Browsing by Subject "Image Reconstruction"

Now showing 1 - 4 of 4

Open Access
Development of image reconstruction algorithms for three dimensional magnetic resonance-electrical impedance tomography
(2003) Onart, Serkan
The electrical resistivity of biological tissues differ among various tissue types. Human body has a large resistivity contrast between a wide range of its tissues. The aim of this study is to reconstruct conductivity images of three dimensional objects with higher resolution and better accuracy than existing conductivity imaging techniques. In order to achieve our goal, we proposed a technique named as Magnetic Resonance - Electrical Impedance Tomography (MR-EIT) which combines the peripheral voltage measurements of classical Electrical Impedance Tomography (EIT) technique with magnetic flux density measurements acquired using a Magnetic Resonance Imaging (MRI) scanner. Five reconstruction algorithms are proposed and computer simulations are made. The proposed algorithms fall in two categories those that utilize current density data and those that utilize magnetic flux density data directly. The first group of algorithms get the current density data from magnetic flux density by Ampere’s law. For calculation of current density with Ampere’s law, we need to all three components of magnetic flux density but that is not possible to get all of them in one measurement phase. Total of three measurement phases are needed for getting all of them but this is not practical because, for measurement of each component the object has to be rotated appropriately in the MRI scanner. The algorithms in the second group suggest an exit to this difficulty and achieve the conductivity reconstruction by using only the data which was acquired in one measurement phase. As can be seen in the results, conductivity reconstruction of three dimensional objects on tomographic planes are made successfully with all of the algorithms. They also work fine against to the measurement noise up to an acceptable level.
Open Access
Effects of scanning and reconstruction parameters on image quality in magnetic particle imaging
(2018-01) Bozkurt, Ecem
Magnetic 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.
Open Access
Fast calibration and image reconstruction for magnetic particle imaging
(2018-05) İlbey, Serhat
Magnetic 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.
Open Access
Low-bandwidth image reconstruction for magnetic particle imaging
(2017-06) Sarıca, Damla
Magnetic 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.