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Medical Imaging, IEEE Transactions on

Issue 12 • Date Dec. 2000

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Displaying Results 1 - 18 of 18
  • Editorial

    Page(s): 1157 - 1159
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    Freely Available from IEEE
  • Reconstruction of MR images from data acquired on a general nonregular grid by pseudoinverse calculation

    Page(s): 1160 - 1167
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    A minimum-norm least-squares image-reconstruction method for the reconstruction of magnetic resonance images from non-Cartesian sampled data is proposed. The method is based on a general formalism for continuous-to-discrete mapping and pseudoinverse calculation. It does not involve any regridding or interpolation of the data and therefore the methodology differs fundamentally from existing regridding-based methods. Moreover, the method uses a continuous representation of objects in the image domain instead of a discretized representation. Simulations and experiments show the possibilities of the method in both radial and spiral imaging. Simulations revealed that minimum-norm least-squares image reconstruction can result in a drastic decrease of artifacts compared with regridding-based reconstruction. Besides, both in vivo and phantom experiments showed that minimum-norm least-squares image reconstruction leads to contrast improvement and increased signal-to-noise ratio compared with image reconstruction based on regridding. As an appendix, an analytical calculation of the raw data corresponding to the well-known Shepp and Logan software head phantom is presented. View full abstract»

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  • Optimal k-space sampling in MRSI for images with a limited region of support

    Page(s): 1168 - 1178
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    Magnetic resonance spectroscopic imaging requires a great deal of time to gather the data necessary to achieve satisfactory resolution. When the image has a limited region of support (ROS), it is possible to reconstruct the image from a subset of k-space samples. Therefore, the authors desire to choose the best possible combination of a small number of k-space samples to guarantee the quality of the reconstructed image. Sequential forward selection (SFS) is appealing as an optimization method because the previously selected sample can be observed while the next sample is selected. However, when the number of selected k-space samples is less than the number of unknowns at the beginning of the selection process, the optimality criterion is undefined and the resulting SFS algorithm cannot be used. Here, the authors present a modified form of the criterion that overcomes this problem and develop an SFS algorithm for the new criterion. Then the authors develop an efficient computational strategy for this algorithm as well as for the standard SFS algorithm. The combined algorithm efficiently selects a reduced set of k-space samples from which the ROS can be reconstructed with minimal noise amplification. View full abstract»

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  • Brain tissue classification of magnetic resonance images using partial volume modeling

    Page(s): 1179 - 1187
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    Presents a fully automatic three-dimensional classification of brain tissues for Magnetic Resonance (MR) images. An MR image volume may be composed of a mixture of several tissue types due to partial volume effects. Therefore, the authors consider that in a brain dataset there are not only the three main types of brain tissue: gray matter, white matter, and cerebro spinal fluid, called pure classes, but also mixtures, called mixclasses. A statistical model of the mixtures is proposed and studied by means of simulations. It is shown that it can be approximated by a Gaussian function under some conditions. The D'Agostino-Pearson normality test is used to assess the risk or of the approximation. In order to classify a brain into three types of brain tissue and deal with the problem of partial volume effects, the proposed algorithm uses two steps: (1) segmentation of the brain into pure and mixclasses using the mixture model; (2) reclassification of the mixclasses into the pure classes using knowledge about the obtained pure classes. Both steps use Markov random field (MRF) models. The multifractal dimension, describing the topology of the brain, is added to the MRFs to improve discrimination of the mixclasses. The algorithm is evaluated using both simulated images and real MR images with different T1-weighted acquisition sequences. View full abstract»

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  • Modeling the hemodynamic response in fMRI using smooth FIR filters

    Page(s): 1188 - 1201
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    Modeling the hemodynamic response in functional magnetic resonance (fMRI) experiments is an important aspect of the analysis of functional neuroimages. This has been done in the past using parametric response function, from a limited family. In this contribution, the authors adopt a semi-parametric approach based on finite impulse response (FIR) filters. In order to cope with the increase in the number of degrees of freedom, the authors introduce a Gaussian process prior on the filter parameters. They show how to carry on the analysis by incorporating prior knowledge on the filters, optimizing hyper-parameters using the evidence framework, or sampling using a Markov Chain Monte Carlo (MCMC) approach. The authors present a comparison of their model with standard hemodynamic response kernels on simulated data, and perform a full analysis of data acquired during an experiment involving visual stimulation. View full abstract»

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  • An active contour model for measuring the area of leg ulcers

    Page(s): 1202 - 1210
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    Leg ulcers are chronic skin a wounds that affect many people and take a long time to heal. The progress of wound healing and the effect of clinical treatments can he monitored partly by measuring the area of the wound. Measurements taken via manually based methods, such as using a computer pointing device to delineate the wound boundary in a digitized image, suffer from variations due to manual dexterity and differences of opinion between observers. An active contour model is presented that models the contour using piecewise B-spline arcs and uses the minimax principle to adaptively regularize the contour according to the local conditions in the wound image. The model makes use of the existing manual delineation process in order to initialize the solution and is shown to reduce the effect of the inherent variations upon the repeatability and consistency of area measurements in many cases. View full abstract»

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  • Edge-guided boundary delineation in prostate ultrasound images

    Page(s): 1211 - 1219
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    Accurate detection of prostate boundaries is required in many diagnostic and treatment procedures for prostate disease. Here, a new paradigm for guided edge delineation is described, a which involves presenting automatically detected prostate edges as a visual guide to the observer, followed by manual editing. This approach enables robust delineation of the prostate boundaries, making it suitable for routine clinical use. The edge-detection algorithm is comprised of three stages. An algorithm called sticks is used to enhance contrast and at the same time reduce speckle in the transrectal ultrasound prostate image. The resulting image is further smoothed using an anisotropic diffusion filter. In the third stage, some basic prior knowledge of the prostate, such as shape and echo pattern, is used to detect the most probable edges describing the prostate. Finally, patient-specific anatomic information is integrated during manual linking of the detected edges. The algorithm was tested on 125 images from 16 patients. The performance of the algorithm was statistically evaluated by employing five expert observers. Based on this study, the authors found that consistency in prostate delineation increases when automatically detected edges are used as visual guide during outlining, while the accuracy of the detected edges was found to be at least as good as those of the human observers. The use of edge guidance for boundary delineation can also be extended to other applications in medical imaging where poor contrast in the images and the complexity in the anatomy limit the clinical usability of fully automatic edge-detection techniques. View full abstract»

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  • A novel approach to extract colon lumen from CT images for virtual colonoscopy

    Page(s): 1220 - 1226
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    An automatic method has been developed for segmentation of abdominal computed tomography (CT) images for virtual colonoscopy obtained after a bowel preparation of a low-residue diet with ingested contrast solutions to enhance the image intensities of residual colonic materials. Removal of the enhanced materials was performed electronically by a computer algorithm. The method is a multistage approach that employs a modified self-adaptive on-line, vector quantization technique for a low-level image classification and utilizes a region-growing strategy for a high-level feature extraction. The low-level classification labels each voxel based on statistical analysis of its three-dimensional intensity vectors consisting of nearby voxels. The high-level processing extracts the labeled stool, fluid and air voxels within the colon, and eliminates bone and lung voxels which have similar image intensities as the enhanced materials and air, but are physically separated from the colon. This method was evaluated by volunteer studies based on both objective and subjective criteria. The validation demonstrated that the method has a high reproducibility and repeatability and a small error due to partial volume effect. As a result of this electronic colon cleansing, routine physical bowel cleansing prior to virtual colonoscopy may not be necessary. View full abstract»

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  • Rapid 3-D cone-beam reconstruction with the simultaneous algebraic reconstruction technique (SART) using 2-D texture mapping hardware

    Page(s): 1227 - 1237
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    Algebraic reconstruction methods, such as the algebraic reconstruction technique (ART) and the related simultaneous ART (SART), reconstruct a two-dimensional (2-D) or three-dimensional (3-D) object from its X-ray projections. The algebraic methods have, in certain scenarios, many advantages over the more popular Filtered Backprojection approaches and have also recently been shown to perform well for 3-D cone-beam reconstruction. However, so far the slow speed of these iterative methods have prohibited their routine use in clinical applications. Here, the authors address this shortcoming and investigate the utility of widely available 2-D texture mapping graphics hardware for the purpose of accelerating the 3-D algebraic reconstruction. They find that this hardware allows 3-D cone-beam reconstructions to be obtained at almost interactive speeds, with speed-ups of over 50 with respect to implementations that only use general-purpose CPUs. However the authors also find that the reconstruction quality is rather sensitive to the resolution of the framebuffer, and to address this critical issue they propose a scheme that extends the precision of a given framebuffer by 4 bits, using the color channels. With this extension, a 12-bit framebuffer delivers useful reconstructions for 0.5% tissue contrast, while an 8-bit framebuffer requires 4%. Since graphics hardware generates an entire image for each volume projection, it is most appropriately used with an algebraic reconstruction method that performs volume correction at that granularity as well, such as SART or SIRT. The authors chose SART for its faster convergence properties. View full abstract»

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  • X-ray CT metal artifact reduction using wavelets: an application for imaging total hip prostheses

    Page(s): 1238 - 1247
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    Traditional computed tomography (CT) reconstructions of total joint prostheses are limited by metal artifacts from corrupted projection data. Published metal artifact reduction methods are based on the assumption that severe attenuation of X-rays by prostheses renders corresponding portions of projection data unavailable, hence the "missing" data are either avoided (in iterative reconstruction) or interpolated (in filtered back-projection with data completion; typically, with filling data "gaps" via linear functions). Here, the authors propose a wavelet-based multiresolution analysis method for metal artifact reduction, in which information is extracted from corrupted projection data. The wavelet method improves image quality by a successive interpolation in the wavelet domain. Theoretical analysis and experimental results demonstrate that the metal artifacts due to both photon starving and beam hardening can be effectively suppressed using the authors' method. As compared to the filtered back-projection after linear interpolation, the wavelet-based reconstruction is significantly more accurate for depiction of anatomical structures, especially in the immediate neighborhood of the prostheses. This superior imaging precision is highly advantageous in geometric modeling for fitting hip prostheses. View full abstract»

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  • A perspective on needle artifacts in MRI: an electromagnetic model for experimentally separating susceptibility effects

    Page(s): 1248 - 1252
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    A thorough understanding of artifacts caused by metallic instruments is essential for the guidance of interventional procedures by magnetic resonance imaging (MRI), because the accurate localization of each instrument is mandatory for this. In the past, this problem has been addressed by several groups, using theoretical, as well as experimental approaches. The artifacts associated with MRI are caused by geometry distortion and intravoxel dephasing. Usually, both effects mingle in the image, and depending on the pulse sequences and its parameters used for data acquisition, these effects are reflected in the image with different magnitude. Here, the authors shortly present the well-known mathematical background of the two underlying effects. Mathematically, both can be treated separately. Here, authors propose a new electromagnetic model which also allows to experimentally separate the effects better than by comparing spin-echo and gradient-echo images of the same object. With this new model, both effects-geometry distortion and intravoxel dephasing-are demonstrated separately using the same gradient-echo pulse sequence for all scans and adjusting the fields of the model properly. Furthermore, as this model allows to adjust both effects independently, it is used to study different weightings of both effects when they appear simultaneously in the image. View full abstract»

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  • AM-FM texture segmentation in electron microscopic muscle imaging

    Page(s): 1253 - 1257
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    Describes the application of an amplitude modulation-frequency modulation (AM-FM) image representation in segmenting electron micrographs of skeletal muscle for the recognition of: (1) normal sarcomere ultrastructural pattern and (2) abnormal regions that occur in sarcomeres in various myopathies. A total of 26 electron micrographs from different myopathies mere used for this study. It is shown that the AM-FM image representation can identify normal repetitive structures and sarcomeres, with a good degree of accuracy. This system can also detect abnormalities in sarcomeres which alter the normal regular pattern, as seen in muscle pathology, with a recognition accuracy of 75%-84% as compared to a human expert. View full abstract»

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  • A Jini service to reconstruct tomographic data

    Page(s): 1258 - 1261
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    Distributed computing that uses dynamic networks will change the way one works and communicates thanks to the interaction of devices and services, that are automatically added and removed from the network as needed. The Jini technology, which is built atop the Java programming language, provides a homogenous view of the network and extends the ability of code to migrate in Java. This software design model simplifies the configuration and access to hardware devices and software services in a network. Thus, it becomes possible to execute new services without pre-installing software on client machines. This new programming paradigm is especially important in medical applications, where the reliable transmission of information is essential. This paper demonstrates how single photon emission computerized tomography data ran be iteratively reconstructed using a Jini service. View full abstract»

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  • Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging

    Page(s): 1261 - 1266
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    During the last few years, optical coherence tomography (OCT) has demonstrated considerable promise as a method of high-resolution intravascular imaging. The goal of this study was to apply and to test the applicability of the rotating kernel transformation (RKT) technique to the speckle reduction and enhancement of OCT images. The technique is locally adaptive. It is based on sequential application of directional masks and selection of the maximum of all outputs. This method enhances the image features by emphasizing thin edges while suppressing a noisy background. Qualitatively, the RKT algorithm provides noticeable improvement over the original image. All processed images are smoother and have better-defined borders of media, intima, and plaque. The quantitative evaluation of RKT performance showed that in terms of average contrast-to-noise ratio, there is a significant improvement in image quality between original and enhanced images. The RKT image enhancement technique shows great promise in improving OCT images for superior boundary identification. View full abstract»

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  • Correction to "Nondistorting flattened maps and the 3-D visualization of colon CT images"

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    In the above paper (see S. Haker et al., ibid., vol.19, p.665-70, 2000), Figs. 2-5 should have appeared in color as shown here. View full abstract»

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  • Estimating the keratoconus index from ultrasound images of the human cornea

    Page(s): 1268 - 1272
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    The keratoconus index (KI) is a new biometric parameter to make diagnosis and to follow the development of the keratoconus in human eyes. Using images from an ultrasound biomicroscope, the authors show a semi-automatic method to speed up the computation of the KI. View full abstract»

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  • Author index

    Page(s): 1 - 6
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    Freely Available from IEEE
  • Subject index

    Page(s): 6 - 15
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    Freely Available from IEEE

Aims & Scope

IEEE Transactions on Medical Imaging (T-MI) encourages the submission of manuscripts on imaging of body structures, morphology and function, and imaging of microscopic biological entities. The journal publishes original contributions on medical imaging achieved by various modalities, such as ultrasound, X-rays (including CT) magnetic resonance, radionuclides, microwaves, and light, as well as medical image processing and analysis, visualization, pattern recognition, and related methods. Studies involving highly technical perspectives are most welcome. The journal focuses on a unified common ground where instrumentation, systems, components, hardware and software, mathematics and physics contribute to the studies.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Milan Sonka
Iowa Institute for Biomedical Imaging
3016B SC, Department of Electrical and Computer Engineering
The University of Iowa
Iowa City, IA  52242  52242  USA
milan-sonka@uiowa.edu