Browsing by Author "Alptekin Soydan, D."
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Item Open Access A denoiser scaling technique for plug-and-play MPI reconstruction(Infinite Science Publishing, 2023-03-19) Güngör, Alper; Aşkın, Barış; Alptekin Soydan, D.; Sarıtaş, Emine Ülkü; Top, C. B.; Çukur, TolgaImage reconstruction based on the system matrix in magnetic particle imaging (MPI) involves an ill-posed inverse problem, which is often solved using iterative optimization procedures that use regularization. Reconstruction performance is highly dependent on the quality of information captured by the regularization prior. Learning-based methods have been recently introduced that significantly improve prior information in MPI reconstruction. Yet, these methods can perform suboptimally under drifts in the image scale between the training and test sets. In this study, we assess the influence of scale drifts on the performance a recent plug-ang-play method (PP-MPI) that uses a pre-trained denoiser. We introduce a new denoiser scaling technique that improves reliability of PP-MPI against deviations in image scale. The proposed technique enables high quality reconstructions that are robust against scale drifts between training and testing sets.Item Open Access A simulation study for an open-sided hybrid MPI-MRI scanner(Infinite Science Publishing, 2023-03-19) Karaca, Sefa; Alptekin Soydan, D.; Top, C. B.; Sarıtaş, Emine ÜlküMagnetic particle imaging (MPI) provides images of magnetic nanoparticle distribution without any signal from the surrounding tissue. MPI would benefit from an additional imaging technique that reveals the anatomical background information, required in many applications. Here, we present a simulation study based on our in-house open-sided prototype MPI system, in which the coils can be utilized interchangeably for MPI and MRI data acquisitions. The system can provide a selection field gradient of 0.5 T m−1 for MPI in field free line topology, and a B0 field of up to 50 mT for MRI. We analyze the system-induced deviations on MRI images for different B0 valuesand pulse sequence parameters.Item 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, JaejunMagnetic 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.