Browsing by Subject "Image reconstruction"

Now showing 1 - 20 of 57

Open Access
Accelerated phase-cycled SSFP imaging with compressed sensing
(Institute of Electrical and Electronics Engineers Inc., 2015) Çukur, T.
Balanced steady-state free precession (SSFP) imaging suffers from irrecoverable signal losses, known as banding artifacts, in regions of large B0 field inhomogeneity. A common solution is to acquire multiple phase-cycled images each with a different frequency sensitivity, such that the location of banding artifacts are shifted in space. These images are then combined to alleviate signal loss across the entire field-of-view. Although high levels of artifact suppression are viable using a large number of images, this is a time costly process that limits clinical utility. Here, we propose to accelerate individual acquisitions such that the overall scan time is equal to that of a single SSFP acquisition. Aliasing artifacts and noise are minimized by using a variable-density random sampling pattern in k-space, and by generating disjoint sampling patterns for separate acquisitions. A sparsity-enforcing method is then used for image reconstruction. Demonstrations on realistic brain phantom images, and in vivo brain and knee images are provided. In all cases, the proposed technique enables robust SSFP imaging in the presence of field inhomogeneities without prolonging scan times. © 2014 IEEE.
Open Access
Adaptive polyphase subband decomposition structures for image compression
(IEEE, 2000) Gerek, Ö. N.; Çetin, A. Enis
Subband decomposition techniques have been extensively used for data coding and analysis. In most filter banks, the goal is to obtain subsampled signals corresponding to different spectral regions of the original data. However, this approach leads to various artifacts in images having spatially varying characteristics, such as images containing text, subtitles, or sharp edges. In this paper, adaptive filter banks with perfect reconstruction property are presented for such images. The filters of the decomposition structure which can be either linear or nonlinear vary according to the nature of the signal. This leads to improved image compression ratios. Simulation examples are presented.
Open Access
Algebraic reconstraction for 3D magnetic resonance-electrical impedance tomography (MREIT) using one component of magnetic flux density
(Institute of Physics and Engineering in Medicine, 2004) Ider, Y. Z.; Onart, S.
Magnetic resonance-electrical impedance tomography (MREIT) algorithms fall into two categories: those utilizing internal current density and those utilizing only one component of measured magnetic flux density. The latter group of algorithms have the advantage that the object does not have to be rotated in the magnetic resonance imaging (MRI) system. A new algorithm which uses only one component of measured magnetic flux density is developed. In this method, the imaging problem is formulated as the solution of a non-linear matrix equation which is solved iteratively to reconstruct resistivity. Numerical simulations are performed to test the algorithm both for noise-free and noisy cases. The uniqueness of the solution is monitored by looking at the singular value behavior of the matrix and it is shown that at least two current injection profiles are necessary. The method is also modified to handle region-of-interest reconstructions. In particular it is shown that, if the image of a certain xy-slice is sought for, then it suffices to measure the z-component of magnetic flux density up to a distance above and below that slice. The method is robust and has good convergence behavior for the simulation phantoms used.
Open Access
Assessment of information redundancy in ECG signals
(IEEE, 1997-09) Acar, Burak; Özçakır, Lütfü; Köymen, Hayrettin
In this paper, the morphological information redundancy in standard 12 lead ECG channels is studied. Study is based on decomposing the ECG channels into orthogonal channels by an SVD based algorithm and then reconstructing them. Then 7 of 8 independently recorded ECG channels are decomposed and the missing channel is reconstructed from these orthogonal channels. Thus the unique morphological information content of each ECG channel is assessed through the loss of clinical information in the reconstructed signal. A comparison of the clinical parameters measured from the reconstructed and original ECG is reported.
Open Access
Autofocused spotlight SAR image reconstruction of off-grid sparse scenes
(Institute of Electrical and Electronics Engineers Inc., 2017) Camlıca, S.; Gurbuz, A. C.; Arıkan, Orhan
Synthetic aperture radar (SAR) has significant role in remote sensing. Phase errors due to uncompensated platform motion, measurement model mismatch, and measurement noise can cause degradations in SAR image reconstruction. For efficient processing of the measurements, image plane is discretized and autofocusing algorithms on this discrete grid are employed. However, in addition to the platform motion errors, the reflectors, which are not exactly on the reconstruction grid, also degrade the image quality. This is called the off-grid target problem. In this paper, a sparsity-based technique is developed for autofocused spotlight SAR image reconstruction that can correct phase errors due to uncompensated platform motion and provide robust images in the presence of off-grid targets. The proposed orthogonal matching pursuit-based reconstruction technique uses gradient descent parameter updates with built in autofocus. The technique can reconstruct high-quality images by using sub Nyquist rate of sampling on the reflected signals at the receiver. The results obtained using both simulated and real SAR system data show that the proposed technique provides higher quality reconstructions over alternative techniques in terms of commonly used performance metrics.
Open Access
Automated image reconstruction for non-cartesian magnetic particle imaging
(2019-09) Özaslan, Ali Alper
Magnetic particle imaging (MPI) is a high-contrast imaging modality that images the spatial distribution of superparamagnetic iron oxide (SPIO) nanoparticles by exploiting their nonlinear response. In MPI, image reconstruction is performed via two di erent methods: system function reconstruction (SFR) and x-space reconstruction. For the SFR approach, analysis of various scanning trajectories provided important insight about their image quality performances. While Cartesian trajectories remain the most popular choice for x-space-based reconstruction, recent work suggests that non-Cartesian trajectories such as the Lissajous trajectory may prove bene cial for improving image quality. In this thesis, a generalized reconstruction scheme is proposed for x-space MPI that can be used in conjunction with any scanning trajectory. The proposed technique automatically tunes the reconstruction parameters from the scanning trajectory, and does not induce any additional blurring. To demonstrate the proposed technique, ve di erent trajectories were utilized with varying density levels. Comparison to alternative reconstruction methods show signi cant improvement in image quality achieved by the proposed technique. Among the tested trajectories, the Lissajous and bidirectional Cartesian trajectories prove more favorable for x-space MPI, and the resolution of the images from these two trajectories can further be improved via deblurring. The fully automated gridding reconstruction proposed in this thesis can be utilized with these trajectories to improve the image quality in x-space MPI.
Open Access
Automatic detection of compound structures by joint selection of region groups from a hierarchical segmentation
(Institute of Electrical and Electronics Engineers, 2016) Akçay, H. G.; Aksoy, S.
A challenging problem in remote sensing image analysis is the detection of heterogeneous compound structures such as different types of residential, industrial, and agricultural areas that are composed of spatial arrangements of simple primitive objects such as buildings and trees. We describe a generic method for the modeling and detection of compound structures that involve arrangements of an unknown number of primitives in large scenes. The modeling process starts with a single example structure, considers the primitive objects as random variables, builds a contextual model of their arrangements using a Markov random field, and learns the parameters of this model via sampling from the corresponding maximum entropy distribution. The detection task is formulated as the selection of multiple subsets of candidate regions from a hierarchical segmentation where each set of selected regions constitutes an instance of the example compound structure. The combinatorial selection problem is solved by the joint sampling of groups of regions by maximizing the likelihood of their individual appearances and relative spatial arrangements. Experiments using very high spatial resolution images show that the proposed method can effectively localize an unknown number of instances of different compound structures that cannot be detected by using spectral and shape features alone.
Open Access
Bilgisayarlı iyonosfer tomografisinde açılım işlevlerinin ve yeniden yapılandırma algoritmalarının model üzerinden karşılaştırılması
(IEEE, 2005-05) Yavuz, E.; Arıkan, F.; Arıkan, Orhan; Erol, C. B.
Bilgisayarlı İyonosfer Tomografisi (BİT), iyonosfer elektron yoğunluğunun iki boyutlu veya üç boyutlu olarak görüntülenmesi için kullanılan bir yöntemdir. Bu yöntem özellikle iyonosfer fiziği veya uydu haberleşmesi gibi alanlar için iyonosferin incelenmesine olanak sağlamaktadır. Bu yöntemde Küresel Yer Belirleme (KYB) uyduları ve bu uydulardan gelen ışınları toplamak üzere yeryüzüne belirli bir geometride yerleştirilen alıcılar kullanılır. Alıcılar KYB uydularından aldıkları sinyaller ile iyonosfere ait olan Toplam Elektron İçeriği (TEİ) verisinin hesaplanmasına olanak sağlar. TEİ verileri ve tomografik yeniden yapılandırma algoritmaları kullanılarak, iyonosfer elektron yoğunluğu görüntülerinin elde edilmesi sağlanır. Gerçekleştirilen bu çalışmada tek bir GPS uydusu ve bu uyduyu izlemek üzere yeryüzünde belirli bir enlem noktasına yerleştirilmiş bir alıcı senaryosu canlandırıldı. Alıcıda oluşan hatalar ve model hataları bu çalışmada ihmal edildi. Bu senaryo içerisinde değişik açılım işlevleri ve yeniden yapılandırma algoritmaları kullanılarak, iyonosfer elektron yoğunluğunun iki boyutlu olarak elde edilmesi gerçekleştirildi. Sonuçların başarım analizleri ileri model olarak kullanılan IRI-95 ile karşılaştırılarak verildi.
Open Access
Binary morphological subband decomposition for image coding
(IEEE, 1996) Gürcan, Metin Nafi; Gerek, Ömer Nezih; Çetin, A. Enis
In this paper a binary waveform coding method based on morphological subband decomposition coupled with embedded zero-tree and entropy coding is described. This method can be utilized in text compression or bit-plane coding of images. Binary morphological subband decomposition operations are carried out in the Gallois Field, resulting in a computationally efficient structure. Simulation studies are presented.
Open Access
Coded scenes for fast system calibration in magnetic particle imaging
(IEEE, 2018) Ilbey, S.; Top, C. B.; Güngör, A.; Sarıtaş, Emine Ülkü; Güven, E.
Magnetic nanoparticle (MNP) agents have a wide range of clinical application areas for both imaging and therapy. MNP distribution inside the body can be imaged using Magnetic Particle Imaging (MPI). For MPI image reconstruction with the system function matrix (SFM) approach, a calibration scan is necessary, in which a single MNP sample is placed and scanned inside the full field of view (FOV), which is a very time consuming task. In this study, we propose the use of coded scenes that include MNP samples at multiple positions inside the FOV, and reconstruct the SFM using compressed sensing techniques. We used simulations to analyze the effect of number of coded scenes on the image quality, and compare the results with standard sparse reconstruction using single MNP sample scan. The results show that with the proposed method, the required number of measurements is decreased substantially, enabling a fast system calibration procedure.
Open Access
Compressed multi-contrast magnetic resonance image reconstruction using Augmented Lagrangian Method
(IEEE, 2016) Güngör, A.; Kopanoğlu, E.; Çukur, Tolga; Güven, H. E.
In this paper, a Multi-Channel/Multi-Contrast image reconstruction algorithm is proposed. The method, which is based on the Augmented Lagrangian Method uses joint convex objective functions to utilize the mutual information in the data from multiple channels to improve reconstruction quality. For this purpose, color total variation and group sparsity are used. To evaluate the performance of the method, the algorithm is compared in terms of convergence speed and image quality using Magnetic Resonance Imaging data to FCSA-MT, an alternative approach on reconstructing multi-contrast MRI data.
Open Access
Computation of holographic patterns between tilted planes
(SPIE - International Society for Optical Engineering, 2006-05) Esmer, Gökhan Bora; Onural, Levent
Computation of the diffraction pattern that gives the desired reconstruction of an object upon proper illumination is an important process in computer generated holography. A fast computational method, based on the plane wave decomposition of 3D field in free-space, is presented to find the desired diffraction pattern. The computational burden includes two FFT algorithms in addition to a shuffling of the frequency components that needs an interpolation in the frequency domain. The algorithm is based on the exact diffraction formulation; there is no need for Fresnel or Fraunhofer approximations. The developed model is utilized to calculate the scalar optical diffraction between tilted planes for monochromatic light. The performance of the presented algorithm is satisfactory for tilt angles up to 60°.
Open Access
Current constrained voltage scaled reconstruction (CCVSR) algorithm for MR-EIT and its performance with different probing current patterns
(Institute of Physics Publishing, 2003) Birgül, Ö.; Eyüboğlu, B. M.; İder, Y. Z.
Conventional injected-current electrical impedance tomography (EIT) and magnetic resonance imaging (MRI) techniques can be combined to reconstruct high resolution true conductivity images. The magnetic flux density distribution generated by the internal current density distribution is extracted from MR phase images. This information is used to form a fine detailed conductivity image using an Ohm's law based update equation. The reconstructed conductivity image is assumed to differ from the true image by a scale factor. EIT surface potential measurements are then used to scale the reconstructed image in order to find the true conductivity values. This process is iterated until a stopping criterion is met. Several simulations are carried out for opposite and cosine current injection patterns to select the best current injection pattern for a 2D thorax model. The contrast resolution and accuracy of the proposed algorithm are also studied. In all simulation studies, realistic noise models for voltage and magnetic flux density measurements are used. It is shown that, in contrast to the conventional EIT techniques, the proposed method has the capability of reconstructing conductivity images with uniform and high spatial resolution. The spatial resolution is limited by the larger element size of the finite element mesh and twice the magnetic resonance image pixel size.
Open Access
DC shift based image reconstruction for magnetic particle imaging
(IEEE, 2017) Sarıca, Damla; Demirel, Ömer Burak; Sarıtaş, Emine Ülkü
Magnetic Particle Imaging (MPI) is a new imaging technology that images the spatial distribution of iron oxide nanoparticles. Since the magnetic field strength that can be safely applied in MPI is limited, the field-of-view (FOV) must be divided into partial FOVs. Because the excitation magnetic field causes direct feedthrough on the receiver coil, the excitation frequency must be filtered out of the MPI signal. During this process, the nanoparticle signal at the same frequency is also lost, as a result of which each partial FOV experiences different levels of DC shift. In the standard MPI image reconstruction, these DC shifts are calculated from neighboring overlapping partial FOVs. Here, we propose a novel method that directly reconstructs the MPI image from the calculated DC shift values. Especially in the case of low bandwidth signal acquisitions, this method yields higher resolution images when compared to the standard method. The simulation results at various signal-to-noise ratios (SNR) show that the proposed method produces 6-8 dB increase in peak SNR and yields images that closely match the ideal image.
Open Access
Experimental results for 2D magnetic resonance electrical impedance tomography (MR-EIT) using magnetic flux density in one direction
(Institute of Physics Publishing, 2003) Birgül, Ö.; Eyüboğlu, B. M.; İder, Y. Z.
Magnetic resonance electrical impedance tomography (MR-EIT) is an emerging imaging technique that reconstructs conductivity images using magnetic flux density measurements acquired employing MRI together with conventional EIT measurements. In this study, experimental MR-EIT images from phantoms with conducting and insulator objects are presented. The technique is implemented using the 0.15 T Middle East Technical University MRI system. The dc current method used in magnetic resonance current density imaging is adopted. A reconstruction algorithm based on the sensitivity matrix relation between conductivity and only one component of magnetic flux distribution is used. Therefore, the requirement for object rotation is eliminated. Once the relative conductivity distribution is found, it is scaled using the peripheral voltage measurements to obtain the absolute conductivity distribution. Images of several insulator and conductor objects in saline filled phantoms are reconstructed. The L2 norm of relative error in conductivity values is found to be 13%, 17% and 14% for three different conductivity distributions.
Open Access
Human action recognition using distribution of oriented rectangular patches
(Springer, 2007-10) İkizler, Nazlı; Duygulu, Pınar
We describe a "bag-of-rectangles" method for representing and recognizing human actions in videos. In this method, each human pose in an action sequence is represented by oriented rectangular patches extracted over the whole body. Then, spatial oriented histograms are formed to represent the distribution of these rectangular patches. In order to carry the information from the spatial domain described by the bag-of-rectangles descriptor to temporal domain for recognition of the actions, four different methods are proposed. These are namely, (i) frame by frame voting, which recognizes the actions by matching the descriptors of each frame, (ii) global histogramming, which extends the idea of Motion Energy Image proposed by Bobick and Davis by rectangular patches, (iii) a classifier based approach using SVMs, and (iv) adaptation of Dynamic Time Warping on the temporal representation of the descriptor. The detailed experiments are carried out on the action dataset of Blank et. al. High success rates (100%) prove that with a very simple and compact representation, we can achieve robust recognition of human actions, compared to complex representations. © Springer-Verlag Berlin Heidelberg 2007.
Open Access
Image denoising using adaptive subband decomposition
(IEEE, 2001) Gezici, Sinan; Yılmaz, İsmail; Gerek, Ö. N.; Çetin, A. Enis
In this paper, we present a new image denoising method based on adaptive subband decomposition (or adaptive wavelet transform) in which the filter coefficients are updated according to an Least Mean Square (LMS) type algorithm. Adaptive subband decomposition filter banks have the perfect reconstruction property. Since the adaptive filterbank adjusts itself to the changing input environments denoising is more effective compared to fixed filterbanks. Simulation examples are presented.
Open Access
Image reconstruction for Magnetic Particle Imaging using an Augmented Lagrangian Method
(IEEE, 2017) Ilbey S.; Top C.B.; Çukur, Tolga; Sarıtaş, Emine Ülkü; Guven H.E.
Magnetic particle imaging (MPI) is a relatively new imaging modality that images the spatial distribution of superparamagnetic iron oxide nanoparticles administered to the body. In this study, we use a new method based on Alternating Direction Method of Multipliers (a subset of Augmented Lagrangian Methods, ADMM) with total variation and l1 norm minimization, to reconstruct MPI images. We demonstrate this method on data simulated for a field free line MPI system, and compare its performance against the conventional Algebraic Reconstruction Technique. The ADMM improves image quality as indicated by a higher structural similarity, for low signal-to-noise ratio datasets, and it significantly reduces computation time. © 2017 IEEE.
Open Access
Inertial imaging with nanomechanical systems
(Nature Publishing Group, 2015) Hanay, M. S.; Kelber, S. I.; O'Connell, C. D.; Mulvaney, P.; Sader, J. E.; Roukes, M. L.
Mass sensing with nanoelectromechanical systems has advanced significantly during the last decade. With nanoelectromechanical systems sensors it is now possible to carry out ultrasensitive detection of gaseous analytes, to achieve atomic-scale mass resolution and to perform mass spectrometry on single proteins. Here, we demonstrate that the spatial distribution of mass within an individual analyte can be imaged - in real time and at the molecular scale - when it adsorbs onto a nanomechanical resonator. Each single-molecule adsorption event induces discrete, time-correlated perturbations to all modal frequencies of the device. We show that by continuously monitoring a multiplicity of vibrational modes, the spatial moments of mass distribution can be deduced for individual analytes, one-by-one, as they adsorb. We validate this method for inertial imaging, using both experimental measurements of multimode frequency shifts and numerical simulations, to analyse the inertial mass, position of adsorption and the size and shape of individual analytes. Unlike conventional imaging, the minimum analyte size detectable through nanomechanical inertial imaging is not limited by wavelength-dependent diffraction phenomena. Instead, frequency fluctuation processes determine the ultimate attainable resolution. Advanced nanoelectromechanical devices appear capable of resolving molecular-scale analytes.
Open Access
Linear/nonlinear adaptive polyphase subband decomposition structures for image compression
(IEEE, 1998-05) Gerek, Ömer N.; Çetin, A. Enis
Subband decomposition techniques have been extensively used for data coding and analysis. In most filter banks, the goal is to obtain subsampled signals corresponding to different spectral bands of the original data. However, this approach leads to various artifacts in images containing text, subtitles, or sharp edges. In this paper, adaptive filter banks with perfect reconstruction property are presented for such images. The filters of the decomposition structure vary according to the nature of the signal. This leads to higher compression ratios for images containing subtitles compared to fixed filter banks. Simulation examples are presented.