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Medical Imaging, IEEE Transactions on

Issue 8 • Date Aug. 2009

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Displaying Results 1 - 22 of 22
  • Table of contents

    Page(s): C1 - C4
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  • IEEE Transactions on Medical Imaging publication information

    Page(s): C2
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  • A Framework for Geometric Analysis of Vascular Structures: Application to Cerebral Aneurysms

    Page(s): 1141 - 1155
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1961 KB) |  | HTML iconHTML  

    There is well-documented evidence that vascular geometry has a major impact in blood flow dynamics and consequently in the development of vascular diseases, like atherosclerosis and cerebral aneurysmal disease. The study of vascular geometry and the identification of geometric features associated with a specific pathological condition can therefore shed light into the mechanisms involved in the pathogenesis and progression of the disease. Although the development of medical imaging technologies is providing increasing amounts of data on the three-dimensional morphology of the in vivo vasculature, robust and objective tools for quantitative analysis of vascular geometry are still lacking. In this paper, we present a framework for the geometric analysis of vascular structures, in particular for the quantification of the geometric relationships between the elements of a vascular network based on the definition of centerlines. The framework is founded upon solid computational geometry criteria, which confer robustness of the analysis with respect to the high variability of in vivo vascular geometry. The techniques presented are readily available as part of the VMTK, an open source framework for image segmentation, geometric characterization, mesh generation and computational hemodynamics specifically developed for the analysis of vascular structures. As part of the Aneurisk project, we present the application of the present framework to the characterization of the geometric relationships between cerebral aneurysms and their parent vasculature. View full abstract»

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  • Towards Modeling of Cardiac Micro-Structure With Catheter-Based Confocal Microscopy: A Novel Approach for Dye Delivery and Tissue Characterization

    Page(s): 1156 - 1164
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    This work presents a methodology for modeling of cardiac tissue micro-structure. The approach is based on catheter-based confocal imaging systems, which are emerging as tools for diagnosis in various clinical disciplines. A limitation of these systems is that a fluorescent marker must be available in sufficient concentration in the imaged region. We introduce a novel method for the local delivery of fluorescent markers to cardiac tissue based on a hydro-gel carrier brought into contact with the tissue surface. The method was tested with living rabbit cardiac tissue and applied to acquire three-dimensional image stacks with a standard inverted confocal microscope and two-dimensional images with a catheter-based confocal microscope. We processed these image stacks to obtain spatial models and quantitative data on tissue microstructure. Volumes of atrial and ventricular myocytes were 4901plusmn1713 and 10299plusmn3598 mum3 (meanplusmnsd), respectively. Atrial and ventricular myocyte volume fractions were 72.4plusmn4.7% and 79.7plusmn2.9% (mean plusmn sd), respectively. Atrial and ventricular myocyte density was 165571plusmn55836 and 86957plusmn32280 cells/mm3 (mean plusmn sd), respectively. These statistical data and spatial descriptions of tissue microstructure provide important input for modeling studies of cardiac tissue function. We propose that the described methodology can also be used to characterize diseased tissue and allows for personalized modeling of cardiac tissue. View full abstract»

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  • A Hybrid System Using Symbolic and Numeric Knowledge for the Semantic Annotation of Sulco-Gyral Anatomy in Brain MRI Images

    Page(s): 1165 - 1178
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1376 KB) |  | HTML iconHTML  

    This paper describes an interactive system for the semantic annotation of brain magnetic resonance images. The system uses both a numerical atlas and symbolic knowledge of brain anatomical structures depicted using the semantic Web standards. This knowledge is combined with graphical data, automatically extracted from the images by imaging tools. The annotations of parts of gyri and sulci, in a region of interest, rely on constraint satisfaction problem solving and description logics inferences. The system is run on a client-server architecture, using Web services and including a sophisticated visualization tool. An evaluation of the system was done using normal (healthy) and pathological cases. The results obtained so far demonstrate that the system produces annotations with high precision and quality. View full abstract»

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  • Dynamic 2D Ultrasound and 3D CT Image Registration of the Beating Heart

    Page(s): 1179 - 1189
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (583 KB) |  | HTML iconHTML  

    Two-dimensional ultrasound (US) is widely used in minimally invasive cardiac procedures due to its convenience of use and noninvasive nature. However, the low quality of US images often limits their utility as a means for guiding procedures, since it is often difficult to relate the images to their anatomical context. To improve the interpretability of the US images while maintaining US as a flexible anatomical and functional real-time imaging modality, we describe a multimodality image navigation system that integrates 2D US images with their 3D context by registering them to high quality preoperative models based on magnetic resonance imaging (MRI) or computed tomography (CT) images. The mapping from such a model to the patient is completed using spatial and temporal registrations. Spatial registration is performed by a two-step rapid registration method that first approximately aligns the two images as a starting point to an automatic registration procedure. Temporal alignment is performed with the aid of electrocardiograph (ECG) signals and a latency compensation method. Registration accuracy is measured by calculating the TRE. Results show that the error between the US and preoperative images of a beating heart phantom is 1.7plusmn0.4 mm, with a similar performance being observed in in vivo animal experiments. View full abstract»

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  • A 3-D High-Frequency Array Based 16 Channel Photoacoustic Microscopy System for In Vivo Micro-Vascular Imaging

    Page(s): 1190 - 1197
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (851 KB) |  | HTML iconHTML  

    This paper discusses the design of a novel photoacoustic microscopy imaging system with promise for studying the structure of tissue microvasculature for applications in visualizing angiogenesis. A new 16 channel analog and digital high-frequency array based photoacoustic microscopy system (PAM) was developed using an Nd: YLF pumped tunable dye laser, a 30 MHz piezo composite linear array transducer, and a custom multichannel receiver electronics system. Using offline delay and sum beam- forming and beamsteering, phantom images were obtained from a 6 mum carbon fiber in water at a depth of 8 mm. The measured -6 dB lateral and axial spatial resolution of the system was 100 plusmn 5 mum and 45 plusmn 5 mum, respectively. The dynamic focusing capability of the system was demonstrated by imaging a composite carbon fiber matrix through a 12.5 mm imaging depth. Next, 2-D in vivo images were formed of vessels around 100 mum in diameter in the human hand. Three-dimensional in vivo images were also formed of micro-vessels 3 mm below the surface of the skin in two Sprague Dawley rats. View full abstract»

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  • Estimation of Channelized Hotelling Observer Performance With Known Class Means or Known Difference of Class Means

    Page(s): 1198 - 1207
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    This paper concerns task-based image quality assessment for the task of discriminating between two classes of images. We address the problem of estimating two widely-used detection performance measures, SNR and AUC, from a finite number of images, assuming that the class discrimination is performed with a channelized Hotelling observer. In particular, we investigate the advantage that can be gained when either 1) the means of the signal-absent and signal-present classes are both known, or 2) when the difference of class means is known. For these two scenarios, we propose uniformly minimum variance unbiased estimators of SNR2, derive the corresponding sampling distributions and provide variance expressions. In addition, we demonstrate how the bias and variance for the related AUC estimators may be calculated numerically by using the sampling distributions for the SNR2 estimators. We find that for both SNR2 and AUC, the new estimators have significantly lower bias and mean-square error than the traditional estimator, which assumes that the class means, and their difference, are unknown. View full abstract»

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  • Accelerated Nonrigid Intensity-Based Image Registration Using Importance Sampling

    Page(s): 1208 - 1216
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (554 KB) |  | HTML iconHTML  

    Nonrigid image registration methods using intensity-based similarity metrics are becoming increasingly common tools to estimate many types of deformations. Nonrigid warps can be very flexible with a large number of parameters and gradient optimization schemes are widely used to estimate them. However, for large datasets, the computation of the gradient of the similarity metric with respect to these many parameters becomes very time consuming. Using a small random subset of image voxels to approximate the gradient can reduce computation time. This work focuses on the use of importance sampling to reduce the variance of this gradient approximation. The proposed importance sampling framework is based on an edge-dependent adaptive sampling distribution designed for use with intensity-based registration algorithms. We compare the performance of registration based on stochastic approximations with and without importance sampling to that using deterministic gradient descent. Empirical results, on simulated magnetic resonance brain data and real computed tomography inhale-exhale lung data from eight subjects, show that a combination of stochastic approximation methods and importance sampling accelerates the registration process while preserving accuracy. View full abstract»

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  • Quantitative Assessment of Oral Orbicular Muscle Deformation After Cleft Lip Reconstruction: An Ultrasound Elastography Study

    Page(s): 1217 - 1222
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    Reconstruction of a cleft lip leads inevitably to scar tissue formation. Scar tissue within the restored oral orbicular muscle might be assessed by quantification of the local contractility of this muscle. Furthermore, information about the contraction capability of the oral orbicular muscle is crucial for planning the revision surgery of an individual patient. We used ultrasound elastography to determine the local deformation (strain) of the upper lip and to differentiate contracting muscle from passive scar tissue. Raw ultrasound data (radio-frequency format; rf-) were acquired, while the lips were brought from normal state into a pout condition and back in normal state, in three patients and three normal individuals. During this movement, the oral orbicular muscle contracts and, consequently, thickens in contrast to scar tissue that will not contract, or even expand. An iterative coarse-to-fine strain estimation method was used to calculate the local tissue strain. Analysis of the raw ultrasound data allows estimation of tissue strain with a high precision. The minimum strain that can be assessed reproducibly is 0.1%. In normal individuals, strain of the orbicular oral muscle was in the order of 20%. Also, a uniform strain distribution in the oral orbicular muscle was found. However, in patients deviating values were found in the region of the reconstruction and the muscle tissue surrounding that. In two patients with a successful reconstruction, strain was reduced by 6% in the reconstructed region with respect to the normal parts of the muscle (from 22% to 16% and from 25% to 19%). In a patient with severe esthetical and functional disability, strain decreased from 30% in the normal region to 5% in the reconstructed region. With ultrasound elastography, the strain of the oral orbicular muscle can be quantified. In healthy subjects, the strain profiles and maximum strain values in all parts of the muscle were similar. The maximum strain of the muscle during pout- was 20% plusmn 1%. In surgically repaired cleft lips, decreased deformation was observed. View full abstract»

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  • Automatic Detection of Pulmonary Embolism in CTA Images

    Page(s): 1223 - 1230
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (332 KB) |  | HTML iconHTML  

    Pulmonary embolism (PE) is a common life-threatening disorder for which an early diagnosis is desirable. We propose a new system for the automatic detection of PE in contrast-enhanced CT images. The system consists of candidate detection, feature computation and classification. Candidate detection focusses on the inclusion of PE-even complete occlusions-and the exclusion of false detections, such as tissue and parenchymal diseases. Feature computation does not only focus on the intensity, shape and size of an embolus, but also on locations and the shape of the pulmonary vascular tree. Several classifiers have been tested and the results show that the performance is optimized by using a bagged tree classifier with two features based on the shape of a blood vessel and the distance to the vessel boundary. The system was trained on 38 CT data sets. Evaluation on 19 other data sets showed that the system generalizes well. The sensitivity of our system on the evaluation data is 63% at 4.9 false positives per data set, which allowed the radiologist to improve the number of detected PE by 22%. View full abstract»

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  • Narrowband Magnetic Particle Imaging

    Page(s): 1231 - 1237
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    The magnetic particle imaging (MPI) method directly images the magnetization of super-paramagnetic iron oxide (SPIO) nanoparticles, which are contrast agents commonly used in magnetic resonance imaging (MRI). MPI, as originally envisioned, requires a high-bandwidth receiver coil and preamplifier, which are difficult to optimally noise match. This paper introduces Narrowband MPI, which dramatically reduces bandwidth requirements and increases the signal-to-noise ratio for a fixed specific absorption rate. We employ a two-tone excitation (called intermodulation) that can be tailored for a high-Q, narrowband receiver coil. We then demonstrate a new MPI instrument capable of full 3-D tomographic imaging of SPIO particles by imaging acrylic and tissue phantoms. View full abstract»

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  • An Adaptive Mean-Shift Framework for MRI Brain Segmentation

    Page(s): 1238 - 1250
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    An automated scheme for magnetic resonance imaging (MRI) brain segmentation is proposed. An adaptive mean-shift methodology is utilized in order to classify brain voxels into one of three main tissue types: gray matter, white matter, and cerebro-spinal fluid. The MRI image space is represented by a high-dimensional feature space that includes multimodal intensity features as well as spatial features. An adaptive mean-shift algorithm clusters the joint spatial-intensity feature space, thus extracting a representative set of high-density points within the feature space, otherwise known as modes. Tissue segmentation is obtained by a follow-up phase of intensity-based mode clustering into the three tissue categories. By its nonparametric nature, adaptive mean-shift can deal successfully with nonconvex clusters and produce convergence modes that are better candidates for intensity based classification than the initial voxels. The proposed method is validated on 3-D single and multimodal datasets, for both simulated and real MRI data. It is shown to perform well in comparison to other state-of-the-art methods without the use of a preregistered statistical brain atlas. View full abstract»

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  • Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets

    Page(s): 1251 - 1265
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4199 KB) |  | HTML iconHTML  

    This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques. View full abstract»

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  • Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data

    Page(s): 1266 - 1277
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    It has been shown that employing multiple atlas images improves segmentation accuracy in atlas-based medical image segmentation. Each atlas image is registered to the target image independently and the calculated transformation is applied to the segmentation of the atlas image to obtain a segmented version of the target image. Several independent candidate segmentations result from the process, which must be somehow combined into a single final segmentation. Majority voting is the generally used rule to fuse the segmentations, but more sophisticated methods have also been proposed. In this paper, we show that the use of global weights to ponderate candidate segmentations has a major limitation. As a means to improve segmentation accuracy, we propose the generalized local weighting voting method. Namely, the fusion weights adapt voxel-by-voxel according to a local estimation of segmentation performance. Using digital phantoms and MR images of the human brain, we demonstrate that the performance of each combination technique depends on the gray level contrast characteristics of the segmented region, and that no fusion method yields better results than the others for all the regions. In particular, we show that local combination strategies outperform global methods in segmenting high-contrast structures, while global techniques are less sensitive to noise when contrast between neighboring structures is low. We conclude that, in order to achieve the highest overall segmentation accuracy, the best combination method for each particular structure must be selected. View full abstract»

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  • Distributed Local MRF Models for Tissue and Structure Brain Segmentation

    Page(s): 1278 - 1295
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4242 KB) |  | HTML iconHTML  

    Accurate tissue and structure segmentation of magnetic resonance (MR) brain scans is critical in several applications. In most approaches this task is handled through two sequential steps. We propose to carry out cooperatively both tissue and subcortical structure segmentation by distributing a set of local and cooperative Markov random field (MRF) models. Tissue segmentation is performed by partitioning the volume into subvolumes where local MRFs are estimated in cooperation with their neighbors to ensure consistency. Local estimation fits precisely to the local intensity distribution and thus handles nonuniformity of intensity without any bias field modelization. Similarly, subcortical structure segmentation is performed via local MRF models that integrate localization constraints provided by a priori fuzzy description of brain anatomy. Subcortical structure segmentation is not reduced to a subsequent processing step but joined with tissue segmentation: the two procedures cooperate to gradually and conjointly improve model accuracy. We propose a framework to implement this distributed modeling integrating cooperation, coordination, and local model checking in an efficient way. Its evaluation was performed using both phantoms and real 3 T brain scans, showing good results and in particular robustness to nonuniformity and noise with a low computational cost. This original combination of local MRF models, including anatomical knowledge, appears as a powerful and promising approach for MR brain scan segmentation. View full abstract»

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  • Unified Framework for Robust Estimation of Brain Networks From fMRI Using Temporal and Spatial Correlation Analyses

    Page(s): 1296 - 1307
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    There is a rapidly growing interest in the neuroimaging field to use functional magnetic resonance imaging (fMRI) to explore brain networks, i.e., how regions of the brain communicate with one another. This paper presents a general and novel statistical framework for robust and more complete estimation of brain functional connectivity from fMRI based on correlation analyses and hypothesis testing. In addition to the ability of examining the correlations with each individual seed as in the standard and existing methods, the proposed framework can detect functional interactions by simultaneously examining multiseed correlations via multiple correlation coefficients. Spatially structured noise in fMRI is also taken into account during the identification of functional interconnection networks through noncentral F hypothesis tests. The associated issues for the multiple testing and the effective degrees-of-freedom are considered as well. Furthermore, partial multiple correlations are introduced and formulated to measure any additional task-induced but not stimulus-locked relation over brain regions so that we can take the analysis of functional connectivity closer to the characterization of direct functional interactions of the brain. Evaluation for accuracy and advantages, and comparisons of the new approaches in the presented general framework are performed using both realistic synthetic data and in vivo fMRI data. View full abstract»

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  • An Automatic Computer-Aided Detection System for Meniscal Tears on Magnetic Resonance Images

    Page(s): 1308 - 1316
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    Knee-related injuries including meniscal tears are common in both young athletes and the aging population, and require accurate diagnosis and surgical intervention when appropriate. With proper techniques and radiologists' experienced skills, confidence in detection of meniscal tears can be quite high. This paper develops a novel computer-aided detection (CAD) diagnostic system for automatic detection of meniscal tears in the knee. Evaluation of this CAD system using an archived database of images from 40 individuals with suspected knee injuries indicates that the sensitivity and specificity of the proposed CAD system are 83.87% and 75.19%, respectively, compared to the mean sensitivity and specificity of 77.41% and 81.39%, respectively, obtained by experienced radiologists in routine diagnosis without using the CAD. The experimental results suggest that the developed CAD system has great potential and promise in automatic detection of both simple and complex meniscal tears of the knee. View full abstract»

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  • Blind Decomposition of Transmission Light Microscopic Hyperspectral Cube Using Sparse Representation

    Page(s): 1317 - 1324
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    In this paper, we address the problem of fully automated decomposition of hyperspectral images for transmission light microscopy. The hyperspectral images are decomposed into spectrally homogeneous compounds. The resulting compounds are described by their spectral characteristics and optical density. We present the multiplicative physical model of image formation in transmission light microscopy, justify reduction of a hyperspectral image decomposition problem to a blind source separation problem, and provide method for hyperspectral restoration of separated compounds. In our approach, dimensionality reduction using principal component analysis (PCA) is followed by a blind source separation (BSS) algorithm. The BSS method is based on sparsifying transformation of observed images and relative Newton optimization procedure. The presented method was verified on hyperspectral images of biological tissues. The method was compared to the existing approach based on nonnegative matrix factorization. Experiments showed that the presented method is faster and better separates the biological compounds from imaging artifacts. The results obtained in this work may be used for improving automatic microscope hardware calibration and computer-aided diagnostics. View full abstract»

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  • Young's Modulus Reconstruction for Radio-Frequency Ablation Electrode-Induced Displacement Fields: A Feasibility Study

    Page(s): 1325 - 1334
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    Radio-frequency (RF) ablation is a minimally invasive treatment for tumors in various abdominal organs. It is effective if good tumor localization and intraprocedural monitoring can be done. In this paper, we investigate the feasibility of using an ultrasound-based Young's modulus reconstruction algorithm to image an ablated region whose stiffness is elevated due to tissue coagulation. To obtain controllable tissue deformations for abdominal organs during and/or intermediately after the RF ablation, the proposed modulus imaging method is specifically designed for using tissue deformation fields induced by the RF electrode. We have developed a new scheme under which the reconstruction problem is simplified to a 2-D problem. Based on this scheme, an iterative Young's modulus reconstruction technique with edge-preserving regularization was developed to estimate the Young's modulus distribution. The method was tested in experiments using a tissue-mimicking phantom and on ex vivo bovine liver tissues. Our preliminary results suggest that high contrast modulus images can be successfully reconstructed. In both experiments, the geometries of the reconstructed modulus images of thermal ablation zones match well with the phantom design and the gross pathology image, respectively. View full abstract»

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  • Special issue on multivariate microscopy image analysis

    Page(s): 1336
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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