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Medical Imaging, IEEE Transactions on

Issue 9 • Date Sept. 2004

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  • Table of contents

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  • IEEE Transactions on Medical Imaging publication information

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  • Accurate estimation of the fisher information matrix for the PET image reconstruction problem

    Page(s): 1057 - 1064
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (329 KB) |  | HTML iconHTML  

    The Fisher information matrix (FIM) plays a key role in the analysis and applications of statistical image reconstruction methods based on Poisson data models. The elements of the FIM are a function of the reciprocal of the mean values of sinogram elements. Conventional plug-in FIM estimation methods do not work well at low counts, where the FIM estimate is highly sensitive to the reciprocal mean estimates at individual detector pairs. A generalized error look-up table (GELT) method is developed to estimate the reciprocal of the mean of the sinogram data. This approach is also extended to randoms precorrected data. Based on these techniques, an accurate FIM estimate is obtained for both Poisson and randoms precorrected data. As an application, the new GELT method is used to improve resolution uniformity and achieve near-uniform image resolution in low count situations. View full abstract»

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  • Spatial transformation of motion and deformation fields using nonrigid registration

    Page(s): 1065 - 1076
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2701 KB) |  | HTML iconHTML  

    In this paper, we present a technique that can be used to transform the motion or deformation fields defined in the coordinate system of one subject into the coordinate system of another subject. Such a transformation accounts for the differences in the coordinate systems of the two subjects due to misalignment and size/shape variation, enabling the motion or deformation of each of the subjects to be directly quantitatively and qualitatively compared. The field transformation is performed by using a nonrigid registration algorithm to determine the intersubject coordinate system mapping from the first subject to the second subject. This fixes the relationship between the coordinate systems of the two subjects, and allows us to recover the deformation/motion vectors of the second subject for each corresponding point in the first subject. Since these vectors are still aligned with the coordinate system of the second subject, the inverse of the intersubject coordinate mapping is required to transform these vectors into the coordinate system of the first subject, and we approximate this inverse using a numerical line integral method. The accuracy of our numerical inversion technique is demonstrated using a synthetic example, after which we present applications of our method to sequences of cardiac and brain images. View full abstract»

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  • Linear structures in mammographic images: detection and classification

    Page(s): 1077 - 1086
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (929 KB) |  | HTML iconHTML  

    We describe methods for detecting linear structures in mammograms, and for classifying them into anatomical types (vessels, spicules, ducts, etc). Several different detection methods are compared, using realistic synthetic images and receiver operating characteristic (ROC) analysis. There are significant differences (p<0.001) between the methods, with the best giving an Az value for pixel-level detection of 0.943. We also investigate methods for classifying the detected linear structures into anatomical types, using their cross-sectional profiles, with particular emphasis on recognising the "spicules" and "ducts" associated with some of the more subtle abnormalities. Automatic classification results are compared with expert annotations using ROC analysis, demonstrating useful discrimination between anatomical classes (Az=0.746). Some of this discrimination relies on simple attributes such as profile width and contrast, but important information is also carried by the shape of the profile (Az=0.653). The methods presented have potentially wide application in improving the specificity of abnormality detection by exploiting additional anatomical information. View full abstract»

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  • Imaging mass lesions by vibro-acoustography: modeling and experiments

    Page(s): 1087 - 1093
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1713 KB) |  | HTML iconHTML  

    Vibro-acoustography is a recently developed imaging method based on the dynamic response of to low-frequency vibration produced by of ultrasound radiation force. The main differentiating feature of this method is that the image includes information about the dynamic properties of the object at the frequency of the vibration, which is normally much lower than the ultrasound frequency. Such information is not available from conventional ultrasound imaging. The purpose of this study is to evaluate the performance of vibro-acoustography in imaging mass lesions in soft tissue. Such lesions normally have elastic properties that are different from the surrounding tissue. Here, we first present a brief formulation of image formation in vibro-acoustography. Then we study vibro-acoustography of solid masses through computer simulation and in vitro experiments. Experiments are conducted on excised fixed liver tissues. Resulting images show lesions with enhanced boundary and often with distinctive textures relative to their background. The results suggest that vibro-acoustography maybe a clinically useful imaging modality for detection of mass lesions. View full abstract»

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  • Propagation of errors from the sensitivity image in list mode reconstruction

    Page(s): 1094 - 1099
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (476 KB) |  | HTML iconHTML  

    List mode image reconstruction is attracting renewed attention. It eliminates the storage of empty sinogram bins. However, a single back projection of all LORs is still necessary for the pre-calculation of a sensitivity image. Since the detection sensitivity is dependent on the object attenuation and detector efficiency, it must be computed for each study. Exact computation of the sensitivity image can be a daunting task for modern scanners with huge numbers of LORs. Thus, some fast approximate calculation may be desirable. In this paper, we analyze the error propagation from the sensitivity image into the reconstructed image. The theoretical analysis is based on the fixed point condition of the list mode reconstruction. The nonnegativity constraint is modeled using the Kuhn-Tucker condition. With certain assumptions and the first-order Taylor series approximation, we derive a closed form expression for the error in the reconstructed image as a function of the error in the sensitivity image. The result shows that the error response is frequency-dependent and provides a simple expression for determining the required accuracy of the sensitivity image calculation. Computer simulations show that the theoretical results are in good agreement with the measured results. View full abstract»

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  • Interactive volume segmentation with differential image foresting transforms

    Page(s): 1100 - 1108
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1160 KB) |  | HTML iconHTML  

    The absence of object information very often asks for considerable human assistance in medical image segmentation. Many interactive two-dimensional and three-dimensional (3-D) segmentation methods have been proposed, but their response time to user's actions should be considerably reduced to make them viable from the practical point of view. We circumvent this problem in the framework of the image foresting transform (IFT)-a general tool for the design of image operators based on connectivity-by introducing a new algorithm (DIFT) to compute sequences of IFTs in a differential way. We instantiate the DIFT algorithm for watershed-based and fuzzy-connected segmentations under two paradigms (single-object and multiple-object) and evaluate the efficiency gains of both approaches with respect to their linear-time implementation based on the nondifferential IFT. We show that the DIFT algorithm provides efficiency gains from 10 to 17, reducing the user's waiting time for segmentation with 3-D visualization on a common PC from 19-36 s to 2-3 s. We also show that the multiple-object approach is more efficient than the single-object paradigm for both segmentation methods. View full abstract»

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  • General reconstruction theory for multislice X-ray computed tomography with a gantry tilt

    Page(s): 1109 - 1116
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (667 KB) |  | HTML iconHTML  

    This paper discusses image reconstruction with a tilted gantry in multislice computed tomography (CT) with helical (spiral) data acquisition. The reconstruction problem with gantry tilt is shown to be transformable into the problem of reconstructing a virtual object from multislice CT data with no gantry tilt, for which various algorithms exist in the literature. The virtual object is related to the real object by a simple affine transformation that transforms the tilted helical trajectory of the X-ray source into a nontilted helix, and the real object can be computed from the virtual object using one-dimensional interpolation. However, the interpolation may be skipped since the reconstruction of the virtual object on a Cartesian grid provides directly nondistorted images of the real object on slices parallel to the tilted plane of the gantry. The theory is first presented without any specification of the detector geometry, then applied to the curved detector geometry of third-generation CT scanners with the use of Katsevich's formula for example. Results from computer-simulated data of the FORBILD thorax phantom are given in support of the theory. View full abstract»

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  • Modality independent elastography (MIE): a new approach to elasticity imaging

    Page(s): 1117 - 1128
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1686 KB) |  | HTML iconHTML  

    The correlation between tissue stiffness and health is an accepted form of organ disease assessment. As a result, there has been a significant amount of interest in developing methods to image elasticity parameters (i.e., elastography). The modality independent elastography (MIE) method combines a nonlinear optimization framework, computer models of soft-tissue deformation, and standard measures of image similarity to reconstruct elastic property distributions within soft tissue. In this paper, simulation results demonstrate successful elasticity image reconstructions in breast cross-sectional images acquired from magnetic resonance (MR) imaging. Results from phantom experiments illustrate its modality independence by reconstructing elasticity images of the same phantom in both MR and computed tomographic imaging units. Additional results regarding the performance of a new multigrid strategy to MIE and the implementation of a parallel architecture are also presented. View full abstract»

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  • Automatic pectoral muscle segmentation on mediolateral oblique view mammograms

    Page(s): 1129 - 1140
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2092 KB) |  | HTML iconHTML  

    Mammograms are X-ray images of the breast which are used to detect breast cancer. When mammograms are analyzed by computer, the pectoral muscle should preferably be excluded from processing intended for the breast tissue. For this and other reasons, it is important to identify and segment out the pectoral muscle. In this paper, a new, adaptive algorithm is proposed to automatically extract the pectoral muscle on digitized mammograms; it uses knowledge about the position and shape of the pectoral muscle on mediolateral oblique views. The pectoral edge is first estimated by a straight line which is validated for correctness of location and orientation. This estimate is then refined using iterative "cliff detection" to delineate the pectoral margin more accurately. Finally, an enclosed region, representing the pectoral muscle, is generated as a segmentation mask. The algorithm was found to be robust to the large variations in appearance of pectoral edges, to dense overlapping glandular tissue, and to artifacts like sticky tape. The algorithm has been applied to the entire Mammographic Image Analysis Society (MIAS) database of 322 images. The segmentation results were evaluated by two expert mammographic radiologists, who rated 83.9% of the curve segmentations to be adequate or better. View full abstract»

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  • Registration of real-time 3-D ultrasound images of the heart for novel 3-D stress echocardiography

    Page(s): 1141 - 1149
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1830 KB) |  | HTML iconHTML  

    Stress echocardiography is a routinely used clinical procedure to diagnose cardiac dysfunction by comparing wall motion information in prestress and poststress ultrasound images. Incomplete data, complicated imaging protocols and misaligned prestress and poststress views, however, are known limitations of conventional stress echocardiography. We discuss how the first two limitations are overcome via the use of real-time three-dimensional (3-D) ultrasound imaging, an emerging modality, and have called the new procedure "3-D stress echocardiography:" We also show that the problem of misaligned views can be solved by registration of prestress and poststress 3-D image sequences. Such images are misaligned because of variations in placing the ultrasound transducer and stress-induced anatomical changes. We have developed a technique to temporally align 3-D images of the two sequences first and then to spatially register them to rectify probe placement error while preserving the stress-induced changes. The 3-D spatial registration is mutual information-based. Image registration used in conjunction with 3-D stress echocardiography can potentially improve the diagnostic accuracy of stress testing. View full abstract»

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  • Artifact analysis and reconstruction improvement in helical cardiac cone beam CT

    Page(s): 1150 - 1164
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2037 KB) |  | HTML iconHTML  

    With the introduction of cone beam (CB) scanners, cardiac volumetric computed tomography (CT) imaging has the potential to become a noninvasive imaging tool in clinical routine for the diagnosis of various heart diseases. Heart rate adaptive reconstruction schemes enable the reconstruction of high-resolution volumetric data sets of the heart. Artifacts, caused by strong heart rate variations, high heart rates and obesity, decrease the image quality and the diagnostic value of the images. The image quality suffers from streak artifacts if suboptimal scan and reconstruction parameters are chosen, demanding improved gating techniques. In this paper, an artifact analysis is carried out which addresses the artifacts due to the gating when using a three-dimensional CB cardiac reconstruction technique. An automatic and patient specific cardiac weighting technique is presented in order to improve the image quality. Based on the properties of the reconstruction algorithm, several assessment techniques are introduced which enable the quantitative determination of the cycle-to-cycle transition smoothness and phase homogeneity of the image reconstruction. Projection data of four patients were acquired using a 16-slice CBCT system in low pitch helical mode with parallel electrocardiogram recording. For each patient, image results are presented and discussed in combination with the assessment criteria. View full abstract»

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  • Regularized image reconstruction algorithms for positron emission tomography

    Page(s): 1165 - 1175
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (960 KB) |  | HTML iconHTML  

    We develop algorithms for obtaining regularized estimates of emission means in positron emission tomography. The first algorithm iteratively minimizes a penalized maximum-likelihood (PML) objective function. It is based on standard de-coupled surrogate functions for the ML objective function and de-coupled surrogate functions for a certain class of penalty functions. As desired, the PML algorithm guarantees nonnegative estimates and monotonically decreases the PML objective function with increasing iterations. The second algorithm is based on an iteration dependent, de-coupled penalty function that introduces smoothing while preserving edges. For the purpose of making comparisons, the MLEM algorithm and a penalized weighted least-squares algorithm were implemented. In experiments using synthetic data and real phantom data, it was found that, for a fixed level of background noise, the contrast in the images produced by the proposed algorithms was the most accurate. View full abstract»

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  • An accurate method for correction of head movement in PET

    Page(s): 1176 - 1185
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1308 KB) |  | HTML iconHTML  

    A method is presented to correct positron emission tomography (PET) data for head motion during data acquisition. The method is based on simultaneous acquisition of PET data in list mode and monitoring of the patient's head movements with a motion tracking system. According to the measured head motion, the line of response (LOR) of each single detected PET event is spatially transformed, resulting in a spatially fully corrected data set. The basic algorithm for spatial transformation of LORs is based on a number of assumptions which can lead to spatial artifacts and quantitative inaccuracies in the resulting images. These deficiencies are discussed, demonstrated and methods for improvement are presented. Using different kinds of phantoms the validity and accuracy of the correction method is tested and its applicability to human studies is demonstrated as well. View full abstract»

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  • Special issue on ion channels bio nanotubes

    Page(s): 1186
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  • 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

    Page(s): 1187
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  • Special issue on molecular and cellular bioimaging

    Page(s): 1188
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  • IEEE Transactions on Medical Imaging Information for authors

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  • Blank page [back cover]

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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