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Medical Imaging, IEEE Transactions on

Issue 12 • Date Dec. 2008

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Displaying Results 1 - 17 of 17
  • Table of contents

    Page(s): C1
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    Freely Available from IEEE
  • IEEE Transactions on Medical Imaging publication information

    Page(s): C2
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    Freely Available from IEEE
  • Wavelet Based Noise Reduction in CT-Images Using Correlation Analysis

    Page(s): 1685 - 1703
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4726 KB) |  | HTML iconHTML  

    The projection data measured in computed tomography (CT) and, consequently, the slices reconstructed from these data are noisy. We present a new wavelet based structure-preserving method for noise reduction in CT-images that can be used in combination with different reconstruction methods. The approach is based on the assumption that data can be decomposed into information and temporally uncorrelated noise. In CT two spatially identical images can be generated by reconstructions from disjoint subsets of projections: using the latest generation dual source CT-scanners one image can be reconstructed from the projections acquired at the first, the other image from the projections acquired at the second detector. For standard CT-scanners the two images can be generated by splitting up the set of projections into even and odd numbered projections. The resulting images show the same information but differ with respect to image noise. The analysis of correlations between the wavelet representations of the input images allows separating information from noise down to a certain signal-to-noise level. Wavelet coefficients with small correlation are suppressed, while those with high correlations are assumed to represent structures and are preserved. The final noise-suppressed image is reconstructed from the averaged and weighted wavelet coefficients of the input images. The proposed method is robust, of low complexity and adapts itself to the noise in the images. The quantitative and qualitative evaluation based on phantom as well as real clinical data showed, that high noise reduction rates of around 40% can be achieved without noticeable loss of image resolution. View full abstract»

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  • Robust Gradient-Based 3-D/2-D Registration of CT and MR to X-Ray Images

    Page(s): 1704 - 1714
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1770 KB) |  | HTML iconHTML  

    One of the most important technical challenges in image-guided intervention is to obtain a precise transformation between the intrainterventional patient's anatomy and corresponding preinterventional 3-D image on which the intervention was planned. This goal can be achieved by acquiring intrainterventional 2-D images and matching them to the preinterventional 3-D image via 3-D/2-D image registration. A novel 3-D/2-D registration method is proposed in this paper. The method is based on robustly matching 3-D preinterventional image gradients and coarsely reconstructed 3-D gradients from the intrainterventional 2-D images. To improve the robustness of finding the correspondences between the two sets of gradients, hypothetical correspondences are searched for along normals to anatomical structures in 3-D images, while the final correspondences are established in an iterative process, combining the robust random sample consensus algorithm (RANSAC) and a special gradient matching criterion function. The proposed method was evaluated using the publicly available standardized evaluation methodology for 3-D/2-D registration, consisting of 3-D rotational X-ray, computed tomography, magnetic resonance (MR), and 2-D X-ray images of two spine segments, and standardized evaluation criteria. In this way, the proposed method could be objectively compared to the intensity, gradient, and reconstruction-based registration methods. The obtained results indicate that the proposed method performs favorably both in terms of registration accuracy and robustness. The method is especially superior when just a few X-ray images and when MR preinterventional images are used for registration, which are important advantages for many clinical applications. View full abstract»

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  • Computational Patient-Specific Models Based on 3-D Ultrasound Data to Quantify Uterine Arterial Flow During Pregnancy

    Page(s): 1715 - 1722
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (609 KB) |  | HTML iconHTML  

    Information on uterine blood flow rate during pregnancy would widely improve our knowledge on feto-placental patho-physiology. Ultrasonographic flow rate evaluation requires the knowledge of the spatial velocity profiles throughout the investigated vessel; these data may be obtained from hemodynamic simulations with accurate computational models. Recently, computational models of superficial vessels have been created using 3-D ultrasound data; unfortunately, common reconstruction methods are unsuitable for the uterine arteries due to the low quality achievable of imaged deep vessels. In this paper a simplified spline-based technique was applied to create computational models for patient-specific simulations of uterine arterial heamodynamics. Moreover, a novel method to quantify the uterine flow rates was developed based on echo-Doppler measurements and computational data. Preliminary results obtained for four patients indicated a quite narrow range for the blood flow rate through the main uterine artery with large variability in the flow split between corporal and cervical branches. Furthermore, parabolic-like velocity profiles were obtained in the branching region of the different patients, suggesting a clinical use of averaged, not patient-specific, spatial velocity distribution coefficients for the blood flow rate calculation. The developed reconstruction method based on 3-D ultrasound imaging is efficient for creating realistic custom models of the uterine arteries. The results of the fluid dynamic simulations allowed us to quantify the uterine arterial flow and its repartition in normal pregnancies. View full abstract»

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  • Development of an Active Intravascular MR Device With an Optical Transmission System

    Page(s): 1723 - 1727
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1459 KB) |  | HTML iconHTML  

    Magnetic resonance imaging (MRI) is a safe and reliable medical imaging method providing good soft tissue contrast while avoiding harmful ionizing radiation. It is highly desirable to use the MRI technology for interventional procedures. However, due to resonance effects that can result in tissue heating, long conducting cables must be avoided. Motivated by the need for more radio-frequency (RF) safety, we developed an optical transmission system for active intravascular MRI devices. An optical transmitter sends the MR signal via an optical fiber. A miniature optical modulator was designed to be integrated into a catheter tip. Furthermore, power is supplied optically to the transmitter. This system can target new medical applications, due to safe catheter tracking and safe intravascular imaging. View full abstract»

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  • Efficient Processing of Laser Speckle Contrast Images

    Page(s): 1728 - 1738
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (669 KB) |  | HTML iconHTML  

    Though laser speckle contrast imaging enables the measurement of scattering particle dynamics with high temporal resolution, the subsequent processing has previously been much slower. In prior studies, generating a laser speckle contrast image required about 1 s to process a raw image potentially collected in 10 ms or less. In this paper, novel algorithms are described which are demonstrated to convert 291 raw images per second to laser speckle contrast images and as many as 410 laser speckle contrast images per second to relative correlation time images. As long as image processing occurs during image acquisition, these algorithms render processing time irrelevant in most circumstances and enable real-time imaging of blood flow dynamics. View full abstract»

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  • A Geometry-Driven Optical Flow Warping for Spatial Normalization of Cortical Surfaces

    Page(s): 1739 - 1753
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1899 KB) |  | HTML iconHTML  

    Spatial normalization is frequently used to map data to a standard coordinate system by removing intersubject morphological differences, thereby allowing for group analysis to be carried out. The work presented in this paper is motivated by the need for an automated cortical surface normalization technique that will automatically identify homologous cortical landmarks and map them to the same coordinates on a standard manifold. The geometry of a cortical surface is analyzed using two shape measures that distinguish the sulcal and gyral regions in a multiscale framework. A multichannel optical flow warping procedure aligns these shape measures between a reference brain and a subject brain, creating the desired normalization. The partial differential equation that carries out the warping is implemented in a Euclidean framework in order to facilitate a multiresolution strategy, thereby permitting large deformations between the two surfaces. The technique is demonstrated by aligning 33 normal cortical surfaces and showing both improved structural alignment in manually labeled sulci and improved functional alignment in positron emission tomography data mapped to the surfaces. A quantitative comparison between our proposed surface-based spatial normalization method and a leading volumetric spatial normalization method is included to show that the surface-based spatial normalization performs better in matching homologous cortical anatomies. View full abstract»

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  • Local Harmonic B_z Algorithm With Domain Decomposition in MREIT: Computer Simulation Study

    Page(s): 1754 - 1761
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (948 KB) |  | HTML iconHTML  

    Magnetic resonance electrical impedance tomography (MREIT) attempts to provide conductivity images of an electrically conducting object with a high spatial resolution. When we inject current into the object, it produces internal distributions of current density J and magnetic flux density B=(Bx,By,Bz). By using a magnetic resonance imaging (MRI) scanner, we can measure Bz data where z is the direction of the main magnetic field of the scanner. Conductivity images are reconstructed based on the relation between the injection current and Bz data. The harmonic Bz algorithm was the first constructive MREIT imaging method and it has been quite successful in previous numerical and experimental studies. Its performance is, however, degraded when the imaging object contains low-conductivity regions such as bones and lungs. To overcome this difficulty, we carefully analyzed the structure of a current density distribution near such problematic regions and proposed a new technique, called the local harmonic Bz algorithm. We first reconstruct conductivity values in local regions with a low conductivity contrast, separated from those problematic regions. Then, the method of characteristics is employed to find conductivity values in the problematic regions. One of the most interesting observations of the new algorithm is that it can provide a scaled conductivity image in a local region without knowing conductivity values outside the region. We present the performance of the new algorithm by using computer simulation methods. View full abstract»

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  • Regional Admittivity Spectra With Tomosynthesis Images for Breast Cancer Detection: Preliminary Patient Study

    Page(s): 1762 - 1768
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    It has been known for some time that many tumors have a significantly different conductivity and permittivity from surrounding normal tissue. This high ldquocontrastrdquo in tissue electrical properties, occurring between a few kilohertz and several megahertz, may permit differentiating malignant from benign tissues. Here we show the ability of electrical impedance spectroscopy (EIS) to roughly localize and clearly distinguish cancers from normal tissues and benign lesions. Localization of these lesions is confirmed by simultaneous, in register digital breast tomosynthesis (DBT) mammography or 3-D mammograms. View full abstract»

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  • Wireless Capsule Endoscopy Color Video Segmentation

    Page(s): 1769 - 1781
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (730 KB) |  | HTML iconHTML  

    This paper describes the use of color image analysis to automatically discriminate between oesophagus, stomach, small intestine, and colon tissue in wireless capsule endoscopy (WCE). WCE uses ldquopill-camrdquo technology to recover color video imagery from the entire gastrointestinal tract. Accurately reviewing and reporting this data is a vital part of the examination, but it is tedious and time consuming. Automatic image analysis tools play an important role in supporting the clinician and speeding up this process. Our approach first divides the WCE image into subimages and rejects all subimages in which tissue is not clearly visible. We then create a feature vector combining color, texture, and motion information of the entire image and valid subimages. Color features are derived from hue saturation histograms, compressed using a hybrid transform, incorporating the discrete cosine transform and principal component analysis. A second feature combining color and texture information is derived using local binary patterns. The video is segmented into meaningful parts using support vector or multivariate Gaussian classifiers built within the framework of a hidden Markov model. We present experimental results that demonstrate the effectiveness of this method. View full abstract»

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  • 3-D Visualization and Identification of Biological Microorganisms Using Partially Temporal Incoherent Light In-Line Computational Holographic Imaging

    Page(s): 1782 - 1790
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1173 KB) |  | HTML iconHTML  

    We present a new method for three-dimensional (3-D) visualization and identification of biological microorganisms using partially temporal incoherent light in-line (PTILI) computational holographic imaging and multivariate statistical methods. For 3-D data acquisition of biological microorganisms, the band-pass filtered white light is used to illuminate a biological sample. The transversely and longitudinally diffracted pattern of the biological sample is magnified by microscope objective (MO) and is optically recorded with an image sensor array interfaced with a computer. Three-dimensional reconstruction of the biological sample from the diffraction pattern is accomplished by using computational Fresnel propagation method. Principal components analysis and nonparametric inference algorithms are applied to the 3-D complex amplitude biological sample for identification purposes. Experiments indicate that the proposed system can be useful for identifying biological microorganisms. To the best of our knowledge, this is the first report on using PTILI computational holographic microscopy for identification of biological microorganisms. View full abstract»

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  • Algorithm for X-ray Scatter, Beam-Hardening, and Beam Profile Correction in Diagnostic (Kilovoltage) and Treatment (Megavoltage) Cone Beam CT

    Page(s): 1791 - 1810
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1945 KB) |  | HTML iconHTML  

    Quantitative reconstruction of cone beam X-ray computed tomography (CT) datasets requires accurate modeling of scatter, beam-hardening, beam profile, and detector response. Typically, commercial imaging systems use fast empirical corrections that are designed to reduce visible artifacts due to incomplete modeling of the image formation process. In contrast, Monte Carlo (MC) methods are much more accurate but are relatively slow. Scatter kernel superposition (SKS) methods offer a balance between accuracy and computational practicality. We show how a single SKS algorithm can be employed to correct both kilovoltage (kV) energy (diagnostic) and megavoltage (MV) energy (treatment) X-ray images. Using MC models of kV and MV imaging systems, we map intensities recorded on an amorphous silicon flat panel detector to water-equivalent thicknesses (WETs). Scattergrams are derived from acquired projection images using scatter kernels indexed by the local WET values and are then iteratively refined using a scatter magnitude bounding scheme that allows the algorithm to accommodate the very high scatter-to-primary ratios encountered in kV imaging. The algorithm recovers radiological thicknesses to within 9% of the true value at both kV and megavolt energies. Nonuniformity in CT reconstructions of homogeneous phantoms is reduced by an average of 76% over a wide range of beam energies and phantom geometries. View full abstract»

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  • 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

    Page(s): 1811
    Save to Project icon | Request Permissions | PDF file iconPDF (589 KB)  
    Freely Available from IEEE
  • 2008 Index IEEE Transactions on Medical Imaging Vol. 27

    Page(s): 1812 - 1836
    Save to Project icon | Request Permissions | PDF file iconPDF (301 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Medical Imaging information for authors

    Page(s): C3
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    Freely Available from IEEE
  • Blank page [back cover]

    Page(s): C4
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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