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Medical Imaging, IEEE Transactions on

Issue 6 • Date June 2012

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Displaying Results 1 - 18 of 18
  • Table of contents

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

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  • Fast GPU Based Adaptive Filtering of 4D Echocardiography

    Page(s): 1165 - 1172
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1161 KB) |  | HTML iconHTML  

    Time resolved three-dimensional (3D) echocardiography generates four-dimensional (3D+time) data sets that bring new possibilities in clinical practice. Image quality of four-dimensional (4D) echocardiography is however regarded as poorer compared to conventional echocardiography where time-resolved 2D imaging is used. Advanced image processing filtering methods can be used to achieve image improvements but to the cost of heavy data processing. The recent development of graphics processing unit (GPUs) enables highly parallel general purpose computations, that considerably reduces the computational time of advanced image filtering methods. In this study multidimensional adaptive filtering of 4D echocardiography was performed using GPUs. Filtering was done using multiple kernels implemented in OpenCL (open computing language) working on multiple subsets of the data. Our results show a substantial speed increase of up to 74 times, resulting in a total filtering time less than 30 s on a common desktop. This implies that advanced adaptive image processing can be accomplished in conjunction with a clinical examination. Since the presented GPU processor method scales linearly with the number of processing elements, we expect it to continue scaling with the expected future increases in number of processing elements. This should be contrasted with the increases in data set sizes in the near future following the further improvements in ultrasound probes and measuring devices. It is concluded that GPUs facilitate the use of demanding adaptive image filtering techniques that in turn enhance 4D echocardiographic data sets. The presented general methodology of implementing parallelism using GPUs is also applicable for other medical modalities that generate multidimensional data. View full abstract»

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  • Catheter Tracking With Phase Information in a Magnetic Resonance Scanner

    Page(s): 1173 - 1180
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2424 KB) |  | HTML iconHTML  

    The purpose of this study is to describe a new active technique for accurately determining both the position and orientation of the tip of a catheter during magnetic resonance (MR)-guided percutaneous cardiovascular procedures. The technique utilizes phase information introduced into the MR signal from a small receive coil located on the distal tip of the catheter. Phase patterns around a small receive coil are rich in information that is directly related to position and orientation. This information can be collected over a large spherical volume with a diameter several times that of the receive coil. The high degree of redundancy yields the potential for an accurate and robust method of catheter tracking. A tracking algorithm is presented that performs catheter tip localization using phase images acquired in two orthogonal planes without any a priori knowledge of catheter position. Associated experimentation demonstrating feasibility is also presented. View full abstract»

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  • Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI Using Conditional Random Fields

    Page(s): 1181 - 1194
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1578 KB) |  | HTML iconHTML  

    Gadolinium-enhancing lesions in brain magnetic resonance imaging of multiple sclerosis (MS) patients are of great interest since they are markers of disease activity. Identification of gadolinium-enhancing lesions is particularly challenging because the vast majority of enhancing voxels are associated with normal structures, particularly blood vessels. Furthermore, these lesions are typically small and in close proximity to vessels. In this paper, we present an automatic, probabilistic framework for segmentation of gadolinium-enhancing lesions in MS using conditional random fields. Our approach, through the integration of different components, encodes different information such as correspondence between the intensities and tissue labels, patterns in the labels, or patterns in the intensities. The proposed algorithm is evaluated on 80 multimodal clinical datasets acquired from relapsing-remitting MS patients in the context of multicenter clinical trials. The experimental results exhibit a sensitivity of 98% with a low false positive lesion count. The performance of the proposed algorithm is also compared to a logistic regression classifier, a support vector machine and a Markov random field approach. The results demonstrate superior performance of the proposed algorithm at successfully detecting all of the gadolinium-enhancing lesions while maintaining a low false positive lesion count. View full abstract»

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  • Diffeomorphic Sulcal Shape Analysis on the Cortex

    Page(s): 1195 - 1212
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3977 KB) |  | HTML iconHTML  

    We present a diffeomorphic approach for constructing intrinsic shape atlases of sulci on the human cortex. Sulci are represented as square-root velocity functions of continuous open curves in R 3, and their shapes are studied as functional representations of an infinite-dimensional sphere. This spherical manifold has some advantageous properties-it is equipped with a Riemannian L 2 metric on the tangent space and facilitates computational analyses and correspondences between sulcal shapes. Sulcal shape mapping is achieved by computing geodesics in the quotient space of shapes modulo scales, translations, rigid rotations, and reparameterizations. The resulting sulcal shape atlas preserves important local geometry inherently present in the sample population. The sulcal shape atlas is integrated in a cortical registration framework and exhibits better geometric matching compared to the conventional euclidean method. We demonstrate experimental results for sulcal shape mapping, cortical surface registration, and sulcal classification for two different surface extraction protocols for separate subject populations. View full abstract»

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  • Simultaneous Nonrigid Registration, Segmentation, and Tumor Detection in MRI Guided Cervical Cancer Radiation Therapy

    Page(s): 1213 - 1227
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3330 KB) |  | HTML iconHTML  

    External beam radiation therapy (EBRT) for the treatment of cancer enables accurate placement of radiation dose on the cancerous region. However, the deformation of soft tissue during the course of treatment, such as in cervical cancer, presents significant challenges for the delineation of the target volume and other structures of interest. Furthermore, the presence and regression of pathologies such as tumors may violate registration constraints and cause registration errors. In this paper, automatic segmentation, nonrigid registration and tumor detection in cervical magnetic resonance (MR) data are addressed simultaneously using a unified Bayesian framework. The proposed novel method can generate a tumor probability map while progressively identifying the boundary of an organ of interest based on the achieved nonrigid transformation. The method is able to handle the challenges of significant tumor regression and its effect on surrounding tissues. The new method was compared to various currently existing algorithms on a set of 36 MR data from six patients, each patient has six T2-weighted MR cervical images. The results show that the proposed approach achieves an accuracy comparable to manual segmentation and it significantly outperforms the existing registration algorithms. In addition, the tumor detection result generated by the proposed method has a high agreement with manual delineation by a qualified clinician. View full abstract»

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  • Ray Contribution Masks for Structure Adaptive Sinogram Filtering

    Page(s): 1228 - 1239
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5128 KB) |  | HTML iconHTML  

    The patient dose in computed tomography (CT) imaging is linked to measurement noise. Various noise-reduction techniques have been developed that adapt structure preserving filters like anisotropic diffusion or bilateral filters to CT noise properties. We introduce a structure adaptive sinogram (SAS) filter that incorporates the specific properties of the CT measurement process. It uses a point-based forward projector to generate a local structure representation called ray contribution mask (RCM). The similarities between neighboring RCMs are used in an enhanced variant of the bilateral filtering concept, where the photometric similarity is replaced with the structural similarity. We evaluate the performance in four different scenarios: The robustness against reconstruction artifacts is demonstrated by a scan of a high-resolution-phantom. Without changing the modulation transfer function (MTF) nor introducing artifacts, the SAS filter reduces the noise level by 13.6%. The image sharpness and noise reduction capabilities are visually assessed on in vivo patient scans and quantitatively evaluated on a simulated phantom. Unlike a standard bilateral filter, the SAS filter preserves edge information and high-frequency components of organ textures well. It shows a homogeneous noise reduction behavior throughout the whole frequency range. The last scenario uses a simulated edge phantom to estimate the filter MTF for various contrasts: the noise reduction for the simple edge phantom exceeds 80%. For low contrasts at 55 Hounsfield units (HU), the mid-frequency range is slightly attenuated, at higher contrasts of approximately 100 HU and above, the MTF is fully preserved. View full abstract»

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  • About the Geometry of Asymmetric Fiber Orientation Distributions

    Page(s): 1240 - 1249
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2142 KB) |  | HTML iconHTML  

    Fiber orientation distributions (FODs) based on diffusion-sensitized magnetic resonance imaging are usually symmetric, primarily due to the nature of the diffusion. In contrast, the underlying fiber configurations are not, as bending or fanning configurations are inherently asymmetric. We propose to dismiss the symmetry of the FOD to additionally encode the asymmetry of the underlying fiber configuration. This is of particular importance for low resolution images that are common in diffusion weighted imaging. We set up the mathematical foundations and geometric interpretations of asymmetric FODs and show how one can benefit from these considerations. We infer a continuity condition that is used as a prior during FOD estimation by constrained spherical deconvolution. This new prior shows superior performance in comparison to other spatial regularization strategies in reliability and accuracy. View full abstract»

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  • Fast \ell _1 -SPIRiT Compressed Sensing Parallel Imaging MRI: Scalable Parallel Implementation and Clinically Feasible Runtime

    Page(s): 1250 - 1262
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2690 KB) |  | HTML iconHTML  

    We present l1 -SPIRiT, a simple algorithm for auto calibrating parallel imaging (acPI) and compressed sensing (CS) that permits an efficient implementation with clinically-feasible runtimes. We propose a CS objective function that minimizes cross-channel joint sparsity in the wavelet domain. Our reconstruction minimizes this objective via iterative soft-thresholding, and integrates naturally with iterative self-consistent parallel imaging (SPIRiT). Like many iterative magnetic resonance imaging reconstructions, l1-SPIRiT's image quality comes at a high computational cost. Excessively long runtimes are a barrier to the clinical use of any reconstruction approach, and thus we discuss our approach to efficiently parallelizing l1 -SPIRiT and to achieving clinically-feasible runtimes. We present parallelizations of l1 -SPIRiT for both multi-GPU systems and multi-core CPUs, and discuss the software optimization and parallelization decisions made in our implementation. The performance of these alternatives depends on the processor architecture, the size of the image matrix, and the number of parallel imaging channels. Fundamentally, achieving fast runtime requires the correct trade-off between cache usage and parallelization overheads. We demonstrate image quality via a case from our clinical experimentation, using a custom 3DFT spoiled gradient echo (SPGR) sequence with up to 8× acceleration via Poisson-disc undersampling in the two phase-encoded directions. View full abstract»

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  • A Comprehensive Cardiac Motion Estimation Framework Using Both Untagged and 3-D Tagged MR Images Based on Nonrigid Registration

    Page(s): 1263 - 1275
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1725 KB) |  | HTML iconHTML  

    In this paper, we present a novel technique based on nonrigid image registration for myocardial motion estimation using both untagged and 3-D tagged MR images. The novel aspect of our technique is its simultaneous usage of complementary information from both untagged and 3-D tagged MR images. To estimate the motion within the myocardium, we register a sequence of tagged and untagged MR images during the cardiac cycle to a set of reference tagged and untagged MR images at end-diastole. The similarity measure is spatially weighted to maximize the utility of information from both images. In addition, the proposed approach integrates a valve plane tracker and adaptive incompressibility into the framework. We have evaluated the proposed approach on 12 subjects. Our results show a clear improvement in terms of accuracy compared to approaches that use either 3-D tagged or untagged MR image information alone. The relative error compared to manually tracked landmarks is less than 15% throughout the cardiac cycle. Finally, we demonstrate the automatic analysis of cardiac function from the myocardial deformation fields. View full abstract»

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  • Learning Semantic and Visual Similarity for Endomicroscopy Video Retrieval

    Page(s): 1276 - 1288
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2919 KB) |  | HTML iconHTML  

    Content-based image retrieval (CBIR) is a valuable computer vision technique which is increasingly being applied in the medical community for diagnosis support. However, traditional CBIR systems only deliver visual outputs, i.e., images having a similar appearance to the query, which is not directly interpretable by the physicians. Our objective is to provide a system for endomicroscopy video retrieval which delivers both visual and semantic outputs that are consistent with each other. In a previous study, we developed an adapted bag-of-visual-words method for endomicroscopy retrieval, called “Dense-Sift,” that computes a visual signature for each video. In this paper, we present a novel approach to complement visual similarity learning with semantic knowledge extraction, in the field of in vivo endomicroscopy. We first leverage a semantic ground truth based on eight binary concepts, in order to transform these visual signatures into semantic signatures that reflect how much the presence of each semantic concept is expressed by the visual words describing the videos. Using cross-validation, we demonstrate that, in terms of semantic detection, our intuitive Fisher-based method transforming visual-word histograms into semantic estimations outperforms support vector machine (SVM) methods with statistical significance. In a second step, we propose to improve retrieval relevance by learning an adjusted similarity distance from a perceived similarity ground truth. As a result, our distance learning method allows to statistically improve the correlation with the perceived similarity. We also demonstrate that, in terms of perceived similarity, the recall performance of the semantic signatures is close to that of visual signatures and significantly better than those of several state-of-the-art CBIR methods. The semantic signatures are thus able to communicate high-level medical knowledge while being consistent with the low-level visual signatures and much sh- rter than them. In our resulting retrieval system, we decide to use visual signatures for perceived similarity learning and retrieval, and semantic signatures for the output of an additional information, expressed in the endoscopist own language, which provides a relevant semantic translation of the visual retrieval outputs. View full abstract»

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  • Analysis of a 3-D System Function Measured for Magnetic Particle Imaging

    Page(s): 1289 - 1299
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2048 KB) |  | HTML iconHTML  

    Magnetic particle imaging (MPI) is a new tomographic imaging approach that can quantitatively map magnetic nanoparticle distributions in vivo. It is capable of volumetric real-time imaging at particle concentrations low enough to enable clinical applications. For image reconstruction in 3-D MPI, a system function (SF) is used, which describes the relation between the acquired MPI signal and the spatial origin of the signal. The SF depends on the instrumental configuration, the applied field sequence, and the magnetic particle characteristics. Its properties reflect the quality of the spatial encoding process. This work presents a detailed analysis of a measured SF to give experimental evidence that 3-D MPI encodes information using a set of 3-D spatial patterns or basis functions that is stored in the SF. This resembles filling 3-D k-space in magnetic resonance imaging, but is faster since all information is gathered simultaneously over a broad acquisition bandwidth. A frequency domain analysis shows that the finest structures that can be encoded with the presented SF are as small as 0.6 mm. SF simulations are performed to demonstrate that larger particle cores extend the set of basis functions towards higher resolution and that the experimentally observed spatial patterns require the existence of particles with core sizes of about 30 nm in the calibration sample. A simple formula is presented that qualitatively describes the basis functions to be expected at a certain frequency. View full abstract»

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  • Confidence Regions for Statistical Model Based Shape Prediction From Sparse Observations

    Page(s): 1300 - 1310
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (761 KB) |  | HTML iconHTML  

    Shape prediction from sparse observation is of increasing interest in minimally invasive surgery, in particular when the target is not directly visible on images. This can be caused by a limited field-of-view of the imaging device, missing contrast or an insufficient signal-to-noise ratio. In such situations, a statistical shape model can be employed to estimate the location of unseen parts of the organ of interest from the observation and identification of the visible parts. However, the quantification of the reliability of such a prediction can be crucial for patient safety. We present here a framework for the estimation of complete shapes and of the associated uncertainties. This paper formalizes and extends previous work in the area by taking into account and incorporating the major sources of uncertainties, in particular the estimation of pose together with shape parameters, as well as the identification of correspondences between the sparse observation and the model. We evaluate our methodology on a large database of 171 human femurs and synthetic experiments based on a liver model. The experiments show that informative and reliable confidence regions can be estimated by the proposed approach. View full abstract»

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  • Regression-Based Cardiac Motion Prediction From Single-Phase CTA

    Page(s): 1311 - 1325
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1339 KB) |  | HTML iconHTML  

    State of the art cardiac computed tomography (CT) enables the acquisition of imaging data of the heart over the entire cardiac cycle at concurrent high spatial and temporal resolution. However, in clinical practice, acquisition is increasingly limited to 3-D images. Estimating the shape of the cardiac structures throughout the entire cardiac cycle from a 3-D image is therefore useful in applications such as the alignment of preoperative computed tomography angiography (CTA) to intra-operative X-ray images for improved guidance in coronary interventions. We hypothesize that the motion of the heart is partially explained by its shape and therefore investigate the use of three regression methods for motion estimation from single-phase shape information. Quantitative evaluation on 150 4-D CTA images showed a small, but statistically significant, increase in the accuracy of the predicted shape sequences when using any of the regression methods, compared to shape-independent motion prediction by application of the mean motion. The best results were achieved using principal component regression resulting in point-to-point errors of 2.3±0.5 mm, compared to values of 2.7±0.6 mm for shape-independent motion estimation. Finally, we showed that this significant difference withstands small variations in important parameter settings of the landmarking procedure. View full abstract»

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  • Formulating Spatially Varying Performance in the Statistical Fusion Framework

    Page(s): 1326 - 1336
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (906 KB) |  | HTML iconHTML  

    To date, label fusion methods have primarily relied either on global [e.g., simultaneous truth and performance level estimation (STAPLE), globally weighted vote] or voxelwise (e.g., locally weighted vote) performance models. Optimality of the statistical fusion framework hinges upon the validity of the stochastic model of how a rater errs (i.e., the labeling process model). Hitherto, approaches have tended to focus on the extremes of potential models. Herein, we propose an extension to the STAPLE approach to seamlessly account for spatially varying performance by extending the performance level parameters to account for a smooth, voxelwise performance level field that is unique to each rater. This approach, Spatial STAPLE, provides significant improvements over state-of-the-art label fusion algorithms in both simulated and empirical data sets. View full abstract»

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  • IEEE Transactions on Medical Imaging information for authors

    Page(s): C3
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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