Browsing by Author "Güngör, A."

Now showing 1 - 11 of 11

Open Access
A diffusion-based reconstruction technique for single pixel camera
(IEEE - Institute of Electrical and Electronics Engineers, 2023-08-28) Güven, Baturalp; Güngör, A.; Bahçeci, M. U.; Çukur, Tolga
Single-pixel imaging enables high-resolution imaging through multiple coded measurements based on lowresolution snapshots. To reconstruct a high-resolution image from these coded measurements, an ill-posed inverse problem is solved. Despite the recent popularity of deep learning-based methods for single-pixel imaging reconstruction, they are insufficient in preserving spatial details and achieving a stable reconstruction. Diffusion-based methods, which have gained attention in recent years, provide a solution to this problem. In this study, to the best of our knowledge, the single-pixel image reconstruction is performed for the first time using a denoising diffusion probabilistic model. The proposed method reconstructs the image by conditioning it towards the least squares solution while preserving data consistency after unconditional training of the model. The proposed method is compared against existing singlepixel imaging methods, and ablation studies are conducted to demonstrate the individual model components. The proposed method outperforms competing methods in both quantitative measurements and visual quality.
Open Access
A transformer-based real-time focus detection technique for wide-field interferometric microscopy
(IEEE - Institute of Electrical and Electronics Engineers, 2023-08-28) Polat, Can; Güngör, A.; Yorulmaz, M.; Kızılelma, B.; Çukur, Tolga
Wide-field interferometric microscopy (WIM) has been utilized for visualization of individual biological nanoparticles with high sensitivity. However, the image quality is highly affected by the focusing of the image. Hence, focus detection has been an active research field within the scope of imaging and microscopy. To tackle this issue, we propose a novel convolution and transformer based deep learning technique to detect focus in WIM. The method is compared to other focus detecton techniques and is able to obtain higher precision with less number of parameters. Furthermore, the model achieves real-time focus detection thanks to its low inference time.
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
Comparison of system-matrix-based and projection-based reconstructions for field free line magnetic particle imaging
(Infinite Science Publishing, 2017) Ilbey, S.; Top, C. B.; Güngör, A.; Çukur, Tolga; Sarıtaş, Emine Ülkü; Güven, H. E.
In magnetic particle imaging (MPI), system sensitivity can be enhanced by scanning the sample along a field free line (FFL) instead of a field free point (FFP). FFL MPI data can then be processed via system-matrix or projection-based reconstructions. Here, we compare the relative performance of these two approaches. We assume an ideal FFL (straight and homogeneous), which is translated and rotated in a two-dimensional field-of-view. We simulate the acquired data from a numerical vessel phantom for a broad range of noise levels. For the system-matrix reconstruction, we propose Alternating Direction Method of Multipliers (ADMM) to solve a constrained convex optimization problem. We also analyze the results of the nonnegative fused lasso (NFL) model to compare the performance of ADMM with one of the state-of-the-art system-matrix-based methods. For the projection-based reconstruction, we use the inverse Radon transform formulation with x-space reconstruction. System-matrix-based methods resulted in a higher structural similarity index and contrast compared to the x-space reconstruction method at the expense of longer reconstruction time. Artifacts occurred due to gridding errors for the x-space reconstruction. As expected, ADMM and NFL reconstructions yielded similar image quality.
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
Deep learning reconstruction for single pixel imaging with generative adversarial networks
(IEEE, 2023-09-11) Güven, Baturalp; Güngör, A.; Bahçeci, M. U.; Çukur, Tolga
Single pixel imaging (SPI) enables high-resolution imaging through multiple coded measurements based on low-resolution snapshots. An inverse problem can then be solved to reconstruct a high-resolution image given the coded measurements. There has been recent interest in adoption of deep neural networks in SPI reconstruction. However, existing methods are commonly trained with pixel-wise loss terms such as the ℓ 1 -norm loss, which can result in spatial blurring and poor sensitivity to structural details. In this study, we propose a novel approach for deep SPI reconstruction based on an unrolled conditional generative adversarial network (cGAN) model. The generator estimates the high-resolution image using coded low-resolution measurements by iterating across a cascade of denoising and data-consistency modules. Meanwhile, the discriminator distinguishes real versus synthesized high-resolution images. The architecture is trained end-to-end via a combined pixel-wise and adversarial loss to enhance sensitivity to structural details. The proposed method is demonstrated against existing SPI reconstruction methods, and ablation studies are performed to demonstrate the individual model components. The proposed method outperforms competing methods in terms of both quantitative metrics and visual quality.
Open Access
Joint dictionary learning reconstruction of compressed multi-contrast magnetic resonance imaging
(Institute of Electrical and Electronics Engineers, 2018) Güngör, A.; Kopanoğlu, E.; Çukur, Tolga; Güven, E.; Yarman-Vural, F. T.
This study deals with reconstruction of compressed multicontrast magnetic resonance image (MRI) reconstruction using joint dictionary learning. Usually pre-determined dictionaries are used for compressed sensing reconstructions. Here, we propose an alternating-minimization based algorithm for recovering image and sparsifying transformation from only data itself. The proposed method can also be viewed as a joint multicontrast reconstruction extension of a previous blind compressive sensing algorithm [1]. For evaluation, the algorithm is compared in terms of convergence speed and image quality to both individual dictionary learning based method [1], and a joint reconstruction algorithm using pre-determined dictionaries for MRI [2].
Open Access
Real-time three-dimensional image reconstruction using alternating direction method of multipliers for magnetic particle imaging
(IEEE, 2018) İlbey, Serhat; Güngör, A.; Top, C. B.; Sarıtaş, Emine Ülkü; Güven, H. E.
Manyetik Parçacık Görüntüleme (MPG), süperparamanyetik demiroksit nanoparçacıklarının uzamsal dağılımını tespit etmekte kullanılan görece yeni bir medikal görüntüleme yöntemidir. MPG’de görüntü geriçatımı için kullanılan yöntemlerden biri sistem matrisi yaklaşımıdır. Bu yöntemde öncelikle kalibrasyon ölçümleri yapılarak sistem matrisi elde edilir. Daha sonra, sistem matrisi ve görüntülenen objeden alınan veri ile bir doğrusal denklem sistemi oluşturulur ve görüntülenen alandaki manyetik parçacık dağılımı yinelemeli düzenlileştirme veya eniyileme algoritmaları ile çözülür. Bu çalışmada, grafik işlemciler kullanılarak yön degiştiren çarpanlar yöntemi ile üç boyutlu bir görüntüleme uzayında gerçek zamanlı görüntü geriçatımı yapılabileceği gösterilmiştir.
Open Access
Simulation of a digital communication system
(IEEE, 2005-09) Güngör, A.; Arıkan, F.; Arıkan, Orhan
In this paper, basic components of a digital communication system are simulated by a computer program. The simulation program is modular and flexible to incorporate any future additions and updates. The simulation program allows the user to choose from various channel models, transmitter and receiver antenna systems, modulation and channel coding techniques. A communication system is defined by various parameters including the source, coding, modulation, antenna systems. In order to facilitate the input of these parameters and follow the flow of the simulation, the Graphical User Interface (GUI) is designed for convenience to the user. The input parameters can both be entered from the GUI or from prepared user files. The major contribution of this simulation system to the existing communication simulators is the addition of flexible antenna systems both at the transmitting and receiving ends. With this simulation program, the antenna arrays can be located anywhere on Earth, on any platform and array elements can be placed on the platform by any desired orientation. The simulation program results are compared with both theoretical computations and commercial simulator results and excellent agreement is observed in both cases.
Open Access
Synthesis of a novel poly(arylene ether ketone) and its conducting composites with polypyrrole
(Elsevier, 1997) Selampinar, F.; Akbulut, U.; Yildiz, E.; Güngör, A.; Toppare, L.
The synthesis of a 1,3-bis(4-fluorobenzoyl)-5-tert-butyl benzene and hexafluoro bisphenol A based poly(arylene ether ketone) (PEK) was described. The electrically conductive composites of polypyrrole (PPy) and PEK were formed by electropolymerization of pyrrole on a PEK coated platinum electrode in a medium containing water andp-toluenesulfonic acid as the solvent and the electrolyte, respectively. The electrical conductivity of the composites was found to be between 1 and 4 S/cm. The polypyrrole/poly (ether ketone) composites were characterized by scanning electron microscopy, FT-IR and thermal analyses (TGA, DSC). © 1997 Elsevier Science S.A.
Open Access
A synthesis-based approach to compressive multi-contrast magnetic resonance imaging
(IEEE, 2017) Güngör, A.; Kopanoğlu, E.; Çukur, Tolga; Güven, H. E.
In this study, we deal with the problem of image reconstruction from compressive measurements of multi-contrast magnetic resonance imaging (MRI). We propose a synthesis based approach for image reconstruction to better exploit mutual information across contrasts, while retaining individual features of each contrast image. For fast recovery, we propose an augmented Lagrangian based algorithm, using Alternating Direction Method of Multipliers (ADMM). We then compare the proposed algorithm to the state-of-the-art Compressive Sensing-MRI algorithms, and show that the proposed method results in better quality images in shorter computation time.