Browsing by Subject "System matrix"

Now showing 1 - 6 of 6

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
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
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
Parameter robustness analysis of system function reconstruction and a novel deblurring network for magnetic particle imaging
(2020-12) Arol, Abdullah Ömer
Magnetic Particle Imaging (MPI) is a novel medical imaging modality that can provide excellent sensitivity, contrast and resolution for imaging the spatial distribution of superparamagnetic iron oxide nanoparticles by utilizing their nonlinear magnetization responses. System function reconstruction (SFR) and x-space reconstruction are the two main image reconstruction approaches in MPI. SFR requires time-consuming calibration measurements, which need to be repeated whenever there is a change in scanning parameters or the nanoparticle. In the first part of this thesis, the effects of using mismatched parameters during calibration measurements and imaging in SFR are investigated. Through numerical simulations, MPI signals gathered with different scanning parameters are used for reconstructing images to analyze the effects of parameter changes in image quality in SFR. In contrast to the SFR approach, standard x-space reconstruction does not require calibration measurements. However, the reconstructed images are blurred by the point spread function of the system. In the second part of this thesis, a new learning-based approach is proposed to improve the image quality in x-space reconstructed images. The proposed method learns an end-to-end mapping between the x-space reconstructed blurred images and the underlying nanoparticle distributions. By using numerical simulations, it is shown that the blurring in x-space reconstruction can be significantly reduced with the proposed method.
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
TranSMS: transformers for super-resolution calibration in magnetic particle imaging
(Institute of Electrical and Electronics Engineers Inc., 2022-07-11) Gungor, Alper; Askin, Baris; Soydan, D.A.; Saritas, Emine Ulku; Top, C. B.; Çukur, Tolga
Magnetic particle imaging (MPI) offers exceptional contrast for magnetic nanoparticles (MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a calibration scan to measure the system matrix (SM), which is then used to set up an inverse problem to reconstruct images of the MNP distribution during subsequent scans. This calibration enables the reconstruction to sensitively account for various system imperfections. Yet time-consuming SM measurements have to be repeated under notable changes in system properties. Here, we introduce a novel deep learning approach for accelerated MPI calibration based on Transformers for SM super-resolution (TranSMS). Low-resolution SM measurements are performed using large MNP samples for improved signal-to-noise ratio efficiency, and the high-resolution SM is super-resolved via model-based deep learning. TranSMS leverages a vision transformer module to capture contextual relationships in low-resolution input images, a dense convolutional module for localizing high-resolution image features, and a data-consistency module to ensure measurement fidelity. Demonstrations on simulated and experimental data indicate that TranSMS significantly improves SM recovery and MPI reconstruction for up to 64-fold acceleration in two-dimensional imaging