Browsing by Author "Saritas, Emine Ulku"

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Open Access
Antibacterial properties and osteoblast interactions of microfluidically synthesized chitosan – SPION composite nanoparticles
(Wiley Periodicals LLC, 2023-05-26) Kafali, M.; Şahinoğlu, O. Berkay; Tufan, Y.; Orsel, Z. C.; Aygun, Elif; Alyuz, Beril; Saritas, Emine Ulku; Erdem, E. Yegan; Ercan, B.
In this research, a multi-step microfluidic reactor was used to fabricate chitosan – superparamagnetic iron oxide composite nanoparticles (Ch – SPIONs), where composite formation using chitosan was aimed to provide antibacterial property and nanoparticle stability for magnetic resonance imaging (MRI). Monodispersed Ch – SPIONs had an average particle size of 8.8 ± 1.2 nm with a magnetization value of 32.0 emu/g. Ch – SPIONs could be used as an MRI contrast agent by shortening T2 relaxation parameter of the surrounding environment, as measured on a 3 T MRI scanner. In addition, Ch – SPIONs with concentrations less than 1 g/L promoted bone cell (osteoblast) viability up to 7 days of culture in vitro in the presence of 0.4 T external static magnetic field. These nanoparticles were also tested against Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa), which are dangerous pathogens that cause infection in tissues and biomedical devices. Upon interaction of Ch – SPIONs with S. aureus and P. aeruginosa at 0.01 g/L concentration, nearly a 2-fold reduction in the number of colonies was observed for both bacteria strains at 48 h of culture. Results cumulatively showed that Ch – SPIONs were potential candidates as a cytocompatible and antibacterial agent that can be targeted to biofilm and imaged using an MRI.
Open Access
A deblurring model for X-space MPI based on coded calibration scenes
(Infinite Science Publishing, 2022) Ergun, Esen; Arola, Abdullah Ömer; Saritas, Emine Ulku
X-space reconstructions suffer from blurring caused by the point spread function (PSF) of the Magnetic Particle Imaging (MPI) system. Here, we propose a deep learning method for deblurring x-space reconstructed images. Our proposed method learns an end-to-end mapping between the gridding-reconstructed collinear images from two partitions of a Lissajous trajectory and the underlying magnetic nanoparticle (MNP) distribution. This nonlinear mapping is learned using measurements from a coded calibration scene (CCS) to speed up the training process. Numerical experiments show that our learning-based method can successfully deblur x-space reconstructed images across a broad range of measurement signal-to-noise ratios (SNR) following training at a moderate SNR.
Open Access
MNP characterization and signal prediction using a model-based dictionary
(Infinite Science Publishing, 2022-03-21) Alpman, Asli; Utkur, Mustafa; Saritas, Emine Ulku
Magnetic Particle Imaging (MPI) utilizes the nonlinear magnetic response of magnetic nanoparticles (MNPs) for signal localization. Accurate modeling of the magnetization behavior of MNPs is crucial for understanding their MPI signal responses. In this work, we propose a model-based dictionary approach using a coupled Brown-Néel rotation model. With experimental results on a Magnetic Particle Spectrometer (MPS), we show that this approach can successfully characterize MNPs and predict their signal responses.
Open Access
PNS limits for Human Head-Size MPI systems: Preliminary results
(Infinite Science Publishing, 2022) Ozaslan, Ali Alper; Utkur, Mustafa; Canpolat, Ugur; Tuncer, Meryem Asli; Oguz, Kader Karli; Saritas, Emine Ulku
Magnetic Particle Imaging (MPI) utilizes kHz-range sinusoidal drive fields to excite magnetic nanoparticles. These time-varying magnetic fields induce electric fields within the human body, which in turn can induce peripheral nerve stimulation (PNS), also known as magnetostimulation. In this work, we report the preliminary results of human subject experiments for human head-size MPI systems. These experiments were performed on a solenoidal head coil that achieved an order of magnitude reduction in the voltages needed to generate the targeted magnetic fields.
Open Access
Rapid TAURUS for real-time color MPI: A feasibility study
(Infinite Science Publishing, 2022) Arslan, Musa Tunç; Saritas, Emine Ulku
Recent developments in color magnetic particle imaging (MPI) provided additional functionalities to MPI, such as distinguishing magnetic nanoparticles (MNPs) by type or by their environmental conditions. In this work, we propose rapid TAURUS (TAU estimation via Recovery of Underlying mirror Symmetry) to achieve relaxation-based real-time color MPI. The method can successfully map the effective relaxation time constants in a relatively wide field-of-view (FOV) at frame rates exceeding 5 frames-per-second (FPS). We present the first simulation results demonstrating that rapid TAURUS is capable of generating high fidelity and high FPS color MPI images in real time.
Open Access
Rapid TAURUS for relaxation-based color magnetic particle imaging
(Institute of Electrical and Electronics Engineers Inc., 2022-08-03) Aslan, M. Tunç; Özaslan, A. Alper; Kurt, S.; Muslu, Y.; Saritas, Emine Ulku
Magnetic particle imaging (MPI) is a rapidly developing medical imaging modality that exploits the non-linear response of magnetic nanoparticles (MNPs). Color MPI widens the functionality of MPI, empowering it with the capability to distinguish different MNPs and/or MNP environments. The system function approach for color MPI relies on extensive calibrations that capture the differences in the harmonic responses of the MNPs. An alternative calibration-free x-space-based method called TAURUS estimates a map of the relaxation time constant, τ , by recovering the underlying mirror symmetry in the MPI signal. However, TAURUS requires a back and forth scanning of a given region, restricting its usage to slow trajectories with constant or piecewise constant focus fields (FFs). In this work, we propose a novel technique to increase the performance of TAURUS and enable τ map estimation for rapid and multi-dimensional trajectories. The proposed technique is based on correcting the distortions on mirror symmetry induced by time-varying FFs. We demonstrate via simulations and experiments in our in-house MPI scanner that the proposed method successfully estimates high-fidelity τ maps for rapid trajectories that provide orders of magnitude reduction in scanning time (over 300 fold for simulations and over 8 fold for experiments) while preserving the calibration-free property of TAURUS.
Open Access
Saturation Coil for Localized Signal Suppression in MPI
(Infinite Science Publishing, 2022) Kor, Ege; Arslan, Musa Tunç; Saritas, Emine Ulku
The magnetic nanoparticles (MNPs) used as imaging tracers in magnetic particle imaging (MPI) accumulate in off-target organs such as liver or spleen. The signal from the high-concentration MNPs in these off-target organs may overpower the signals from the nearby low-concentration regions targeted during imaging. In this work, we propose using a saturation coil to suppress the localized high intensity MPI signal from the off-target accumulation organs. The results of the proof-of-concept imaging experiments show that, when the saturation coil is placed over the high-concentration region, it can selectively and completely suppress the signal from that region.
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