Search Results for author: Seyed Amir Hossein Hosseini

Found 9 papers, 3 papers with code

Zero-Shot Physics-Guided Deep Learning for Subject-Specific MRI Reconstruction

no code implementations NeurIPS Workshop Deep_Invers 2021 Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Mehmet Akcakaya

In the presence of models pre-trained on a database, we show that the proposed approach can be adapted as subject-specific fine-tuning via transfer learning to further improve reconstruction quality.

Anatomy MRI Reconstruction +2

Zero-Shot Self-Supervised Learning for MRI Reconstruction

1 code implementation ICLR 2022 Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Mehmet Akçakaya

Moreover, recent studies show that database-trained models may not generalize well when the unseen measurements differ in terms of sampling pattern, acceleration rate, SNR, image contrast, and anatomy.

Anatomy MRI Reconstruction +2

Improved Supervised Training of Physics-Guided Deep Learning Image Reconstruction with Multi-Masking

no code implementations26 Oct 2020 Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Steen Moeller, Mehmet Akçakaya

Physics-guided deep learning (PG-DL) via algorithm unrolling has received significant interest for improved image reconstruction, including MRI applications.

Image Reconstruction Rolling Shutter Correction

Multi-Mask Self-Supervised Learning for Physics-Guided Neural Networks in Highly Accelerated MRI

no code implementations13 Aug 2020 Burhaneddin Yaman, Hongyi Gu, Seyed Amir Hossein Hosseini, Omer Burak Demirel, Steen Moeller, Jutta Ellermann, Kâmil Uğurbil, Mehmet Akçakaya

In this study, we propose an improved self-supervised learning strategy that more efficiently uses the acquired data to train a physics-guided reconstruction network without a database of fully-sampled data.

MRI Reconstruction Self-Supervised Learning

Noise2Inpaint: Learning Referenceless Denoising by Inpainting Unrolling

no code implementations16 Jun 2020 Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Mehmet Akçakaya

These methods rely on a masking approach that divides the image pixels into two disjoint sets, where one is used as input to the network while the other is used to define the loss.

Image Denoising Image Inpainting +2

High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural Networks

no code implementations12 May 2020 Seyed Amir Hossein Hosseini, Burhaneddin Yaman, Steen Moeller, Mehmet Akçakaya

In addition, the proposed approach has the potential to reduce the risks of generalization to rare pathological conditions, which may be unavailable in the training data.

MRI Reconstruction Transfer Learning

Dense Recurrent Neural Networks for Accelerated MRI: History-Cognizant Unrolling of Optimization Algorithms

no code implementations16 Dec 2019 Seyed Amir Hossein Hosseini, Burhaneddin Yaman, Steen Moeller, Mingyi Hong, Mehmet Akçakaya

These methods unroll iterative optimization algorithms to solve the inverse problem objective function, by alternating between domain-specific data consistency and data-driven regularization via neural networks.

MRI Reconstruction Rolling Shutter Correction

Self-Supervised Learning of Physics-Guided Reconstruction Neural Networks without Fully-Sampled Reference Data

2 code implementations16 Dec 2019 Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Steen Moeller, Jutta Ellermann, Kâmil Uğurbil, Mehmet Akçakaya

Results: Results on five different knee sequences at acceleration rate of 4 shows that proposed self-supervised approach performs closely with supervised learning, while significantly outperforming conventional compressed sensing and parallel imaging, as characterized by quantitative metrics and a clinical reader study.

MRI Reconstruction Self-Supervised Learning

Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled Data

1 code implementation21 Oct 2019 Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Steen Moeller, Jutta Ellermann, Kâmil Uǧurbil, Mehmet Akçakaya

In this work, we tackle this issue and propose a self-supervised learning strategy that enables physics-based DL reconstruction without fully-sampled data.

MRI Reconstruction Self-Supervised Learning

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