Mehmet Akcakaya | DeepAI

AI Chat

Featured Co-authors

Daniel Rueckert
199 publications
Chi Zhang
179 publications
Jong Chul Ye
130 publications
Sijia Liu
112 publications
Ulas Bagci
88 publications
Mingyi Hong
67 publications
Thomas Pock
46 publications
Christian Igel
33 publications
Kerstin Hammernik
21 publications
Burhaneddin Yaman
20 publications
Hyungjin Chung
19 publications

∙ 04/17/2022

Accelerated MRI With Deep Linear Convolutional Transform Learning

Recent studies show that deep learning (DL) based MRI reconstruction out...

0 Hongyi Gu, et al. ∙

∙ 03/23/2022

Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging

Physics-driven deep learning methods have emerged as a powerful tool for...

0 Kerstin Hammernik, et al. ∙

∙ 05/17/2021

Unsupervised Deep Learning Methods for Biological Image Reconstruction

Recently, deep learning approaches have become the main research frontie...

157 Mehmet Akcakaya, et al. ∙

∙ 05/12/2021

20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction

High spatial and temporal resolution across the whole brain is essential...

0 Omer Burak Demirel, et al. ∙

∙ 05/10/2021

Improved Simultaneous Multi-Slice Functional MRI Using Self-supervised Deep Learning

Functional MRI (fMRI) is commonly used for interpreting neural activitie...

0 Omer Burak Demirel, et al. ∙

∙ 02/25/2021

On Instabilities of Conventional Multi-Coil MRI Reconstruction to Small Adverserial Perturbations

Although deep learning (DL) has received much attention in accelerated M...

8 Chi Zhang, et al. ∙

∙ 02/15/2021

Scan-Specific MRI Reconstruction using Zero-Shot Physics-Guided Deep Learning

Physics-guided deep learning (PG-DL) has emerged as a powerful tool for ...

0 Burhaneddin Yaman, et al. ∙

∙ 11/18/2020

Self-Supervised Physics-Guided Deep Learning Reconstruction For High-Resolution 3D LGE CMR

Late gadolinium enhancement (LGE) cardiac MRI (CMR) is the clinical stan...

0 Burhaneddin Yaman, et al. ∙

∙ 10/26/2020

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

Physics-guided deep learning (PG-DL) via algorithm unrolling has receive...

0 Burhaneddin Yaman, et al. ∙

∙ 08/13/2020

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

Purpose: To develop an improved self-supervised learning strategy that e...

0 Burhaneddin Yaman, et al. ∙

∙ 06/16/2020

Noise2Inpaint: Learning Referenceless Denoising by Inpainting Unrolling

Deep learning based image denoising methods have been recently popular d...

0 Burhaneddin Yaman, et al. ∙

∙ 05/12/2020

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

Long scan duration remains a challenge for high-resolution MRI. Deep lea...

0 Seyed Amir Hossein Hosseini, et al. ∙

∙ 04/29/2020

The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

Purpose: To organize a knee MRI segmentation challenge for characterizin...

1 Arjun Desai, et al. ∙

∙ 12/16/2019

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

Purpose: To develop a strategy for training a physics-driven MRI reconst...

12 Burhaneddin Yaman, et al. ∙

∙ 12/16/2019

Dense Recurrent Neural Networks for Inverse Problems: History-Cognizant Unrolling of Optimization Algorithms

Inverse problems in medical imaging applications incorporate domain-spec...

11 Seyed Amir Hossein Hosseini, et al. ∙

∙ 10/21/2019

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

Deep learning (DL) has emerged as a tool for improving accelerated MRI r...

0 Burhaneddin Yaman, et al. ∙

∙ 04/01/2019

Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction

Following the success of deep learning in a wide range of applications, ...

42 Florian Knoll, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world