Browsing by Subject "Magnetic particle imaging"

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A novel hybrid magnetic particle imaging and low-field magnetic resonance imaging scanner
(2024-08) Karaca, Sefa
Magnetic particle imaging (MPI) is an emerging medical imaging modality, in which the spatial distribution of the magnetic nanoparticles (MNPs) are imaged using their non-linear magnetization curve. Since biological tissues do not exhibit such magnetic behavior, MNPs serve as the sole source of the MPI signal, making it a promising in vivo imaging modality with high contrast and sensitivity. However, anatomical information is also essential for many applications. To address this issue, MPI can be combined with other imaging modalities, such as computed tomography (CT) or magnetic resonance imaging (MRI), using standalone or hybrid systems. It is particularly advantageous to combine MPI with MRI, given that they are both non-ionizing imaging modalities. Moreover, since both MPI and MRI utilize magnetic fields, a hybrid system that integrates these modalities can potentially reduce costs via shared utilization of hardware. This thesis introduces a novel preclinical-size hybrid MPI and low-field (LF) MRI scanner. The proposed system features an MPI mode with a field free line (FFL) topology and a selection field gradient of 0.25 T/m, alongside a LF-MRI mode with a B0 field strength of 50 mT. The primary advantage of this hybrid system lies in the interchangeable use of coil groups between MPI and LF-MRI modes, facilitating the generation of a multimodal image that features high sensitivity and contrast imaging of MNP distribution by MPI, alongside anatomical information from LF-MRI. Additionally, the coil configuration of this hybrid system features an open-sided design, with the exception of the Tx/Rx coil of MRI, which utilizes a solenoid design for experimental studies. This coil can be substituted with a surface coil, facilitating the development of an open-sided hybrid system. First, the feasibility of multimodal imaging with the proposed hybrid scanner is evaluated by characterizing the magnetic fields in the system. Next, the effects of system-induced deviations on image quality are investigated via an in-house MRI simulator. The experimental imaging results demonstrate that the proposed preclinical-size hybrid MPI and LF-MRI scanner can successfully perform multimodal imaging.
Open Access
A preclinical arbitrary waveform magnetic particle imaging scanner for multi-frequency imaging
(2023-08) Yılmaz, Beril Alyüz
Magnetic Particle Imaging (MPI) is a tracer based tomographic imaging modal-ity that images the spatial distribution of the magnetic nanoparticles (MNPs) using their nonlinear magnetization response. MPI is a rapidly growing and safe imaging modality with high temporal and spatial resolution, together with high sensitivity. In MPI, different types of MNPs and the properties of their local environment such as viscosity and temperature can be identified via the relax-ation behavior of the MNPs. The optimal drive field (DF) frequency depends on the application of interest. In addition, the sensitivity of quantitative mapping can benefit from imaging at multiple DF frequencies. However conventional MPI systems utilize an impedance matching circuitry tuned to a specific DF frequency to mitigate the reactive power, which in turn restricts the operation of the MPI systems to that frequency. In this thesis, a preclinical arbitrary waveform (AW) MPI scanner is proposed to enable flexible functionality in a wide range of oper-ating frequencies. The AW MPI scanner features three specialized components:(1) an AW drive coil with a reduced inductance achieved by utilizing Rutherford cable windings to enable wideband imaging in a preclinical-size MPI scanner, (2) a gradiometric receive coil designed to have zero mutual inductance with the AW drive coil to alleviate the effect of the direct feedthrough signal while sensitively receiving the MNP signal, and (3) additional capacitor banks to block DC cur-rent while avoiding distortions in the DF waveform. This thesis also proposes a technique for multi-frequency imaging in a single scan using the developed AW MPI scanner. Experimental imaging results demonstrate that MPI images and relaxation maps can be successfully achieved at multiple DF frequencies using the developed AW MPI scanner and the proposed multi-frequency imaging technique.
Open Access
Anatomical measurements correlate with individual magnetostimulation thresholds for kHz‐range homogeneous magnetic fields
(Wiley, 2020) Demirel, Ömer Burak; Kılıç, Toygan; Çukur, Tolga; Sarıtaş, Emine Ülkü
Purpose: Magnetostimulation, also known as peripheral nerve stimulation (PNS), is the dominant safety constraint in magnetic resonance imaging (MRI) for the gradient magnetic fields that operate around 0.1–1 kHz, and for the homogeneous drive field in magnetic particle imaging (MPI) that operates around 10–150 kHz. Previous studies did not report correlations between anatomical measures and magnetostimulation thresholds for the gradient magnetic fields in MRI. In contrast, a strong linear correlation was shown between the thresholds and the inverse of body part size in MPI. Yet, the effects of other anatomical measures on the thresholds for the drive field remain unexplored. Here, we investigate the effects of fat percentage on magnetostimulation thresholds for kHz‐range homogeneous magnetic fields such as the drive field in MPI, with the ultimate goal of predicting subject‐specific thresholds based on simple anatomical measures. Methods: Human subject experiments were performed on the upper arms of 10 healthy male subjects (age: 26 ± 2 yr) to determine magnetostimulation thresholds. Experiments were repeated three times for each subject, with brief resting periods between repetitions. Using a solenoidal magnetostimulation coil, a homogeneous magnetic field at 25 kHz with 100 ms pulse duration was applied at 4‐s intervals, while the subject reported stimulation via a mouse click. To determine the thresholds, individual subject responses were fitted to a cumulative distribution function modeled by a sigmoid curve. Next, anatomical images of the upper arms of the subjects were acquired on a 3 T MRI scanner. A two‐point Dixon method was used to obtain separate images of water and fat tissues, from which several anatomical measures were derived: the effective outer radius of the upper arm, the effective inner radius (i.e., the muscle radius), and fat percentage. Pearson’s correlation coefficient was used to assess the relationship between the threshold and anatomical measures. This statistical analysis was repeated after factoring out the expected effects of body part size. An updated model for threshold prediction is provided, where in addition to scaling in proportion with the inverse of the outer radius, the threshold has an affine dependence on fat percentage. Results: A strong linear correlation (r = 0.783, P < 0.008) was found between magnetostimulation threshold and fat percentage, and the correlation became stronger after factoring out the effects of outer radius (r = 0.839, P < 0.003). While considering body part size alone did not explain any significant variance in measured thresholds (P > 0.398), the updated model that also incorporates fat percentage yielded substantially improved threshold predictions with = 0.654 (P < 0.001). Conclusions: This work shows for the first time that fat percentage strongly correlates with magnetostimulation thresholds for kHz‐range homogenous magnetic fields such as the drive field in MPI, and that the correlations get even stronger after factoring out the effects of body part size. These results have important practical implications for predicting subject‐specific thresholds, which in turn can increase the performance of the drive field and improve image quality while remaining within the safety limits.
Open Access
Automated image reconstruction for non-cartesian magnetic particle imaging
(2019-09) Özaslan, Ali Alper
Magnetic particle imaging (MPI) is a high-contrast imaging modality that images the spatial distribution of superparamagnetic iron oxide (SPIO) nanoparticles by exploiting their nonlinear response. In MPI, image reconstruction is performed via two di erent methods: system function reconstruction (SFR) and x-space reconstruction. For the SFR approach, analysis of various scanning trajectories provided important insight about their image quality performances. While Cartesian trajectories remain the most popular choice for x-space-based reconstruction, recent work suggests that non-Cartesian trajectories such as the Lissajous trajectory may prove bene cial for improving image quality. In this thesis, a generalized reconstruction scheme is proposed for x-space MPI that can be used in conjunction with any scanning trajectory. The proposed technique automatically tunes the reconstruction parameters from the scanning trajectory, and does not induce any additional blurring. To demonstrate the proposed technique, ve di erent trajectories were utilized with varying density levels. Comparison to alternative reconstruction methods show signi cant improvement in image quality achieved by the proposed technique. Among the tested trajectories, the Lissajous and bidirectional Cartesian trajectories prove more favorable for x-space MPI, and the resolution of the images from these two trajectories can further be improved via deblurring. The fully automated gridding reconstruction proposed in this thesis can be utilized with these trajectories to improve the image quality in x-space MPI.
Open Access
Calibration-free relaxation-based multi-color magnetic particle imaging
(Institute of Electrical and Electronics Engineers, 2018) Muslu, Yavuz; Utkur, Mustafa; Demirel, Ömer Burak; Sarıtaş, Emine Ülkü
Magnetic particle imaging (MPI) is a novel imaging modality with important potential applications, such as angiography, stem cell tracking, and cancer imaging. Recently, there have been efforts to increase the functionality of MPI via multi-color imaging methods that can distinguish the responses of different nanoparticles, or nanoparticles in different environmental conditions. The proposed techniques typically rely on extensive calibrations that capture the differences in the harmonic responses of the nanoparticles. In this paper, we propose a method to directly estimate the relaxation time constant of the nanoparticles from the MPI signal, which is then used to generate a multi-color relaxation map. The technique is based on the underlying mirror symmetry of the adiabatic MPI signal when the same region is scanned back and forth. We validate the proposed method via simulations, and via experiments on our in-house magnetic particle spectrometer setup at 10.8 kHz and our in-house MPI scanner at 9.7 kHz. Our results show that nanoparticles can be successfully distinguished with the proposed technique, without any calibration or prior knowledge about the nanoparticles.
Open Access
Cancer imaging and treatment monitoring with color magnetic particle imaging
(2021-09) Ütkür, Mustafa
Magnetic particle imaging (MPI) is emerging as a highly promising non-invasive tomographic imaging modality for cancer research. Superparamagnetic iron oxide nanoparticles (SPIONs) are used as imaging tracers in MPI. By exploiting the relaxation behavior of SPIONs, the capabilities of MPI can also be broadened to functional imaging applications that can distinguish different nanoparticles and/or environments. One of the important applications of functional MPI is viscosity mapping, since certain cancer types are shown to have increased cellular viscosity levels. MPI can potentially detect these cancerous tissues through estimating the viscosity levels of the tissue environment. Another important application area of MPI is temperature mapping, since SPIONs are also utilized in magnetic fluid hyperthermia (MFH) treatments and MPI enables localized application of MFH. To achieve accurate temperature estimations, however, one must also take into account the confounding effects of viscosity and temperature on the MPI signal. This dissertation studies relaxation-based viscosity and temperature mapping with MPI, covering the biologically relevant viscosity range (<5 mPa·s) and the therapeutically applicable temperature range (25-45!C). The characterization of the SPION relaxation response was performed on an in-house arbitrarywaveform magnetic particle spectrometer (MPS) setup, and the imaging experiments were performed on an in-house MPI scanner. Both the MPS setup and the MPI scanner were designed and developed as parts of this thesis. The effects of viscosity and temperature on relaxation time constant estimations were investigated, and the sensitivities of MPI to these functional parameters were determined at a wide range of operating points. The relaxation time constants, t’s, were estimated with a technique called TAURUS (TAU, t, estimation via Recovery of Underlying mirror Symmetry), which is based on a linear relaxation equation. Although the nonlinear relaxation behaviors of the SPIONs are highly dependent on the excitation field parameters, SPION type, and the hardware configuration, the results suggest that one-to-one relation between the estimated t and the targeted functional parameters (i.e., viscosity or temperature) can be obtained. According to these results, MPI can successfully map viscosity and temperature, with higher than 30%/mPa/s sensitivity for viscosity mapping and approximately 10%/!C sensitivity for temperature mapping, at 10 kHz drive field frequency. In addition, the results suggest that the simultaneous mapping of viscosity and temperature can be achieved by performing multiple measurements at different drive field frequencies and/or amplitudes. Overall, these findings show that hybrid MPI-MFH systems offer a promising approach for real-time monitored and localized thermal ablation treatment of cancer. The viscosity and temperature mapping capabilities of MPI via relaxation time constant estimation can provide feedback for high accuracy thermal dose adjustment to the cancerous tissues, thereby, increasing the efficacy of the treatment.
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
DC shift based image reconstruction for magnetic particle imaging
(IEEE, 2017) Sarıca, Damla; Demirel, Ömer Burak; Sarıtaş, Emine Ülkü
Magnetic Particle Imaging (MPI) is a new imaging technology that images the spatial distribution of iron oxide nanoparticles. Since the magnetic field strength that can be safely applied in MPI is limited, the field-of-view (FOV) must be divided into partial FOVs. Because the excitation magnetic field causes direct feedthrough on the receiver coil, the excitation frequency must be filtered out of the MPI signal. During this process, the nanoparticle signal at the same frequency is also lost, as a result of which each partial FOV experiences different levels of DC shift. In the standard MPI image reconstruction, these DC shifts are calculated from neighboring overlapping partial FOVs. Here, we propose a novel method that directly reconstructs the MPI image from the calculated DC shift values. Especially in the case of low bandwidth signal acquisitions, this method yields higher resolution images when compared to the standard method. The simulation results at various signal-to-noise ratios (SNR) show that the proposed method produces 6-8 dB increase in peak SNR and yields images that closely match the ideal image.
Open Access
Effects of duty cycle on magnetostimulation thresholds in MPI
(Infinite Science Publishing, 2017) Demirel, Omer Burak; Sarıtaş, Emine Ülkü
Magnetic Particle Imaging (MPI) relies on time-varying magnetic fields to generate an image of the spatial distribution of superparamagnetic iron oxide nanoparticles. However, these oscillating magnetic fields form electric field patterns within the body, which in turn can cause peripheral nerve stimulations (PNS), also known as magnetostimulation. To prevent potential safety hazards and to optimize the scanning parameters such as field-of-view (FOV) and scanning speed in MPI, the factors that affect drive field magnetostimulation limits need to be determined accurately. In this work, we investigate the effects of the duty cycle on magnetostimulation thresholds in MPI. We performed human subject experiments by using a highly homogenous solenoidal coil on the upper arm of six subjects. Six different duty cycles ranging between 5 % and 100 % were applied at 25 kHz. Accordingly, magnetostimulation limits first decrease and then increase with increasing duty cycle, reaching a maximum at 100 % duty cycle. Since high duty cycles would be the preferred operating mode for rapid imaging with MPI, these results have promising implications for future human-sized MPI systems.
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
Fully automated gridding reconstruction for non-Cartesian x-space magnetic particle imaging
(IOP, 2019) Özaslan, Ali Alper; Alacaoğlu, Ahmet; Demirel, Ömer Burak; Çukur, Tolga; Sarıtaş, Emine Ülkü
Magnetic particle imaging (MPI) is a fast emerging biomedical imaging modality that exploits the nonlinear response of superparamagnetic iron oxide (SPIO) nanoparticles to image their spatial distribution. Previously, various scanning trajectories were analyzed for the system function reconstruction (SFR) approach, providing important insight regarding their image quality performances. While Cartesian trajectories remain the most popular choice for x-spacebased reconstruction, recent work suggests that non-Cartesian trajectories such as the Lissajous trajectory may prove beneficial for improving image quality. In this work, we propose a generalized reconstruction scheme for x-space MPI that can be used in conjunction with any scanning trajectory. The proposed technique automatically tunes the reconstruction parameters from the scanning trajectory, and does not induce any additional blurring. To demonstrate the proposed technique, we utilize five different trajectories with varying density levels. Comparison to alternative reconstruction methods show significant improvement in image quality achieved by the proposed technique. Among the tested trajectories, the Lissajous and bidirectional Cartesian trajectories prove more favorable for x-space MPI, and the resolution of the images from these two trajectories can further be improved via deblurring. The proposed fully automated gridding reconstruction can be utilized with these trajectories to improve the image quality in x-space MPI.
Open Access
Image reconstruction for Magnetic Particle Imaging using an Augmented Lagrangian Method
(IEEE, 2017) Ilbey S.; Top C.B.; Çukur, Tolga; Sarıtaş, Emine Ülkü; Guven H.E.
Magnetic particle imaging (MPI) is a relatively new imaging modality that images the spatial distribution of superparamagnetic iron oxide nanoparticles administered to the body. In this study, we use a new method based on Alternating Direction Method of Multipliers (a subset of Augmented Lagrangian Methods, ADMM) with total variation and l1 norm minimization, to reconstruct MPI images. We demonstrate this method on data simulated for a field free line MPI system, and compare its performance against the conventional Algebraic Reconstruction Technique. The ADMM improves image quality as indicated by a higher structural similarity, for low signal-to-noise ratio datasets, and it significantly reduces computation time. © 2017 IEEE.
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
Low drive field amplitude for improved image resolution in magnetic particle imaging
(Wiley-Blackwell Publishing, Inc., 2016) Croft, L. R.; Goodwill, P. W.; Konkle, J. J.; Arami, H.; Price, D. A.; Li, A. X.; Saritas, E. U.; Conolly, S. M.
Purpose: Magnetic particle imaging (MPI) is a new imaging technology that directly detects superparamagnetic iron oxide nanoparticles. The technique has potential medical applications in angiography, cell tracking, and cancer detection. In this paper, the authors explore how nanoparticle relaxation affects image resolution. Historically, researchers have analyzed nanoparticle behavior by studying the time constant of the nanoparticle physical rotation. In contrast, in this paper, the authors focus instead on how the time constant of nanoparticle rotation affects the final image resolution, and this reveals nonobvious conclusions for tailoring MPI imaging parameters for optimal spatial resolution. Methods: The authors first extend x-space systems theory to include nanoparticle relaxation. The authors then measure the spatial resolution and relative signal levels in an MPI relaxometer and a 3D MPI imager at multiple drive field amplitudes and frequencies. Finally, these image measurements are used to estimate relaxation times and nanoparticle phase lags. Results: The authors demonstrate that spatial resolution, as measured by full-width at half-maximum, improves at lower drive field amplitudes. The authors further determine that relaxation in MPI can be approximated as a frequency-independent phase lag. These results enable the authors to accurately predict MPI resolution and sensitivity across a wide range of drive field amplitudes and frequencies. Conclusions: To balance resolution, signal-to-noise ratio, specific absorption rate, and magnetostimulation requirements, the drive field can be a low amplitude and high frequency. Continued research into how the MPI drive field affects relaxation and its adverse effects will be crucial for developing new nanoparticles tailored to the unique physics of MPI. Moreover, this theory informs researchers how to design scanning sequences to minimize relaxation-induced blurring for better spatial resolution or to exploit relaxation-induced blurring for MPI with molecular contrast.
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
Manyetik parçacık görüntüleme için sinyal-gürültü oranını eniyileyen görüntü geriçatım tekniği
(Gazi Universitesi Muhendislik-Mimarlik, 2017) Bozkurt, Ecem; Sarıtaş, Emine Ülkü
Magnetic particle imaging (MPI) is a new biomedical imaging modality that images the spatial distribution of superpamagnetic iron oxide nanoparticles. In MPI, the amplitude of the excitation magnetic field that causes the time-varying magnetization response of the nanoparticles is restricted by the nerve stimulation safety limits. Hence, the region to be imaged is divided into small sections and scanned as overlapping partial fields-of-view. The nanoparticle signal at the excitation frequency is lost during the filtering process of the direct feedthrough signal induced on the receive coil due to the excitation field. To recover this loss, the overlapping partial fields-of-view are merged via utilizing the continuity and positivity of the desired image. In this work, an image reconstruction technique that merges the partial fields-of-view while optimizing the signal-to-noise ratio is proposed. Accordingly, each partial field-of-view must be weighted by the square of the position-dependent scanning speed. Via extensive simulations at various overlap percentages and signal-to-noise ratios, this work demonstrates that the proposed method overcomes the vertical line artifacts caused by the standard MPI reconstruction techniques and improves image quality.
Open Access
A novel preclinical dual-topology magnetic particle imaging scanner
(2020-12) Çağıl, Ahmet Rahmetullah
Magnetic Particle Imaging (MPI) is a relatively new biomedical imaging modality that can provide excellent sensitivity, contrast and resolution utilising superparamagnetic iron oxide nanoparticles. In MPI, spatial selectivity is achieved through selection fields created by either a permanent magnets or electromagnets. Selection fields generated by different arrangements of magnets result in different topologies of operation for MPI scanners. Among these scanner topologies, most prominent ones are Field Free Point (FFP) and Field Free Line (FFL) scanners, which differ in their advantages and disadvantages. Most importantly, FFL scanners provide improved sensitivity and rapid imaging, but offer only projection format images. FFP scanners offer 3D volumetric acquisition (and thus flexibility in acquiring an image from only a region or slice of interest), but can suffer from relatively lower sensitivity and longer scan times. In standard MPI scanners, as one topology has to be chosen when building a scanner, advantages of the other topology will be forgone. This thesis proposes a hybrid topology that functions as FFL by default, but can be swapped electronically into an FFP topology with the use of a saddle coil pair. This dual-topology scanner allows projection format images that can be used for rapid 2D projecton imaging or serve the purpose of a localizer, which can then be followed by 3D volumetric imaging of a region of interest. A preclinical hybrid scanner utilising the proposed topology is designed and constructed. Additionally, a novel double tuning mechanism that improves ease of tuning and allows for consistently achieving higher decoupling for gradiometer receive coils is introduced, built, and demonstrated.
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
Partial FOV Center Imaging (PCI): a robust X-space image reconstruction for magnetic particle imaging
(IEEE, 2020) Kurt, Semih; Muslu, Yavuz; Sarıtaş, Emine Ülkü
Magnetic Particle Imaging (MPI) is an emerging medical imaging modality that images the spatial distribution of superparamagnetic iron oxide (SPIO) nanoparticles using their nonlinear response to applied magnetic fields. In standard x-space approach to MPI, the image is reconstructed by gridding the speed-compensated nanoparticle signal to the instantaneous position of the field free point (FFP). However, due to safety limits on the drive field, the field-of-view (FOV) needs to be covered by multiple relatively small partial field-of-views (pFOVs). The image of the entire FOV is then pieced together from individually processed pFOVs. These processing steps can be sensitive to non-ideal signal conditions such as harmonic interference, noise, and relaxation effects. In this work, we propose a robust x-space reconstruction technique, Partial FOV Center Imaging (PCI), with substantially simplified pFOV processing. PCI first forms a raw image of the entire FOV by mapping MPI signal directly to pFOV center locations. The corresponding MPI image is then obtained by deconvolving this raw image by a compact kernel, whose fully-known shape solely depends on the pFOV size. We analyze the performance of the proposed reconstruction via extensive simulations, as well as imaging experiments on our in-house FFP MPI scanner. The results show that PCI offers a trade-off between noise robustness and interference robustness, outperforming standard x-space reconstruction in terms of both robustness against non-ideal signal conditions and image quality.
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.