructed by fusing a single static 3D model with two time sequences of 2D projections of the same imaged region. The fusion process utilizes a variational approach that constrains the volumes to have both smoothly varying regions separated by edges and sparse regions of nonzero support. The variational problem is solved using a modified version of the Gauss–Seidel algorithm that exploits the spatio-temporal structure of the angiography problem. The 3D time series results are visualized using time series of isosurfaces, synthetic X-rays from arbitrary perspectives or poses, and 3D surfaces that show arrival times of the contrasted blood front using color coding. The derived visualizations provide physicians with a previously unavailable wealth of information that can lead to safer procedures, including quicker localization of flow altering abnormalities such as blood clots, and lower procedural X-ray exposure. Quantitative SNR and other performance analysis of the algorithm on computational phantom data are also presented.
MRI Pulse Sequence Design With First-Order Gradient Moment Nulling in Arbitra...
1 Jun 2010 at 12:00am
We suggest a polynomial program for the calculation of optimized gradient waveforms for magnetic resonance tomography pulse sequences. Such non-linear mathematical programs can describe gradient system capabilities, meet $k$-space trajectory specifications, and capture sequence timing conditions. Moreover they allow the incorporation of gradient moment nulling constraints in one or several arbitrary spatial directions, which can reduce flow motion artifacts in the images. We report first experiences in solving such automatic pulse sequence design programs with the interior point solver Ipopt.
A Bayesian Mixture Approach to Modeling Spatial Activation Patterns in Multis...
1 Jun 2010 at 12:00am
We propose a probabilistic model for analyzing spatial activation patterns in multiple functional magnetic resonance imaging (fMRI) activation images such as repeated observations on an individual or images from different individuals in a clinical study. Instead of taking the traditional approach of voxel-by-voxel analysis, we directly model the shape of activation patterns by representing each activation cluster in an image as a Gaussian-shaped surface. We assume that there is an unknown true template pattern and that each observed image is a noisy realization of this template. We model an individual image using a mixture of experts model with each component representing a spatial activation cluster. Taking a nonparametric Bayesian approach, we use a hierarchical Dirichlet process to extract common activation clusters from multiple images and estimate the number of such clusters automatically. We further extend the model by adding random effects to the shape parameters to allow for image-specific variation in the activation patterns. Using a Bayesian framework, we learn the shape parameters for both image-level activation patterns and the template for the set of images by sampling from the posterior distribution of the parameters. We demonstrate our model on a dataset collected in a large multisite fMRI study.
Fast Semi-Analytical Model-Based Acoustic Inversion for Quantitative Optoacou...
1 Jun 2010 at 12:00am
We present a fast model-based inversion algorithm for quantitative 2-D and 3-D optoacoustic tomography. The algorithm is based on an accurate and efficient forward model, which eliminates the need for regularization in the inversion process while providing modeling flexibility essential for quantitative image formation. The resulting image-reconstruction method eliminates stability problems encountered in previously published model-based techniques and, thus, enables performing image reconstruction in real time. Our model-based framework offers a generalization of the forward solution to more comprehensive optoacoustic propagation models, such as including detector frequency response, without changing the inversion procedure. The reconstruction speed and other algorithmic performances are demonstrated using numerical simulation studies and experimentally on tissue-mimicking optically heterogeneous phantoms and small animals. In the experimental examples, the model-based reconstructions manifested correctly the effect of light attenuation through the objects and did not suffer from the artifacts which usually afflict the commonly used filtered backprojection algorithms, such as negative absorption values.
Automatic Segmentation of Pulmonary Lobes Robust Against Incomplete Fissures
1 Jun 2010 at 12:00am
A method for automatic segmentation of pulmonary lobes from computed tomography (CT) scans is presented that is robust against incomplete fissures. The method is based on a multiatlas approach in which existing lobar segmentations are deformed to test scans in which the fissures, the lungs, and the bronchial tree have been automatically segmented. The key element of our method is a cost function that exploits information from fissures, lung borders, and bronchial tree in an effective way, such that less reliable information (lungs, airways) is only used when the most reliable information (fissures) is missing. To cope with the anatomical variation in lobe shape, an atlas selection mechanism is introduced. The method is evaluated on two test sets of 120 scans in total. The results show that the lobe segmentation closely follows the fissures when they are present. In a simulated experiment in which parts of complete fissures are removed, the robustness of the method against different levels of incomplete fissures is shown. When the fissures are incomplete, an observer study shows agreement of the automatically determined lobe borders with a radiologist for 81% of the lobe borders on average.
Robust Reconstruction of MRSI Data Using a Sparse Spectral Model and High Res...
1 Jun 2010 at 12:00am
We introduce a novel algorithm to address the challenges in magnetic resonance (MR) spectroscopic imaging. In contrast to classical sequential data processing schemes, the proposed method combines the reconstruction and postprocessing steps into a unified algorithm. This integrated approach enables us to inject a range of prior information into the data processing scheme, thus constraining the reconstructions. We use high resolution, 3-D estimate of the magnetic field inhomogeneity map to generate an accurate forward model, while a high resolution estimate of the fat/water boundary is used to minimize spectral leakage artifacts. We parameterize the spectrum at each voxel as a sparse linear combination of spikes and polynomials to capture the metabolite and baseline components, respectively. The constrained model makes the problem better conditioned in regions with significant field inhomogeneity, thus enabling the recovery even in regions with high field map variations. To exploit the high resolution MR information, we formulate the problem as an anatomically constrained total variation optimization scheme on a grid with the same spacing as the magnetic resonance imaging data. We analyze the performance of the proposed scheme using phantom and human subjects. Quantitative and qualitative comparisons indicate a significant improvement in spectral quality and lower leakage artifacts.
N4ITK: Improved N3 Bias Correction
1 Jun 2010 at 12:00am
A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as “N4ITK,” available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized $^{3}{rm He}$ lung image data, and 9.4T postmortem hippocampus data.
Three-Dimensional Analysis of Retinal Layer Texture: Identification of Fluid-...
1 Jun 2010 at 12:00am
Optical coherence tomography (OCT) is becoming one of the most important modalities for the noninvasive assessment of retinal eye diseases. As the number of acquired OCT volumes increases, automating the OCT image analysis is becoming increasingly relevant. In this paper, a method for automated characterization of the normal macular appearance in spectral domain OCT (SD-OCT) volumes is reported together with a general approach for local retinal abnormality detection. Ten intraretinal layers are first automatically segmented and the 3-D image dataset flattened to remove motion-based artifacts. From the flattened OCT data, 23 features are extracted in each layer locally to characterize texture and thickness properties across the macula. The normal ranges of layer-specific feature variations have been derived from 13 SD-OCT volumes depicting normal retinas. Abnormalities are then detected by classifying the local differences between the normal appearance and the retinal measures in question. This approach was applied to determine footprints of fluid-filled regions—SEADs (Symptomatic Exudate-Associated Derangements)—in 78 SD-OCT volumes from 23 repeatedly imaged patients with choroidal neovascularization (CNV), intra-, and sub-retinal fluid and pigment epithelial detachment. The automated SEAD footprint detection method was validated against an independent standard obtained using an interactive 3-D SEAD segmentation approach. An area under the receiver-operating characteristic curve of $0.961 pm 0.012$ was obtained for the classification of vertical, cross-layer, macular columns. A study performed on 12 pairs of OCT volumes obtained from the same eye on the same day shows that the repeatability of the automated method is comparable to that of the human experts. This work demonstrates that useful 3-D textural information can be extracted from SD-OCT scans and—together with - - an anatomical atlas of normal retinas—can be used for clinically important applications.
Correction to “Advanced Level-Set-Based Cell Tracking in Time-Lapse Fluoresce...
1 Jun 2010 at 12:00am
Correction to “An Inverse Problem Approach for Elasticity Imaging through Vib...
1 Jun 2010 at 12:00am
IEEE Foundation
1 Jun 2010 at 12:00am

Presented by MedRat®

MedRat BioArchives MedRat News Home e-Journals Home MedRat Feedback Download e-Journals Links Disclaimer / Copyrights