DEQ-MPI: A Deep Equilibrium Reconstruction with Learned Consistency for Magnetic Particle Imaging

A. Güngör, B. Askin, D. A. Soydan, E. U. Saritas, C. B. Top and T. Çukur
IEEE Transactions on Medical Imaging, 2023.

DEQ-MPI is the first deep equilibrium model with learned data consistency in the literature. In contrast to unrolling-based methods, DEQ-MPI computes a convergent solution while avoiding memory constraints via implicit differentiation, and it uses a learned consistency measure to better account for correlations in the data distribution. With these advances, we provide demonstrations for SOTA physics-driven image reconstruction in magnetic particle imaging (MPI).

[Code]

Adaptive Diffusion Priors for Accelerated MRI Reconstruction

A. Güngör, SUH Dar, Ş Öztürk, Y Korkmaz, G Elmas, M Özbey, T Çukur
Medical Image Analysis, 2023.

Diffusion models promise enhanced sample quality and diversity in MRI reconstruction. However, current diffusion methods use static priors that are frozen for inference on the test set. Here, we introduce an adaptive diffusion prior that is optimized during inference to ensure conformity to the distribution of test data. As a result, AdaDiff outperforms SOTA physics-driven unrolled methods and diffusion probabilistic models in terms of image quality and generalization.

[Code]

Unsupervised Medical Image Translation with Adversarial Diffusion Models

M Özbey, SUH Dar, HA Bedel, O Dalmaz, Ş Özturk, A. Güngör, T Çukur
IEEE Transactions on Medical Imaging, 2023.

Generative adversarial networks (GANs) have become the gold-standard in medical image synthesis, yet GANs often suffer from poor stability and limited representational diversity. Here, we introduce a novel adversarial diffusion model for medical image translation that improves sample fidelity and diversity, without the prohibitive computational burden of conventional diffusion probabilistic models such as DDPM.

[Code]

CalibFPA: A Focal Plane Array Imaging System based on Online Deep-Learning Calibration

A. Güngör, M.U. Bahçeci, Y. Ergen, A. Sözak, O. Ö. Ekiz, T. Yelboğa, T. Çukur
Arxiv Preprint, arXiv:2309.11421.

Compressive focal plane arrays enable cost-effective high-resolution (HR) imaging by acquisition of several multiplexed measurements on a low-resolution sensor. For practical implementation, system non-idealities such as optical aberrations are captured in an offline and time-consuming manner. In this study, we propose a novel compressive FPA system based on online deep-learning calibration of multiplexed LR measurements (CalibFPA).

Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction

M. U. Mirza, O. Dalmaz, H. A. Bedel, G. Elmas, Y. Korkmaz, A. Güngör, S. UH Dar, T. Çukur
Arxiv Preprint, arXiv:2308.01096.

Conventional diffusion models are trained to transform Gaussian noise onto the data distribution, and this normality assumption causes divergence between the learned and desired transformations in inverse problems. To address this fundamental limitation, here we introduce a Fourier-constrained diffusion bridge (FDB) that learns to transform directly between undersampled and fully-sampled data via a generalized diffusion process.

[Code]

Adaptive Measurement Matrix Design in Direction of Arrival Estimation

B. Kilic, A. Güngör, M. Kalfa, O. Arikan
IEEE Transactions on Signal Processing, 2022.

We propose a novel measurement matrix design methodology for direction of arrival estimation that adapts to the prior probability distribution on the source scene, and we compare its performance over alternative approaches using both on-grid and gridless reconstruction methods. The proposed technique is derived in closed-form and shown to provide improved compression rates compared to the state-of-the-art.

PP-MPI: A Deep Plug-and-Play Prior for Magnetic Particle Imaging Reconstruction

B. Askin, A. Güngör, D. A. Soydan, E. U. Saritas, C. B. Top, T. Çukur
Machine Learning for Medical Image Reconstruction (MLMIR 2022) Workshop, hosted by 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022.

PP-MPI is an ADMM-based reconstruction algorithm with a deep plug-and-play denoiser for 2D and 3D MPI. Besides its high-quality reconstruction performance, data-driven denoiser enables fast convergence of the algorithm.

[Code]

TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging

A. Güngör, B. Askin, D. A. Soydan, E. U. Saritas, C. B. Top, T. Çukur
IEEE Transactions on Medical Imaging, 2022.

TranSMS is a model-based transformer architecture that super-resolves system matrix rows for accelerated MPI calibration, while maintaining fidelity to the physical signal model for reliable generalization.

[Code]

Compressive Synthetic Aperture Radar Imaging and Autofocusing by Augmented Lagrangian Methods

A. Güngör, M. Çetin and H. E. Güven
IEEE Transactions on Computational Imaging, 2022.

Here, we propose an image reconstruction technique that jointly estimates both the phase errors and the underlying image simulatenously for undersampled synthetic aperture radar imaging in the presence of phase errors. Our method provides significant improvements in terms of image reconstruction quality and computation speed, as well as phase error estimation performance.

Simultaneous use of individual and joint regularization terms in compressive sensing: Joint reconstruction of multi-channel multi-contrast MRI acquisitions

E Kopanoglu, A. Güngör, T Kilic, EU Saritas, KK Oguz, T Çukur, HE Güven
NMR in Biomedicine, 2020.

Multi-contrast images are commonly acquired together to maximize complementary diagnostic information, albeit at the expense of longer scan times. SIMIT-CS utilizes a time-efficient strategy to acquire high-quality multi-contrast images and then reconstruct undersampled data with joint regularization terms that leverage common information across contrasts.

Real-time Compressive Video Reconstruction for Spatial Multiplexing Cameras

O.F. Kar, A. Gungor, H.E. Guven
IEEE GLOBALSIP, 2019
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Learning-based Regularization for Spatial Multiplexing Cameras
O.F. Kar, A. Gungor, H.E. Guven
IEEE GLOBALSIP, 2019

A Transform Learning-based Deconvolution Technique with Super-resolution and Microscanning Applications
A. Gungor, O.F. Kar
IEEE ICIP, 2019

A Matrix-free Reconstruction Method for Compressive Focal Plane Array Imaging
A. Gungor, O.F. Kar, H.E. Guven
IEEE ICIP, 2018