By Topic

Medical Imaging, IEEE Transactions on

Issue 9 • Date Sept. 2008

Filter Results

Displaying Results 1 - 23 of 23
  • Table of contents

    Page(s): C1
    Save to Project icon | Request Permissions | PDF file iconPDF (101 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Medical Imaging publication information

    Page(s): C2
    Save to Project icon | Request Permissions | PDF file iconPDF (42 KB)  
    Freely Available from IEEE
  • Fast Joint Reconstruction of Dynamic R_2^* and Field Maps in Functional MRI

    Page(s): 1177 - 1188
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (851 KB) |  | HTML iconHTML  

    Blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) is conventionally done by reconstructing T2 * -weighted images. However, since the images are unitless they are nonquantifiable in terms of important physiological parameters. An alternative approach is to reconstruct R2 * maps which are quantifiable and have comparable BOLD contrast as T2* -weighted images. However, conventional R2 * mapping involves long readouts and ignores relaxation during readout. Another problem with fMRI imaging is temporal drift/fluctuations in off-resonance. Conventionally, a field map is collected at the start of the fMRI study to correct for off-resonance, ignoring any temporal changes. Here, we propose a new fast regularized iterative algorithm that jointly reconstructs R2 * and field maps for all time frames in fMRI data. To accelerate the algorithm we linearize the MR signal model, enabling the use of fast regularized iterative reconstruction methods. The regularizer was designed to account for the different resolution properties of both R2 * and field maps and provide uniform spatial resolution. For fMRI data with the same temporal frame rate as data collected for T2 * -weighted imaging the resulting R2 * maps performed comparably to T2 * -weighted images in activation detection while also correcting for spatially global and local temporal changes in off-resonance. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Automatic Model-Based Segmentation of the Heart in CT Images

    Page(s): 1189 - 1201
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1092 KB) |  | HTML iconHTML  

    Automatic image processing methods are a pre-requisite to efficiently analyze the large amount of image data produced by computed tomography (CT) scanners during cardiac exams. This paper introduces a model-based approach for the fully automatic segmentation of the whole heart (four chambers, myocardium, and great vessels) from 3-D CT images. Model adaptation is done by progressively increasing the degrees-of-freedom of the allowed deformations. This improves convergence as well as segmentation accuracy. The heart is first localized in the image using a 3-D implementation of the generalized Hough transform. Pose misalignment is corrected by matching the model to the image making use of a global similarity transformation. The complex initialization of the multicompartment mesh is then addressed by assigning an affine transformation to each anatomical region of the model. Finally, a deformable adaptation is performed to accurately match the boundaries of the patient's anatomy. A mean surface-to-surface error of 0.82 mm was measured in a leave-one-out quantitative validation carried out on 28 images. Moreover, the piecewise affine transformation introduced for mesh initialization and adaptation shows better interphase and interpatient shape variability characterization than commonly used principal component analysis. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Novel Technique for Distal Locking of Intramedullary Nail Based on Two Non-constrained Fluoroscopic Images and Navigation

    Page(s): 1202 - 1212
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3629 KB) |  | HTML iconHTML  

    Distal locking is one of the most difficult steps in intramedullary nailing. Numerous methods can help the surgeon, but all are time-consuming and involve much irradiation. We have developed and tested a new method based on only two fluoroscopic shots that do not need to be taken in the axes of the holes. This avoids requiring the presence of an experienced fluoroscopy operator to accurately adjust the imaging device in front of the locking holes, and decreases the exposure to radiation of the patient and medical team. A 3-D model of the distal nail and of its locking holes was constructed from a pair of calibrated fluoroscopic views. Prior to this, the contours of the nail and locking holes projections had to be determined. A 3-D optical localizer allowed the tracking of reference frames fixed to the nail, imaging device, and drilling motor. A navigation system based on the model guided the surgeon during distal targeting. The robustness, accuracy, and duration of the technique were evaluated in laboratory. The range of acceptable orientations of the X-ray beam has also been determined. Twenty drilling tests were carried out on sawbones. The accuracy and the duration required by our system to perform the distal targeting shows potential suitability for clinical use. The drill passed through the nail locking holes for all of them. The accuracy was about 1.5 mm in translation and 1deg in rotation. The total time spent on drilling did not exceed 15 min. The system was also assessed in vivo on three patients. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Sparsity-Enforced Slice-Selective MRI RF Excitation Pulse Design

    Page(s): 1213 - 1229
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1551 KB) |  | HTML iconHTML  

    We introduce a novel algorithm for the design of fast slice-selective spatially-tailored magnetic resonance imaging (MRI) excitation pulses. This method, based on sparse approximation theory, uses a second-order cone optimization to place and modulate a small number of slice-selective sine-like radio-frequency (RF) pulse segments ("spokes") in excitation fc-space, enforcing sparsity on the number of spokes allowed while si multaneously encouraging those that remain to be placed and modulated in a way that best forms a user-defined in-plane target magnetization. Pulses are designed to mitigate B1 inhomogeneity in a water phantom at 7 T and to produce highly-structured excitations in an oil phantom on an eight-channel parallel excitation system at 3 T. In each experiment, pulses generated by the spar- sity-enfoldquoced method outperform those created via conventional Fourier-based techniques, e.g., when attempting to produce a uniform magnetization in the presence of severe B1 inhomogeneity, a 5.7-ms 15-spoke pulse generated by the sparsity-enforced method produces an excitation with 1.28 times lower root mean square error than conventionally-designed 15-spoke pulses. To achieve this same level of uniformity, the conventional methods need to use 29-spoke pulses that are 7.8 ms long. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs

    Page(s): 1230 - 1241
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2447 KB) |  | HTML iconHTML  

    In this paper, we propose an automatic method to detect microaneurysms in retina photographs. Microaneurysms are the most frequent and usually the first lesions to appear as a consequence of diabetic retinopathy. So, their detection is necessary for both screening the pathology and follow up (progression measurement). Automating this task, which is currently performed manually, would bring more objectivity and reproducibility. We propose to detect them by locally matching a lesion template in sub- bands of wavelet transformed images. To improve the method performance, we have searched for the best adapted wavelet within the lifting scheme framework. The optimization process is based on a genetic algorithm followed by Powell's direction set descent. Results are evaluated on 120 retinal images analyzed by an expert and the optimal wavelet is compared to different conventional mother wavelets. These images are of three different modalities: there are color photographs, green filtered photographs, and angiographs. Depending on the imaging modality, microaneurysms were detected with a sensitivity of respectively 89.62%, 90.24%, and 93.74% and a positive predictive value of respectively 89.50%, 89.75%, and 91.67%, which is better than previously published methods. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Noise Correction on Rician Distributed Data for Fibre Orientation Estimators

    Page(s): 1242 - 1251
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1333 KB) |  | HTML iconHTML  

    New complex tissue microstructure estimators have been presented recently in order to elucidate white matter fibre orientations. Since these algorithms are based on the diffusion- weighted signal profile, the estimations are affected by noise artefacts. The proven robustness of these methods cannot counteract distortions since the statistical Rician behavior has not been taken into account. In this study, two techniques to counteract the noise distortions are presented to improve the fibre orientation estimations. Simulations and in vivo experiments show an improvement in the angular resolution and convergence of the results. One of these strategies represents a good compromise between computational cost and result improvements. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Computer Aided Evaluation of Ankylosing Spondylitis Using High-Resolution CT

    Page(s): 1252 - 1267
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1753 KB) |  | HTML iconHTML  

    Ankylosing spondylitis is a disease characterized by abnormal bone structures (syndesmophytes) growing at intervertebral disk spaces. Because this growth is so slow as to be undetectable on plain radiographs taken over years, it is desirable to resort to computerized techniques to complement qualitative human judgment with precise quantitative measures. We developed an algorithm with minimal user intervention that provides such measures using high-resolution computed tomography (CT) images. To the best of our knowledge it is the first time that determination of the disease's status is attempted by direct measurement of the syndesmophytes. The first part of our algorithm segments the whole vertebral body using a 3-D multiscale cascade of successive level sets. The second part extracts the continuous ridgeline of the vertebral body where syndesmophytes are located. For that we designed a novel level set implementation capable of evolving on the isosurface of an object represented by a triangular mesh using curvature features. The third part of the algorithm segments the syndesmophytes from the vertebral body using local cutting planes and quantitates them. We present experimental work done with 10 patients from each of which we processed five vertebrae. The results of our algorithm were validated by comparison with a semi-quantitative evaluation made by a medical expert who visually inspected the CT scans. Correlation between the two evaluations was found to be 0.936 (p < 10-18). View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Tractography Gone Wild: Probabilistic Fibre Tracking Using the Wild Bootstrap With Diffusion Tensor MRI

    Page(s): 1268 - 1274
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (659 KB) |  | HTML iconHTML  

    Diffusion tensor magnetic resonance imaging (DT-MRI) permits the noninvasive assessment of tissue microstructure and, with fibre-tracking algorithms, allows for the 3-D trajectories of white matter fasciculi to be reconstructed noninvasively. Probabilistic algorithms allow one to assign a ldquoconfidencerdquo to a given reconstructed pathway - but often rely on a priori assumptions about sources of uncertainty in the data. Bootstrap methods have been proposed as a way of circumventing this problem, deriving the uncertainty from the data themselves - but acquisition times for data amenable to precise and robust bootstrapping are clinically prohibitive. By combining the wild bootstrap, recently introduced to the DT-MRI literature, with tractography, we show how confidence can be assigned to reconstructed trajectories using data collected in a fraction of the time required for regular bootstrapping. We compare in vivo wild bootstrap tracking results with regular tracking results and show that results are comparable. This approach therefore allows users who have collected data sets for use with deterministic tracking algorithms, rather than those specifically designed for bootstrapping, to be able to apply bootstrap analyses and retrospectively assign confidence to their reconstructed trajectories with minimum additional effort. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Mechanical Imaging of the Breast

    Page(s): 1275 - 1287
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1892 KB) |  | HTML iconHTML  

    In this paper, we analyze the physical basis for elasticity imaging of the breast by measuring breast skin stress patterns that result from a force sensor array pressed against the breast tissue. Temporal and spatial changes in the stress pattern allow detection of internal structures with different elastic properties and assessment of geometrical and mechanical parameters of these structures. The method entitled mechanical imaging is implemented in the breast mechanical imager (BMI), a compact device consisting of a hand held probe equipped with a pressure sensor array, a compact electronic unit, and a touchscreen laptop computer. Data acquired by the BMI allows calculation of size, shape, consistency/hardness, and mobility of detected lesions. The BMI prototype has been validated in laboratory experiments on tissue models and in an ongoing clinical study. The obtained results prove that the BMI has potential to become a screening and diagnostic tool that could largely supplant clinical breast examination through its higher sensitivity, quantitative record storage, ease-of-use, and inherent low cost. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Probabilistic Framework Based on Hidden Markov Model for Fiducial Identification in Image-Guided Radiation Treatments

    Page(s): 1288 - 1300
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3402 KB) |  | HTML iconHTML  

    Fiducial tracking is a common target tracking method widely used in image-guided procedures such as radiotherapy and radiosurgery. In this paper, we present a multifiducial identification method that incorporates context information in the process. We first convert the problem into a state sequence problem by establishing a probabilistic framework based on a hidden Markov model (HMM), where prior probability represents an individual candidate's resemblance to a fiducial; transition probability quantifies the similarity of a candidate set to the fiducials' geometrical configuration; and th.e Viterbi algorithm provides an efficient solution. We then discuss the problem of identifying fiducials using stereo projections, and propose a special, higher order HMM, which consists of two parallel HMMs, connected by an association measure that captures the inherent correlation between the two projections. A novel algorithm, the concurrent viterbi with association (CVA) algorithm, is introduced to efficiently identify fiducials in the two projections simultaneously. This probabilistic framework is highly flexible and provides a buffer to accommodate deformations. A simple implementation of the CVA algorithm is presented to evaluate the efficacy of the framework. Experiments were carried out using clinical images acquired during patient treatments, and several examples are presented to illustrate a variety of clinical situations. In the experiments, the algorithm demonstrated a large tracking range, computational efficiency, ease of use, and robustness that meet the requirements for clinical use. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Current Density Impedance Imaging

    Page(s): 1301 - 1309
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3159 KB) |  | HTML iconHTML  

    Current density impedance imaging (CDII) is a new impedance imaging technique that can noninvasively measure the conductivity distribution inside a medium. It utilizes current density vector measurements which can be made using a magnetic resonance imager (MRI) (Scott et al., 1991). CDII is based on a simple mathematical expression for nablasigma/sigma = nabla ln sigma, the gradient of the logarithm of the conductivity sigma, at each point in a region where two current density vectors J1 and J2 have been measured and J1 x J2 ne 0. From the calculated nabla In sigma and a priori knowledge of the conductivity at the boundary, the logarithm of the conductivity In sigma is integrated by two different methods to produce an image of the conductivity sigma in the region of interest. The CDII technique was tested on three different conductivity phantoms. Much emphasis has been placed on the experimental validation of CDII results against direct bench measurements by commercial LCR meters before and after CDII was performed. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Continuous and Discrete Data Rebinning in Time-of-Flight PET

    Page(s): 1310 - 1322
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1566 KB) |  | HTML iconHTML  

    This paper investigates data compression methods for time-of-flight (TOF) positron emission tomography (PET), which rebin the 3-D TOF measurements into a set of 2-D TOF data for a stack of transaxial slices. The goal of this work is to develop re- binning algorithms that are more accurate than the TOF single- slice-rebinning (TOF-SSRB) method proposed by Mullani in 1982. Two approaches are explored. The first one is based on a partial differential equation, which expresses a consistency condition for TOF-PET data with a Gaussian TOF profile. From this equation we derive an analytical rebinning algorithm, which is unbiased in the limit of continuous sampling. The second approach is discrete: each 2-D rebinned data sample is calculated as a linear combination of the 3-D TOF samples in the same axial plane parallel to the axis of the scanner. The coefficients of the linear combination are precomputed by optimizing a cost function which enforces both accuracy and good variance reduction, models the TOF profile, the axial PSF of the LORs, and the specific sampling scheme of the scanner. Measurements of a thorax phantom on a prototype TOF-PET scanner with a resolution of 550 ps show that the proposed discrete method improves the bias-variance trade-off and is a promising alternative to TOF-SSRB when data compression is required to achieve clinically acceptable reconstruction time. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Binary Encoding of Multiplexed Images in Mixed Noise

    Page(s): 1323 - 1332
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1192 KB) |  | HTML iconHTML  

    Binary coding of multiplexed signals and images has been studied in the context of spectroscopy with models of either purely constant or purely proportional noise, and has been shown to result in improved noise performance under certain conditions. We consider the case of mixed noise in an imaging system consisting of multiple individually-controllable sources (X-ray or near-infrared, for example) shining on a single detector. We develop a mathematical model for the noise in such a system and show that the noise is dependent on the properties of the binary coding matrix and on the average number of sources used for each code. Each binary matrix has a characteristic linear relationship between the ratio of proportional-to-constant noise and the noise level in the decoded image. We introduce a criterion for noise level, which is minimized via a genetic algorithm search. The search procedure results in the discovery of matrices that outperform the Hadamard S-matrices at certain levels of mixed noise. Simulation of a seven-source radiography system demonstrates that the noise model predicts trends and rank order of performance in regions of nonuniform images and in a simple tomosynthesis reconstruction. We conclude that the model developed provides a simple framework for analysis, discovery, and optimization of binary coding patterns used in multiplexed imaging systems. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Correction for Resolution Nonuniformities Caused by Anode Angulation in Computed Tomography

    Page(s): 1333 - 1341
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (828 KB) |  | HTML iconHTML  

    Most X-ray tubes comprise a rotating anode that is bombarded with electrons to produce X-rays. A substantial amount of heat is generated, and to increase the area of the anode exposed to the electrons, without increasing the apparent size of the focal spot, the focal track of the anode is generally beveled with a very shallow angle (typically 5deg-7deg in a computed tomography (CT) tube). Due to the line focus principle, this allows a fairly large area of the focal track to be exposed to electrons while retaining a fairly small effective projected focal spot. One side effect of anode angulation is that the focal spot appears different from different positions in the detector array; the effective focal spot size at a constant distance from the tube will be larger for a peripheral detector channel than for a central one. These differences in the effective size of the focal spot across the fleld-of-view lead to worse resolution in the periphery than in the center of reconstructed images. In this work we describe a method for achieving more uniform resolution in fanbeam CT images by correcting for these focal spot angulation effects. We do so by modeling the effects as a series of local blurrings in the space of transmitted CT intensities and determining the effective coefficients of the corresponding discrete convolutions. The effect of these blurrings can then be compensated for in the sinogram domain through the use of a penalized-likelihood sinogram restoration model we have recently developed. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Detection and Measurement of Fetal Anatomies from Ultrasound Images using a Constrained Probabilistic Boosting Tree

    Page(s): 1342 - 1355
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1998 KB) |  | HTML iconHTML  

    We propose a novel method for the automatic detection and measurement of fetal anatomical structures in ultrasound images. This problem offers a myriad of challenges, including: difficulty of modeling the appearance variations of the visual object of interest, robustness to speckle noise and signal dropout, and large search space of the detection procedure. Previous solutions typically rely on the explicit encoding of prior knowledge and formulation of the problem as a perceptual grouping task solved through clustering or variational approaches. These methods are constrained by the validity of the underlying assumptions and usually are not enough to capture the complex appearances of fetal anatomies. We propose a novel system for fast automatic detection and measurement of fetal anatomies that directly exploits a large database of expert annotated fetal anatomical structures in ultrasound images. Our method learns automatically to distinguish between the appearance of the object of interest and background by training a constrained probabilistic boosting tree classifier. This system is able to produce the automatic segmentation of several fetal anatomies using the same basic detection algorithm. We show results on fully automatic measurement of biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL), humerus length (HL), and crown rump length (CRL). Notice that our approach is the first in the literature to deal with the HL and CRL measurements. Extensive experiments (with clinical validation) show that our system is, on average, close to the accuracy of experts in terms of segmentation and obstetric measurements. Finally, this system runs under half second on a standard dual-core PC computer. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Dynamic Positron Emission Tomography Data-Driven Analysis Using Sparse Bayesian Learning

    Page(s): 1356 - 1369
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1265 KB) |  | HTML iconHTML  

    A method is presented for the analysis of dynamic positron emission tomography (PET) data using sparse Bayesian learning. Parameters are estimated in a compartmental framework using an over-complete exponential basis set and sparse Bayesian learning. The technique is applicable to analyses requiring either a plasma or reference tissue input function and produces estimates of the system's macro-parameters and model order. In addition, the Bayesian approach returns the posterior distribution which allows for some characterisation of the error component. The method is applied to the estimation of parametric images of neuroreceptor radioligand studies. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Special Issue on Imaging and the Virtual Physiological Human

    Page(s): 1370
    Save to Project icon | Request Permissions | PDF file iconPDF (204 KB)  
    Freely Available from IEEE
  • Join the IEEE Engineering in Medicine and Biology Society [advertisement]

    Page(s): 1371
    Save to Project icon | Request Permissions | PDF file iconPDF (618 KB)  
    Freely Available from IEEE
  • Leading the field since 1884 [advertisement]

    Page(s): 1372
    Save to Project icon | Request Permissions | PDF file iconPDF (223 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Medical Imaging Information for authors

    Page(s): C3
    Save to Project icon | Request Permissions | PDF file iconPDF (29 KB)  
    Freely Available from IEEE
  • Blank page [back cover]

    Page(s): C4
    Save to Project icon | Request Permissions | PDF file iconPDF (5 KB)  
    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