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Medical Imaging, IEEE Transactions on

Issue 3 • Date March 2009

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Displaying Results 1 - 21 of 21
  • 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
  • Shear Wave Spectroscopy for In Vivo Quantification of Human Soft Tissues Visco-Elasticity

    Page(s): 313 - 322
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1434 KB) |  | HTML iconHTML  

    In vivo assessment of dispersion affecting the propagation of visco-elastic waves in soft tissues is key to understand the rheology of human tissues. In this paper, the ability of the supersonic shear imaging (SSI) technique to generate planar shear waves propagating in tissues is fully exploited. First, by strongly limiting shear wave diffraction in the imaging plane, this imaging technique enables to discriminate between the usually concomitant influences of both medium rheological properties and diffraction affecting the shear wave dispersion. Second, transient propagation of these plane shear waves in soft tissues can be measured using echographic images acquired at very high frame. In vitro and in vivo experiments demonstrate that dispersion curves, which characterize the rheological behavior of tissues by measuring the frequency dependence of shear wave speed and attenuation, can be recovered in the 75-600 Hz frequency range. Based on a phase difference algorithm, the dispersion curves are computed in 1 cm2 regions of interest from the acquired propagation movie. In vivo measurements in biceps brachii muscle and liver of three healthy volunteers show important differences in the rheological behavior of these different tissues. Liver tissue appears to be much more dispersive with a phase velocity ranging from ~ 1.5 m/s at 75 Hz to ~ 3 m/s at 500 Hz whereas muscle tissue shows an important anisotropy, shear waves propagating longitudinally to the muscular fibers are almost nondispersive while those propagating transversally are very dispersive with a shear wave speed ranging from 0.5 to 2 m/s between 75 and 500 Hz. The estimation of dispersion curves is local and can be performed separately in different regions of the organ. This signal processing approach based on the SSI modality introduces the new concept of In vivo shear wave spectroscopy (SWS) that could become an additional t- ol for tissue characterization. This paper demonstrates the in vivo ability of this SWS to quantify both local shear elasticity and dispersion in real time. View full abstract»

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  • Region Segmentation in the Frequency Domain Applied to Upper Airway Real-Time Magnetic Resonance Images

    Page(s): 323 - 338
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2813 KB) |  | HTML iconHTML  

    We describe a method for unsupervised region segmentation of an image using its spatial frequency domain representation. The algorithm was designed to process large sequences of real-time magnetic resonance (MR) images containing the 2-D midsagittal view of a human vocal tract airway. The segmentation algorithm uses an anatomically informed object model, whose fit to the observed image data is hierarchically optimized using a gradient descent procedure. The goal of the algorithm is to automatically extract the time-varying vocal tract outline and the position of the articulators to facilitate the study of the shaping of the vocal tract during speech production. View full abstract»

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  • Incorporating Human Contrast Sensitivity in Model Observers for Detection Tasks

    Page(s): 339 - 347
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1319 KB) |  | HTML iconHTML  

    Contrast sensitivity of the human visual system is a characteristic that can adversely affect human performance in detection tasks. In this paper, we propose a method for incorporating human contrast sensitivity in anthropomorphic model observers. In our method, we model human contrast sensitivity using the Barten model with the mean luminance of a region of interest centered at the signal location. In addition, one free parameter is varied to control the effect of the contrast sensitivity on the model observer's performance. We investigate our model of human contrast sensitivity in a channelized-Hotelling observer (CHO) with difference-of-Gaussian channels. We call the CHO incorporating the contrast sensitivity a contrast-sensitive CHO (CS-CHO). The human data from a psychophysical study by Park are used for comparing the performance of the CS-CHO to human performance. That study used Gaussian signals with six different signal intensities in non-Gaussian lumpy backgrounds. A value of the free parameter is chosen to match the performance of the CS-CHO to the mean human performance only at the strongest signal. Results show that the CS-CHO with the chosen value of the free parameter predicts the mean human performance at the five lower signal intensities. Our results show that the CS-CHO predicts human performance well as a function of signal intensity. View full abstract»

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  • A Diffusion Tensor Imaging Tractography Algorithm Based on Navier–Stokes Fluid Mechanics

    Page(s): 348 - 360
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (838 KB) |  | HTML iconHTML  

    We introduce a fluid mechanics based tractography method for estimating the most likely connection paths between points in diffusion tensor imaging (DTI) volumes. We customize the Navier-Stokes equations to include information from the diffusion tensor and simulate an artificial fluid flow through the DTI image volume. We then estimate the most likely connection paths between points in the DTI volume using a metric derived from the fluid velocity vector field. We validate our algorithm using digital DTI phantoms based on a helical shape. Our method segmented the structure of the phantom with less distortion than was produced using implementations of heat-based partial differential equation (PDE) and streamline based methods. In addition, our method was able to successfully segment divergent and crossing fiber geometries, closely following the ideal path through a digital helical phantom in the presence of multiple crossing tracts. To assess the performance of our algorithm on anatomical data, we applied our method to DTI volumes from normal human subjects. Our method produced paths that were consistent with both known anatomy and directionally encoded color images of the DTI dataset. View full abstract»

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  • Joint Sulcal Detection on Cortical Surfaces With Graphical Models and Boosted Priors

    Page(s): 361 - 373
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1134 KB) |  | HTML iconHTML  

    In this paper, we propose an automated approach for the joint detection of major sulci on cortical surfaces. By representing sulci as nodes in a graphical model, we incorporate Markovian relations between sulci and formulate their detection as a maximum a posteriori (MAP) estimation problem over the joint space of major sulci. To make the inference tractable, a sample space with a finite number of candidate curves is automatically generated at each node based on the Hamilton-Jacobi skeleton of sulcal regions. Using the AdaBoost algorithm, we learn both individual and pairwise shape priors of sulcal curves from training data, which are then used to define potential functions in the graphical model based on the connection between AdaBoost and logistic regression. Finally belief propagation is used to perform the MAP inference and select the joint detection results from the sample spaces of candidate curves. In our experiments, we quantitatively validate our algorithm with manually traced curves and demonstrate the automatically detected curves can capture the main body of sulci very accurately. A comparison with independently detected results is also conducted to illustrate the advantage of the joint detection approach. View full abstract»

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  • Coregistered FDG PET/CT-Based Textural Characterization of Head and Neck Cancer for Radiation Treatment Planning

    Page(s): 374 - 383
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1442 KB) |  | HTML iconHTML  

    Coregistered fluoro-deoxy-glucose (FDG) positron emission tomography/computed tomography (PET/CT) has shown potential to improve the accuracy of radiation targeting of head and neck cancer (HNC) when compared to the use of CT simulation alone. The objective of this study was to identify textural features useful in distinguishing tumor from normal tissue in head and neck via quantitative texture analysis of coregistered 18 F-FDG PET and CT images. Abnormal and typical normal tissues were manually segmented from PET/CT images of 20 patients with HNC and 20 patients with lung cancer. Texture features including some derived from spatial grey-level dependence matrices (SGLDM) and neighborhood gray-tone-difference matrices (NGTDM) were selected for characterization of these segmented regions of interest (ROIs). Both K nearest neighbors (KNNs) and decision tree (DT)-based KNN classifiers were employed to discriminate images of abnormal and normal tissues. The area under the curve (AZ) of receiver operating characteristics (ROC) was used to evaluate the discrimination performance of features in comparison to an expert observer. The leave-one-out and bootstrap techniques were used to validate the results. The AZ of DT-based KNN classifier was 0.95. Sensitivity and specificity for normal and abnormal tissue classification were 89% and 99%, respectively. In summary, NGTDM features such as PET coarseness, PET contrast, and CT coarseness extracted from FDG PET/CT images provided good discrimination performance. The clinical use of such features may lead to improvement in the accuracy of radiation targeting of HNC. View full abstract»

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  • A Filtered Backprojection Algorithm for Triple-Source Helical Cone-Beam CT

    Page(s): 384 - 393
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (554 KB) |  | HTML iconHTML  

    Multisource cone-beam computed tomography (CT) is an attractive approach of choice for superior temporal resolution, which is critically important for cardiac imaging and contrast enhanced studies. In this paper, we present a filtered-backprojection (FBP) algorithm for triple-source helical cone-beam CT. The algorithm is both exact and efficient. It utilizes data from three inter-helix PI-arcs associated with the inter-helix PI-lines and the minimum detection windows defined for the triple-source configuration. The proof of the formula is based on the geometric relations specific to triple-source helical cone-beam scanning. Simulation results demonstrate the validity of the reconstruction algorithm. This algorithm is also extended to a multisource version for ( 2N + 1 ) -source helical cone-beam CT. With parallel computing, the proposed FBP algorithms can be significantly faster than our previously published multisource backprojection-filtration algorithms. Thus, the FBP algorithms are promising in applications of triple-source helical cone-beam CT. View full abstract»

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  • Iterative Off-Resonance and Signal Decay Estimation and Correction for Multi-Echo MRI

    Page(s): 394 - 404
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2393 KB) |  | HTML iconHTML  

    Signal dephasing due to field inhomogeneity and signal decay due to transverse relaxation lead to perturbations of the Fourier encoding commonly applied in magnetic resonance imaging. Hence, images acquired with long readouts suffer from artifacts such as blurring, distortion, and intensity variation. These artifacts can be removed in reconstruction, usually based on separately collected information in form of field and relaxation maps. In this work, a recently proposed gridding-based algorithm for off-resonance correction is extended to also address signal decay. It is integrated into a new fixed-point iteration, which permits the joint estimation of an image and field and relaxation maps from multi-echo acquisitions. This approach is then applied in simulations and in vivo experiments and demonstrated to improve both images and maps. The rapid convergence of the fixed-point iteration in combination with the efficient gridding-based correction promises to render the running time of such a joint estimation acceptable. View full abstract»

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  • Three-Dimensional Blood Vessel Quantification via Centerline Deformation

    Page(s): 405 - 414
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1641 KB) |  | HTML iconHTML  

    It is clinically important to quantify the geometric parameters of an abnormal vessel, as this information can aid radiologists in choosing appropriate treatments or apparatuses. Centerline and cross-sectional diameters are commonly used to characterize the morphology of vessel in various clinical applications. Due to the existence of stenosis or aneurysm, the associated vessel centerline is unable to truly portray the original, healthy vessel shape and may result in inaccurate quantitative measurement. To remedy such a problem, a novel method using an active tube model is proposed. In the method, a smoothened centerline is determined as the axis of a deformable tube model that is registered onto the vessel lumen. Three types of regions, normal, stenotic, and aneurysmal regions, are defined to classify the vessel segment under-analyzed by use of the algorithm of a cross-sectional-based distance field. The registration process used on the tube model is governed by different region-adaptive energy functionals associated with the classified vessel regions. The proposed algorithm is validated on the 3-D computer-generated phantoms and 3-D rotational digital subtraction angiography (DSA) datasets. Experimental results show that the deformed centerline provides better vessel quantification results compared with the original centerline. It is also shown that the registered model is useful for measuring the volume of aneurysmal regions. View full abstract»

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  • 1/f Noise in Diffuse Optical Imaging and Wavelet-Based Response Estimation

    Page(s): 415 - 422
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (587 KB) |  | HTML iconHTML  

    In diffuse optical imaging (DOI) data analysis, the functional response is contaminated with physiological noise as in functional magnetic resonance imaging (fMRI). In this work we extend a previously proposed method for fMRI to estimate the parameters of a linear model of DOI time series. The regression is performed in the wavelet domain to infer drift coefficients at different scales and to estimate the strength of the hemodynamic response function (HRF). This multiresolution approach benefits from the whitening property of the discrete wavelet transform (DWT), which approximately decorrelates long-memory noise processes. We also show that a more accurate estimation is obtained by removing some regressors correlating with the protocol. Moreover, we observe that this improvement is related to a quantitative measure of 1/f noise. The performances of the method are first evaluated against a standard spline-cosine drift approach with simulated HRF and real background physiology. Lastly, the technique is applied to experimental event-related data acquired by near-infrared spectroscopy (NIRS). View full abstract»

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  • Joint Estimation and Correction of Geometric Distortions for EPI Functional MRI Using Harmonic Retrieval

    Page(s): 423 - 434
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1488 KB) |  | HTML iconHTML  

    Magnetic resonance imaging (MRI) uses applied spatial variations in the magnetic field to encode spatial position. Therefore, nonuniformities in the main magnetic field can cause image distortions. In order to correct the image distortions, it is desirable to simultaneously acquire data with a field map in registration. We propose a joint estimation (JE) framework with a fast, noniterative approach using harmonic retrieval (HR) methods, combined with a multi-echo echo-planar imaging (EPI) acquisition. The connection with HR establishes an elegant framework to solve the JE problem through a sequence of 1-D HR problems in which efficient solutions are available. We also derive the condition on the smoothness of the field map in order for HR techniques to recover the image with high signal-to-noise ratio. Compared to other dynamic field mapping methods, this method is not constrained by the absolute level of the field inhomogeneity over the slice, but relies on a generous pixel-to-pixel smoothness. Moreover, this method can recover image, field map, and T2* map simultaneously. View full abstract»

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  • Fast, Accurate and Shift-Varying Line Projections for Iterative Reconstruction Using the GPU

    Page(s): 435 - 445
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1328 KB) |  | HTML iconHTML  

    List-mode processing provides an efficient way to deal with sparse projections in iterative image reconstruction for emission tomography. An issue often reported is the tremendous amount of computation required by such algorithm. Each recorded event requires several back- and forward line projections. We investigated the use of the programmable graphics processing unit (GPU) to accelerate the line-projection operations and implement fully-3D list-mode ordered-subsets expectation-maximization for positron emission tomography (PET). We designed a reconstruction approach that incorporates resolution kernels, which model the spatially-varying physical processes associated with photon emission, transport and detection. Our development is particularly suitable for applications where the projection data is sparse, such as high-resolution, dynamic, and time-of-flight PET reconstruction. The GPU approach runs more than 50 times faster than an equivalent CPU implementation while image quality and accuracy are virtually identical. This paper describes in details how the GPU can be used to accelerate the line projection operations, even when the lines-of-response have arbitrary endpoint locations and shift-varying resolution kernels are used. A quantitative evaluation is included to validate the correctness of this new approach. View full abstract»

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  • Selective Deblurring for Improved Calcification Visualization and Quantification in Carotid CT Angiography: Validation Using Micro-CT

    Page(s): 446 - 453
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1307 KB) |  | HTML iconHTML  

    Visualization and quantification of small structures with computed tomography (CT) is hampered by the limited spatial resolution of the system. Histogram-based selective deblurring (HiSD) is a deconvolution method that restores small high-density structures, i.e., calcifications, of a CT image, using the high-intensity voxel information of the deconvolved image, while preserving the original hounsfield Units (HUs) in the remaining tissues. In this study, high resolution micro-CT data are used to validate the potential of HiSD to improve calcium visualization and quantification in the carotid arteries on in vivo contrast-enhanced CTA data. The evaluation is performed qualitatively and quantitatively on 15 atherosclerotic plaques obtained from ten different patients. HiSD in combination with vessel segmentation significantly improves calcification visualization and quantification on in vivo contrast-enhanced CT images. Calcification blur is reduced, while avoiding noise amplification and edge-ringing artifacts in the surrounding tissues. Calcification quantification errors are reduced by 23.5% on average. View full abstract»

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  • Automatic Detection of Anatomical Landmarks in Uterine Cervix Images

    Page(s): 454 - 468
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1751 KB) |  | HTML iconHTML  

    The work focuses on a unique medical repository of digital cervicographic images (ldquoCervigramsrdquo) collected by the National Cancer Institute (NCI) in longitudinal multiyear studies. NCI, together with the National Library of Medicine (NLM), is developing a unique Web-accessible database of the digitized cervix images to study the evolution of lesions related to cervical cancer. Tools are needed for automated analysis of the cervigram content to support cancer research. We present a multistage scheme for segmenting and labeling regions of anatomical interest within the cervigrams. In particular, we focus on the extraction of the cervix region and fine detection of the cervix boundary; specular reflection is eliminated as an important preprocessing step; in addition, the entrance to the endocervical canal (the ldquoosrdquo), is detected. Segmentation results are evaluated on three image sets of cervigrams that were manually labeled by NCI experts. View full abstract»

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  • Postarthroplasty Examination Using X-Ray Images

    Page(s): 469 - 474
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1262 KB) |  | HTML iconHTML  

    Arthroplasty, the implantation of prostheses into joints, is a surgical procedure that is affecting a larger and larger number of patients over time. As a result, it is increasingly important to develop imaging techniques to noninvasively examine joints with prostheses after surgery, both statically and dynamically in 3-D. The static problem is considered here, with the aim to create a 3-D shape model of the bone as well as the prosthesis using a set of 2-D X-rays from various viewpoints. The most important challenge to be addressed is the lack of texture, the most common feature to recover shape from multiple views. In order to overcome this limitation, we reformulate the problem using a novel multiview segmentation approach where an active contours 3-D surface evolution with level-set implementation is used to recover the shape of bones and prostheses in postoperative joints. The recovered shape may then be used to track 3-D motions in dynamic X-ray sequences to obtain kinematic information. View full abstract»

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    Page(s): 475
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    Page(s): 476
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  • IEEE Transactions on Medical Imaging information for authors

    Page(s): C3
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    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