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Medical Imaging, IEEE Transactions on

Issue 11 • Date Nov. 2001

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Displaying Results 1 - 10 of 10
  • Organ motion detection in CT images using opposite rays in fan-beam projection systems

    Page(s): 1109 - 1122
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (252 KB)  

    Motion artifacts have been identified as a problem in medical tomography systems. While computed tomography (CT) imaging has been getting faster, there remains a need to detect and compensate for motions in clinical follow-up of neurological patients (multiple sclerosis, tumors, stroke, etc.), in cardiac imaging, and in any area in which failing to detect a motion artifact may lead to misdiagnosis. The authors have developed a novel algorithm to detect motion in brain images. The algorithm deals with detecting and isolating motion in the object domain using only the information available in the sinogram domain. The new "opposite ray algorithm" (ORA) addresses the issue of motion in the interior elements of the object. The ORA combines information from projections that are opposite in space and separated in time to isolate and identify the motion. A sinogram of motion is created, integrated and reconstructed to isolate the moving component. The algorithm can be used with conventional clinical scanners employing quarter-detector offset. The significant effect of quarter-detector offset on the ORA is investigated. The effects that a finite beamwidth and noise have on the ORA are also investigated. Both the similarity index and a correlation coefficient are used to evaluate the algorithm. The algorithm is successful when applied to cases exhibiting translational and translational-rotational motion. A similarity index of 0.88 is obtained in a typical case with both translational and rotational motion. Further development is recommended in the deformation case. View full abstract»

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  • Enhancement of contrast echocardiography by image variability analysis

    Page(s): 1123 - 1130
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (193 KB) |  | HTML iconHTML  

    Although there have been recent advances in echocardiography, many studies remain suboptimal due to poor image quality and unclear blood-myocardium border. The authors developed a novel image processing technique, cardiac variability imaging (CVI), based on the variance of pixel intensity values during passage of ultrasound microbubble contrast into the left ventricle chamber, with the aim of enhancing endocardial border delineation and image quality. CVI analysis was performed on simulated data to test and verify the mechanism of image enhancement. Then CVI analysis was applied to echocardiographic images obtained in two different clinical studies, and still images were interpreted by expert reviewers. In the first study (N=15), using contrast agent EchoGen, the number of observable wall segments in end-diastolic images, for example, was significantly increased by CVI (4.93) as compared to precontrast (3.28) and contrast images (3.36), P<0.001 for both comparisons to CVI. In the second study (N=8), using contrast agent Optison, interobserver variability of manually traced end-diastolic volumes was significantly decreased using CVI (22.3 ml) as compared to precontrast (63.4) and contrast images (49.0), P<0.01 for both comparisons to CVI. In conclusion, CVI can substantially enhance endocardial border delineation and improve echocardiographic image quality and image interpretation. View full abstract»

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  • Spatial transformations of diffusion tensor magnetic resonance images

    Page(s): 1131 - 1139
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (324 KB) |  | HTML iconHTML  

    The authors address the problem of applying spatial transformations (or "image warps") to diffusion tensor magnetic resonance images. The orientational information that these images contain must be handled appropriately when they are transformed spatially during image registration. The authors present solutions for global transformations of three-dimensional images up to 12-parameter affine complexity and indicate how their methods can be extended for higher order transformations. Several approaches are presented and tested using synthetic data. One method, the preservation of principal direction algorithm, which takes into account shearing, stretching and rigid rotation, is shown to be the most effective. Additional registration experiments are performed on human brain data obtained from a single subject, whose head was imaged in three different orientations within the scanner. All of the authors' methods improve the consistency between registered and target images over naive warping algorithms. View full abstract»

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  • Scale-based diffusive image filtering preserving boundary sharpness and fine structures

    Page(s): 1140 - 1155
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (386 KB)  

    Image acquisition techniques often suffer from low signal-to-noise ratio (SNR) and/or contrast-to-noise ratio (CNR). Although many acquisition techniques are available to minimize these, post acquisition filtering is a major off-line image processing technique commonly used to improve the SNR and CNR. A major drawback of filtering is that it often diffuses/blurs important structures along with noise. Here, the authors introduce two scale-based filtering methods that use local structure size or "object scale" information to arrest smoothing around fine structures and across even low-gradient boundaries. The first of these methods uses a weighted average over a scale-dependent neighborhood while the other employs scale-dependent diffusion conductance to perform filtering. Both methods adaptively modify the degree of filtering at any image location depending on local object scale. Object scale allows the authors to accurately use a restricted homogeneity parameter for filtering in regions with fine details and in the vicinity of boundaries while a generous parameter in the interiors of homogeneous regions. Qualitative experiments based on both phantoms and patient magnetic resonance images show significant improvements using the scale-based methods over the extant anisotropic diffusive filtering method in preserving fine details and sharpness of object boundaries. Quantitative analyses utilizing 25 phantom images generated under a range of conditions of blurring, noise, and background variation confirm the superiority of the new scale-based approaches. View full abstract»

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  • Chemical-shift imaging utilizing the positional shifts along the readout gradient direction

    Page(s): 1156 - 1166
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (290 KB)  

    Describes a method that uses the linear phase acquired during the readout period due to chemical shift to generate individual magnetic resonance (MR) images of chemically shifted species. The method utilizes sets of Fourier (or κ-space) data acquired with different directions of the readout gradient and a postprocessing algorithm to generate chemical shift images. The methodology is developed for both Cartesian data acquisition and for radial data acquisition. The method is presented here for two chemically shifted species but it can be extended to more species. Here, the authors present the theory, show the results in phantoms and in human images, and discuss the artifacts and signal-to-noise ratio of the images obtained with the technique. View full abstract»

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  • Automated graph-based analysis and correction of cortical volume topology

    Page(s): 1167 - 1177
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (200 KB)  

    The human cerebral cortex is topologically equivalent to a sheet and can be considered topologically spherical if it is closed at the brainstem. Low-level segmentation of magnetic resonance (MR) imagery typically produces cerebral volumes whose tessellations are not topologically spherical. The authors present a novel algorithm that analyzes and constrains the topology of a volumetric object. Graphs are formed that represent the connectivity of voxel segments in the foreground and background of the image. These graphs are analyzed and minimal corrections to the volume are made prior to tessellation. The authors apply the algorithm to a simple test object and to cerebral white matter masks generated by a low-level tissue identification sequence. The authors tessellate the resulting objects using the marching cubes algorithm and verify their topology by computing their Euler characteristics. A key benefit of the algorithm is that it localizes the change to a volume to the specific areas of its topological defects. View full abstract»

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  • Extension of finite-support extrapolation using the generalized series model for MR spectroscopic imaging

    Page(s): 1178 - 1183
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (175 KB) |  | HTML iconHTML  

    In magnetic resonance (MR) imaging, limited data sampling in κ-space leads to the well-known Fourier truncation artifact, which includes ringing and blurring. This problem is particularly severe for MR spectroscopic imaging, where only 16-24 points are typically acquired along each spatial dimension. Several methods have been proposed to overcome this problem by incorporating prior information in the image reconstruction. These include the generalized series (GS) model and the finite-support extrapolation method. This paper shows the connection between finite-support extrapolation and the GS model. In particular, finite-support extrapolation is a limiting case of the GS model, when the only available prior information is the support region. The support region refers to those image portions with nonzero intensities, and it can be estimated in practice as the nonbackground region of an image. By itself, the support region constitutes a rather weak constraint that may not lead to considerable resolution gain. This situation can be improved by using additional prior information, which can be incorporated systematically with the GS model. Examples of such additional prior information include intensity estimates of anatomical structures inside the support region. View full abstract»

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  • A signal/noise analysis of quasi-static MR elastography

    Page(s): 1183 - 1187
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (118 KB) |  | HTML iconHTML  

    In quasi-static magnetic resonance elastography, strain images of a tissue or material undergoing deformation are produced. In this paper, the signal/noise (S/N) ratio [SNR] of elastographic strain images, as measured by a phase-contrast technique, is analyzed. Experiments are conducted to illustrate how diffusion-mediated signal attenuation limits maximum strain SNR in small displacement cases, while the imaging point-spread function limits large displacement cases. A simple theoretical treatment agrees well with experiments and shows how an optimal displacement encoding moment can be predicted for a given experimental set of parameters to achieve a maximum strain SNR. A further experiment demonstrates how the limitation on strain SNR posed by the imaging point-spread function may potentially be overcome. View full abstract»

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  • Image reconstruction using the wavelet transform for positron emission tomography

    Page(s): 1188 - 1193
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (202 KB) |  | HTML iconHTML  

    The authors conducted positron emission tomography (PET) image reconstruction experiments using the wavelet transform. The Wavelet-Vaguelette decomposition was used as a framework from which expressions for the necessary wavelet coefficients might be derived, and then the wavelet shrinkage was applied to the wavelet coefficients for the reconstruction (WVS). The performances of WVS were evaluated and compared with those of the filtered back-projection (FBP) using software phantoms, physical phantoms, and human PET studies. The results demonstrated that WVS gave stable reconstruction over the range of shrinkage parameters and provided better noise and spatial resolution characteristics than FBP. View full abstract»

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  • Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching

    Page(s): 1193 - 1200
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (221 KB) |  | HTML iconHTML  

    Reports on the design and test of an image processing algorithm for the localization of the optic disk (OD) in low-resolution (about 20 μ/pixel) color fundus images. The design relies on the combination of two procedures: 1) a Hausdorff-based template matching technique on edge map, guided by 2) a pyramidal decomposition for large scale object tracking. The two approaches are tested against a database of 40 images of various visual quality and retinal pigmentation, as well as of normal and small pupils. An average error of 7% on OD center positioning is reached with no false detection. In addition, a confidence level is associated to the final detection that indicates the "level of difficulty" the detector has to identify the OD position and shape. View full abstract»

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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