Browsing by Subject "Compressed sensing"

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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 measurement matrix design in compressed sensing based direction of arrival estimation
(IEEE, 2021) Kılıç, Berkan; Güngör, Alper; Kalfa, Mert; Arıkan, Orhan
Design of measurement matrices is an important aspect of compressed sensing (CS) based direction of arrival (DoA) applications that enables reduction in the analog channels to be processed in sparse target environments. Here, a novel measurement matrix design methodology for CS based DoA estimation is proposed and its superior performance over alternative measurement matrix design methodologies is demonstrated. The proposed method uses prior probability distribution of the targets to improve performance. Compared to the state-of-the-art techniques, it is quantitatively demonstrated that the proposed measurement matrix design approach enables significant reduction in the number of analog channels to be processed and adapts to a priori information on the target scene.
Open Access
Adaptive measurement matrix design in direction of arrival estimation
(IEEE, 2022-09-26) Kılıç, Berkan; Güngör, Alper; Kalfa, Mert; Arıkan, Orhan
Advances in compressed sensing (CS) theory have brought new perspectives to encoding and decoding of signals with sparse representations. The encoding strategies are determined by measurement matrices whose design is a critical aspect of the CS applications. In this study, we propose a novel measurement matrix design methodology for direction of arrival estimation that adapts to the prior probability distribution on the source scene, and we compare its performance over alternative approaches using both on-grid and gridless reconstruction methods. The proposed technique is derived in closed-form and shown to provide improved compression rates compared to the state-of-the-art. This technique is also robust to the uncertainty in the prior source information. In the presence of significant mutual coupling between antenna elements, the proposed technique is adapted to mitigate these mutual coupling effects.
Open Access
Adaptive techniques in compressed sensing based direction of arrival estimation
(2021-07) Kılıç, Berkan
Direction of arrival (DOA) estimation is an important research area having exten-sive applications including radar, sonar, wireless communications, and electronic warfare systems. Development and popularization of the compressed sensing (CS) theory has led to a vast literature on the use of the CS techniques in DOA esti-mation which has been shown to be superior over the classical techniques under various scenarios. In the CS based techniques, measurement matrices determine the received information while sparsity promoting reconstruction algorithms are used to estimate the unknown DOAs. Hence, design of measurement matrices and sparse reconstruction algorithms are among the most important aspects of the CS theory. In this thesis, both aspects are investigated and novel techniques are proposed for improved performance. Following a brief explanation of the classical and the CS based DOA estimation techniques, a new optimization perspective is introduced on the Capon’s beam-former by using the minimum mean square error criterion. After that, a mea-surement matrix design methodology exploiting prior information on the source environment is introduced. Hardware and sofware implementation constraints of the introduced method are investigated and more eﬃcient alternatives are pro-posed. Additionally, an adaptive dictionary design algorithm is introduced for more eﬀective use of the prior information. Lastly, the Cramer-Rao Lower Bound expression for the compressed DOA signal models is derived and its implications on the measurement matrix design are investigated leading to a sector based mea-surement matrix design technique along with a novel reconstruction algorithm.
Open Access
Automated parameter selection for accelerated mri reconstruction via low-rank modeling of local k-space neighborhoods
(Elsevier GmbH, 2025-02) Ilıcak, Efe; Sarıtaş, Emine Ülkü; Çukur, Tolga
The publisher regrets that the declaration of competing interest statement was not included in the original article. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The publisher would like to apologise for any inconve nience caused.
Open Access
A comparison between the CS-TOF and the CTA/DSA for WEB device management
(Sage Publications, 2021-05-06) Algin, Oktay; Yuce, G.; Koc, U.; Ayberk, G.
Purpose There is no study on the role of three-dimensional compressed sensing time of flight MR angiography (3D-CS-TOF) in the management of the WEB device. We evaluated the efficacy of 3-tesla 3D-CS-TOF for the management and follow-up of the WEB device implantations. Materials and methods Seventy-three aneurysms of 69 patients treated with the WEB device were retrospectively examined. Morphological parameters and embolization results of the aneurysms were assessed and compared on 3D-CS-TOF, CTA, and DSA images. Results Occluded, neck remnant, and recurrent aneurysms were observed in 61 (83.6%), 7 (9.6%), and 5 (6.8%) aneurysms, respectively. Inter- and intra-reader agreement values related to aneurysm size measurements were perfect. Aneurysms size, age, and proximal vessel tortuosity were negatively correlated with the visibility of the aneurysms and parent vessels on 3D-CS-TOF images (p = 0.043; p = 0.032; p < 0.001, respectively). Subarachnoid hemorrhage and age are associated with 3D-CS-TOF artifacts (p = 0.031; p = 0.005, respectively). 3D-CS-TOF findings are in perfect agreement with DSA or CT angiography (CTA) results (p < 0.001). Conclusion According to our results, 3D-CS-TOF can be an easy, fast, and reliable alternative for the management or follow-up of WEB assisted embolization.
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
Compressed sensing on ambiguity function domain for high resolution detection
(IEEE, 2010) Güldoǧan, Mehmet B.; Pilancı, Mert; Arıkan, Orhan
In this paper, by using compressed sensing techniques, a new approach to achieve robust high resolution detection in sparse multipath channels is presented. Currently used sparse reconstruction techniques are not immediately applicable in wireless channel modeling and radar signal processing. Here, we make use of the cross-ambiguity function (CAF) and transformed the reconstruction problem from time to delay-Doppler domain for efficient exploitation of the delay-Doppler diversity of the multipath components. Simulation results quantify the performance gain and robustness obtained by this new CAF based compressed sensing approach. ©2010 IEEE.
Open Access
Compressed sensing techniques for accelerated magnetic resonance imaging
(2017-07) Ilıcak, Efe
Magnetic resonance imaging has seen a growing interest in the recent years due to its non-invasive and non-ionizing nature. However, imaging speed remains a major concern. Recently, compressed sensing theory has opened new doors for accelerated imaging applications. This dissertation studies compressed sensing based reconstruction strategies for accelerated magnetic resonance imaging, speci cally for angiography and multiple-acquisition methods. For magnetic resonance angiography, we propose a novel approach that improves scan time e ciency while suppressing background signals. In this study, we attain high-contrast angiograms from undersampled data by utilizing a two-stage reconstruction strategy. Simulations and in vivo experiments demonstrate that the developed strategy is able to relax trade-o s between image contrast and scan e ciency without compromising vessel depiction. For multiple-acquisition balanced steady state free precession imaging, we develop a framework that jointly reconstructs undersampled phasecycled images. This approach is able to improve banding artifact suppression while maintaining scan e ciency. Results show that the proposed method is able to attain high-quality reconstructions even at high acceleration factors. Overall, the ndings presented in this thesis indicate that compressed sensing reconstructions represent a promising future for rapid magnetic resonance imaging. Consequently, compressed sensing reconstruction techniques hold a great potential to change the time-consuming clinical imaging practices.
Open Access
Compressive sampling and adaptive multipath estimation
(IEEE, 2010) Pilancı, Mert; Arıkan, Orhan
In many signal processing problems such as channel estimation and equalization, the problem reduces to a linear system of equations. In this proceeding we formulate and investigate linear equations systems with sparse perturbations on the coefficient matrix. In a large class of matrices, it is possible to recover the unknowns exactly even if all the data, including the coefficient matrix and observation vector is corrupted. For this aim, we propose an optimization problem and derive its convex relaxation. The numerical results agree with the previous theoretical findings of the authors. The technique is applied to adaptive multipath estimation in cognitive radios and a significant performance improvement is obtained. The fact that rapidly varying channels are sparse in delay and doppler domain enables our technique to maintain reliable communication even far from the channel training intervals. ©2010 IEEE.
Open Access
Compressive sensing based flame detection in infrared videos
(IEEE, 2013) Günay, Osman; Çetin, A. Enis
In this paper, a Compressive Sensing based feature extraction algorithm is proposed for flame detection using infrared cameras. First, bright and moving regions in videos are detected. Then the videos are divided into spatio-temporal blocks and spatial and temporal feature vectors are exctracted from these blocks. Compressive Sensing is used to exctract spatial feature vectors. Compressed measurements are obtained by multiplying the pixels in the block with the sensing matrix. A new method is also developed to generate the sensing matrix. A random vector generated according to standard Gaussian distribution is passed through a wavelet transform and the resulting matrix is used as the sensing matrix. Temporal features are obtained from the vector that is formed from the difference of mean intensity values of the frames in two neighboring blocks. Spatial feature vectors are classified using Adaboost. Temporal feature vectors are classified using hidden Markov models. To reduce the computational cost only moving and bright regions are classified and classification is performed at specified intervals instead of every frame. © 2013 IEEE.
Open Access
Compressive sensing-based robust off-the-grid stretch processing
(Institution of Engineering and Technology, 2017) Ilhan, I.; Gurbuz, A. C.; Arıkan, Orhan
Classical stretch processing (SP) obtains high range resolution by compressing large bandwidth signals with narrowband receivers using lower rate analogue-to-digital converters. SP achieves the resolution of the large bandwidth signal by focusing into a limited range window, and by deramping in the analogue domain. SP offers moderate data rate for signal processing for high bandwidth waveforms. Furthermore, if the scene in the examined window is sparse, compressive sensing (CS)-based techniques have the potential to further decrease the required number of measurements. However, CS-based reconstructions are highly affected by model mismatches such as targets that are off-the-grid. This study proposes a sparsity-based iterative parameter perturbation technique for SP that is robust to targets off-the-grid in range or Doppler. The error between reconstructed and actual scenes is measured using Earth mover's distance metric. Performance analyses of the proposed technique are compared with classical CS and SP techniques in terms of data rate, resolution and signal-to-noise ratio. It is shown through simulations that the proposed technique offers robust and high-resolution reconstructions for the same data rate compared with both classical SP- and CS-based techniques.
Open Access
Cross-term free based bistatic radar system using sparse least squares
(SPIE, 2015) Sevimli, R. Akın; Çetin, A. Enis
Passive Bistatic Radar (PBR) systems use illuminators of opportunity, such as FM, TV, and DAB broadcasts. The most common illuminator of opportunity used in PBR systems is the FM radio stations. Single FM channel based PBR systems do not have high range resolution and may turn out to be noisy. In order to enhance the range resolution of the PBR systems algorithms using several FM channels at the same time are proposed. In standard methods, consecutive FM channels are translated to baseband as is and fed to the matched filter to compute the range-Doppler map. Multichannel FM based PBR systems have better range resolution than single channel systems. However superious sidelobe peaks occur as a side effect. In this article, we linearly predict the surveillance signal using the modulated and delayed reference signal components. We vary the modulation frequency and the delay to cover the entire range-Doppler plane. Whenever there is a target at a specific range value and Doppler value the prediction error is minimized. The cost function of the linear prediction equation has three components. The first term is the real-part of the ordinary least squares term, the second-Term is the imaginary part of the least squares and the third component is the l2-norm of the prediction coefficients. Separate minimization of real and imaginary parts reduces the side lobes and decrease the noise level of the range-Doppler map. The third term enforces the sparse solution on the least squares problem. We experimentally observed that this approach is better than both the standard least squares and other sparse least squares approaches in terms of side lobes. Extensive simulation examples will be presented in the final form of the paper.
Open Access
Data from: Performance of compressed sensing based image reconstruction for photoacoustic imaging
(Bilkent University, 2022-08) Çelebi, Sadık Çağatay
Open Access
Effect of different sparsity priors on compressive photon-sieve spectral imaging
(IEEE, 2018) Kar, O. F.; Oktem, F. S.; Kamaci, U.; Akyön, Fatih ÇaĞatay
Compressive spectral imaging is a rapidly growing area yielding higher performance novel spectral imagers than conventional ones. Inspired by compressed sensing theory, compressive spectral imagers aim to reconstruct the spectral images from compressive measurements using sparse signal recovery algorithms. In this paper, first, the image formation model and a sparsity-based reconstruction approach are presented for compressive photon-sieve spectral imager. Then the reconstruction performance of the approach is analyzed using different sparsity priors. In the system, a coded aperture is used for modulation and a photon-sieve for dispersion. In the measurements, coded and blurred images of spectral bands are superimposed. Simulation results show promising image reconstruction performance from these compressive measurements.
Open Access
Efficient heterogeneous parallel programming for compressed sensing based direction of arrival estimation
(John Wiley & Sons Ltd., 2021-07) Fişne, A.; Kılıç, Berkan; Güngör, Alper; Özsoy, A.
In the direction of arrival (DoA) estimation, typically sensor arrays are used where the number of required sensors can be large depending on the application. With the help of compressed sensing (CS), hardware complexity of the sensor array system can be reduced since reliable estimations are possible by using the compressed measurements where the compression is done by measurement matrices. After the compression, DoAs are reconstructed by using sparsity promoting algorithms such as alternating direction method of multipliers (ADMM). For the given procedure, both the measurement matrix design and the reconstruction algorithm may include computationally intensive operations, which are addressed in this study. The presented simulation results imply the feasibility of the system in real-time processing with energy efficient implementations. We propose employing parallel programming to satisfy the real-time processing requirements. While the measurement matrix design has been accelerated 16urn:x-wiley:cpe:media:cpe6490:cpe6490-math-0001 with CPU based parallel version with respect to the fastest serial implementation, ADMM based DoA estimation has been improved 1.1urn:x-wiley:cpe:media:cpe6490:cpe6490-math-0002 with GPU based parallel version compared to the fastest CPU parallel implementation. In addition, we achieved, to the best of our knowledge, the first energy-efficient real-time DoA estimation on embedded Jetson GPGPUs in 15 W power consumption without affecting the DoA accuracy performance.
Open Access
An empirical eigenvalue-threshold test for sparsity level estimation from compressed measurements
(IEEE, 2014) Lavrenko, A.; Römer, F.; Del Galdo, G.; Thoma, R.; Arıkan, Orhan
Compressed sensing allows for a significant reduction of the number of measurements when the signal of interest is of a sparse nature. Most computationally efficient algorithms for signal recovery rely on some knowledge of the sparsity level, i.e., the number of non-zero elements. However, the sparsity level is often not known a priori and can even vary with time. In this contribution we show that it is possible to estimate the sparsity level directly in the compressed domain, provided that multiple independent observations are available. In fact, one can use classical model order selection algorithms for this purpose. Nevertheless, due to the influence of the measurement process they may not perform satisfactorily in the compressed sensing setup. To overcome this drawback, we propose an approach which exploits the empirical distributions of the noise eigenvalues. We demonstrate its superior performance compared to state-of-the-art model order estimation algorithms numerically.
Open Access
Entropy polarization in butterfly transforms
(Academic Press, 2021-12) Arıkan, Erdal
In signal processing, it is common to employ various transforms for analyzing or compressing real- or complex-valued signals. If the transform is chosen suitably, certain characteristics of the signal, such as spectral content or sparsity, become readily accessible by looking at the energy distribution among the coordinates of the signal in the transform domain. In contrast, in information-theoretic settings entropy replaces energy as the key parameter of interest; information is processed directly by acting on the entropy through various transforms. Here we follow the information-theoretic approach and focus on the evolution of entropy in the course of butterfly transforms over arbitrary number fields. In particular, we state conditions for entropy polarization—a phenomenon that has been useful in constructing capacity-achieving source and channel codes. We discuss the possibility of using entropy polarization as a useful tool in signal processing applications.
Open Access
Fast system calibration with coded calibration scenes for magnetic particle imaging
(IEEE, 2019) İlbey, Serhat; Top, C. B.; Güngör, Alper; Çukur, Tolga; Sarıtaş, Emine Ülkü; Güven, H. Emre
Magnetic particle imaging (MPI) is a relatively new medical imaging modality, which detects the nonlinear response of magnetic nanoparticles (MNPs) that are exposed to external magnetic fields. The system matrix (SM) method for MPI image reconstruction requires a time consuming system calibration scan prior to image acquisition, where a single MNP sample is measured at each voxel position in the field-of-view (FOV). The scanned sample has the maximum size of a voxel so that the calibration measurements have relatively poor signal-to-noise ratio (SNR). In this paper, we present the coded calibration scene (CCS) framework, where we place multiple MNP samples inside the FOV in a random or pseudo-random fashion. Taking advantage of the sparsity of the SM, we reconstruct the SM by solving a convex optimization problem with alternating direction method of multipliers using CCS measurements. We analyze the effects of filling rate, number of measurements, and SNR on the SM reconstruction using simulations and demonstrate different implementations of CCS for practical realization. We also compare the imaging performance of the proposed framework with that of a standard compressed sensing SM reconstruction that utilizes a subset of calibration measurements from a single MNP sample. The results show that CCS significantly reduces calibration time while increasing both the SM reconstruction and image reconstruction performances.
Open Access
Faz-MIMO sistemlerinde geliş açısı kestirim performansı karşılaştırması
(IEEE, 2021-07-19) Bahçeci, M. Umut; Güngör, Alper; Çetintepe, Çağrı; Tuncer, T. Engin
Bu çalışmada, literatürde yer alan Fazlı Çoklu Giriş-Çoklu Çıkış (Fazlı-MIMO) dizi konsepti ile Sıkıştırılmış Algılama (SA) teknikleri ilk defa birlikte ele alınarak bir radar sisteminde çoklu hedefler için geliş açısı kestirim (GAK) problemi özgün olarak ele alınmıştır. Göndermeç tarafında ortalama güç kıstasları gözetilerek farklı altdizi sayıları için geleneksel huzme şekillendirme ve rasgele fazlı ağırlıklı tasarım yöntemleri incelenmi ş; almaç tarafında ise huzme şekillendirme, ayrık SA ve grup SA olmak üzere üç farklı geri çatım yöntemi ile çoklu hedefler için GAK başarımı araştırılmıştır. Anılan her senaryo için farklı Sinyal-Gürültü-Oranları (SGO) altında gerçekleştirilen parametrik Monte Carlo benzetimleri ile hedef başına hata sonuçları elde edilmiştir. Düşük SGO durumunda, Fazlı-MIMO ve SA teknikleri birlikte kullanılarak GAK başarımının artırılabileceği gösterilmiştir. Ayrıca grup seyreklik çözümü ile ayrık çözüme kıyasla kestirim hatasında 3 kata kadar bir azalma sağlanmıştır.