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Medical Imaging, IEEE Transactions on

Issue 3 • Date June 1998

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Displaying Results 1 - 18 of 18
  • Application of reconstruction-based scatter compensation to thallium-201 SPECT: implementations for reduced reconstructed image noise

    Page(s): 325 - 333
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    Scatter compensation in Tl-201 single photon emission computed tomography (SPECT) presents an interesting challenge because of the multiple emission energies and relatively large proportion of scattered photons. In this paper, the authors present a simulation study investigating reconstructed image noise levels arising from various implementations of iterative reconstruction-based scatter compensation (RBSC) in Tl-201 SPECT. A two-stage analysis was used to study single and multiple energy window implementations of reconstruction-based scatter compensation, and RBSC was compared to the upper limits on performance for other approaches to handling scatter. In the first stage, singular value decomposition of the system transfer matrix was used to analyze noise levels in a manner independent of the choice of reconstruction algorithm, providing results valid across a wide range of regularizations. In the second stage, the data were reconstructed using maximum-likelihood expectation-maximization, and the noise properties of the resultant images were analyzed. The best RBSC performance was obtained using multiple energy windows, one for each emission photopeak, and RBSC outperformed the upper limit on subtraction-based compensation methods. Implementing RBSC with the correct choice of energy window acquisition scheme is promising method for performing scatter compensation for Tl-201 SPECT. View full abstract»

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  • Optimized acquisition time and image sampling for dynamic SPECT of Tl-201

    Page(s): 334 - 343
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (266 KB)  

    With the recent development in scatter and attenuation correction algorithms, dynamic single photon emission computerized tomography (SPECT) can potentially yield physiological parameters, with tracers exhibiting suitable kinetics such as thallium-201 (Tl-201). A systematic way is proposed to investigate the minimum data acquisition times and sampling requirements for estimating physiological parameters with quantitative dynamic SPECT. Two different sampling schemes were investigated with Monte Carlo simulations: (1) Continuous data collection for total study duration ranging from 30-240 min. (2) Continuous data collection for first 10-45 min followed by a delayed study at approximately 3 h. Tissue time activity curves with realistic noise were generated from a mean plasma time activity curve and rate constants (K 1-k 4) derived from Tl-201 kinetic studies in 16 dogs. Full dynamic sampling schedules (DynSS) were compared to optimum sampling schedules (OSS). The authors found that OSS can reliably estimate the blood flow related K 1 and V d comparable to DynSS. A 30-min continuous collection was sufficient if only K 1 was of interest. A split session schedule of a 30-min dynamic followed by a static study at 3 h allowed reliable estimation of both K 1 and V d avoiding the need for a prolonged (>60-min) continuous dynamic acquisition. The methodology developed should also be applicable to optimizing sampling schedules for other SPECT tracers. View full abstract»

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  • Coupled B-snake grids and constrained thin-plate splines for analysis of 2-D tissue deformations from tagged MRI

    Page(s): 344 - 356
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (470 KB)  

    Magnetic resonance imaging (MRI) is unique in its ability to noninvasively and selectively alter tissue magnetization and create tagged patterns within a deforming body such as the heart muscle. The resulting patterns define a time-varying curvilinear coordinate system on the tissue, which the authors track with coupled B-snake grids. B-spline bases provide local control of shape, compact representation, and parametric continuity. Efficient spline warps are proposed which warp an area in the plane such that two embedded snake grids obtained from two tagged frames are brought into registration, interpolating a dense displacement vector field. The reconstructed vector field adheres to the known displacement information at the intersections, forces corresponding snakes to be warped into one another, and for all other points in the plane, where no information is available, a C 1 continuous vector field is interpolated. The implementation proposed in this paper improves on the authors' previous variational-based implementation and generalizes warp methods to include biologically relevant contiguous open curves, in addition to standard landmark points. The methods are validated with a cardiac motion simulator, in addition to in-vivo tagging data sets. View full abstract»

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  • Maximum-likelihood estimation of Rician distribution parameters

    Page(s): 357 - 361
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    The problem of parameter estimation from Rician distributed data (e.g., magnitude magnetic resonance images) is addressed. The properties of conventional estimation methods are discussed and compared to maximum-likelihood (ML) estimation which is known to yield optimal results asymptotically. In contrast to previously proposed methods, ML estimation is demonstrated to be unbiased for high signal-to-noise ratio (SNR) and to yield physical relevant results for low SNR. View full abstract»

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  • Algebraic reconstruction for magnetic resonance imaging under B/sub 0/ inhomogeneity

    Page(s): 362 - 370
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    In magnetic resonance imaging, spatial localization is usually achieved using Fourier encoding which is realized by applying a magnetic field gradient along the dimension of interest to create a linear correspondence between the resonance frequency and spatial location following the Larmor equation. In the presence of B 0 inhomogeneities along this dimension, the linear mapping does not hold and spatial distortions arise in the acquired images. In this paper, the problem of image reconstruction under an inhomogeneous field is formulated as an inverse problem of a linear Fredholm equation of the first kind. The operators in these problems are estimated using field mapping and the k-space trajectory of the imaging sequence. Since such inverse problems are known to be ill-posed in general, robust solvers, singular value decomposition and conjugate gradient method, are employed to obtain corrected images that are optimal in the Frobenius norm sense. Based on this formulation, the choice of the imaging sequence for well-conditioned matrix operators is discussed, and it is shown that nonlinear k-space trajectories provide better results. The reconstruction technique is applied to sequences where the distortion is more severe along one of the image dimensions and the two-dimensional reconstruction problem becomes equivalent to a set of independent one-dimensional problems. Experimental results demonstrate the performance and stability of the algebraic reconstruction methods. View full abstract»

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  • Application of the extremum stack to neurological MRI

    Page(s): 371 - 382
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    The extremum stack, as proposed by Koenderink (1984), is a multiresolution image description and segmentation scheme which examines intensity extrema (minima and maxima) as they move and merge through a series of progressively isotropically diffused images known as scale space. Such a data-driven approach is attractive because it is claimed to he a generally applicable and natural method of image segmentation. The performance of the extremum stack is evaluated here using the case of neurological magnetic resonance imaging data as a specific example, and means of improving its performance proposed. It is confirmed experimentally that the extremum stack has the desirable property of being shift-, scale-, and rotation-invariant, and produces natural results for many compact regions of anatomy. It handles elongated objects poorly, however, and subsections of regions may merge prematurely before each region is represented as a single node. It is shown that this premature merging can often be avoided by the application of either a variable conductance-diffusing preprocessing step, or more effectively, the use of an adaptive variable conductance diffusion method within the extremum stack itself in place of the isotropic Gaussian diffusion proposed by Koenderink. View full abstract»

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  • Reversible decorrelation method for progressive transmission of 3-D medical image

    Page(s): 383 - 394
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    In this paper, the authors present a new reversible decorrelation method of three-dimensional (3-D) medical images for progressive transmission. Progressive transmission of an image permits gradual improvement of image quality while being displayed. When the amount of image data is very large, as a 3-D medical image, the progressive transmission plays an important role in viewing or browsing the image. The data structure presented in this paper takes account of interframe correlation as well as intraframe correlation of the 3-D image. This type of data structure has been termed the 3-D hierarchy embedded differential image (3-D-HEDI) as was derived from the earlier HEDI structure (Kim et al., 1995). Experiments were conducted to verify the performance of 3-D HEDI in terms of the decorrelation efficiency as well as the progressive transmission efficiency. It is compared with those of conventional hierarchy interpolation (HINT), two-dimensional (2-D) HEDI and differential pulse code modulation (DPCM). Experimental results indicate that 3-D HEDI outperforms HINT, 2-D HEDI and DPCM in both decorrelation efficiency as well as the progressive transmission efficiency on 3-D medical images. View full abstract»

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  • Quasi-bandlimited properties of Radon transforms and their implications for increasing angular sampling densities

    Page(s): 395 - 406
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    The n-dimensional (n-D) radon transform, which forms the mathematical basis for a broad variety of tomographic imaging applications, can be viewed as an n-D function in n-D sinogram space. Accurate reconstruction of continuous or discrete tomographic images requires full knowledge of the Radon transform in the corresponding n-D sinogram space. In practice, however, one can have only a finite set of discrete samples of the Radon transform in the sinogram space. One often derives the desired full knowledge of the Radon transform from its discrete samples by invoking various interpolation algorithms. According to the Wittaker-Shannon sampling theorem, a necessary condition for a full and unique recovery of the Radon transform from its discrete samples is that the Radon transform itself be bandlimited. Therefore, it is necessary to analyze the bandlimited properties of the Radon transform. In this work, the authors analyze explicitly the bandlimited properties of the Radon transform and show that the Radon transform is mathematically quasi-bandlimited [or essentially bandlimited] in two quantitative senses and can essentially be treated as bandlimited in practice. The quasi-bandlimited properties can be used for increasing the angular sampling density of the Radon transform. View full abstract»

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  • Anatomic region-based dynamic range compression for chest radiographs using warping transformation of correlated distribution

    Page(s): 407 - 418
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    The purpose of this paper is to investigate the effectiveness of the authors' novel dynamic range compression (DRC) for chest radiographs. The purpose of DRC is to compress the gray scale range of the image when using narrow dynamic range viewing systems such as monitors. First, an automated segmentation method was used to detect the lung region. The combined region of mediastinum, heart, and subdiaphragm was defined based on the lung region. The correlated distributions, between a pixel value and its neighboring averaged pixel value, for the lung region and the combined region were calculated. According to the appearance of overlapping of two distributions, the warping function was decided. After pixel values were warped, the pixel value range of the lung region was compressed while preserving the detail information, because the warping function compressed the range of the averaged pixel values while preserving the pixel value range for the pixels which had had the same averaged pixel value. The performance was evaluated with the authors' criterion function which was the contrast divided by the moment, where the contrast and the moment represent the sum of the differences between the pixel values and the averaged values of eight pixels surrounding that pixel, and the sum of the differences between the pixel values and the averaged value of all pixels in the region-of-interest, respectively. For 71 screening chest images from Johns Hopkins University Hospital (Baltimore, MD), this method improved our criterion function at 11.7% on average. The warping transformation algorithm based on the correlated distribution was effective in compressing the dynamic range while simultaneously preserving the detail information. View full abstract»

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  • A robust numerical solution to reconstruct a globally relative shear modulus distribution from strain measurements

    Page(s): 419 - 428
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    To noninvasively quantify tissue elasticity for differentiating malignancy of soft tissue, the authors previously proposed a two-dimensional (2-D) mechanical inverse problem in which simultaneous partial differential equations (PDE's) represented the target distribution globally of relative shear moduli with respect to reference shear moduli such that the relative values could be determined from strain distributions obtained by conventional ultrasound (US) or nuclear magnetic resonance (NMR) imaging-based analysis. Here, the authors further consider the analytic solution in the region of interest, subsequently demonstrating that the problem is inevitably ill-conditioned in real-world applications, i.e., noise in measurement data and improper configurations of mechanical sources/reference regions make it impossible to guarantee the existence of a stable and unique target global distribution. Next, based on clarification of the inherent problematic conditions, the authors describe a newly developed numerical-based implicit-integration approach that novelly incorporates a computationally efficient regularization method designed to solve this differential inverse problem using just low-pass filtered spectra derived from strain measurements. To evaluate method effectiveness, reconstructions of the global distribution are carried out using intentionally created ill-conditioned models. The resultant reconstructions indicate the robust solution is highly suitable, while also showing it has high potential to be applied in the development of an effective yet versatile diagnostic tool for quantifying the distribution of elasticity in various soft tissues. View full abstract»

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  • Model-guided labeling of coronary structure

    Page(s): 429 - 441
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    Assigning anatomic labels to coronary arteries in X-ray angiograms is an important task in medical imaging, motivated by the desire to standardize the assessment of coronary artery disease and to facilitate the three-dimensional (3-D) reconstruction and visualization of the coronary vasculature. However, automatic labeling poses a number of significant challenges, including the presence of noise, artifacts, competing structures, misleading visual cues, and other difficulties associated with a dynamic and inherently complex structure. The authors have developed a model-guided approach that addresses these challenges and automatically labels the vascular structure in coronary angiographic images. The approach consists of two models: (1) a symbolic model, represented through a directed acyclic graph, that captures vascular tree hierarchies and branch interrelationships and (2) a generalized 3-D model that captures spatial and geometric relationships. Importantly, the approach detects ambiguities (such as vessel overlaps) that may be found in a frame of a cine sequence, and resolves these ambiguities by considering the information derived from other (unambiguous) frames in the temporal sequence, employing dynamic programming methods to match the image features found in the different (ambiguous and unambiguous) frames. This paper presents this model-guided labeling algorithm and discusses the experimental results obtained from implementing and applying the resulting labeling system to a variety of clinical images. The results indicate the feasibility of achieving robust and consistently accurate image labeling through this model-guided, temporal disambiguation method. View full abstract»

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  • A novel approach to microcalcification detection using fuzzy logic technique

    Page(s): 442 - 450
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    Breast cancer continues to be a significant public health problem in the United States. Approximately, 182,000 new cases of breast cancer are diagnosed and 46,000 women die of breast cancer each year. Even more disturbing is the fact that one out of eight women in the United States will develop breast cancer at some point during her lifetime. Since the cause of breast cancer remains unknown, primary prevention becomes impossible. Computer-aided mammography is an important and challenging task in automated diagnosis. It has great potential over traditional interpretation of film-screen mammography in terms of efficiency and accuracy. Microcalcifications are the earliest sign of breast carcinomas and their detection is one of the key issues for breast cancer control. In this study, a novel approach to microcalcification detection based on fuzzy logic technique is presented. Microcalcifications are first enhanced based on their brightness and nonuniformity. Then, the irrelevant breast structures are excluded by a curve detector. Finally, microcalcifications are located using an iterative threshold selection method. The shapes of microcalcifications are reconstructed and the isolated pixels are removed by employing the mathematical morphology technique. The essential idea of the proposed approach is to apply a fuzzified image of a mammogram to locate the suspicious regions and to interact the fuzzified image with the original image to preserve fidelity. The major advantage of the proposed method is its ability to detect microcalcifications even in very dense breast mammograms. A series of clinical mammograms are employed to test the proposed algorithm and the performance is evaluated by the free-response receiver operating characteristic curve. The experiments aptly show that the microcalcifications can be accurately detected even in very dense mammograms using the proposed approach. View full abstract»

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  • Data-driven homologue matching for chromosome identification

    Page(s): 451 - 462
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    Karyotyping involves the visualization and classification of chromosomes into standard classes. In "normal" human metaphase spreads, chromosomes occur in homologous pairs for the autosomal classes 1-22, and X chromosome for females. Many existing approaches for performing automated human chromosome image analysis presuppose cell normalcy, containing 46 chromosomes within a metaphase spread with two chromosomes per class. This is an acceptable assumption for routine automated chromosome image analysis. However, many genetic abnormalities are directly linked to structural or numerical aberrations of chromosomes within the metaphase spread. Thus, two chromosomes per class cannot be assumed for anomaly analysis. This paper presents the development of image analysis techniques which are extendible to detecting numerical aberrations evolving from structural abnormalities. Specifically, an approach to identifying "normal" chromosomes from selected class(es) within a metaphase spread is presented. Chromosome assignment to a specific class is initially based on neural networks, followed by banding pattern and centromeric index criteria checking, and concluding with homologue matching. Experimental results are presented comparing neural networks as the sole classifier to the authors' homologue matcher for identifying class 17 within normal and abnormal metaphase spreads. View full abstract»

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  • Design and construction of a realistic digital brain phantom

    Page(s): 463 - 468
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    After conception and implementation of any new medical image processing algorithm, validation is an important step to ensure that the procedure fulfils all requirements set forth at the initial design stage. Although the algorithm must be evaluated on real data, a comprehensive validation requires the additional use of simulated data since it is impossible to establish ground truth with in vivo data. Experiments with simulated data permit controlled evaluation over a wide range of conditions (e.g., different levels of noise, contrast, intensity artefacts, or geometric distortion). Such considerations have become increasingly important with the rapid growth of neuroimaging, i.e., computational analysis of brain structure and function using brain scanning methods such as positron emission tomography and magnetic resonance imaging. Since simple objects such as ellipsoids or parallelepipedes do not reflect the complexity of natural brain anatomy, the authors present the design and creation of a realistic, high-resolution, digital, volumetric phantom of the human brain. This three-dimensional digital brain phantom is made up of ten volumetric data sets that define the spatial distribution for different tissues (e.g., grey matter, white matter, muscle, skin, etc.), where voxel intensity is proportional to the fraction of tissue within the voxel. The digital brain phantom can be used to simulate tomographic images of the head. Since the contribution of each tissue type to each voxel in the brain phantom is known, it can be used as the gold standard to test analysis algorithms such as classification procedures which seek to identify the tissue "type" of each image voxel. Furthermore, since the same anatomical phantom may be used to drive simulators for different modalities, it is the ideal tool to test intermodality registration algorithms. The brain phantom and simulated MR images have been made publicly available on the Internet (http://www.bic.mni.mcgill.ca/brainweb). View full abstract»

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  • Edge detection in medical images using a genetic algorithm

    Page(s): 469 - 474
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    An algorithm is developed that detects well-localized, unfragmented, thin edges in medical images based on optimization of edge configurations using a genetic algorithm (GA). Several enhancements were added to improve the performance of the algorithm over a traditional GA. The edge map is split into connected subregions to reduce the solution space and simplify the problem. The edge-map is then optimized in parallel using incorporated genetic operators that perform transforms on edge structures. Adaptation is used to control operator probabilities based on their participation. The GA was compared to the simulated annealing (SA) approach using ideal and actual medical images from different modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound. Quantitative comparisons were provided based on the Pratt figure of merit and on the cost-function minimization. The detected edges were thin, continuous, and well localized. Most of the basic edge features were detected, Results for different medical image modalities are promising and encourage further investigation to improve the accuracy and experiment with different cost functions and genetic operators. View full abstract»

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  • MR image texture analysis applied to the diagnosis and tracking of Alzheimer's disease

    Page(s): 475 - 478
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    The authors assess the value of magnetic resonance (MR) image texture in Alzheimer's disease (AD) both as a diagnostic marker and as a measure of progression. T 1-weighted MR scans were acquired from 40 normal controls and 24 AD patients. These were split into a training set (20 controls, 40 AD) and a test set (20 controls, 14 AD). In addition, five control subjects and five AD patients were scanned repeatedly over several years. On each scan a texture feature vector was evaluated over the brain; this consisted of 260 measures derived from the spatial gray-level dependence method. A stepwise discriminant analysis was applied to the training set, to obtain a linear discriminant function. In the test set, this function yielded significantly different values for the control and AD groups (p<10 -4) with only small group overlap; a classification rate of 91% was obtained. For the repeatedly scanned control subjects, the median increment in the discriminant function between successive scans of 0.12 was not significantly different from zero (p>0.05); for the repeatedly scanned AD patients the corresponding median increment of 1.4 was significantly different from zero (p<0.05). MR image texture may be a useful aid in the diagnosis and tracking of Alzheimer's disease. View full abstract»

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  • Magnetocardiographic localization of arrhythmia substrates: a methodology study with accessory pathway ablation as reference

    Page(s): 479 - 484
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    In magnetocardiographic (MCG) localization of arrhythmia substrates, a model of the thorax volume conductor is a crucial component of the calculations. In this study, the authors investigated different models of the thorax, to determine the most suitable to use in the computations. Their methods and results are as follows. They studied 11 patients with overt Wolff-Parkinson-White syndrome, scheduled for catheter ablation. The MCG registrations were made with a 37-channel "superconducting quantum interference device" system. The underlying equivalent current dipole was computed for the delta-wave. Three models of the thorax were used: the infinite halfspace, a sphere and a box. For anatomical correlation and to define the suitable sphere and box, magnetic resonance images were obtained. As reference the authors used the position of the tip of the catheter, at successful radio-frequency-ablation, documented by cine-fluoroscopy. Nine patients could be evaluated. The mean errors (range) when using the infinite halfspace, the sphere and the box were 96 (49-125), 21 (5-39), and 36 mm (20-58 mm), respectively (p<0.0001). In conclusion, the sphere was significantly better suited than the other models tested in this study, but even with this model the accuracy of MCG localization must further improve to be clinically useful. More realistic models of the thorax are probably required to achieve this goal. View full abstract»

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  • Linear and neural models for classifying breast masses

    Page(s): 485 - 488
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    Computational methods can be used to provide an initial screening or a second opinion in medical settings and may improve the sensitivity and specificity of diagnoses. In the current study, linear discriminant models and artificial neural networks are trained to detect breast cancer in suspicious masses using radiographic features and patient age. Results on 139 suspicious breast masses (79 malignant, 60 benign, biopsy proven) indicate that a significant probability of detecting malignancies can be achieved at the risk of a small percentage of false positives. Receiver operating characteristic (ROC) analysis favors the use of linear models, however, a new measure related to the area under the ROC curve (A Z) suggests a possible benefit from hybridizing linear and nonlinear classifiers. 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