Browsing by Author "Güngör, Alper"

Now showing 1 - 16 of 16

Open Access
A denoiser scaling technique for plug-and-play MPI reconstruction
(Infinite Science Publishing, 2023-03-19) Güngör, Alper; Aşkın, Barış; Alptekin Soydan, D.; Sarıtaş, Emine Ülkü; Top, C. B.; Çukur, Tolga
Image reconstruction based on the system matrix in magnetic particle imaging (MPI) involves an ill-posed inverse problem, which is often solved using iterative optimization procedures that use regularization. Reconstruction performance is highly dependent on the quality of information captured by the regularization prior. Learning-based methods have been recently introduced that significantly improve prior information in MPI reconstruction. Yet, these methods can perform suboptimally under drifts in the image scale between the training and test sets. In this study, we assess the influence of scale drifts on the performance a recent plug-ang-play method (PP-MPI) that uses a pre-trained denoiser. We introduce a new denoiser scaling technique that improves reliability of PP-MPI against deviations in image scale. The proposed technique enables high quality reconstructions that are robust against scale drifts between training and testing sets.
Open Access
Adaptive diffusion priors for accelerated MRI reconstruction
(Elsevier B.V., 2023-07-20) Güngör, Alper; Dar, Salman Ul Hassan; Öztürk, Şaban; Korkmaz, Yılmaz; Bedel, Hasan Atakan; Elmas, Gökberk; Özbey, Muzaffer; Çukur, Tolga
Deep MRI reconstruction is commonly performed with conditional models that de-alias undersampled acquisitions to recover images consistent with fully-sampled data. Since conditional models are trained with knowledge of the imaging operator, they can show poor generalization across variable operators. Unconditional models instead learn generative image priors decoupled from the operator to improve reliability against domain shifts related to the imaging operator. Recent diffusion models are particularly promising given their high sample fidelity. Nevertheless, inference with a static image prior can perform suboptimally. Here we propose the first adaptive diffusion prior for MRI reconstruction, AdaDiff, to improve performance and reliability against domain shifts. AdaDiff leverages an efficient diffusion prior trained via adversarial mapping over large reverse diffusion steps. A two-phase reconstruction is executed following training: a rapid-diffusion phase that produces an initial reconstruction with the trained prior, and an adaptation phase that further refines the result by updating the prior to minimize data-consistency loss. Demonstrations on multi-contrast brain MRI clearly indicate that AdaDiff outperforms competing conditional and unconditional methods under domain shifts, and achieves superior or on par within-domain performance. © 2023 Elsevier B.V.
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
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
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.
Open Access
Learning-based reconstruction methods for magnetic particle imaging
(2023-01) Güngör, Alper
Magnetic particle imaging (MPI) is a novel modality for imaging of magnetic nanoparticles (MNP) with high resolution, contrast and frame rate. An inverse problem is usually cased for reconstruction, which requires a time-consuming calibration scan for measuring a system matrix (SM). Previous calibration procedures involve scanning an MNP filled sample with a size that matches desired resolution through field of view. This time-consuming calibration scan which accounts for both system and MNP response imperfections is a critical factor prohibiting its practical use. Moreover, the quality of the reconstructed images heavily depend on the prior information about the MNP distribution as well as the specific re-construction algorithm, since the inverse problem is highly ill-posed. Previous approaches commonly solve an optimization problem based on the measurement model that iteratively estimates the image while enforcing data consistency in an interleaved fashion. However, while conventional hand-crated priors do not fully capture the underlying complex features of MPI images, recently proposed learned priors suffer from limited generalization performance. To tackle these issues, we first propose a deep learning based technique for accelerated MPI calibration. The technique utilizes transformers for SM super-resolution (TranSMS) for accelerated calibration of SMs with high signal-to-noise-ratio. For signal-to-noise-ratio efficiency, we propose scanning a low resolution SM with larger MNP sample size. For improved SM estimation, TranSMS leverages the vision trans-former to capture global contextual information while utilizing the convolutional module for local high-resolution features. Finally, a novel data-consistency module enforces measurement fidelity. TranSMS is shown to outperform competing methods significantly in terms of both SM recovery and image reconstruction performance. Next, to improve image reconstruction quality, we propose a novel physics-driven deep equilibrium based technique with learned consistency block for MPI (DEQ-MPI). DEQ-MPI embeds deep network operators into iterative optimization procedures for improved modeling of image statistics. Moreover, DEQ-MPI utilizes learned consistency to better capture the data statistics which helps improve the overall image reconstruction performance. Finally, compared to previous unrolling-based techniques, DEQ-MPI leverages implicit layers which enables training on the converged output. Demonstrations on both simulated and experimental data show that DEQ-MPI significantly improves image quality and reconstruction time over state-of-the-art reconstructions based on hand-crafted or learned priors.
Open Access
Manyetik parçacık görüntüleme için evrişimsel sinir ağı tabanlı bir süper-çözünürlük tekniği
(IEEE, 2021-07-19) Aşkın, Barış; Güngör, Alper; Soydan, Damla Alptekin; Top, Can Barış; Çukur, Tolga
Manyetik Parçacık Görüntüleme (MPG), süperparamanyetik demir-oksit (SPDO) parçacıklarının yüksek çözünürlük ve kare hızında görüntülenmesini sağlayan bir görüntüleme yöntemidir. Görüntüleme işlemi doğrusal olarak modellenebilmektedir. Ancak deneysel sistemlerin ideal dışı davranışı ve teorik sistemlere kıyasla değişimlerinden dolayı, MPG sistemlerinde çoğu durumda öncelikli olarak ileri model matrisi ölçülür (sistem kalibre edilir) ve ardından bu matrisler kullanılarak görüntülerin geriçatımı yapılır. Görüntü çözünürlüğü ve boyutu doğrudan sistem matrisinin boyutundan etkilenmektedir. Ancak, kalibrasyon işlemi görüntüleme alanına bağlı olarak çok zaman almaktadır. Bu çalışmada, düşük çözünürlükte ölçülen sistem matrisleri üzerinde süper-çözünürlük teknikleri kullanılarak yüksek çözünürlüklü sistem matrisi elde edilmesi önerilmektedir. Bu amaç doğrultusunda evrişimsel sinir ağı (ESA) tabanlı bir süperçözünürlük tekniği MPG için uyarlanmış ve doğrusal aradeğerlemeye (interpolasyon) karşı etkinliği gösterilmiştir. Yöntemler gürültüsüz bir benzetim ortamında kıyaslanmış ve 4 4 kat süper-çözünürlük için, önerilen yöntem %2.92 normalize edilmiş ortalama kare hatasına yol açarken, bikübik aradeğerlemenin %12.47 hataya yol açtığı gösterilmiştir.
Open Access
Parçacık süzgeci kullanarak uyarlamalı sıkıştırılmış algılama tabanlı geliş yönü kestirimi
(IEEE, 2020-12-18) Kılıç, Berkan; Güngör, Alper; Kalfa, Mert; Arıkan, Orhan
Geliş yönü kestirimi (GYK) problemlerinde sinyaller arkaplan uzayında, nispeten az boyutlu bir manifoldda yatar. Bu nedenle, sıkıştırılmış algılama teknikleri güvenilir geliş yönü kestirimine olanak verir. Ayrıca, ardışık Monte Carlo yöntemlerini kullanmak, tek bir noktasal kestirim yerine GYK için bir olasılık dağılımı elde etmeye olanak tanır. Sonuç olarak, bu olasılık dağılımı ölçüm matrisi tasarımında kullanıldığında, yüksek kestirim başarımı ile birlikte anten dizisi sinyal işlemede boyut indirgeme sağlanır. Bu çalışmada, olasılık dağılımı elde etmek için parçacık süzgeci kullanan, uyarlamalı sıkıştırılmış algılama tabanlı bir ızgarasız GYK yöntemi önerilmiştir. Ölçüm matrisinin rasgele seçilmesinin ve tasarlanmasının başarımları karşılaştırılmıştır. Ölçüm matrisi tasarımının, antenler üzerindeki ölçüm gürültüsüne bağlı olarak kestirim başarım artırımı, bir dizi benzetim ile gösterilmiştir.
Open Access
PP-MPI: A deep plug-and-play prior for magnetic particle imaging reconstruction
(Springer Cham, 2022-09) Aşkın, Barış; Güngör, Alper; Alptekin Soydan, D.; Sarıtaş, Emine Ülkü; Top, C. B.; Çukur, Tolga; Haq, Nandinee; Maier, Andreas; Qin, Chen; Johnson, Patricia; Würfl, Tobias; Yoo, Jaejun
Magnetic particle imaging (MPI) is a recent modality that enables high contrast and frame-rate imaging of the magnetic nanoparticle (MNP) distribution. Based on a measured system matrix, MPI reconstruction can be cast as an inverse problem that is commonly solved via regularized iterative optimization. Yet, hand-crafted regularization terms can elicit suboptimal performance. Here, we propose a novel MPI reconstruction “PP-MPI” based on a deep plug-and-play (PP) prior embedded in a model-based iterative optimization. We propose to pre-train the PP prior based on a residual dense convolutional neural network (CNN) on an MPI-friendly dataset derived from magnetic resonance angiograms. The PP prior is then embedded into an alternating direction method of multiplier (ADMM) optimizer for reconstruction. A fast implementation is devised for 3D image reconstruction by fusing the predictions from 2D priors in separate rectilinear orientations. Our demonstrations show that PP-MPI outperforms state-of-the-art iterative techniques with hand-crafted regularizers on both simulated and experimental data. In particular, PP-MPI achieves on average 3.10 dB higher peak signal-to-noise ratio than the top-performing baseline under variable noise levels, and can process 12 frames/sec to permit real-time 3D imaging.
Open Access
Simultaneous use of individual and joint regularization terms in compressive sensing: joint reconstruction of multi‐channel multi‐contrast MRI acquisitions
(Wiley, 2020) Kopanoğlu, E.; Güngör, Alper; Kılıç, Toygan; Sarıtaş, Emine Ülkü; Oğuz, Kader K.; Çukur, Tolga; Güven, H. E.
Multi‐contrast images are commonly acquired together to maximize complementary diagnostic information, albeit at the expense of longer scan times. A time‐efficient strategy to acquire high‐quality multi‐contrast images is to accelerate individual sequences and then reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause features that are unique to a subset of contrasts to leak into the other contrasts. Such leakage‐of‐features may appear as artificial tissues, thereby misleading diagnosis. The goal of this study is to develop a compressive sensing method for multi‐channel multi‐contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi‐channel multi‐contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single‐channel simulated and multi‐channel in‐vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. The proposed method demonstrates rapid convergence and improved image quality for both simulated and in‐vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage‐of‐features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms, thereby holding great promise for clinical use.
Open Access
Statistically segregated k-space sampling for accelerating multiple-acquisition MRI
(IEEE, 2019) Şenel, L. Kerem; Kılıç, Toygan; Güngör, Alper; Kopanoğlu, Emre; Güven, H. Emre; Sarıtaş, Emine U.; Koç, Aykut; Çukur, Tolga
A central limitation of multiple-acquisition magnetic resonance imaging (MRI) is the degradation in scan efficiency as the number of distinct datasets grows. Sparse recovery techniques can alleviate this limitation via randomly undersampled acquisitions. A frequent sampling strategy is to prescribe for each acquisition a different random pattern drawn from a common sampling density. However, naive random patterns often contain gaps or clusters across the acquisition dimension that, in turn, can degrade reconstruction quality or reduce scan efficiency. To address this problem, a statistically segregated sampling method is proposed for multiple-acquisition MRI. This method generates multiple patterns sequentially while adaptively modifying the sampling density to minimize k-space overlap across patterns. As a result, it improves incoherence across acquisitions while still maintaining similar sampling density across the radial dimension of k-space. Comprehensive simulations and in vivo results are presented for phase-cycled balanced steady-state free precession and multi-echo $\text{T}_{\text{2}}$ -weighted imaging. Segregated sampling achieves significantly improved quality in both Fourier and compressed-sensing reconstructions of multiple-acquisition datasets.
Open Access
Super-resolving reconstruction technique for MPI
(Infinite Science Publishing, 2020) Güngör, Alper; Top, Can Barış
System matrix reconstruction of Magnetic Particle Imaging (MPI) require a time-consuming calibration process. The total number of pixels of the desired image has a direct effect on the calibration time. Although there are various techniques that can shorten the calibration process such as compressive sensing or coded calibration scenes, the increase in total number of pixels still require higher number of samples. In this study, we propose a simple super-resolution technique for MPI images during reconstruction. Using simulations on a field free line MPI scanner system with low drive field amplitude, we show that one can achieve higher resolution images by simply applying super-resolution techniques on the rows of the system matrix. We demonstrate that even simple linear models can help resolve high-resolution structures.
Open Access
Tomographic field free line magnetic particle imaging with an open-sided scanner configuration
(IEEE, 2020) Top, C. B.; Güngör, Alper
Superparamagnetic iron oxide nanoparticles (SPIONs) have a high potential for use in clinical diagnostic and therapeutic applications. In vivo distribution of SPIONs can be imaged with the Magnetic Particle Imaging (MPI) method, which uses an inhomogeneous magnetic field with a field free region (FFR). The spatial distribution of the SPIONs are obtained by scanning the FFR inside the field of view (FOV) and sensing SPION related magnetic field disturbance. MPI magnets can be configured to generate a field free point (FFP), or a field free line (FFL) to scan the FOV. FFL scanners provide more sensitivity, and are also more suitable for scanning large regions compared to FFP scanners. Interventional procedures will benefit greatlyfrom FFL based open magnet configurations. Here, we present the first open-sided MPI system that can electronically scan the FOV with an FFL to generate tomographic MPI images. Magnetic field measurements show that FFL can be rotated electronically in the horizontal plane and translated in three dimensions to generate 3D MPI images. Using the developed scanner, we obtained 2D images of dot and cylinder phantoms with varying iron concentrations between 11 μg/ml and 770 μg/ml. We used a measurement based system matrix image reconstruction method that minimizes 11-norm and total variation in the images. Furthermore, we present 2D imaging results of two 4 mm-diameter vessel phantoms with 0% and 75% stenosis. The experiments show high quality imaging results with a resolution down to 2.5 mm for a relatively low gradient field of 0.6 T/m.
Open Access
Unsupervised medical image translation with adversarial diffusion models
(Institute of Electrical and Electronics Engineers , 2023-11-30) Özbey, Muzaffer; Dalmaz, Onat; Dar, Salman Ul Hassan; Bedel, Hasan Atakan; Özturk, Şaban; Güngör, Alper; Çukur, Tolga
Imputation of missing images via source-to-target modality translation can improve diversity in medical imaging protocols. A pervasive approach for synthesizing target images involves one-shot mapping through generative adversarial networks (GAN). Yet, GAN models that implicitly characterize the image distribution can suffer from limited sample fidelity. Here, we propose a novel method based on adversarial diffusion modeling, SynDiff, for improved performance in medical image translation. To capture a direct correlate of the image distribution, SynDiff leverages a conditional diffusion process that progressively maps noise and source images onto the target image. For fast and accurate image sampling during inference, large diffusion steps are taken with adversarial projections in the reverse diffusion direction. To enable training on unpaired datasets, a cycle-consistent architecture is devised with coupled diffusive and non-diffusive modules that bilaterally translate between two modalities. Extensive assessments are reported on the utility of SynDiff against competing GAN and diffusion models in multi-contrast MRI and MRI-CT translation. Our demonstrations indicate that SynDiff offers quantitatively and qualitatively superior performance against competing baselines.