Search Results for author: Salman UH Dar

Found 8 papers, 7 papers with code

Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction

1 code implementation2 Aug 2023 Muhammad U. Mirza, Onat Dalmaz, Hasan A. Bedel, Gokberk Elmas, Yilmaz Korkmaz, Alper Gungor, Salman UH Dar, Tolga Çukur

Instead of the target transformation from undersampled to fully-sampled data required for MRI reconstruction, common diffusion priors are trained to learn a task-agnostic transformation from an asymptotic start-point of Gaussian noise onto the finite end-point of fully-sampled data.

MRI Reconstruction

Learning Deep MRI Reconstruction Models from Scratch in Low-Data Regimes

1 code implementation6 Jan 2023 Salman UH Dar, Şaban Öztürk, Muzaffer Özbey, Tolga Çukur

To alleviate error propagation, PSFNet combines its SS and SG priors via a novel parallel-stream architecture with learnable fusion parameters.

MRI Reconstruction Rolling Shutter Correction

Unsupervised Medical Image Translation with Adversarial Diffusion Models

1 code implementation17 Jul 2022 Muzaffer Özbey, Onat Dalmaz, Salman UH Dar, Hasan A Bedel, Şaban Özturk, Alper Güngör, Tolga Çukur

Extensive assessments are reported on the utility of SynDiff against competing GAN and diffusion models in multi-contrast MRI and MRI-CT translation.

Image-to-Image Translation Imputation +1

Adaptive Diffusion Priors for Accelerated MRI Reconstruction

1 code implementation12 Jul 2022 Alper Güngör, Salman UH Dar, Şaban Öztürk, Yilmaz Korkmaz, Gokberk Elmas, Muzaffer Özbey, Tolga Çukur

A two-phase reconstruction is executed following training: a rapid-diffusion phase that produces an initial reconstruction with the trained prior, and an adaptation phase that further refines the result by updating the prior to minimize data-consistency loss.

De-aliasing MRI Reconstruction

Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers

1 code implementation15 May 2021 Yilmaz Korkmaz, Salman UH Dar, Mahmut Yurt, Muzaffer Özbey, Tolga Çukur

Supervised reconstruction models are characteristically trained on matched pairs of undersampled and fully-sampled data to capture an MRI prior, along with supervision regarding the imaging operator to enforce data consistency.

MRI Reconstruction

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