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Item type:Publication, Access status: without files , NFFT.jl: Generic and fast Julia implementation of the nonequidistant fast fourier transform*(Society for Industrial & Applied Mathematics (SIAM), 2023); ; The nonequidistant fast Fourier transform (NFFT) is an extension of the famous fast Fourier transform (FFT) that can be applied to nonequidistantly sampled data in time/space or frequency domain. It is an approximative algorithm that allows one to control the approximation error in such a way that machine precision is reached while keeping the algorithmic complexity in the same order as a regular FFT. The NFFT plays a major role in many signal processing applications and has been intensively studied from a theoretical and computational perspective. The fastest CPU implementations of the NFFT are implemented in the low-level programming languages C and C++ and require a compromise between code generalizability, code readability, and code efficiency. The programming language Julia promises new opportunities in optimizing these three conflicting goals. In this work we show that Julia indeed allows one to develop an NFFT implementation which is completely generic and dimension-agnostic and requires about two to three times less code than the other famous libraries NFFT3 and FINUFFT while still being one of the fastest NFFT implementations developed to date.Publicationtype: Journal Article7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Warmstart Approach for Accelerating Deep Image Prior Reconstruction in Dynamic Tomography(2022-07); Deep image prior (DIP) has been successfully used in the field of tomography to obtain high-quality images from under-sampled and noisy measurements. The key advantage of DIP compared to conventional deep-learning based image reconstruction techniques is that it requires no training data and thus can be used in a flexible manner without incorporating domain specific knowledge. The downside of DIP is that it shifts the training step to reconstruction time where usually fast algorithms are required to reduced the latency between acquisition and display of the reconstructed image. In this work we tackle this problem for dynamic tomography scenarios in which a large number of temporally resolved images are taken over time. By initializing the DIP network using a previous frame of the time series, it is possible to significantly reduce the overall reconstruction time. To cope with abrupt changes in the captured time-series, we propose to use an adaptive restart method having the ability to switch between warm- and coldstart depending on the amount of inter-frame changes.Publicationtype: Conference Paper10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Efficient joint estimation of tracer distribution and background signals in magnetic particle imaging using a dictionary approachBackground signals are a primary source of artifacts in magnetic particle imaging and limit the sensitivity of the method since background signals are often not precisely known and vary over time. The state-of-the art method for handling background signals uses one or several background calibration measurements with an empty scanner bore and subtracts a linear combination of these background measurements from the actual particle measurement. This approach yields satisfying results in case that the background measurements are taken in close proximity to the particle measurement and when the background signal drifts linearly. In this work, we propose a joint estimation of particle distribution and background signal based on a dictionary that is capable of representing typical background signals. Reconstruction is performed frame-by-frame with minimal assumptions on the temporal evolution of background signals. Thus, even non-linear temporal evolution of the latter can be captured. Using a singular-value decomposition, the dictionary is derived from a large number of background calibration scans that do not need to be recorded in close proximity to the particle measurement. The dictionary is sufficiently expressive and represented by its principle components. The proposed joint estimation of particle distribution and background signal is expressed as a linear Tikhonov-regularized least squares problem, which can be efficiently solved. In phantom experiments it is shown that the method strongly suppresses background artifacts and even allows to estimate and remove the direct feed-through of the excitation field.Publicationtype: Journal ArticleScopus© Citations 13 49 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Efficient optimization of mri sampling patterns using the bayesian fisher information matrixThis work proposes an efficient way to adapt MRI sampling patterns to a given anatomy and imaging context using a small set of representative training data. Such techniques were shown to help shorten MRI experiments while guaranteeing high image quality. An often encountered drawback of such methods are high computation times. We extend the recently proposed OEDIPUS framework by making use of the Bayesian Fisher information matrix. Based on the latter we devise an algorithm, which can be more than an order of magnitude faster than OEDIPUS for practical applications. This opens up the possibility to generate tailored sampling patterns for applications for which this would be infeasible otherwise. We evaluate our method in the context of multi-echo gradient echo imaging. The resulting sampling patterns show superior image reconstruction results compared to those obtained by other popularly used sampling schemes.Publicationtype: Conference Paper58 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Dictionary-based background signal estimation for magnetic particle imagingMagnetic Particle Imaging is a highly sensitive tracer-based imaging method able to image as little as a nanogram of tracer material. In practice, background signals stemming from imperfections in the scanner instrumentation make it challenging to achieve these very high sensitivities. In case where the background signal is static over time it can be simply subtracted using an empty reference measurement. In this work we develop a method that is capable of handling dynamic background signals. Since the particle signal and the background signal are superimposed, we propose to perform a joint estimation of both quantities. The background signal space is modeled by a dictionary of background signals, build from a set of representative drifting background measurements. The proposed method is evaluated on experimentally and it is shown that it is capable of accurately estimating the background and tracer signal in a dynamic phantom experiment.Publicationtype: Conference Paper42 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Abstract: Reduktion der Kalibrierungszeit für die Magnetpartikelbildgebung mittels Deep Learning(Springer, 2021-03); ; ; ; Die Magnetpartikelbildgebung (MPI) ist eine junge tomographische Bildgebungstechnik, die magnetische Nanopartikel mit einer hohen räumlichen und zeitlichen Auflösung quantitativ abbildet. Eine gängige Methode zur Rekonstruktion von MPI-Daten ist die Systemmatrix (SM)-basierte Rekonstruktion. Die komplexwertige SM wird in einer zeitaufwändigen Kalibrierungsmessung bestimmt.Publicationtype: Conference Paper41 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Reduktion der Kalibrierungszeit für die Magnetpartikelbildgebung mittels Deep LearningPublicationtype: Conference Paper70 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , 3d-SMRnet: Achieving a New Quality of MPI System Matrix Recovery by Deep Learning(Springer, 2020-10); ; ; ; Magnetic particle imaging (MPI) data is commonly reconstructed using a system matrix acquired in a time-consuming calibration measurement. Compared to model-based reconstruction, the calibration approach has the important advantage that it takes into account both complex particle physics and system imperfections. However, this has the disadvantage that the system matrix has to be re-calibrated each time the scan parameters, the particle types or even the particle environment (e.g. viscosity or temperature) changes. One way to shorten the calibration time is to scan the system matrix at a subset of the spatial positions of the intended field-of-view and use the system matrix recovery. Recent approaches used compressed sensing (CS) and achieved subsampling factors up to 28, which still allowed the reconstruction of MPI images with sufficient quality. In this work we propose a novel framework with a 3d system matrix recovery network and show that it recovers a 3d system matrix with a subsampling factor of 64 in less than a minute and outperforms CS in terms of system matrix quality, reconstructed image quality, and processing time. The advantage of our method is demonstrated by reconstructing open access MPI datasets. Furthermore, it is also shown that the model is capable of recovering system matrices for different particle types.Publicationtype: Conference Paper198 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Using low-rank tensors for the recovery of MPI system matricesIn Magnetic Particle Imaging (MPI), the system matrix plays an important role, as it encodes the relationship between particle concentration and the measured signal. Its acquisition requires a time-consuming calibration scan, whereas its size leads to a high memory-demand. Both of these aspects can be limiting factors in practice. In order to reduce measurement time, compressed sensing exploits the knowledge that the MPI system matrix has a sparse representation in a suitably chosen domain. In this work we demonstrate that the rows of the system matrix allow a representation as low-rank tensors. We show that such an approximation leads to a denoising of the system matrix while introducing only a negligible bias. As an application, we develop a new matrix recovery method exploiting aforementioned low rank property in addition to sparsity in the DCT domain. Experiments show that the proposed matrix recovery method yields system matrices with reduced error when compared to a standard compressed sensing recovery.Publicationtype: Journal Article55