Browsing by Subject "Magnetic Particle Imaging"
Now showing 1 - 7 of 7
- Results Per Page
- Sort Options
Item Open Access Bimodal interventional instrument markers for magnetic particle imaging and magnetic resonance imaging—a proof-of-concept study(MDPI, 2022-05-02) Wegner, Franz; Lüdtke-Buzug, Kerstin; Cremers, Sjef; Friedrich, Thomas; Sieren, Malte M.; Haegele, Julian; Koch, Martin A.; Borm, Paul; Buzug, Thorsten M.; Barkhausen, Joerg; Ahlborg, Mandy; Sarıtaş, Emine ÜlküThe purpose of this work was to develop instrument markers that are visible in both magnetic particle imaging (MPI) and magnetic resonance imaging (MRI). The instrument markers were based on two different magnetic nanoparticle types (synthesized in-house KLB and commercial Bayoxide E8706). Coatings containing one of both particle types were fabricated and measured with a magnetic particle spectrometer (MPS) to estimate their MPI performance. Coatings based on both particle types were then applied on a segment of a nonmetallic guidewire. Imaging experiments were conducted using a commercial, preclinical MPI scanner and a preclinical 1 tesla MRI system. MPI image reconstruction was performed based on system matrices measured with dried KLB and Bayoxide E8706 coatings. The bimodal markers were clearly visible in both methods. They caused circular signal voids in MRI and areas of high signal intensity in MPI. Both the signal voids as well as the areas of high signal intensity were larger than the real marker size. Images that were reconstructed with a Bayoxide E8706 system matrix did not show sufficient MPI signal. Instrument markers with bimodal visibility are essential for the perspective of monitoring cardiovascular interventions with MPI/MRI hybrid systems. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Item Open Access Effects of scanning and reconstruction parameters on image quality in magnetic particle imaging(2018-01) Bozkurt, EcemMagnetic particle imaging (MPI) is a novel medical imaging modality, based on the magnetization of the superparamagnetic iron oxide nanoparticles. In MPI, an external magnetic field called the drive field is applied to excite the nanoparticles. The link between the image quality and the drive field parameters is complex, as nanoparticle behavior changes with the drive field parameters. In addition, the maximum applicable drive field strength is limited by human safety restrictions. Recent studies have shown that the resolution improves at low drive field amplitudes and SNR enhances as drive field frequency increases. Other studies have confirmed that scanning at frequencies as high as 150 kHz is feasible for human-size MPI scanners. However, how the image quality is affected by drive field parameters, especially for high frequencies around 150 kHz, was not investigated. This thesis investigates the effects of the drive field parameters on the image quality in MPI with relaxometer experiments. The effects of the safety limits are also explored across different drive field frequencies via simulations. The results provide important insight in determining the optimal drive field parameters for safe MPI scanners. This thesis also introduces a new method for improving the image quality in MPI. MPI images can suffer from asymmetric hazing and irregular trending artifacts when nanoparticle response is delayed due to relaxation effects. This thesis proposes a new method based on averaging of relaxation effects from negative and positive half-cycles of the MPI signal, combined with a Savitzky-Golay detrending filter. Both experimental and simulation results demonstrate a significant improvement in image quality.Item Open Access Fast calibration and image reconstruction for magnetic particle imaging(2018-05) İlbey, SerhatMagnetic particle imaging (MPI) is a relatively new medical imaging modality that images the spatial distribution of magnetic nanoparticles (MNPs) administered to the body. For image reconstruction with the system matrix (SM) approach, a time-consuming calibration scan is necessary, in which a single MNP sample is placed and scanned inside the full eld-of-view (FOV). Moreover, for a relatively large 3D high-resolution FOV, the reconstructed SM is too large to get high quality images in real-time using the standard state-of-the-art techniques. In this thesis, for the calibration scan, the use of coded calibration scenes (CCSs) is proposed, which utilizes MNP samples at multiple positions inside the FOV. The SM, which is sparse in the discrete cosine transform domain, is reconstructed using the Alternating Direction Method of Multipliers (ADMM) with l1-norm minimization. The e ectiveness of the CCSs for di erent parameter sets is analyzed via simulations, and the results are compared with the standard sparse reconstruction technique. As the MPI images are naturally sparse, ADMM is also proposed for image reconstruction, minimizing the total variation and l1-norm. Image quality is compared with the images obtained by widely used MPI image reconstruction algorithms: Algebraic Reconstruction Technique, Nonnegative Fused LASSO, and X-space-based projection reconstruction. Moreover, ADMM is parallelized on a GPU for real-time image reconstruction. The results show that the required number of measurements for system calibration is substantially reduced with the proposed methods, and the reconstruction performance is signi cantly improved in terms of both image quality and speed.Item Open Access Image deconvolution methods based on fourier transform phase and bounded energy(2018-08) Yorulmaz, OnurWe developed deconvolution algorithms based on Fourier transform phase and bounded energy. Deconvolution is a major area of study in image processing applications. In general, restoration of original images from noisy filtered observation images is an ill-posed problem. We use Fourier transform phase as a constraint in developed image recovery methods. The Fourier phase information is robust to noise, which makes it suitable as a frequency domain constraint. One of our focus is microscopy images where the blur is caused by slight disturbances of the focus. Because of the symmetrical optical parameters, it may be assumed that the Point Spread Function (PSF) is symmetrical. This symmetry of PSF results in zero phase distortion in the Fourier transform coefficients of the original image. Since the convolution leads to multiplication in Fourier domain, we assume that the Fourier phase of some of the frequencies of observed image around the origin represents the Fourier phase of the original image in the same set of frequencies. Therefore the Fourier transform phases of the original image can be estimated from the phase of the observed image and this information can be used as a Fourier domain constraint. In order to complete the algorithm, we also use a Total Variation (TV) reduction based regularization in spatial domain. We embed the proposed Fourier phase relation and spatial domain regularization as additional constraints in well-known blind Ayers-Dainty deconvolution method. Another problem we focused on is the restoration of highly blurry Magnetic Particle Imaging (MPI) applications. In this study we developed a standalone iterative algorithm. The algorithm again relies on the symmetry property of the MPI PSF. The phase estimates of the true image are obtained from the observed image. In this case we employ an `1 projection based regularization algorithm. The `1 projection reduces the small coefficients to zero which is suitable for MPI application because the contrast between foreground and background is sufficiently large by nature. Finally, a more general restoration algorithm is developed for deconvolution of non-symmetrical filters. The algorithm uses the known Fourier phase properties of the PSF in order to estimate the Fourier transform phase of the original image. We also update the estimated Fourier transform magnitudes iteratively using the knowledge of observed image and the PSF. A TV reduction based regularization method completes the algorithm in spatial domain. Simulations and experimental results show that the proposed algorithm outperforms the Wiener filter. We also conclude that the addition of estimate of Fourier transform phase is useful in any deconvolution method.Item Open Access Low-bandwidth image reconstruction for magnetic particle imaging(2017-06) Sarıca, DamlaMagnetic Particle Imaging (MPI) is a high contrast tracer imaging modality with applications such as stem cell tracking, angiography and cancer imaging. In MPI, a time-varying magnetic field called the drive field is applied, and the magnetization response of superparamagnetic iron oxide nanoparticles (SPIOs) is recorded. The signal from these nanoparticles is at both drive field frequency and its higher harmonics. However, due to simultaneous excitation and signal reception, the direct feedthrough contaminates the nanoparticle signal at the fundamental harmonic. The direct feedthrough signal can be eliminated using a high-pass filter, where the effect of this filtering has been shown to be a DC loss in image domain. Reliable x-space image reconstruction can then be achieved via enforcing positivity and continuity of the image. However, low SPIO concentrations and/or hardware constraints can further limit the usable signal bandwidth to only a few harmonics. Under low bandwidth signal acquisitions, the loss of higher harmonics results in blurred images after regular x-space reconstruction. This thesis proposes an iterative x-space reconstruction method that recovers not only the lost fundamental harmonic but also the un-acquired higher harmonics for low-bandwidth acquisitions. Proposed method converges to the ideal (i.e., high bandwidth) MPI image in 3-4 iterations. In extensive simulations that incorporate measurement noise and nanoparticle relaxation effects, the proposed method displays improved image quality with respect to the regular x-space reconstruction scheme, with at least 6 dB improvement in peak signal-to-noise ratio (PSNR) metric. Finally, the proposed method is also demonstrated with imaging experiments on an in-house MPI scanner.Item Open Access Magnetic Particle Imaging (MPI) for NMR and MRI researchers(Elsevier, 2013) Sarıtaş, Emine ÜlküMagnetic Particle Imaging (MPI) is a new tracer imaging modality that is gaining significant interest from NMR and MRI researchers. While the physics of MPI differ substantially from MRI, it employs hardware and imaging concepts that are familiar to MRI researchers, such as magnetic excitation and detection, pulse sequences, and relaxation effects. Furthermore, MPI employs the same superparamagnetic iron oxide (SPIO) contrast agents that are sometimes used for MR angiography and are often used for MRI cell tracking studies. These SPIOs are much safer for humans than iodine or gadolinium, especially for Chronic Kidney Disease (CKD) patients. The weak kidneys of CKD patients cannot safely excrete iodine or gadolinium, leading to increased morbidity and mortality after iodinated X-ray or CT angiograms, or after gadolinium-MRA studies. Iron oxides, on the other hand, are processed in the liver, and have been shown to be safe even for CKD patients. Unlike the “black blood” contrast generated by SPIOs in MRI due to increased dephasing, SPIOs in MPI generate positive, “bright blood” contrast. With this ideal contrast, even prototype MPI scanners can already achieve fast, high-sensitivity, and high-contrast angiograms with millimeter-scale resolutions in phantoms and in animals. Moreover, MPI shows great potential for an exciting array of applications, including stem cell tracking in vivo, first-pass contrast studies to diagnose or stage cancer, and inflammation imaging in vivo. So far, only a handful of prototype small-animal MPI scanners have been constructed worldwide. Hence, MPI is open to great advances, especially in hardware, pulse sequence, and nanoparticle improvements, with the potential to revolutionize the biomedical imaging field.Item Open Access Relaxation mapping in magnetic particle imaging(2018-01) Muslu, YavuzMagnetic Particle Imaging (MPI) is a novel biomedical imaging modality that shows great potential in terms of sensitivity, resolution, and contrast. Since its first introduction in 2005, several applications of MPI have already been demonstrated such as angiography, stem cell tracking, and cancer imaging. Recently, multi-color MPI techniques have been proposed to increase the functionality of MPI, where different nanoparticles are distinguished according to the differences in their responses to oscillating magnetic fields. These methods can also be extended to probe environmental factors such as viscosity and temperature, provided that the responses of different nanoparticles or nanoparticles in different environments are pre-calibrated. This thesis proposes a new multi-color MPI technique that does not require a calibration phase. This new technique directly estimates the relaxation time constants of nanoparticles to distinguish nanoparticle types and environmental factors from the MPI signal, and generates a multi-color relaxation map. The validity of the proposed technique is confirmed through an extensive experimental work with an in-house Magnetic Particle Spectrometer (MPS) at 10.8 kHz and an in-house MPI scanner at 9.7 kHz drive field frequencies, successfully distinguishing different nanoparticle types. The proposed calibration-free multi-color MPI technique is a promising method for future functional imaging applications of MPI.